In [0]:

    

!wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/monet2photo.zip


    

    




--2019-08-22 12:55:44--  https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/monet2photo.zip
Resolving people.eecs.berkeley.edu (people.eecs.berkeley.edu)... 128.32.189.73
Connecting to people.eecs.berkeley.edu (people.eecs.berkeley.edu)|128.32.189.73|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 305231073 (291M) [application/zip]
Saving to: ‘monet2photo.zip’

monet2photo.zip     100%[===================>] 291.09M  21.2MB/s    in 15s     

2019-08-22 12:55:59 (19.6 MB/s) - ‘monet2photo.zip’ saved [305231073/305231073]

In [0]:

    

!mkdir data
!cd data

!unzip monet2photo.zip


    

    




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import time
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import PIL

from glob import glob
from keras import Input, Model
from keras.callbacks import TensorBoard
from keras.layers import Conv2D, BatchNormalization, Activation
from keras.layers import Add, Conv2DTranspose, ZeroPadding2D, LeakyReLU
from keras.optimizers import Adam
from imageio import imread
from skimage.transform import resize


    

    




Using TensorFlow backend.

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!pip install git+https://www.github.com/keras-team/keras-contrib.git


    

    




Collecting git+https://www.github.com/keras-team/keras-contrib.git
  Cloning https://www.github.com/keras-team/keras-contrib.git to /tmp/pip-req-build-s_z8f_t6
  Running command git clone -q https://www.github.com/keras-team/keras-contrib.git /tmp/pip-req-build-s_z8f_t6
Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (from keras-contrib==2.0.8) (2.2.4)
Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.12.0)
Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.3.1)
Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.0.8)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (2.8.0)
Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.16.4)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.1.0)
Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (3.13)
Building wheels for collected packages: keras-contrib
  Building wheel for keras-contrib (setup.py) ... done
  Created wheel for keras-contrib: filename=keras_contrib-2.0.8-cp36-none-any.whl size=101066 sha256=cfc88de5fa2eac3e2695288ecdf4f8de4699b739e42455ffe03a614c72c4b121
  Stored in directory: /tmp/pip-ephem-wheel-cache-pzwn1cn5/wheels/11/27/c8/4ed56de7b55f4f61244e2dc6ef3cdbaff2692527a2ce6502ba
Successfully built keras-contrib
Installing collected packages: keras-contrib
Successfully installed keras-contrib-2.0.8

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from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization


    

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def residual_block(x):
  
  """
  Residual block
  """
  
  res = Conv2D(filters = 128, kernel_size = 3, strides = 1, padding = "same")(x)
  res = BatchNormalization(axis = 3, momentum = 0.9, epsilon = 1e-5)(res)
  res = Activation('relu')(res)
  res = Conv2D(filters = 128, kernel_size = 3, strides = 1, padding = "same")(res)
  res = BatchNormalization(axis = 3, momentum = 0.9, epsilon = 1e-5)(res)
  
  return Add()([res, x])


    

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def build_generator():
  
  """
  Creating a generator network with the hyperparameters defined below
  """
  
  input_shape = (128, 128, 3)
  residual_blocks = 6
  input_layer = Input(shape = input_shape)
  
  
  ## 1st Convolutional Block
  x = Conv2D(filters = 32, kernel_size = 7, strides = 1, padding = "same")(input_layer)
  x = InstanceNormalization(axis = 1)(x)
  x = Activation("relu")(x)
  
  ## 2nd Convolutional Block
  x = Conv2D(filters = 64, kernel_size = 3, strides = 2, padding = "same")(x)
  x = InstanceNormalization(axis = 1)(x)
  x = Activation("relu")(x)
  
  ## 3rd Convolutional Block
  x = Conv2D(filters = 128, kernel_size = 3, strides = 2, padding = "same")(x)
  x = InstanceNormalization(axis = 1)(x)
  x = Activation("relu")(x)
  
  
  ## Residual blocks
  for _ in range(residual_blocks):
    x = residual_block(x)
  
  
  ## 1st Upsampling Block
  x = Conv2DTranspose(filters = 64, kernel_size = 3, strides = 2, padding = "same", use_bias = False)(x)
  x = InstanceNormalization(axis = 1)(x)
  x = Activation("relu")(x)
  
  ## 2nd Upsampling Block
  x = Conv2DTranspose(filters = 32, kernel_size = 3, strides = 2, padding = "same", use_bias = False)(x)
  x = InstanceNormalization(axis = 1)(x)
  x = Activation("relu")(x)
  
  ## Last Convolutional Layer
  x = Conv2D(filters = 3, kernel_size = 7, strides = 1, padding = "same")(x)
  output = Activation("tanh")(x)
  
  
  model = Model(inputs = [input_layer], outputs = [output])
  return model


    

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def build_discriminator():
  
  """
  Create a discriminator network using the hyperparameters defined below
  """
  
  input_shape = (128, 128, 3)
  hidden_layers = 3
  
  input_layer = Input(shape = input_shape)
  
  x = ZeroPadding2D(padding = (1, 1))(input_layer)
  
  
  ## 1st Convolutional Block
  x = Conv2D(filters = 64, kernel_size = 4, strides = 2, padding = "valid")(x)
  x = LeakyReLU(alpha = 0.2)(x)
  
  x = ZeroPadding2D(padding = (1, 1))(x)
  
  
  ## 3 Hidden Convolutional Blocks
  for i in range(1, hidden_layers + 1):
    x = Conv2D(filters = 2 ** i * 64, kernel_size = 4, strides = 2, padding = "valid")(x)
    x = InstanceNormalization(axis = 1)(x)
    x = LeakyReLU(alpha = 0.2)(x)
    
    x = ZeroPadding2D(padding = (1, 1))(x)
    
  
  ## Last Convolutional Layer
  output = Conv2D(filters = 1, kernel_size = 4, strides = 1, activation = "sigmoid")(x)
  
  
  model = Model(inputs = [input_layer], outputs = [output])
  return model


    

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def load_images(data_dir):
  
  imagesA = glob(data_dir + '/testA/*.*')
  imagesB = glob(data_dir + '/testB/*.*')
  
  allImagesA = []
  allImagesB = []
  
  for index, filename in enumerate(imagesA):
    imgA = imread(filename, pilmode = "RGB")
    imgB = imread(imagesB[index], pilmode = "RGB")
    
    imgA = resize(imgA, (128, 128))
    imgB = resize(imgB, (128, 128))
    
    if np.random.random() > 0.5:
      imgA = np.fliplr(imgA)
      imgB = np.fliplr(imgB)
      
    
    allImagesA.append(imgA)
    allImagesB.append(imgB)
    
  
  ## Normalize images
  allImagesA = np.array(allImagesA) / 127.5 - 1.
  allImagesB = np.array(allImagesB) / 127.5 - 1.
  
  return allImagesA, allImagesB


    

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def load_test_batch(data_dir, batch_size):
  
  imagesA = glob(data_dir + '/testA/*.*')
  imagesB = glob(data_dir + '/testB/*.*')
  
  imagesA = np.random.choice(a = imagesA, size = batch_size)
  imagesB = np.random.choice(a = imagesB, size = batch_size)
  
  allA = []
  allB = []
  
  for i in range(len(imagesA)):
    ## Load and resize images
    imgA = resize(imread(imagesA[i], pilmode = 'RGB').astype(np.float32), (128, 128))
    imgB = resize(imread(imagesB[i], pilmode = 'RGB').astype(np.float32), (128, 128))
    
    allA.append(imgA)
    allB.append(imgB)
    
  return np.array(allA) / 127.5 - 1.0, np.array(allB) / 127.5 - 1.0


    

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!mkdir results

def save_images(originalA, generatedB, reconstructedA,
                originalB, generatedA, reconstructedB, path):
  
  """
  Save images
  """
  
  fig = plt.figure()
  
  ax = fig.add_subplot(2, 3, 1)
  ax.imshow(originalA)
  ax.axis("off")
  ax.set_title("Original")
  
  ax = fig.add_subplot(2, 3, 2)
  ax.imshow(generatedB)
  ax.axis("off")
  ax.set_title("Generated")
  
  ax = fig.add_subplot(2, 3, 3)
  ax.imshow(reconstructedA)
  ax.axis("off")
  ax.set_title("Reconstructed")
  
  ax = fig.add_subplot(2, 3, 4)
  ax.imshow(originalB)
  ax.axis("off")
  ax.set_title("Original")
  
  ax = fig.add_subplot(2, 3, 5)
  ax.imshow(generatedA)
  ax.axis("off")
  ax.set_title("Generated")
  
  ax = fig.add_subplot(2, 3, 6)
  ax.imshow(reconstructedB)
  ax.axis("off")
  ax.set_title("Reconstructed")
  
  plt.savefig(path)


    

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def write_log(callback, name, loss, batch_no):
  
  """
  Write training summary to TensorBoard
  """
  
  summary = tf.Summary()
  summary_value = summary.value.add()
  summary_value.simple_value = loss
  summary_value.tag = name
  callback.writer.add_summary(summary, batch_no)
  callback.writer.flush()


    

In [0]:

    

if __name__ == '__main__':
  
  data_dir = "monet2photo"
  batch_size = 1
  epochs = 500
  mode = 'train'
  
  if mode == 'train':
    
    """
    Load dataset
    """
    
    imagesA, imagesB = load_images(data_dir = data_dir)
    
    ## Define the common optimizer
    common_optimizer = Adam(0.002, 0.5)
    
    
    ## Build and compile discriminator networks
    discriminatorA = build_discriminator()
    discriminatorB = build_discriminator()
    
    discriminatorA.compile(loss = 'mse', 
                           optimizer = common_optimizer,
                           metrics = ['accuracy'])
    discriminatorB.compile(loss = 'mse',
                           optimizer = common_optimizer,
                           metrics = ['accuracy'])
    
    
    ## Build generator networks
    generatorA_to_B = build_generator()
    generatorB_to_A = build_generator()
    
    
    """
    Create an adversarial network
    """
    
    inputA = Input(shape = (128, 128, 3))
    inputB = Input(shape = (128, 128, 3))
    
    
    ## --> Generated images using both of the generator networks
    generatedB = generatorA_to_B(inputA)
    generatedA = generatorB_to_A(inputB)
    
    
    ## --> Reconstruct the images back to the original ones
    reconstructedA = generatorB_to_A(generatedB)
    reconstructedB = generatorA_to_B(generatedA)
    
    generatedA_Id = generatorB_to_A(inputA)
    generatedB_Id = generatorA_to_B(inputB)
    
    
    ## Make both of the discriminator networks non-trainable
    discriminatorA.trainable = False
    discriminatorB.trainable = False
    
    probsA = discriminatorA(generatedA)
    probsB = discriminatorB(generatedB)
    
    
    adversarial_model = Model(inputs = [inputA, inputB],
                              outputs = [probsA, probsB, 
                                         reconstructedA, reconstructedB,
                                         generatedA_Id, generatedB_Id])
    
    adversarial_model.compile(loss = ['mse', 'mse', 'mae', 'mae', 'mae', 'mae'],
                              loss_weights = [1, 1, 10.0, 10.0, 1.0, 1.0],
                              optimizer = common_optimizer)
    
    
    tensorboard = TensorBoard(log_dir = "logs/{}".format(time.time()),
                              write_images = True, write_grads = True,
                              write_graph = True)
    tensorboard.set_model(generatorA_to_B)
    tensorboard.set_model(generatorB_to_A)
    tensorboard.set_model(discriminatorA)
    tensorboard.set_model(discriminatorB)
    
    
    real_labels = np.ones((batch_size, 7, 7, 1))
    fake_labels = np.zeros((batch_size, 7, 7, 1))
    
    for epoch in range(epochs):
      print("Epoch: {}".format(epoch))
      
      D_losses = []
      G_losses = []
      
      num_batches = int(min(imagesA.shape[0], imagesB.shape[0]) / batch_size)
      print("Number of batches: {}".format(num_batches))
      
      
      for index in range(num_batches):
        print("Batch: {}".format(index))
        
        ## Sample images
        batchA = imagesA[index * batch_size: (index + 1) * batch_size]
        batchB = imagesB[index * batch_size: (index + 1) * batch_size]
        
        ## Translate images to opposite domain
        generatedB = generatorA_to_B.predict(batchA)
        generatedA = generatorB_to_A.predict(batchB)
        
        ## Train the discriminator A on real and fake images
        D_A_Loss1 = discriminatorA.train_on_batch(batchA, real_labels)
        D_A_Loss2 = discriminatorA.train_on_batch(generatedA, fake_labels)
        
        ## Train the discriminator B on real and fake images
        D_B_Loss1 = discriminatorB.train_on_batch(batchB, real_labels)
        D_B_Loss2 = discriminatorB.train_on_batch(generatedB, fake_labels)
        
        ## Calculate the total discriminator loss
        D_loss = 0.5 * np.add(0.5 * np.add(D_A_Loss1, D_A_Loss2), 
                              0.5 * np.add(D_B_Loss1, D_B_Loss2))
        
        print("D_Loss: {}".format(D_loss))
        
        
        """
        Train the generator networks
        """
        
        G_loss = adversarial_model.train_on_batch([batchA, batchB],
                                                  [real_labels, real_labels,
                                                   batchA, batchB,
                                                   batchA, batchB])
        
        print("G_Loss: {}".format(G_loss))
        
        D_losses.append(D_loss)
        G_losses.append(G_loss)
        
        
      """
      Save losses to TensorBoard after every epoch
      """
      
      write_log(tensorboard, 'discriminator_loss', np.mean(D_losses), epoch)
      write_log(tensorboard, 'generator_loss', np.mean(G_losses), epoch)
      
      
      ## Sample and save images after every 10 epochs
      if epoch % 10 == 0:
        
        ## Get a batch of test data
        batchA, batchB = load_test_batch(data_dir = data_dir, batch_size = 2)
        
        ## Generate images
        generatedB = generatorA_to_B.predict(batchA)
        generatedA = generatorB_to_A.predict(batchB)
        
        ## Get reconstructed images
        recons_A = generatorB_to_A.predict(generatedB)
        recons_B = generatorA_to_B.predict(generatedA)
        
        ## Save original, generated and reconstructed images
        for i in range(len(generatedA)):
          save_images(originalA = batchA[i], generatedB = generatedB[i], reconstructedA = recons_A[i],
                      originalB = batchB[i], generatedA = generatedA[i], reconstructedB = recons_B[i],
                      path = "results/gen_{}_{}".format(epoch, i))
          
    
    ## Save models
    generatorA_to_B.save_weights("generatorA_to_B.h5")
    generatorB_to_A.save_weights("generatorB_to_A.h5")
    discriminatorA.save_weights("discriminatorA.h5")
    discriminatorB.save_weights("discriminatorB.h5")
    
    
  elif mode == 'predict':
    
    ## Build generator networks
    generatorA_to_B = build_generator()
    generatorB_to_A = build_generator()
    
    generatorA_to_B.load_weights("generatorA_to_B.h5")
    generatorB_to_A.load_weights("generatorB_to_A.h5")
    
    
    ## Get a batch of test data
    batchA, batchB = load_test_batch(data_dir = data_dir, batch_size = 2)
    
    
    ## Save images
    generatedB = generatorA_to_B.predict(batchA)
    generatedA = generatorB_to_A.predict(batchB)
    
    reconsA = generatorB_to_A.predict(generatedB)
    reconsB = generatorA_to_B.predict(generatedA)
    
    for i in range(len(generatedA)):
      save_images(originalA = batchA[i], generatedB = generatedB[i], reconstructedA = recons_A[i],
                  originalB = batchB[i], generatedA = generatedA[i], reconstructedB = recons_B[i],
                  path = "results/test_{}".format(i))


    

    




WARNING: Logging before flag parsing goes to stderr.
W0822 12:57:49.734445 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

W0822 12:57:49.736517 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

W0822 12:57:49.752670 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

W0822 12:57:50.060953 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

W0822 12:57:50.235987 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.

W0822 12:57:50.237730 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

W0822 12:57:53.725808 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.

W0822 12:58:08.037763 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:850: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.

W0822 12:58:08.039646 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:853: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.







    




Epoch: 0
Number of batches: 121
Batch: 0






    




/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?
  'Discrepancy between trainable weights and collected trainable'
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?
  'Discrepancy between trainable weights and collected trainable'
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?
  'Discrepancy between trainable weights and collected trainable'






    




D_Loss: [0.5305587  0.49489796]
G_Loss: [25.205275, 5.0539395e-09, 6.623143e-08, 1.2187737, 1.0739226, 1.2182144, 1.0600963]
Batch: 1
D_Loss: [0.4999975 0.5      ]






    




/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?
  'Discrepancy between trainable weights and collected trainable'






    




G_Loss: [3.9817982, 8.908386e-11, 2.0940565e-11, 0.20176497, 0.16031894, 0.19941089, 0.16154826]
Batch: 2
D_Loss: [0.4999979 0.5      ]
G_Loss: [0.14613247, 1.06901446e-10, 3.266916e-11, 0.008199459, 0.0050833183, 0.008200543, 0.0051041497]
Batch: 3
D_Loss: [0.49999782 0.5       ]
G_Loss: [0.07484748, 1.3021768e-10, 4.5673253e-11, 0.0035987599, 0.0032056177, 0.0035989643, 0.0032047378]
Batch: 4
D_Loss: [0.49999744 0.5       ]
G_Loss: [0.055565186, 1.7880945e-10, 7.669714e-11, 0.0039672605, 0.001084133, 0.003967266, 0.0010839804]
Batch: 5
D_Loss: [0.4999969 0.5      ]
G_Loss: [0.08253879, 2.7708774e-10, 1.625803e-10, 0.0032744212, 0.004229109, 0.0032744072, 0.0042290743]
Batch: 6
D_Loss: [0.4999958 0.5      ]
G_Loss: [0.10562783, 5.279189e-10, 5.5828697e-10, 0.0035202028, 0.00608233, 0.0035201842, 0.0060823206]
Batch: 7
D_Loss: [0.49999303 0.5       ]
G_Loss: [0.086792976, 1.5879688e-09, 1.0446463e-08, 0.00399099, 0.0038992828, 0.0039909696, 0.0038992679]
Batch: 8
D_Loss: [0.49997103 0.5       ]
G_Loss: [1.0719333, 3.3966444e-08, 0.9998367, 0.0031124214, 0.003441812, 0.00311242, 0.0034418332]
Batch: 9
D_Loss: [0.74946296 0.25      ]
G_Loss: [2.074207, 0.9999405, 0.9958127, 0.0041293586, 0.0030027935, 0.004129373, 0.003002779]
Batch: 10
D_Loss: [0.6524923  0.33163264]
G_Loss: [2.0854123, 0.9999276, 0.99994147, 0.0026702269, 0.0051064147, 0.0026702278, 0.0051064114]
Batch: 11
D_Loss: [0.49996725 0.5       ]
G_Loss: [2.0732508, 0.9998624, 0.99994475, 0.0046732277, 0.0020034672, 0.0046732393, 0.0020034793]
Batch: 12
D_Loss: [0.4999519 0.5      ]
G_Loss: [2.076531, 0.99949414, 0.9999042, 0.0031265437, 0.0038855155, 0.0031265533, 0.0038854443]
Batch: 13
D_Loss: [0.4999232 0.5      ]
G_Loss: [2.0755465, 0.9983372, 0.999865, 0.005020872, 0.0020104526, 0.005020882, 0.0020104344]
Batch: 14
D_Loss: [0.52557886 0.46428573]
G_Loss: [2.0670457, 1.0, 0.9998054, 0.003332845, 0.0027798975, 0.003332844, 0.0027799131]
Batch: 15
D_Loss: [0.49995142 0.5       ]
G_Loss: [2.0627306, 1.0, 0.99967253, 0.0035078381, 0.0022247022, 0.0035078109, 0.0022247187]
Batch: 16
D_Loss: [0.49991855 0.5       ]
G_Loss: [2.0548084, 1.0, 0.9991885, 0.0022493354, 0.002807031, 0.0022493303, 0.0028070305]
Batch: 17
D_Loss: [0.4998095 0.5      ]
G_Loss: [2.070156, 1.0, 0.99236864, 0.0031272955, 0.0039442857, 0.003127303, 0.0039442796]
Batch: 18
D_Loss: [0.74809253 0.25      ]
G_Loss: [1.0848153, 1.0, 0.0, 0.0033807512, 0.00432973, 0.0033807652, 0.0043297005]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0574374, 1.0, 0.0, 0.0034567295, 0.0017648425, 0.0034567132, 0.0017648609]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0851662, 1.0, 0.0, 0.0030887434, 0.0046536615, 0.0030885967, 0.004653617]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0874918, 1.0, 0.0, 0.0042378055, 0.003715997, 0.004237742, 0.0037159426]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0767947, 1.0, 0.0, 0.003374986, 0.0036064032, 0.003374503, 0.0036063932]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0570321, 1.0, 0.0, 0.0031483816, 0.0020362842, 0.0031492207, 0.0020362176]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.051461, 1.0, 0.0, 0.0022763265, 0.0024026777, 0.0022682818, 0.0024027082]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0806954, 1.0, 0.0, 0.0036099807, 0.0037259345, 0.0036102007, 0.003725971]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0783005, 1.0, 0.0, 0.004731375, 0.0023868494, 0.00473141, 0.0023868706]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0667522, 1.0, 0.0, 0.0033675889, 0.002700807, 0.0033674338, 0.0027008553]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.052085, 1.0, 0.0, 0.0022735798, 0.002461434, 0.0022734143, 0.0024614318]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.1021444, 1.0, 0.0, 0.0048889676, 0.0043968046, 0.004889828, 0.004396752]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0780519, 1.0, 0.0, 0.003395278, 0.0037004033, 0.0033947781, 0.0037003746]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0768307, 1.0, 0.0, 0.0032819586, 0.0037026913, 0.0032816841, 0.0037025642]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0663079, 1.0, 0.0, 0.0033780397, 0.0026499787, 0.0033778278, 0.0026499734]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0675365, 1.0, 0.0, 0.0030047006, 0.0031349768, 0.003004685, 0.0031349908]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0556906, 1.0, 0.0, 0.0034778118, 0.0015851352, 0.0034762435, 0.0015849032]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0632472, 1.0, 0.0, 0.003059525, 0.0026906393, 0.0030549334, 0.0026905788]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0780853, 1.0, 0.0, 0.0059963553, 0.0011018349, 0.0060012974, 0.0011019123]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0686725, 1.0, 0.0, 0.004952337, 0.001290628, 0.0049523315, 0.0012905572]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.059172, 1.0, 0.0, 0.004489617, 0.00088965584, 0.0044896095, 0.0008896413]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0479424, 1.0, 0.0, 0.0031680036, 0.0011903526, 0.0031684681, 0.0011903867]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0867711, 1.0, 0.0, 0.0036952486, 0.004193059, 0.0036950114, 0.004193094]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.090488, 1.0, 0.0, 0.0052892114, 0.002936988, 0.005289113, 0.002936964]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0798702, 1.0, 0.0, 0.0031518026, 0.0041091316, 0.0031518235, 0.0041091973]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.084857, 1.0, 0.0, 0.003741756, 0.003972517, 0.0037417463, 0.0039725807]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0679239, 1.0, 0.0, 0.0036366396, 0.0025382612, 0.0036366002, 0.0025382675]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0486474, 1.0, 0.0, 0.0030035162, 0.001419005, 0.0030033097, 0.001418854]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0841243, 1.0, 0.0, 0.004741164, 0.0029064927, 0.0047411527, 0.002906536]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0815023, 1.0, 0.0, 0.0037443202, 0.0036649501, 0.0037445861, 0.0036651045]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0864365, 1.0, 0.0, 0.0035981806, 0.0042596306, 0.0035984716, 0.0042598965]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0906178, 1.0, 0.0, 0.004105132, 0.004132814, 0.0041052364, 0.0041330634]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0778519, 1.0, 0.0, 0.0035791048, 0.0034983526, 0.0035793327, 0.0034979074]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0800817, 1.0, 0.0, 0.0042693685, 0.0030108076, 0.004269264, 0.003010592]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0871855, 1.0, 0.0, 0.0038375768, 0.0040883394, 0.0038375626, 0.0040887096]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0628582, 1.0, 0.0, 0.0029133393, 0.002801029, 0.0029132674, 0.0028012986]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.077869, 1.0, 0.0, 0.0043209996, 0.0027580336, 0.0043206667, 0.0027580424]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0779094, 1.0, 0.0, 0.003730467, 0.0033522083, 0.003730199, 0.0033524567]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0749876, 1.0, 0.0, 0.004295883, 0.00252108, 0.004296179, 0.0025218101]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0770252, 1.0, 0.0, 0.004742824, 0.0022594454, 0.004742638, 0.0022598822]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0795077, 1.0, 0.0, 0.0046040365, 0.0026238794, 0.004604093, 0.0026244419]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.064366, 1.0, 0.0, 0.0032435777, 0.0026074562, 0.0032441802, 0.0026115323]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.1274866, 1.0, 0.0, 0.0040348433, 0.0075588943, 0.00403386, 0.0075154817]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0737286, 1.0, 0.0, 0.0034005335, 0.0033020773, 0.0034006143, 0.0033019525]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0905625, 1.0, 0.0, 0.004027214, 0.004205725, 0.0040273867, 0.0042056846]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0964904, 1.0, 0.0, 0.0042829257, 0.0044889115, 0.0042831446, 0.0044887755]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0696694, 1.0, 0.0, 0.0035361843, 0.0027973326, 0.0035371496, 0.0027970588]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0733979, 1.0, 0.0, 0.0027131853, 0.0039593363, 0.0027134323, 0.0039593377]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0742708, 1.0, 0.0, 0.0034474717, 0.0033044256, 0.0034474852, 0.0033043916]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0489588, 1.0, 0.0, 0.0025036074, 0.0019471708, 0.0025037182, 0.0019471394]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.09276, 1.0, 0.0, 0.0052122213, 0.0032205018, 0.005212277, 0.0032204827]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0650876, 1.0, 0.0, 0.0028143679, 0.0031026572, 0.0028146973, 0.003102632]
Batch: 70
D_Loss: [0.5 0.5]
G_Loss: [1.076307, 1.0, 0.0, 0.005073653, 0.0018633741, 0.0050733928, 0.0018633879]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0785102, 1.0, 0.0, 0.0031344837, 0.0040026596, 0.0031359803, 0.0040026912]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0717121, 1.0, 0.0, 0.003545532, 0.0029737581, 0.003545533, 0.002973699]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0820658, 1.0, 0.0, 0.0033342442, 0.004126278, 0.0033342445, 0.0041262778]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0856704, 1.0, 0.0, 0.0035295077, 0.004258689, 0.0035295356, 0.0042587053]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.0973699, 1.0, 0.0, 0.004074935, 0.004776881, 0.0040749134, 0.004776835]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0872959, 1.0, 0.0, 0.0050162543, 0.0029197321, 0.0050163274, 0.0029197254]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0685794, 1.0, 0.0, 0.003675417, 0.0025590837, 0.0036753644, 0.0025591091]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0550449, 1.0, 0.0, 0.0041380986, 0.0008659789, 0.004138087, 0.0008659939]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0651807, 1.0, 0.0, 0.0034196381, 0.0025058696, 0.0034195997, 0.0025058584]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0762947, 1.0, 0.0, 0.0045200223, 0.0024158582, 0.0045200656, 0.0024157786]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.061006, 1.0, 0.0, 0.0021862653, 0.0033597136, 0.002186448, 0.0033597415]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0631664, 1.0, 0.0, 0.0030834829, 0.0026589192, 0.003083443, 0.002658933]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.071692, 1.0, 0.0, 0.0037598638, 0.00275759, 0.0037598151, 0.002757649]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0868279, 1.0, 0.0, 0.0035172347, 0.0043762056, 0.003517234, 0.00437618]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0737256, 1.0, 0.0, 0.0039054756, 0.002796845, 0.003905605, 0.0027968176]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0768528, 1.0, 0.0, 0.0038795, 0.003107092, 0.0038796565, 0.0031071838]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.058604, 1.0, 0.0, 0.0031390134, 0.0021886071, 0.003139085, 0.0021885857]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0670298, 1.0, 0.0, 0.0031270678, 0.002966572, 0.00312701, 0.0029665818]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0691516, 1.0, 0.0, 0.003329737, 0.0029567499, 0.0033298177, 0.0029568754]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0603658, 1.0, 0.0, 0.0028722296, 0.0026155564, 0.002872322, 0.002615537]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0871449, 1.0, 0.0, 0.004564752, 0.0033575501, 0.0045642396, 0.0033574726]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0613427, 1.0, 0.0, 0.0039046337, 0.0016719277, 0.0039053552, 0.0016719126]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0717534, 1.0, 0.0, 0.004279409, 0.002243618, 0.004279781, 0.0022434099]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0699325, 1.0, 0.0, 0.004034694, 0.0023227795, 0.004034913, 0.0023227953]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.078219, 1.0, 0.0, 0.0036165146, 0.0034943172, 0.0036162194, 0.0034944594]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0747249, 1.0, 0.0, 0.0038836333, 0.0029095171, 0.003883834, 0.002909556]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0739962, 1.0, 0.0, 0.0036926835, 0.0030341838, 0.0036932414, 0.0030341984]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0573971, 1.0, 0.0, 0.003084928, 0.0021330016, 0.0030848081, 0.00213297]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0624814, 1.0, 0.0, 0.0032848888, 0.0023952425, 0.0032848283, 0.0023952671]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0733134, 1.0, 0.0, 0.0041300706, 0.002534788, 0.0041300394, 0.0025348193]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0451329, 1.0, 0.0, 0.0030677933, 0.0010351529, 0.0030683372, 0.0010350395]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0779171, 1.0, 0.0, 0.0043695667, 0.0027137883, 0.0043696933, 0.0027138027]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0759537, 1.0, 0.0, 0.0033069362, 0.003597927, 0.003307046, 0.0035979534]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0812958, 1.0, 0.0, 0.0032483656, 0.0041421754, 0.0032482634, 0.004142168]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0655774, 1.0, 0.0, 0.0026388727, 0.0033226293, 0.0026396988, 0.0033226837]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0708199, 1.0, 0.0, 0.003556444, 0.0028817393, 0.0035563638, 0.0028817055]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0803638, 1.0, 0.0, 0.004227072, 0.0030787254, 0.0042271223, 0.0030787117]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0846242, 1.0, 0.0, 0.0028743737, 0.0048187403, 0.0028743937, 0.0048187356]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0863596, 1.0, 0.0, 0.004478797, 0.0033721244, 0.004478313, 0.0033721193]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0712557, 1.0, 0.0, 0.0030880217, 0.003389774, 0.0030878845, 0.0033897818]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0743222, 1.0, 0.0, 0.004573022, 0.0021835642, 0.0045728656, 0.0021833978]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0732552, 1.0, 0.0, 0.0040654046, 0.0025941664, 0.004065361, 0.0025941008]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0623237, 1.0, 0.0, 0.003333007, 0.0023328, 0.003332877, 0.0023327845]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0415868, 1.0, 0.0, 0.002432391, 0.0013482854, 0.002431692, 0.0013482757]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0499488, 1.0, 0.0, 0.0025880132, 0.0019527874, 0.0025879866, 0.001952795]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0585754, 1.0, 0.0, 0.0030760448, 0.0022489824, 0.0030762176, 0.0022489754]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.0502319, 1.0, 0.0, 0.0038499665, 0.0007165597, 0.0038500559, 0.00071656296]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0947158, 1.0, 0.0, 0.004319322, 0.0042911433, 0.0043199672, 0.0042911833]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0638133, 1.0, 0.0, 0.002425766, 0.0033754623, 0.0024256422, 0.0033754224]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0824113, 1.0, 0.0, 0.0044944594, 0.0029974456, 0.0044946223, 0.0029974855]






    




W0822 12:59:28.696646 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.715202 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.736211 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.753946 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.772412 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.792045 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.878107 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.897553 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.917304 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.937082 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.957372 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 12:59:28.978284 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).






    




Epoch: 1
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0690359, 1.0, 0.0, 0.002908807, 0.003367193, 0.0029086964, 0.0033672038]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0828736, 1.0, 0.0, 0.004325145, 0.0032088323, 0.0043251896, 0.0032088072]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0789301, 1.0, 0.0, 0.0048052473, 0.0023702187, 0.0048053046, 0.002370237]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0674511, 1.0, 0.0, 0.0030081037, 0.0031238394, 0.0030078182, 0.0031238368]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0506923, 1.0, 0.0, 0.0035094658, 0.0010988972, 0.0035097501, 0.0010989867]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0756971, 1.0, 0.0, 0.0027716947, 0.0041098623, 0.0027717045, 0.0041097756]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0988199, 1.0, 0.0, 0.0030399049, 0.005943728, 0.0030398485, 0.0059436564]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0760825, 1.0, 0.0, 0.0031523965, 0.0037642352, 0.0031520324, 0.0037641423]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0683393, 1.0, 0.0, 0.0029137386, 0.003299105, 0.0029120487, 0.003298933]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.069293, 1.0, 0.0, 0.0033497447, 0.0029496374, 0.0033495626, 0.0029495822]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0790051, 1.0, 0.0, 0.0022527794, 0.0049294764, 0.0022530123, 0.004929535]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0660744, 1.0, 0.0, 0.0039958097, 0.0020109871, 0.0039953827, 0.0020109636]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0673909, 1.0, 0.0, 0.0023699692, 0.0037565092, 0.0023696898, 0.0037564773]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0651402, 1.0, 0.0, 0.0039214743, 0.002000365, 0.003921532, 0.0020003505]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0650758, 1.0, 0.0, 0.0032533435, 0.002662763, 0.0032518697, 0.002662723]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.058662, 1.0, 0.0, 0.0031617132, 0.0021711942, 0.003161782, 0.0021711853]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0514642, 1.0, 0.0, 0.0019652606, 0.0027133147, 0.0019651125, 0.0027132959]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0737677, 1.0, 0.0, 0.00286523, 0.0038409191, 0.0028652376, 0.0038408777]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0794905, 1.0, 0.0, 0.0030385405, 0.004187871, 0.003038527, 0.0041879574]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0535123, 1.0, 0.0, 0.0030541336, 0.0018106545, 0.0030537304, 0.0018106659]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.077323, 1.0, 0.0, 0.00253687, 0.0044925357, 0.0025364468, 0.004492525]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0763917, 1.0, 0.0, 0.0033263233, 0.0036183875, 0.003326186, 0.0036184522]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0754428, 1.0, 0.0, 0.0033466062, 0.0035119387, 0.003345548, 0.0035118305]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0612429, 1.0, 0.0, 0.0034765787, 0.002090917, 0.0034770353, 0.0020909025]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0503787, 1.0, 0.0, 0.002196201, 0.0023836978, 0.0021959045, 0.0023837374]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.075517, 1.0, 0.0, 0.003242367, 0.0036228593, 0.0032421346, 0.003622795]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0704234, 1.0, 0.0, 0.0040702857, 0.0023318431, 0.004070302, 0.0023318212]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0575237, 1.0, 0.0, 0.0025779274, 0.0026514744, 0.002578204, 0.0026514705]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.052472, 1.0, 0.0, 0.002380249, 0.0023899446, 0.0023801723, 0.0023899553]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0976458, 1.0, 0.0, 0.004596005, 0.004280909, 0.0045956727, 0.004280923]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0752236, 1.0, 0.0, 0.0032524911, 0.003586011, 0.0032526483, 0.0035859928]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0738326, 1.0, 0.0, 0.0031165336, 0.003595496, 0.0031167995, 0.0035954663]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0606256, 1.0, 0.0, 0.0029350608, 0.002576422, 0.0029342338, 0.0025764583]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0642027, 1.0, 0.0, 0.0028187367, 0.0030178954, 0.0028185486, 0.0030177254]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.055972, 1.0, 0.0, 0.0034350348, 0.0016532931, 0.0034353798, 0.0016533652]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0696948, 1.0, 0.0, 0.003709991, 0.0026259064, 0.003709926, 0.0026258607]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0499235, 1.0, 0.0, 0.003411762, 0.0011267848, 0.0034111529, 0.0011269166]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.059263, 1.0, 0.0, 0.004023206, 0.0013645109, 0.0040213643, 0.0013644792]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0471228, 1.0, 0.0, 0.0033818176, 0.0009020152, 0.0033824537, 0.0009020141]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0424316, 1.0, 0.0, 0.0026934939, 0.0011638699, 0.0026941667, 0.0011638134]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0818876, 1.0, 0.0, 0.003300084, 0.004144351, 0.0032989727, 0.004144259]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0875378, 1.0, 0.0, 0.0050630597, 0.0028948048, 0.0050642765, 0.0028948546]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0723424, 1.0, 0.0, 0.0025149612, 0.004061666, 0.00251455, 0.004061615]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0744872, 1.0, 0.0, 0.002846898, 0.0039246846, 0.002846796, 0.0039245617]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0575687, 1.0, 0.0, 0.002779054, 0.0024544718, 0.00277879, 0.002454618]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0415702, 1.0, 0.0, 0.0023469822, 0.0014322456, 0.0023456414, 0.001432311]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0754511, 1.0, 0.0, 0.0040341495, 0.0028250732, 0.0040339613, 0.0028250706]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0702459, 1.0, 0.0, 0.0028041434, 0.0035818587, 0.002804057, 0.0035817802]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0778897, 1.0, 0.0, 0.0029199668, 0.004161032, 0.0029186336, 0.0041609406]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0782099, 1.0, 0.0, 0.003100296, 0.0040096906, 0.0031004376, 0.0040096305]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0696911, 1.0, 0.0, 0.0029329679, 0.0034026187, 0.0029326736, 0.0034025484]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0722039, 1.0, 0.0, 0.003576219, 0.0029878162, 0.0035756277, 0.0029878698]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0788169, 1.0, 0.0, 0.0031494433, 0.0040157316, 0.003149445, 0.0040157894]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0547557, 1.0, 0.0, 0.0022319546, 0.0027458156, 0.0022322708, 0.0027455962]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0672694, 1.0, 0.0, 0.0033480022, 0.0027674837, 0.003347007, 0.0027675093]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0647525, 1.0, 0.0, 0.0025712266, 0.0033155691, 0.0025689583, 0.0033155228]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0649921, 1.0, 0.0, 0.0033634999, 0.0025451235, 0.003360709, 0.0025451174]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0700454, 1.0, 0.0, 0.004102381, 0.0022649579, 0.004107074, 0.0022649076]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0717363, 1.0, 0.0, 0.0038430905, 0.0026784507, 0.0038425163, 0.0026783664]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0575136, 1.0, 0.0, 0.0022858365, 0.002942732, 0.0022853059, 0.0029425807]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.051129, 1.0, 0.0, 0.0029304698, 0.0017177439, 0.0029290586, 0.0017176372]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0633619, 1.0, 0.0, 0.0026121608, 0.0031480354, 0.0026119584, 0.0031479988]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0811806, 1.0, 0.0, 0.0033721686, 0.0040073157, 0.0033784842, 0.004007233]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0837145, 1.0, 0.0, 0.0033826162, 0.004227848, 0.003382165, 0.0042276876]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0616775, 1.0, 0.0, 0.0030957947, 0.0025113367, 0.0030949702, 0.0025112615]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0660031, 1.0, 0.0, 0.0023560212, 0.0036443167, 0.002355568, 0.003644269]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0669566, 1.0, 0.0, 0.0029435083, 0.003143541, 0.0029428368, 0.0031434712]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0467743, 1.0, 0.0, 0.0024026977, 0.0018496225, 0.0024014488, 0.0018495583]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0828505, 1.0, 0.0, 0.0044814064, 0.0030505161, 0.0044807936, 0.0030503203]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0597439, 1.0, 0.0, 0.0023904028, 0.00304088, 0.0023900098, 0.0030408874]
Batch: 70
D_Loss: [0.5 0.5]
G_Loss: [1.0652426, 1.0, 0.0, 0.004161967, 0.0017692402, 0.0041613444, 0.001769146]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0781186, 1.0, 0.0, 0.0032573468, 0.003844549, 0.0032552246, 0.0038443434]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0607215, 1.0, 0.0, 0.0027751883, 0.002745027, 0.0027743669, 0.0027448612]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0699923, 1.0, 0.0, 0.0025502543, 0.003812763, 0.0025493565, 0.0038126225]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0699056, 1.0, 0.0, 0.0025083034, 0.0038471567, 0.0025039297, 0.0038471108]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.0772461, 1.0, 0.0, 0.0027869977, 0.004235564, 0.0027852524, 0.0042352653]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0692925, 1.0, 0.0, 0.0032747467, 0.0030248929, 0.003270689, 0.003025419]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0579554, 1.0, 0.0, 0.0028439309, 0.0024247929, 0.0028435267, 0.0024247244]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0437514, 1.0, 0.0, 0.0031132712, 0.0008641704, 0.0031128041, 0.0008641989]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0517775, 1.0, 0.0, 0.0022416362, 0.0024654143, 0.0022417475, 0.0024652854]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0586634, 1.0, 0.0, 0.0029461482, 0.0023870608, 0.0029443253, 0.0023869488]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0749251, 1.0, 0.0, 0.0035061294, 0.0033053532, 0.003505185, 0.0033051241]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0564024, 1.0, 0.0, 0.0025257547, 0.0026019975, 0.0025229806, 0.0026019413]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0673636, 1.0, 0.0, 0.0034374665, 0.0026864354, 0.0034379463, 0.0026864987]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0816848, 1.0, 0.0, 0.003139732, 0.004286159, 0.003139875, 0.004286024]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0662302, 1.0, 0.0, 0.0033241857, 0.002696741, 0.0033242744, 0.0026966264]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0635854, 1.0, 0.0, 0.002820759, 0.0029598828, 0.002819197, 0.002959826]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0525088, 1.0, 0.0, 0.0026739584, 0.0021000733, 0.0026685582, 0.0021000463]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0603169, 1.0, 0.0, 0.002738479, 0.0027444796, 0.0027428984, 0.002744441]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0578811, 1.0, 0.0, 0.0025970214, 0.0026648347, 0.0025976426, 0.002664812]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0529727, 1.0, 0.0, 0.002345852, 0.0024698945, 0.0023451631, 0.002469975]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0741739, 1.0, 0.0, 0.0036837007, 0.003059436, 0.0036833805, 0.0030592834]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0489503, 1.0, 0.0, 0.0027278408, 0.0017222384, 0.0027272003, 0.0017223866]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0584309, 1.0, 0.0, 0.0030426648, 0.0022693058, 0.0030419582, 0.0022693032]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.05576, 1.0, 0.0, 0.002895498, 0.0021735672, 0.0028959112, 0.0021734422]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0704371, 1.0, 0.0, 0.0030447748, 0.0033586551, 0.0030438825, 0.0033589015]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0585555, 1.0, 0.0, 0.0026243734, 0.0026989118, 0.0026239108, 0.00269882]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0595974, 1.0, 0.0, 0.0025129043, 0.002905117, 0.002511977, 0.0029050908]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0482585, 1.0, 0.0, 0.002263237, 0.0021240371, 0.0022617173, 0.0021240667]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0519004, 1.0, 0.0, 0.0025234034, 0.0021946216, 0.0025255508, 0.0021946784]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0615052, 1.0, 0.0, 0.0032799293, 0.0023113347, 0.003281176, 0.0023113214]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0413585, 1.0, 0.0, 0.0025414922, 0.0012183666, 0.0025413905, 0.0012184889]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0636867, 1.0, 0.0, 0.0032056642, 0.0025839475, 0.0032067788, 0.002583893]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0638754, 1.0, 0.0, 0.0023310354, 0.003475741, 0.0023321854, 0.0034755766]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0690624, 1.0, 0.0, 0.0022851112, 0.0039933478, 0.0022846754, 0.003993195]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0590044, 1.0, 0.0, 0.0022372527, 0.0031268988, 0.0022361416, 0.003126856]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0603896, 1.0, 0.0, 0.0026443498, 0.0028455583, 0.002644991, 0.0028455728]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0666258, 1.0, 0.0, 0.0031364998, 0.0029203868, 0.0031366302, 0.002920326]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.071319, 1.0, 0.0, 0.0019209204, 0.0045627104, 0.0019202413, 0.0045624417]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0704637, 1.0, 0.0, 0.0031581768, 0.0032477318, 0.0031568694, 0.0032476843]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0571347, 1.0, 0.0, 0.0020918113, 0.0031023163, 0.00209124, 0.003102203]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0594218, 1.0, 0.0, 0.0033497117, 0.0020519346, 0.0033533927, 0.0020518934]
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G_Loss: [1.0574048, 1.0, 0.0, 0.0026998739, 0.0025189386, 0.0026978017, 0.0025187684]
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G_Loss: [1.0474591, 1.0, 0.0, 0.0020996686, 0.0022148513, 0.0020992109, 0.002214741]
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G_Loss: [1.0353155, 1.0, 0.0, 0.0018949468, 0.0013156135, 0.001893992, 0.0013157844]
Batch: 115
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G_Loss: [1.0435133, 1.0, 0.0, 0.002103814, 0.0018518267, 0.0021051364, 0.0018518277]
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G_Loss: [1.0500723, 1.0, 0.0, 0.002416893, 0.0021349012, 0.0024195246, 0.0021348763]
Batch: 117
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G_Loss: [1.042225, 1.0, 0.0, 0.0030416262, 0.00079694495, 0.0030422262, 0.0007970494]
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D_Loss: [0.5 0.5]
G_Loss: [1.0771601, 1.0, 0.0, 0.0028419718, 0.004172641, 0.0028415876, 0.004172408]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0657964, 1.0, 0.0, 0.0027472656, 0.0032341143, 0.002748827, 0.0032337713]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0783491, 1.0, 0.0, 0.0042481273, 0.0028731367, 0.0042632525, 0.0028732328]
Epoch: 2
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.061259, 1.0, 0.0, 0.0024423595, 0.0031266783, 0.0024418945, 0.0031266848]
Batch: 1
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G_Loss: [1.0762488, 1.0, 0.0, 0.0036676268, 0.0032638868, 0.0036692405, 0.0032643655]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0600425, 1.0, 0.0, 0.0032377578, 0.0022205813, 0.0032385404, 0.0022204658]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0767678, 1.0, 0.0, 0.004056763, 0.002922262, 0.004055498, 0.0029220288]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0424306, 1.0, 0.0, 0.0025824332, 0.0012747147, 0.0025843093, 0.0012748487]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0670744, 1.0, 0.0, 0.002128982, 0.003968633, 0.0021297112, 0.003968571]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0887794, 1.0, 0.0, 0.0022860523, 0.0057847043, 0.002287315, 0.0057846173]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.063317, 1.0, 0.0, 0.00218467, 0.0035714412, 0.0021846485, 0.0035712798]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0615164, 1.0, 0.0, 0.002598328, 0.0029943946, 0.0025949068, 0.0029941797]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0576092, 1.0, 0.0, 0.0023047668, 0.0029323497, 0.0023055123, 0.0029326025]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0730894, 1.0, 0.0, 0.00200796, 0.0046368353, 0.002005082, 0.0046363384]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0563979, 1.0, 0.0, 0.003114642, 0.002012339, 0.0031156263, 0.0020124847]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.057968, 1.0, 0.0, 0.0016790037, 0.0035908425, 0.0016788708, 0.0035907128]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0530388, 1.0, 0.0, 0.0029528085, 0.001868739, 0.002954541, 0.0018688994]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0539542, 1.0, 0.0, 0.0024093266, 0.0024959384, 0.0024059373, 0.002495679]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0480483, 1.0, 0.0, 0.002361334, 0.0020067028, 0.0023612578, 0.0020066197]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.042879, 1.0, 0.0, 0.0013706357, 0.0025274602, 0.0013706465, 0.002527379]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0621293, 1.0, 0.0, 0.0020485106, 0.003599551, 0.0020491988, 0.0035994272]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0631925, 1.0, 0.0, 0.0020884506, 0.0036562078, 0.0020895954, 0.0036562188]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0552627, 1.0, 0.0, 0.0021312716, 0.0028921773, 0.0021358037, 0.002892536]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0694221, 1.0, 0.0, 0.0019700672, 0.0043408787, 0.0019721591, 0.0043405876]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0656296, 1.0, 0.0, 0.0024027938, 0.0035635978, 0.002402244, 0.0035635524]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0619597, 1.0, 0.0, 0.0022251266, 0.0034076776, 0.0022240696, 0.0034076218]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0443435, 1.0, 0.0, 0.002135187, 0.0018959282, 0.0021366007, 0.0018958377]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0499337, 1.0, 0.0, 0.0022313213, 0.0023083333, 0.0022288444, 0.0023082634]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0640622, 1.0, 0.0, 0.0023771378, 0.003446613, 0.0023783888, 0.003446363]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0580457, 1.0, 0.0, 0.0031465678, 0.0021301175, 0.0031490405, 0.0021299575]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0486431, 1.0, 0.0, 0.0019776686, 0.0024445048, 0.0019770972, 0.002444311]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.04566, 1.0, 0.0, 0.0019027262, 0.002248522, 0.0018989019, 0.0022486188]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0834832, 1.0, 0.0, 0.003605139, 0.0039843502, 0.003603933, 0.003984281]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0596406, 1.0, 0.0, 0.0021709942, 0.0032508438, 0.0021715278, 0.0032507668]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0583608, 1.0, 0.0, 0.0020126696, 0.0032925834, 0.002015729, 0.0032926148]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0465386, 1.0, 0.0, 0.0018356898, 0.0023951703, 0.0018348638, 0.0023951225]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.05011, 1.0, 0.0, 0.0018752685, 0.0026802174, 0.0018750782, 0.002680068]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0447266, 1.0, 0.0, 0.0023408895, 0.0017249156, 0.0023432611, 0.0017252682]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.055094, 1.0, 0.0, 0.0025154874, 0.002492826, 0.0025183521, 0.002492636]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0372342, 1.0, 0.0, 0.0022806767, 0.0011045688, 0.0022770518, 0.0011046659]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0450032, 1.0, 0.0, 0.0027802878, 0.0013109366, 0.0027800004, 0.0013110216]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0335577, 1.0, 0.0, 0.0022059518, 0.0008446739, 0.0022065616, 0.0008448336]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0380403, 1.0, 0.0, 0.0023271893, 0.0011309357, 0.002328386, 0.0011306133]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0725842, 1.0, 0.0, 0.002555321, 0.004043279, 0.0025551007, 0.0040429803]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0722293, 1.0, 0.0, 0.003784782, 0.0027814046, 0.0037859515, 0.0027814207]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0604196, 1.0, 0.0, 0.0016394679, 0.0038532522, 0.0016391543, 0.0038532254]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.059217, 1.0, 0.0, 0.0017665966, 0.0036166157, 0.0017682037, 0.0036165244]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0452725, 1.0, 0.0, 0.0016868194, 0.0024290504, 0.001684617, 0.002429049]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0355186, 1.0, 0.0, 0.0016571472, 0.0015718825, 0.0016563495, 0.0015719752]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0603846, 1.0, 0.0, 0.0028282804, 0.0026610773, 0.0028300057, 0.0026610973]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0557445, 1.0, 0.0, 0.0016256084, 0.0034420872, 0.0016256236, 0.003441954]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0669335, 1.0, 0.0, 0.002121899, 0.0039631696, 0.002119782, 0.003962987]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0593663, 1.0, 0.0, 0.0017041132, 0.003692768, 0.0017047763, 0.0036927382]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.05051, 1.0, 0.0, 0.0015142155, 0.0030780744, 0.0015088164, 0.003078164]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0550034, 1.0, 0.0, 0.002115359, 0.0028847477, 0.0021173372, 0.002884934]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0635129, 1.0, 0.0, 0.0018894451, 0.0038843653, 0.00189041, 0.0038844706]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.047862, 1.0, 0.0, 0.00171995, 0.0026312955, 0.0017184562, 0.002631185]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.05466, 1.0, 0.0, 0.0023490847, 0.0026196023, 0.0023535872, 0.0026195466]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0561117, 1.0, 0.0, 0.0019735054, 0.0031274513, 0.001974703, 0.0031273537]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0480297, 1.0, 0.0, 0.0018382845, 0.0025281142, 0.0018374963, 0.0025281622]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0464865, 1.0, 0.0, 0.00216116, 0.0020648616, 0.0021614614, 0.0020647668]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0480962, 1.0, 0.0, 0.0019813338, 0.0023911158, 0.0019807606, 0.0023909642]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0492793, 1.0, 0.0, 0.0018538698, 0.002626535, 0.0018488222, 0.002626478]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0362929, 1.0, 0.0, 0.0019228206, 0.0013762095, 0.0019258481, 0.0013767632]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.047023, 1.0, 0.0, 0.0014762672, 0.0027984467, 0.0014775873, 0.0027982595]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0573773, 1.0, 0.0, 0.0016067433, 0.0036092275, 0.0016083473, 0.003609267]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0644308, 1.0, 0.0, 0.0021126862, 0.0037450194, 0.0021088577, 0.0037449421]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0463731, 1.0, 0.0, 0.0021069155, 0.0021088654, 0.0021061725, 0.0021091893]
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D_Loss: [0.5 0.5]
G_Loss: [1.0556995, 1.0, 0.0, 0.0018634859, 0.0032004148, 0.0018596449, 0.0032007976]
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G_Loss: [1.0523547, 1.0, 0.0, 0.0018449273, 0.0029143007, 0.0018478204, 0.0029145917]
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G_Loss: [1.0341142, 1.0, 0.0, 0.0014344461, 0.0016668406, 0.0014343087, 0.0016670444]
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G_Loss: [1.0686315, 1.0, 0.0, 0.0033318568, 0.0029072764, 0.003332968, 0.0029072736]
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G_Loss: [1.0476406, 1.0, 0.0, 0.0014259843, 0.0029050913, 0.0014247888, 0.0029050652]
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D_Loss: [0.5 0.5]
G_Loss: [1.0446297, 1.0, 0.0, 0.0023683147, 0.0016887967, 0.0023696073, 0.0016888438]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0752852, 1.0, 0.0, 0.0031588557, 0.0036857799, 0.0031529812, 0.003685802]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0431309, 1.0, 0.0, 0.0014783232, 0.0024426784, 0.0014782897, 0.0024425355]
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D_Loss: [0.5 0.5]
G_Loss: [1.0525625, 1.0, 0.0, 0.0013579663, 0.003420405, 0.0013585312, 0.003420348]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0551721, 1.0, 0.0, 0.0016604142, 0.0033552034, 0.0016607353, 0.0033552023]
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G_Loss: [1.0628487, 1.0, 0.0, 0.0018207688, 0.003892755, 0.0018205697, 0.0038929477]
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G_Loss: [1.0522014, 1.0, 0.0, 0.0019648117, 0.0027808629, 0.0019637104, 0.0027809558]
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D_Loss: [0.5 0.5]
G_Loss: [1.0481422, 1.0, 0.0, 0.00206586, 0.0023108707, 0.002063952, 0.0023108162]
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D_Loss: [0.5 0.5]
G_Loss: [1.0264069, 1.0, 0.0, 0.00148029, 0.00092031475, 0.0014804709, 0.00092034356]
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D_Loss: [0.5 0.5]
G_Loss: [1.0370853, 1.0, 0.0, 0.00095449446, 0.002416918, 0.0009541576, 0.0024169153]
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D_Loss: [0.5 0.5]
G_Loss: [1.0468363, 1.0, 0.0, 0.0019274622, 0.0023303214, 0.001928134, 0.002330317]
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D_Loss: [0.5 0.5]
G_Loss: [1.0620333, 1.0, 0.0, 0.0024296562, 0.0032098175, 0.0024287133, 0.0032097748]
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D_Loss: [0.5 0.5]
G_Loss: [1.0417036, 1.0, 0.0, 0.0012979513, 0.0024933536, 0.0012970854, 0.0024933456]
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D_Loss: [0.5 0.5]
G_Loss: [1.0493389, 1.0, 0.0, 0.0019274976, 0.0025577503, 0.0019285575, 0.0025578276]
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D_Loss: [0.5 0.5]
G_Loss: [1.0582824, 1.0, 0.0, 0.0012374436, 0.0040609213, 0.0012378432, 0.004060886]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0447869, 1.0, 0.0, 0.0014940441, 0.0025775745, 0.0014930558, 0.0025775316]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0474793, 1.0, 0.0, 0.0016763309, 0.002640178, 0.0016739096, 0.002640233]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0405004, 1.0, 0.0, 0.0015026813, 0.00217932, 0.0015010163, 0.002179355]
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D_Loss: [0.5 0.5]
G_Loss: [1.040252, 1.0, 0.0, 0.0010502003, 0.0026090117, 0.0010507766, 0.0026089835]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0459871, 1.0, 0.0, 0.0016498787, 0.0025307355, 0.0016503447, 0.00253074]
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D_Loss: [0.5 0.5]
G_Loss: [1.0429528, 1.0, 0.0, 0.0015410698, 0.0023639132, 0.001539162, 0.0023638806]
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D_Loss: [0.5 0.5]
G_Loss: [1.0553709, 1.0, 0.0, 0.0021943008, 0.0028393369, 0.0021952435, 0.0028392829]
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D_Loss: [0.5 0.5]
G_Loss: [1.0398393, 1.0, 0.0, 0.001770867, 0.0018507807, 0.0017718923, 0.0018508034]
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D_Loss: [0.5 0.5]
G_Loss: [1.0420873, 1.0, 0.0, 0.0015307306, 0.0022954515, 0.0015301667, 0.0022954238]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0383918, 1.0, 0.0, 0.0014439363, 0.0020463332, 0.001442817, 0.002046308]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0621653, 1.0, 0.0, 0.0024200382, 0.0032314458, 0.0024188394, 0.0032315524]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0459155, 1.0, 0.0, 0.0016276736, 0.002546456, 0.0016277721, 0.00254644]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0459532, 1.0, 0.0, 0.0014020189, 0.002775622, 0.001401169, 0.0027755997]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0419289, 1.0, 0.0, 0.0016989178, 0.002113074, 0.0016956226, 0.0021131714]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0354477, 1.0, 0.0, 0.0012092374, 0.0020133057, 0.0012090843, 0.0020132298]
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D_Loss: [0.5 0.5]
G_Loss: [1.0446681, 1.0, 0.0, 0.0019317153, 0.002128841, 0.0019338353, 0.0021287624]
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D_Loss: [0.5 0.5]
G_Loss: [1.0386283, 1.0, 0.0, 0.0022101018, 0.0013016385, 0.0022092075, 0.001301758]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0493385, 1.0, 0.0, 0.0020478745, 0.0024372793, 0.0020496733, 0.002437241]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0551047, 1.0, 0.0, 0.0016676272, 0.0033417717, 0.0016689023, 0.0033418112]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0559654, 1.0, 0.0, 0.0012619337, 0.0038258685, 0.0012614162, 0.003825849]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0533264, 1.0, 0.0, 0.001911834, 0.0029362226, 0.0019096434, 0.0029362473]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0467163, 1.0, 0.0, 0.0014709327, 0.0027759043, 0.0014721402, 0.0027758442]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0528431, 1.0, 0.0, 0.0020548003, 0.0027490682, 0.0020553889, 0.0027490254]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0623486, 1.0, 0.0, 0.0013942681, 0.0042738398, 0.0013938048, 0.0042736717]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0571885, 1.0, 0.0, 0.002141499, 0.0030574682, 0.0021415167, 0.003057334]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0443932, 1.0, 0.0, 0.0012539402, 0.0027820002, 0.0012519797, 0.0027818577]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0434794, 1.0, 0.0, 0.0020980705, 0.001854219, 0.0021023015, 0.0018542846]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0416527, 1.0, 0.0, 0.0014644209, 0.002322259, 0.0014636046, 0.0023222526]
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G_Loss: [1.0386976, 1.0, 0.0, 0.0014516012, 0.0020667296, 0.0014477673, 0.0020664888]
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G_Loss: [1.0289363, 1.0, 0.0, 0.0012929605, 0.0013377871, 0.0012912059, 0.0013376256]
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G_Loss: [1.0362641, 1.0, 0.0, 0.0015417014, 0.0017549911, 0.0015421574, 0.0017550038]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.040037, 1.0, 0.0, 0.0016481484, 0.0019914294, 0.0016500272, 0.0019913064]
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G_Loss: [1.0354906, 1.0, 0.0, 0.0023681677, 0.0008581633, 0.002368992, 0.0008582865]
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D_Loss: [0.5 0.5]
G_Loss: [1.0638645, 1.0, 0.0, 0.001774144, 0.004031552, 0.0017760888, 0.0040314132]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0663065, 1.0, 0.0, 0.002969727, 0.003058016, 0.0029712291, 0.003057837]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0525385, 1.0, 0.0, 0.002075063, 0.00270091, 0.0020777057, 0.0027010192]
Epoch: 3
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0448604, 1.0, 0.0, 0.0012180633, 0.0028602816, 0.001216554, 0.002860312]
Batch: 1
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G_Loss: [1.0561738, 1.0, 0.0, 0.0019169755, 0.0031893505, 0.0019211348, 0.0031893002]
Batch: 2
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G_Loss: [1.0408058, 1.0, 0.0, 0.0016860184, 0.0020235386, 0.0016866224, 0.0020236687]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0619934, 1.0, 0.0, 0.0029238907, 0.0027121748, 0.002920683, 0.0027120668]
Batch: 4
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G_Loss: [1.0304688, 1.0, 0.0, 0.0014125878, 0.0013572723, 0.0014131258, 0.0013571728]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0529622, 1.0, 0.0, 0.001044853, 0.0037698264, 0.001045636, 0.0037697887]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0745748, 1.0, 0.0, 0.0012361947, 0.005543242, 0.001237172, 0.0055432133]
Batch: 7
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G_Loss: [1.0488625, 1.0, 0.0, 0.0011559338, 0.003286126, 0.0011558679, 0.0032860376]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0492578, 1.0, 0.0, 0.0018717357, 0.0026066029, 0.0018678609, 0.0026063947]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.045736, 1.0, 0.0, 0.0013813493, 0.002776374, 0.0013822915, 0.0027764156]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0650197, 1.0, 0.0, 0.0016082036, 0.004303183, 0.0016028085, 0.0043031406]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0435903, 1.0, 0.0, 0.0020342884, 0.0019282617, 0.0020367284, 0.0019281919]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0525838, 1.0, 0.0, 0.0014289808, 0.0033514136, 0.0014282695, 0.0033515624]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0416791, 1.0, 0.0, 0.0020474494, 0.0017413237, 0.0020500962, 0.0017413762]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0402898, 1.0, 0.0, 0.0014238189, 0.0022391924, 0.0014204005, 0.0022392112]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0358033, 1.0, 0.0, 0.0014490376, 0.0018058624, 0.0014484474, 0.0018058759]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0408269, 1.0, 0.0, 0.0014316612, 0.0022799424, 0.0014310277, 0.0022800162]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0518101, 1.0, 0.0, 0.0013876111, 0.0033223084, 0.0013887058, 0.0033223578]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0515076, 1.0, 0.0, 0.0014865563, 0.0031957338, 0.0014890502, 0.0031956555]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0536673, 1.0, 0.0, 0.001700399, 0.003178093, 0.0017039608, 0.003178387]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0619975, 1.0, 0.0, 0.0016965059, 0.003939595, 0.0016969235, 0.003939644]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0534726, 1.0, 0.0, 0.0015992555, 0.0032620127, 0.0015979701, 0.0032620004]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0492343, 1.0, 0.0, 0.001495443, 0.0029805934, 0.0014932607, 0.002980715]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0324969, 1.0, 0.0, 0.0011105454, 0.0018436334, 0.0011116227, 0.001843574]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0469764, 1.0, 0.0, 0.0020259256, 0.002245003, 0.0020220357, 0.0022450571]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0520284, 1.0, 0.0, 0.0016257174, 0.0031041326, 0.0016259408, 0.003104017]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0452393, 1.0, 0.0, 0.0022305278, 0.0018819647, 0.0022324196, 0.0018819345]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.043676, 1.0, 0.0, 0.0017110461, 0.0022596354, 0.0017095729, 0.0022596447]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0400087, 1.0, 0.0, 0.0016910083, 0.0019464851, 0.001687178, 0.0019465241]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0714169, 1.0, 0.0, 0.0028368316, 0.003655468, 0.0028383667, 0.0036553994]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0486455, 1.0, 0.0, 0.0015446346, 0.0028775805, 0.0015459446, 0.0028775197]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0485846, 1.0, 0.0, 0.0014189631, 0.0029975409, 0.0014219545, 0.0029976615]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0371429, 1.0, 0.0, 0.0010961846, 0.0022806455, 0.0010940287, 0.0022805557]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0401723, 1.0, 0.0, 0.0012591704, 0.0023929905, 0.001257626, 0.0023930352]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.034815, 1.0, 0.0, 0.0014981817, 0.0016666745, 0.0014997064, 0.0016666101]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0434588, 1.0, 0.0, 0.0016823725, 0.0022682748, 0.0016837992, 0.0022683996]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0271293, 1.0, 0.0, 0.0013218017, 0.0011448361, 0.0013179923, 0.001144836]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0338068, 1.0, 0.0, 0.0017580059, 0.0013152121, 0.0017593992, 0.0013152852]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0256686, 1.0, 0.0, 0.0014980288, 0.0008355837, 0.0014969188, 0.00083557254]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0359348, 1.0, 0.0, 0.0022262526, 0.0010405377, 0.002226182, 0.0010405736]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0642096, 1.0, 0.0, 0.0019103345, 0.003927054, 0.0019086767, 0.003927071]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0625513, 1.0, 0.0, 0.0029983404, 0.002688012, 0.0029996834, 0.0026880554]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0599779, 1.0, 0.0, 0.0017303358, 0.0037222104, 0.0017299257, 0.003722371]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0506968, 1.0, 0.0, 0.0011305698, 0.0034780537, 0.0011325844, 0.003478032]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0338602, 1.0, 0.0, 0.0010281637, 0.0020502352, 0.001025914, 0.0020502936]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0335854, 1.0, 0.0, 0.0013840471, 0.0016692514, 0.0013832765, 0.0016691294]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.049949, 1.0, 0.0, 0.0021945222, 0.0023460493, 0.002197111, 0.0023462162]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0440352, 1.0, 0.0, 0.00088463334, 0.0031186396, 0.00088388636, 0.003118681]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0589926, 1.0, 0.0, 0.0018239546, 0.0035392954, 0.0018206134, 0.0035396013]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0461208, 1.0, 0.0, 0.0010245492, 0.003168127, 0.0010259073, 0.0031681473]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0411375, 1.0, 0.0, 0.0008710142, 0.0028694835, 0.0008628529, 0.0028695927]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.04818, 1.0, 0.0, 0.0016905597, 0.0026891949, 0.001693359, 0.002689153]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0557241, 1.0, 0.0, 0.0014661234, 0.003599605, 0.0014671113, 0.0035997736]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0459291, 1.0, 0.0, 0.0016725353, 0.0025029639, 0.0016711064, 0.0025030277]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0493436, 1.0, 0.0, 0.002074691, 0.0024107604, 0.0020782528, 0.0024107932]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0499312, 1.0, 0.0, 0.0016417892, 0.0028972905, 0.0016432898, 0.002897202]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0429589, 1.0, 0.0, 0.0014536129, 0.002451808, 0.0014528027, 0.0024518045]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0385786, 1.0, 0.0, 0.0016472951, 0.0018597123, 0.0016487604, 0.0018597905]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0462707, 1.0, 0.0, 0.0020587451, 0.0021477311, 0.00205789, 0.002148021]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0434289, 1.0, 0.0, 0.0015702348, 0.0023784635, 0.0015635954, 0.0023782975]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0341845, 1.0, 0.0, 0.0018259895, 0.0012813739, 0.0018289913, 0.0012818389]
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D_Loss: [0.5 0.5]
G_Loss: [1.0427506, 1.0, 0.0, 0.0013870979, 0.0024992004, 0.0013887242, 0.0024990253]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0528765, 1.0, 0.0, 0.0015078299, 0.0032990184, 0.0015087489, 0.0032992659]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0584245, 1.0, 0.0, 0.0019247998, 0.0033868344, 0.0019212, 0.0033870311]
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G_Loss: [1.0416371, 1.0, 0.0, 0.0018825429, 0.0019027971, 0.0018806776, 0.0019030473]
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G_Loss: [1.0531042, 1.0, 0.0, 0.0018998754, 0.002928121, 0.0018955003, 0.002928661]
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G_Loss: [1.0444041, 1.0, 0.0, 0.0015549404, 0.0024815456, 0.0015577029, 0.0024816054]
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G_Loss: [1.0300926, 1.0, 0.0, 0.0011495962, 0.0015861285, 0.0011491568, 0.001586141]
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D_Loss: [0.5 0.5]
G_Loss: [1.0618354, 1.0, 0.0, 0.003099252, 0.0025219382, 0.0031014825, 0.0025219813]
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G_Loss: [1.0447012, 1.0, 0.0, 0.0013592942, 0.0027046, 0.0013578138, 0.0027044734]
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G_Loss: [1.039856, 1.0, 0.0, 0.0020354195, 0.0015877254, 0.002036849, 0.0015877993]
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D_Loss: [0.5 0.5]
G_Loss: [1.0754404, 1.0, 0.0, 0.003368022, 0.0034903982, 0.0033655772, 0.0034906173]
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D_Loss: [0.5 0.5]
G_Loss: [1.0371281, 1.0, 0.0, 0.0012292163, 0.0021461083, 0.0012288536, 0.0021460284]
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D_Loss: [0.5 0.5]
G_Loss: [1.0473185, 1.0, 0.0, 0.0011686006, 0.0031330634, 0.0011689229, 0.0031329482]
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D_Loss: [0.5 0.5]
G_Loss: [1.0489453, 1.0, 0.0, 0.0015460143, 0.0029036142, 0.0015453936, 0.002903558]
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D_Loss: [0.5 0.5]
G_Loss: [1.0526471, 1.0, 0.0, 0.0017807202, 0.0030053305, 0.0017808985, 0.0030057386]
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D_Loss: [0.5 0.5]
G_Loss: [1.051291, 1.0, 0.0, 0.0018862335, 0.0027764079, 0.0018876566, 0.0027768551]
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D_Loss: [0.5 0.5]
G_Loss: [1.0401871, 1.0, 0.0, 0.0018684652, 0.0017852697, 0.0018644629, 0.0017853396]
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D_Loss: [0.5 0.5]
G_Loss: [1.0253744, 1.0, 0.0, 0.0012594284, 0.0010473407, 0.0012595513, 0.00104712]
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D_Loss: [0.5 0.5]
G_Loss: [1.0327599, 1.0, 0.0, 0.0008303677, 0.0021478543, 0.0008298436, 0.0021479093]
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D_Loss: [0.5 0.5]
G_Loss: [1.043262, 1.0, 0.0, 0.0019067113, 0.0020261141, 0.001907411, 0.0020262098]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0538027, 1.0, 0.0, 0.0020693636, 0.0028219505, 0.0020675682, 0.002822066]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0363598, 1.0, 0.0, 0.0012498116, 0.0020556827, 0.0012491654, 0.002055646]
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D_Loss: [0.5 0.5]
G_Loss: [1.0422004, 1.0, 0.0, 0.0017293787, 0.0021068177, 0.0017314374, 0.0021070507]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0479255, 1.0, 0.0, 0.001021441, 0.0033353511, 0.0010221592, 0.0033354056]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0380319, 1.0, 0.0, 0.0013917799, 0.0020657147, 0.001390921, 0.0020659752]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0377374, 1.0, 0.0, 0.0014914986, 0.0019395554, 0.0014867329, 0.0019400226]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0364668, 1.0, 0.0, 0.0015063027, 0.0018091535, 0.0015031466, 0.001809108]
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D_Loss: [0.5 0.5]
G_Loss: [1.0308026, 1.0, 0.0, 0.0008899169, 0.0019102658, 0.0008905189, 0.0019102027]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0357379, 1.0, 0.0, 0.0015901958, 0.0016586275, 0.0015908729, 0.0016587474]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0383425, 1.0, 0.0, 0.0015967563, 0.0018890213, 0.0015949906, 0.0018896351]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0458148, 1.0, 0.0, 0.002228053, 0.001936743, 0.0022293585, 0.0019374341]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0384184, 1.0, 0.0, 0.0014769156, 0.0020155502, 0.0014780825, 0.0020155795]
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D_Loss: [0.5 0.5]
G_Loss: [1.0393739, 1.0, 0.0, 0.0011380723, 0.0024414514, 0.0011373907, 0.0024413047]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0313901, 1.0, 0.0, 0.0014848039, 0.0013689894, 0.0014832506, 0.0013689441]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.05415, 1.0, 0.0, 0.0023059295, 0.0026168588, 0.0023040215, 0.0026181317]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.036317, 1.0, 0.0, 0.0015657935, 0.0017357082, 0.0015662457, 0.0017357314]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0373544, 1.0, 0.0, 0.0013732079, 0.0020225984, 0.0013735087, 0.0020227216]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0394585, 1.0, 0.0, 0.0015529164, 0.00203438, 0.001551047, 0.002034537]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0292891, 1.0, 0.0, 0.0010822369, 0.0015804018, 0.0010822365, 0.0015804588]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.034809, 1.0, 0.0, 0.0018564131, 0.0013079578, 0.0018573311, 0.0013080542]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0380292, 1.0, 0.0, 0.002098152, 0.0013592157, 0.0020962697, 0.0013592346]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0392976, 1.0, 0.0, 0.0018215036, 0.0017509274, 0.0018224837, 0.0017508736]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0457252, 1.0, 0.0, 0.0016019029, 0.002554865, 0.0016026342, 0.0025548697]
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D_Loss: [0.5 0.5]
G_Loss: [1.0442926, 1.0, 0.0, 0.0011929383, 0.0028337007, 0.0011923946, 0.0028337776]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0381316, 1.0, 0.0, 0.0018674509, 0.0015991395, 0.0018663887, 0.001599262]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0415275, 1.0, 0.0, 0.0014053297, 0.0023698122, 0.0014061715, 0.0023698392]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0409368, 1.0, 0.0, 0.0020255137, 0.0016959885, 0.0020258897, 0.0016960364]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0460821, 1.0, 0.0, 0.0013136412, 0.0028756508, 0.0013136711, 0.0028756051]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0420878, 1.0, 0.0, 0.001970326, 0.0018558055, 0.0019704592, 0.0018561202]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0330106, 1.0, 0.0, 0.0012242303, 0.0017767693, 0.001223725, 0.0017768978]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.041586, 1.0, 0.0, 0.0020470268, 0.0017332791, 0.0020493518, 0.0017336772]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0306844, 1.0, 0.0, 0.0012230524, 0.0015664668, 0.0012226538, 0.00156652]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0294102, 1.0, 0.0, 0.001284631, 0.0013893243, 0.0012813596, 0.0013893042]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0278063, 1.0, 0.0, 0.0012923914, 0.0012355788, 0.0012909079, 0.0012356297]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0293608, 1.0, 0.0, 0.0014988377, 0.0011702874, 0.0014991595, 0.001170296]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0336057, 1.0, 0.0, 0.0016702468, 0.0013846701, 0.0016717348, 0.0013847399]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.0381013, 1.0, 0.0, 0.0022951681, 0.0011685595, 0.002295517, 0.0011684945]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0550685, 1.0, 0.0, 0.0016828883, 0.0033232477, 0.0016839603, 0.0033232328]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0575942, 1.0, 0.0, 0.0030224393, 0.0022131423, 0.0030251504, 0.002213138]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0421624, 1.0, 0.0, 0.0019605733, 0.0018723273, 0.0019608564, 0.0018725279]
Epoch: 4
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0330777, 1.0, 0.0, 0.0011036424, 0.0019033534, 0.001104306, 0.0019033647]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0507387, 1.0, 0.0, 0.0017924949, 0.0028200524, 0.0017928712, 0.0028203619]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0304247, 1.0, 0.0, 0.0015128051, 0.0012530746, 0.0015126803, 0.0012532892]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0555289, 1.0, 0.0, 0.003126495, 0.0019214593, 0.0031277877, 0.0019215889]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0278066, 1.0, 0.0, 0.0013024904, 0.0012253812, 0.0013024567, 0.0012254877]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0429995, 1.0, 0.0, 0.0009929154, 0.0029161326, 0.0009929466, 0.0029160806]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0625939, 1.0, 0.0, 0.0012213369, 0.004469002, 0.001221669, 0.0044689644]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0339642, 1.0, 0.0, 0.0011108165, 0.0019769222, 0.0011098761, 0.0019769806]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0405968, 1.0, 0.0, 0.0018581162, 0.0018326414, 0.0018566409, 0.0018326604]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0339158, 1.0, 0.0, 0.0012867684, 0.0017964477, 0.0012869956, 0.0017966237]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0516727, 1.0, 0.0, 0.0016473285, 0.0030504316, 0.0016445233, 0.003050521]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0367287, 1.0, 0.0, 0.0019712925, 0.0013676754, 0.001971323, 0.0013676553]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.042877, 1.0, 0.0, 0.0013688824, 0.002529014, 0.0013689203, 0.0025292016]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0325801, 1.0, 0.0, 0.0019873925, 0.0009744016, 0.0019876284, 0.00097450987]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0288583, 1.0, 0.0, 0.0013420356, 0.0012815001, 0.0013414274, 0.0012814594]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0270394, 1.0, 0.0, 0.0014089935, 0.0010491944, 0.0014083094, 0.0010491768]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0304267, 1.0, 0.0, 0.0014186823, 0.0013473155, 0.0014194113, 0.0013472908]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.039814, 1.0, 0.0, 0.0013177244, 0.00230174, 0.0013175241, 0.0023018424]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0363724, 1.0, 0.0, 0.0014373279, 0.0018692524, 0.0014374196, 0.0018692268]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0538534, 1.0, 0.0, 0.0016720218, 0.0032237666, 0.0016718116, 0.0032237412]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0471892, 1.0, 0.0, 0.0016541779, 0.0026357388, 0.0016542737, 0.0026358108]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0384039, 1.0, 0.0, 0.0015688301, 0.0019225482, 0.0015675854, 0.0019225436]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.034234, 1.0, 0.0, 0.0014696645, 0.0016426677, 0.0014677958, 0.0016428909]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0331441, 1.0, 0.0, 0.0010398712, 0.001973173, 0.0010403632, 0.0019733105]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0413616, 1.0, 0.0, 0.0019734958, 0.0017868007, 0.0019716637, 0.0017868595]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0419585, 1.0, 0.0, 0.001553891, 0.002260548, 0.0015535272, 0.002260484]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0347553, 1.0, 0.0, 0.0021443628, 0.0010152394, 0.0021439781, 0.0010153678]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0370524, 1.0, 0.0, 0.0016822792, 0.0016861043, 0.0016821541, 0.0016862894]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0306146, 1.0, 0.0, 0.0017084804, 0.0010745925, 0.0017091547, 0.0010747414]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0579332, 1.0, 0.0, 0.002763271, 0.002503399, 0.0027632047, 0.0025033422]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0342313, 1.0, 0.0, 0.0014881361, 0.0016238318, 0.0014875607, 0.0016239503]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0330878, 1.0, 0.0, 0.0013762026, 0.0016317432, 0.0013762164, 0.0016321242]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0365356, 1.0, 0.0, 0.0010609633, 0.0022604729, 0.0010608627, 0.0022604722]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0298898, 1.0, 0.0, 0.001243714, 0.0014735295, 0.001243563, 0.0014737318]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.029283, 1.0, 0.0, 0.0014267126, 0.0012353458, 0.0014271401, 0.0012353108]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0386486, 1.0, 0.0, 0.0016592994, 0.0018542128, 0.0016592295, 0.0018541624]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0262525, 1.0, 0.0, 0.0012538886, 0.0011328207, 0.0012524901, 0.0011328316]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0296991, 1.0, 0.0, 0.0016864154, 0.0010134531, 0.001686955, 0.0010134205]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.023532, 1.0, 0.0, 0.0014999206, 0.0006393603, 0.0014998522, 0.0006394065]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0310748, 1.0, 0.0, 0.0022103782, 0.0006145891, 0.0022105838, 0.0006145594]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0581974, 1.0, 0.0, 0.0018384214, 0.0034524142, 0.0018365596, 0.0034523914]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0598983, 1.0, 0.0, 0.0029424313, 0.002502864, 0.0029424918, 0.0025028659]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0531839, 1.0, 0.0, 0.0017259539, 0.0031089676, 0.0017256541, 0.003108983]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0411459, 1.0, 0.0, 0.0010905152, 0.00264996, 0.0010911429, 0.0026499815]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0250903, 1.0, 0.0, 0.0010106881, 0.0012702569, 0.001010569, 0.0012702481]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0347874, 1.0, 0.0, 0.0013626008, 0.0017998342, 0.0013633796, 0.0017996924]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.040244, 1.0, 0.0, 0.0021382123, 0.0015203756, 0.0021378754, 0.0015203212]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0326241, 1.0, 0.0, 0.00083288335, 0.0021330044, 0.0008322748, 0.002133015]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0450839, 1.0, 0.0, 0.0017751174, 0.0023235166, 0.0017739679, 0.0023234813]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0295857, 1.0, 0.0, 0.0009508746, 0.0017387683, 0.00095057604, 0.0017388143]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0256686, 1.0, 0.0, 0.00080033275, 0.0015332298, 0.0008000506, 0.0015329474]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0389767, 1.0, 0.0, 0.0016733097, 0.0018700107, 0.0016735307, 0.001869962]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0434147, 1.0, 0.0, 0.001451079, 0.0024957033, 0.0014509985, 0.0024959105]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0393986, 1.0, 0.0, 0.0016491537, 0.00193251, 0.0016495424, 0.0019323607]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0354514, 1.0, 0.0, 0.002018386, 0.0012043009, 0.002020359, 0.0012042182]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0402992, 1.0, 0.0, 0.0016145395, 0.0020490584, 0.0016143303, 0.0020489926]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.035596, 1.0, 0.0, 0.0014106481, 0.001825345, 0.0014108705, 0.0018252386]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0374851, 1.0, 0.0, 0.0016503234, 0.001757436, 0.0016502265, 0.0017573435]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0356803, 1.0, 0.0, 0.0019511163, 0.0012925435, 0.0019511859, 0.0012924786]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0344706, 1.0, 0.0, 0.0015624147, 0.001571475, 0.0015603006, 0.0015713418]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0349888, 1.0, 0.0, 0.0017869894, 0.0013937308, 0.0017878938, 0.0013935424]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0333954, 1.0, 0.0, 0.0014039921, 0.0016318999, 0.0014046261, 0.0016318469]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.043887, 1.0, 0.0, 0.0015781943, 0.002411544, 0.0015783093, 0.0024112454]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0429944, 1.0, 0.0, 0.0017921603, 0.0021164801, 0.0017914667, 0.0021165023]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0443746, 1.0, 0.0, 0.0018435052, 0.0021906106, 0.00184333, 0.0021900686]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0485786, 1.0, 0.0, 0.0019230657, 0.0024932977, 0.0019215334, 0.0024935305]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.032913, 1.0, 0.0, 0.0014792627, 0.0015127747, 0.0014797816, 0.001512771]
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D_Loss: [0.5 0.5]
G_Loss: [1.0320251, 1.0, 0.0, 0.0011004398, 0.0018109512, 0.0011000347, 0.0018109761]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0502541, 1.0, 0.0, 0.0030763177, 0.0014921203, 0.0030775592, 0.0014922041]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0425205, 1.0, 0.0, 0.0013644925, 0.0025010968, 0.0013635599, 0.0025011068]
Batch: 70
D_Loss: [0.5 0.5]
G_Loss: [1.038648, 1.0, 0.0, 0.0020058346, 0.0015076067, 0.0020060446, 0.0015075639]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0690837, 1.0, 0.0, 0.003371088, 0.002909351, 0.0033698943, 0.00290942]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0283442, 1.0, 0.0, 0.0011832935, 0.0013934904, 0.0011828681, 0.0013934721]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0353428, 1.0, 0.0, 0.0011373033, 0.0020757322, 0.0011367784, 0.0020756903]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0345355, 1.0, 0.0, 0.0015219986, 0.0016178159, 0.0015196144, 0.001617868]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.0403298, 1.0, 0.0, 0.0017941601, 0.0018722031, 0.0017937205, 0.0018723694]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0493597, 1.0, 0.0, 0.0018905163, 0.002596775, 0.001889895, 0.0025969006]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0329591, 1.0, 0.0, 0.001825029, 0.0011714193, 0.0018231187, 0.0011715115]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0283525, 1.0, 0.0, 0.0012339215, 0.0013436022, 0.001233734, 0.0013434973]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0281037, 1.0, 0.0, 0.0008253503, 0.0017295585, 0.0008250782, 0.0017296304]
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D_Loss: [0.5 0.5]
G_Loss: [1.0393, 1.0, 0.0, 0.001894875, 0.0016778281, 0.0018950392, 0.001677901]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0489414, 1.0, 0.0, 0.0020155706, 0.002433747, 0.0020144992, 0.0024337033]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0297709, 1.0, 0.0, 0.0012307838, 0.0014757416, 0.0012298864, 0.0014757225]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0355603, 1.0, 0.0, 0.0016815527, 0.0015511337, 0.0016823305, 0.0015510571]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0359275, 1.0, 0.0, 0.0009775301, 0.0022886158, 0.0009773426, 0.002288697]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0377254, 1.0, 0.0, 0.0013764155, 0.002053297, 0.0013750569, 0.0020532035]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0346549, 1.0, 0.0, 0.0014442799, 0.0017063217, 0.0014423763, 0.0017063433]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0331541, 1.0, 0.0, 0.0015185141, 0.0014956768, 0.0015165377, 0.001495677]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0273308, 1.0, 0.0, 0.000855754, 0.001628853, 0.0008559791, 0.0016287754]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0321162, 1.0, 0.0, 0.0015713507, 0.0013482671, 0.0015717379, 0.0013482735]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0356641, 1.0, 0.0, 0.0016349626, 0.0016073426, 0.0016338096, 0.0016072044]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.041522, 1.0, 0.0, 0.002246338, 0.0015283622, 0.002246788, 0.0015283767]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0382972, 1.0, 0.0, 0.0014333008, 0.0020483, 0.0014328877, 0.0020481977]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.041647, 1.0, 0.0, 0.0010740439, 0.0027120966, 0.0010734699, 0.0027120966]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0270022, 1.0, 0.0, 0.001383692, 0.0010711808, 0.0013824552, 0.0010711259]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0529441, 1.0, 0.0, 0.0023117294, 0.0025015364, 0.002310128, 0.0025012922]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0334865, 1.0, 0.0, 0.0015569003, 0.0014873286, 0.0015568992, 0.0014873615]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0353447, 1.0, 0.0, 0.001330408, 0.0018827377, 0.0013304533, 0.0018827332]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0414983, 1.0, 0.0, 0.0015399309, 0.0022327276, 0.001538809, 0.0022329078]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.028789, 1.0, 0.0, 0.0010587696, 0.0015584778, 0.0010583117, 0.0015581588]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0321313, 1.0, 0.0, 0.001831722, 0.0010892618, 0.0018322744, 0.001089205]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0385205, 1.0, 0.0, 0.0020578434, 0.0014441203, 0.002056764, 0.0014440867]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0364233, 1.0, 0.0, 0.0017803957, 0.0015307614, 0.0017811335, 0.0015306963]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.042672, 1.0, 0.0, 0.0015849844, 0.0022942014, 0.0015859233, 0.002294151]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0408012, 1.0, 0.0, 0.0011818176, 0.002527443, 0.0011813955, 0.0025272665]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.033378, 1.0, 0.0, 0.0018601099, 0.0011742993, 0.001859802, 0.0011741875]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.039913, 1.0, 0.0, 0.001390784, 0.0022377595, 0.0013900303, 0.0022376678]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.037596, 1.0, 0.0, 0.0020189402, 0.0013989724, 0.002017988, 0.0013989757]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0431477, 1.0, 0.0, 0.0012859765, 0.0026365276, 0.0012860936, 0.0026364494]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0379936, 1.0, 0.0, 0.0018826702, 0.0015712979, 0.0018825248, 0.0015713396]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.032048, 1.0, 0.0, 0.0012284692, 0.0016849383, 0.0012288693, 0.0016849947]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0409995, 1.0, 0.0, 0.0020395727, 0.0016876117, 0.0020403906, 0.0016872794]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0285599, 1.0, 0.0, 0.0011208621, 0.0014754883, 0.0011207468, 0.0014755173]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0274608, 1.0, 0.0, 0.001301678, 0.0011949949, 0.0012993965, 0.0011947424]
Batch: 114
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G_Loss: [1.0273479, 1.0, 0.0, 0.0012444728, 0.0012418061, 0.0012434854, 0.0012417145]
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G_Loss: [1.0282086, 1.0, 0.0, 0.0015287551, 0.0010356676, 0.0015288924, 0.0010355954]
Batch: 116
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G_Loss: [1.0338664, 1.0, 0.0, 0.0017493367, 0.0013293321, 0.0017504502, 0.0013293487]
Batch: 117
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G_Loss: [1.038089, 1.0, 0.0, 0.00233057, 0.0011320782, 0.0023305812, 0.0011319892]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0541134, 1.0, 0.0, 0.0016695508, 0.0032497472, 0.0016705474, 0.0032497344]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0612043, 1.0, 0.0, 0.0033742678, 0.0021895003, 0.0033770213, 0.0021894677]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0401676, 1.0, 0.0, 0.0017955087, 0.0018560593, 0.0017958273, 0.0018560293]
Epoch: 5
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0321149, 1.0, 0.0, 0.0010831645, 0.0018363311, 0.0010837085, 0.0018362277]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0505283, 1.0, 0.0, 0.0018016666, 0.0027917677, 0.0018019155, 0.0027920166]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0293219, 1.0, 0.0, 0.0014541083, 0.0012116103, 0.0014531264, 0.0012116742]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0570531, 1.0, 0.0, 0.0033368277, 0.0018497005, 0.0033381763, 0.0018496567]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0260382, 1.0, 0.0, 0.0012166495, 0.001150459, 0.0012165516, 0.0011504539]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0419397, 1.0, 0.0, 0.0009809325, 0.0028317731, 0.000980989, 0.0028317538]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0623679, 1.0, 0.0, 0.0012505442, 0.00441924, 0.0012508145, 0.0044192085]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0337526, 1.0, 0.0, 0.0011117123, 0.0019567867, 0.0011107955, 0.0019568095]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0390381, 1.0, 0.0, 0.0019186395, 0.0016303873, 0.0019175031, 0.0016303451]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0333004, 1.0, 0.0, 0.0012416434, 0.0017856328, 0.0012417673, 0.0017857701]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0486488, 1.0, 0.0, 0.0016584905, 0.0027643328, 0.0016561537, 0.002764447]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0383344, 1.0, 0.0, 0.0019683463, 0.0015166133, 0.001968104, 0.0015165485]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0419221, 1.0, 0.0, 0.001328176, 0.0024829167, 0.0013282152, 0.0024829623]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.031464, 1.0, 0.0, 0.0019795166, 0.0008808427, 0.0019796449, 0.0008808921]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0280018, 1.0, 0.0, 0.0013024367, 0.0012432236, 0.0013021356, 0.001243155]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0265932, 1.0, 0.0, 0.0013880511, 0.0010296013, 0.0013872359, 0.0010295358]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0299579, 1.0, 0.0, 0.0014312291, 0.0012921444, 0.0014319639, 0.0012921878]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0394728, 1.0, 0.0, 0.0012909066, 0.002297556, 0.0012906085, 0.0022976059]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0357324, 1.0, 0.0, 0.0014241233, 0.0018242857, 0.0014242181, 0.0018240821]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0551349, 1.0, 0.0, 0.0016605698, 0.0033516972, 0.0016601692, 0.003351915]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0437675, 1.0, 0.0, 0.0016374348, 0.0023414108, 0.0016375604, 0.0023414304]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0357783, 1.0, 0.0, 0.0015756565, 0.0016770235, 0.0015745442, 0.0016768933]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0318655, 1.0, 0.0, 0.0014529895, 0.0014439842, 0.0014517459, 0.0014440232]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0329696, 1.0, 0.0, 0.0010025853, 0.001994634, 0.0010029547, 0.0019944664]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0408646, 1.0, 0.0, 0.0019507878, 0.0017643142, 0.0019492753, 0.0017643131]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0422926, 1.0, 0.0, 0.0015210281, 0.0023237825, 0.0015207855, 0.002323702]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0337644, 1.0, 0.0, 0.0021097201, 0.0009597956, 0.0021094214, 0.00095984444]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.036468, 1.0, 0.0, 0.0016635879, 0.0016517064, 0.0016633437, 0.0016517568]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0306519, 1.0, 0.0, 0.0017192488, 0.0010672382, 0.0017198878, 0.0010672486]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.056576, 1.0, 0.0, 0.0027223758, 0.0024209274, 0.0027221497, 0.0024209078]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0334932, 1.0, 0.0, 0.0014609697, 0.0015838965, 0.0014604405, 0.0015840009]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0322293, 1.0, 0.0, 0.0013568816, 0.0015730282, 0.0013570355, 0.0015731778]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0369302, 1.0, 0.0, 0.0010423587, 0.0023149513, 0.0010423331, 0.0023148647]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0287381, 1.0, 0.0, 0.0012350588, 0.0013775285, 0.0012347281, 0.001377553]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0297359, 1.0, 0.0, 0.0013959456, 0.0013073093, 0.0013961531, 0.0013071977]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.038103, 1.0, 0.0, 0.001648207, 0.0018157223, 0.0016480787, 0.0018155982]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0261639, 1.0, 0.0, 0.0012208926, 0.0011577581, 0.0012196039, 0.001157739]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0291063, 1.0, 0.0, 0.0016466996, 0.0009993494, 0.0016466548, 0.000999218]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0237782, 1.0, 0.0, 0.0015105079, 0.0006511212, 0.0015107058, 0.00065115426]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0306513, 1.0, 0.0, 0.0021998486, 0.0005865948, 0.0022002247, 0.0005865991]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0576851, 1.0, 0.0, 0.0018084819, 0.003435756, 0.0018071017, 0.0034357794]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0596653, 1.0, 0.0, 0.0029279278, 0.0024962048, 0.0029278751, 0.002496158]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0530102, 1.0, 0.0, 0.0016820033, 0.0031371266, 0.0016816906, 0.003137214]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0417839, 1.0, 0.0, 0.0010675299, 0.0027309617, 0.0010680384, 0.0027310052]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.024475, 1.0, 0.0, 0.001012279, 0.001212738, 0.0010123206, 0.0012124707]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0329804, 1.0, 0.0, 0.0013317405, 0.0016664469, 0.0013322816, 0.0016663544]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0401931, 1.0, 0.0, 0.0021632784, 0.001490673, 0.0021628104, 0.0014906557]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0311702, 1.0, 0.0, 0.00083098374, 0.0020027123, 0.0008304911, 0.0020027114]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0438734, 1.0, 0.0, 0.0018040992, 0.0021844627, 0.0018034532, 0.0021843428]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0281981, 1.0, 0.0, 0.0009041101, 0.0016594003, 0.000903607, 0.0016594178]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0249823, 1.0, 0.0, 0.0007678484, 0.0015032886, 0.00076806557, 0.0015028771]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0385642, 1.0, 0.0, 0.001678926, 0.0018269201, 0.0016788722, 0.0018268549]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.043465, 1.0, 0.0, 0.0014469638, 0.0025043953, 0.001446964, 0.0025044843]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0386494, 1.0, 0.0, 0.0016505562, 0.0018629887, 0.0016510165, 0.0018628369]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0348594, 1.0, 0.0, 0.0019912175, 0.0011777122, 0.0019925942, 0.001177599]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399438, 1.0, 0.0, 0.0016068814, 0.002024398, 0.0016066161, 0.002024408]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0353708, 1.0, 0.0, 0.0013986926, 0.0018168166, 0.0013988642, 0.001816743]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0375707, 1.0, 0.0, 0.0016679757, 0.0017475757, 0.0016676772, 0.0017475539]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0343401, 1.0, 0.0, 0.0018718146, 0.0012500142, 0.0018718686, 0.0012499716]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0343455, 1.0, 0.0, 0.0015891443, 0.0015333329, 0.0015875122, 0.0015332643]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0340399, 1.0, 0.0, 0.0017610535, 0.0013334176, 0.0017617668, 0.0013333894]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.032835, 1.0, 0.0, 0.0013999889, 0.0015849606, 0.0014005674, 0.0015849341]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0438683, 1.0, 0.0, 0.0016061324, 0.0023819136, 0.0016061745, 0.002381677]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0425488, 1.0, 0.0, 0.0017301007, 0.0021380251, 0.001729484, 0.002137999]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0426165, 1.0, 0.0, 0.0018324, 0.0020418959, 0.0018319774, 0.0020414605]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.04828, 1.0, 0.0, 0.0019431924, 0.0024460224, 0.0019416916, 0.002446162]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0318094, 1.0, 0.0, 0.0014266351, 0.0014651155, 0.0014270188, 0.0014648493]
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D_Loss: [0.5 0.5]
G_Loss: [1.0321313, 1.0, 0.0, 0.001077873, 0.0018432045, 0.0010773428, 0.0018431854]
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D_Loss: [0.5 0.5]
G_Loss: [1.0494711, 1.0, 0.0, 0.003059897, 0.0014373921, 0.0030607877, 0.0014374198]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0422422, 1.0, 0.0, 0.0013566585, 0.0024836035, 0.00135596, 0.0024835137]
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D_Loss: [0.5 0.5]
G_Loss: [1.0388948, 1.0, 0.0, 0.00202587, 0.001510007, 0.002026056, 0.0015098876]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0681715, 1.0, 0.0, 0.00333902, 0.002858509, 0.0033376985, 0.0028585638]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0276773, 1.0, 0.0, 0.0011749206, 0.001341227, 0.0011746539, 0.0013411966]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0347325, 1.0, 0.0, 0.0011185004, 0.0020390397, 0.0011180234, 0.002038981]
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D_Loss: [0.5 0.5]
G_Loss: [1.0337244, 1.0, 0.0, 0.0015100066, 0.0015560342, 0.001507835, 0.0015560351]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.0396878, 1.0, 0.0, 0.0018050559, 0.0018030661, 0.0018046374, 0.0018019908]
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D_Loss: [0.5 0.5]
G_Loss: [1.0506049, 1.0, 0.0, 0.0019021035, 0.0026983693, 0.0019015407, 0.002698719]
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D_Loss: [0.5 0.5]
G_Loss: [1.0325242, 1.0, 0.0, 0.0018010896, 0.0011558195, 0.0017994851, 0.0011557264]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0288154, 1.0, 0.0, 0.0012225754, 0.001397032, 0.0012223746, 0.001396904]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0277851, 1.0, 0.0, 0.0008358952, 0.0016900375, 0.00083568064, 0.0016900762]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0388051, 1.0, 0.0, 0.0018749563, 0.0016527644, 0.0018751095, 0.0016527231]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0487385, 1.0, 0.0, 0.0020061105, 0.0024247381, 0.0020053145, 0.0024246154]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0297376, 1.0, 0.0, 0.0012186874, 0.0014848142, 0.001217863, 0.0014847808]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0351161, 1.0, 0.0, 0.0016623533, 0.0015299988, 0.0016627781, 0.0015297984]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0364196, 1.0, 0.0, 0.0009658711, 0.0023450088, 0.0009657898, 0.0023450023]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0363717, 1.0, 0.0, 0.0013685564, 0.0019380823, 0.0013674798, 0.0019378434]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0342997, 1.0, 0.0, 0.0014208555, 0.0016974467, 0.0014194432, 0.0016972602]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0333934, 1.0, 0.0, 0.001525395, 0.0015105144, 0.0015237932, 0.0015105018]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0263617, 1.0, 0.0, 0.0008417252, 0.0015547927, 0.0008418665, 0.001554702]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.031276, 1.0, 0.0, 0.0015567818, 0.0012864426, 0.0015572968, 0.0012864068]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0358428, 1.0, 0.0, 0.0016501853, 0.0016083324, 0.001649424, 0.0016081388]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0414544, 1.0, 0.0, 0.002254095, 0.0015144569, 0.002254474, 0.0015143869]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0378687, 1.0, 0.0, 0.0014303331, 0.002012343, 0.0014296332, 0.0020122563]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0416992, 1.0, 0.0, 0.0010522292, 0.002738642, 0.0010518385, 0.0027386625]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0262793, 1.0, 0.0, 0.001369548, 0.0010195905, 0.001368488, 0.0010195215]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.052463, 1.0, 0.0, 0.0023089359, 0.0024605717, 0.0023076003, 0.0024602236]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0329528, 1.0, 0.0, 0.0015549744, 0.0014407234, 0.0015549334, 0.0014407581]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0349382, 1.0, 0.0, 0.001318945, 0.0018572733, 0.0013189535, 0.0018572202]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0414308, 1.0, 0.0, 0.001524538, 0.0022419852, 0.0015234166, 0.0022421554]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0286593, 1.0, 0.0, 0.0010568042, 0.0015486565, 0.0010563771, 0.0015482837]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0317839, 1.0, 0.0, 0.0018408778, 0.001048528, 0.0018413765, 0.0010484471]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0383039, 1.0, 0.0, 0.002008916, 0.0014733453, 0.0020079627, 0.0014733193]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0353507, 1.0, 0.0, 0.0017306283, 0.0014830208, 0.0017311142, 0.0014829466]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0422237, 1.0, 0.0, 0.0015880009, 0.0022504781, 0.001588496, 0.0022504514]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0406858, 1.0, 0.0, 0.0011852948, 0.0025134601, 0.001185063, 0.0025132084]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0331628, 1.0, 0.0, 0.0018492995, 0.0011655411, 0.0018490285, 0.0011653847]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0393175, 1.0, 0.0, 0.0013934409, 0.0021809489, 0.0013927075, 0.002180926]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0374167, 1.0, 0.0, 0.0020168188, 0.0013847892, 0.0020159874, 0.0013846678]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0417945, 1.0, 0.0, 0.0012651349, 0.0025343804, 0.0012652206, 0.002534309]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0368506, 1.0, 0.0, 0.0018442811, 0.0015057841, 0.0018441788, 0.0015056918]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0316753, 1.0, 0.0, 0.0012262643, 0.0016532936, 0.0012267086, 0.0016532219]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0412905, 1.0, 0.0, 0.0020377631, 0.0017159069, 0.0020382602, 0.0017155213]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0278174, 1.0, 0.0, 0.0010759531, 0.0014529119, 0.001075853, 0.0014528816]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0283786, 1.0, 0.0, 0.0014197556, 0.0011603308, 0.0014175845, 0.0011600319]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0272392, 1.0, 0.0, 0.0012151527, 0.0012612203, 0.0012143797, 0.0012611236]
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D_Loss: [0.5 0.5]
G_Loss: [1.028085, 1.0, 0.0, 0.0015415684, 0.0010116257, 0.0015416661, 0.0010115238]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.034316, 1.0, 0.0, 0.0018057849, 0.0013137725, 0.0018067034, 0.0013137449]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.0390033, 1.0, 0.0, 0.0024026623, 0.0011431037, 0.0024026185, 0.0011430186]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0547489, 1.0, 0.0, 0.0017489104, 0.0032281654, 0.0017499821, 0.0032281633]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0627022, 1.0, 0.0, 0.0035247542, 0.0021752222, 0.0035271281, 0.0021752422]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0394355, 1.0, 0.0, 0.0017232383, 0.0018617997, 0.0017234461, 0.0018616887]
Epoch: 6
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0324286, 1.0, 0.0, 0.0011188127, 0.0018292186, 0.0011192763, 0.0018290167]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0510256, 1.0, 0.0, 0.001881999, 0.002756659, 0.0018820391, 0.002756937]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0308774, 1.0, 0.0, 0.0015990809, 0.0012080447, 0.0015981786, 0.0012079928]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0558732, 1.0, 0.0, 0.0032561878, 0.0018230622, 0.0032575957, 0.0018229714]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0250604, 1.0, 0.0, 0.0011125517, 0.001165655, 0.001112697, 0.0011656426]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0410887, 1.0, 0.0, 0.0009405903, 0.0027947365, 0.00094069197, 0.002794748]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0616277, 1.0, 0.0, 0.0012078607, 0.0043946435, 0.001208029, 0.0043946104]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0335512, 1.0, 0.0, 0.0011027171, 0.001947475, 0.0011018873, 0.0019474344]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.037788, 1.0, 0.0, 0.0018952747, 0.0015401045, 0.0018942201, 0.0015400178]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0330459, 1.0, 0.0, 0.0012241512, 0.0017800236, 0.0012241298, 0.0017800814]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0473258, 1.0, 0.0, 0.0016406914, 0.0026618722, 0.0016383477, 0.0026619602]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0392356, 1.0, 0.0, 0.001973615, 0.0015933006, 0.0019731154, 0.0015932776]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.040907, 1.0, 0.0, 0.0012686827, 0.0024501241, 0.0012686859, 0.0024501737]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.031062, 1.0, 0.0, 0.0019613544, 0.00086244795, 0.0019615453, 0.00086246815]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0280436, 1.0, 0.0, 0.0013230741, 0.0012263791, 0.0013227214, 0.0012263115]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0262475, 1.0, 0.0, 0.0013772105, 0.0010089967, 0.0013763388, 0.0010089321]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0295414, 1.0, 0.0, 0.0014108482, 0.0012746523, 0.0014115695, 0.0012747197]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0393585, 1.0, 0.0, 0.001280211, 0.002297863, 0.0012798891, 0.0022978943]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0358485, 1.0, 0.0, 0.001416761, 0.001842227, 0.0014165475, 0.001842002]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.054148, 1.0, 0.0, 0.0016545996, 0.0032679795, 0.001654296, 0.0032679406]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0423471, 1.0, 0.0, 0.0016256243, 0.0022241008, 0.0016257211, 0.002224083]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0349082, 1.0, 0.0, 0.001576148, 0.0015974415, 0.0015749467, 0.0015973165]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0312914, 1.0, 0.0, 0.0014359807, 0.001408774, 0.0014349775, 0.0014087597]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0322298, 1.0, 0.0, 0.0009813734, 0.0019485811, 0.0009817726, 0.0019483778]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0412394, 1.0, 0.0, 0.001963397, 0.0017857247, 0.001962383, 0.0017857105]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0417135, 1.0, 0.0, 0.0014866891, 0.002305463, 0.0014865482, 0.0023054155]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.033207, 1.0, 0.0, 0.0020806459, 0.0009381961, 0.0020804089, 0.00093820627]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0360153, 1.0, 0.0, 0.0016421886, 0.0016319421, 0.0016419702, 0.0016319694]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0307719, 1.0, 0.0, 0.001726896, 0.0010704963, 0.0017273647, 0.0010704588]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0563962, 1.0, 0.0, 0.0027092707, 0.0024177204, 0.0027087238, 0.0024177115]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0334104, 1.0, 0.0, 0.0014532276, 0.0015841287, 0.0014526718, 0.0015842173]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.031915, 1.0, 0.0, 0.0013484161, 0.0015529236, 0.0013485698, 0.001552968]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0367448, 1.0, 0.0, 0.0010323817, 0.0023080865, 0.0010322803, 0.002307984]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.02812, 1.0, 0.0, 0.0012281821, 0.0013282651, 0.0012276518, 0.0013280176]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0301182, 1.0, 0.0, 0.0013738603, 0.001364162, 0.0013740448, 0.001363936]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0377498, 1.0, 0.0, 0.0016398317, 0.0017920013, 0.0016397357, 0.0017917533]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0262245, 1.0, 0.0, 0.0012053397, 0.0011788297, 0.0012040928, 0.0011787978]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0289661, 1.0, 0.0, 0.0016302639, 0.0010030763, 0.0016299912, 0.0010028353]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.023892, 1.0, 0.0, 0.0015114876, 0.0006604943, 0.0015116788, 0.000660526]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0304341, 1.0, 0.0, 0.002188438, 0.00057825726, 0.0021888157, 0.0005782491]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0574179, 1.0, 0.0, 0.0017901552, 0.0034297951, 0.0017885913, 0.0034298194]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0595213, 1.0, 0.0, 0.0029182327, 0.0024928134, 0.0029181775, 0.002492746]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0526544, 1.0, 0.0, 0.0016414544, 0.0031453131, 0.001641216, 0.003145478]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0418324, 1.0, 0.0, 0.0010461534, 0.0027567386, 0.001046692, 0.0027568047]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0245063, 1.0, 0.0, 0.0010222837, 0.0012056066, 0.0010223158, 0.001205046]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0321218, 1.0, 0.0, 0.0013047394, 0.0016153937, 0.0013052258, 0.0016153082]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0405895, 1.0, 0.0, 0.0022064643, 0.0014835297, 0.0022060536, 0.0014834629]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0311838, 1.0, 0.0, 0.0008697638, 0.0019651619, 0.0008693535, 0.0019651514]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0435069, 1.0, 0.0, 0.0018115176, 0.0021437074, 0.0018111534, 0.0021434573]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0274804, 1.0, 0.0, 0.0008670549, 0.001631204, 0.00086664956, 0.0016311698]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0243326, 1.0, 0.0, 0.00072983484, 0.0014822526, 0.00073008786, 0.0014817824]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0383694, 1.0, 0.0, 0.0016846736, 0.0018034581, 0.0016846182, 0.0018033513]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0430458, 1.0, 0.0, 0.0014308651, 0.0024823837, 0.0014308619, 0.0024824035]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0390394, 1.0, 0.0, 0.0016652232, 0.0018838062, 0.0016655962, 0.0018834679]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0344651, 1.0, 0.0, 0.001961442, 0.001171658, 0.0019626329, 0.0011714014]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0398067, 1.0, 0.0, 0.0016036648, 0.0020151623, 0.0016034283, 0.0020150382]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0352546, 1.0, 0.0, 0.001384046, 0.0018209262, 0.0013842466, 0.0018207106]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0373602, 1.0, 0.0, 0.0016810566, 0.0017153571, 0.0016807537, 0.0017152538]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0334696, 1.0, 0.0, 0.001803467, 0.0012392075, 0.0018035825, 0.001239184]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0341825, 1.0, 0.0, 0.0016019177, 0.0015057087, 0.0016007525, 0.0015055274]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0339596, 1.0, 0.0, 0.0017479146, 0.0013392895, 0.0017484005, 0.001339083]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.03228, 1.0, 0.0, 0.0013849481, 0.0015495378, 0.00138555, 0.0015494699]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0436025, 1.0, 0.0, 0.0016091047, 0.0023547986, 0.0016091843, 0.0023542787]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0423547, 1.0, 0.0, 0.0017157726, 0.0021347213, 0.0017152169, 0.0021346346]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0421404, 1.0, 0.0, 0.0018318756, 0.0019991696, 0.001831452, 0.001998413]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.048096, 1.0, 0.0, 0.0019436451, 0.0024288294, 0.0019423203, 0.0024289049]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0314497, 1.0, 0.0, 0.0014098504, 0.0014492304, 0.0014102471, 0.001448621]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0320933, 1.0, 0.0, 0.0010596786, 0.0018579622, 0.0010590898, 0.001857867]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0490484, 1.0, 0.0, 0.0030533196, 0.001405547, 0.003054121, 0.0014055427]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0422475, 1.0, 0.0, 0.001365112, 0.0024756435, 0.00136443, 0.0024754675]
Batch: 70
D_Loss: [0.5 0.5]
G_Loss: [1.0389894, 1.0, 0.0, 0.0020298534, 0.0015146545, 0.0020300564, 0.0015144171]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0680184, 1.0, 0.0, 0.003345402, 0.0028382265, 0.0033437994, 0.0028382982]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0274659, 1.0, 0.0, 0.0011724541, 0.0013244615, 0.001172259, 0.0013244215]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0345644, 1.0, 0.0, 0.0011097437, 0.0020325312, 0.0011092178, 0.0020324423]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0332307, 1.0, 0.0, 0.0015045373, 0.0015166111, 0.0015025117, 0.001516576]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.039331, 1.0, 0.0, 0.0018115349, 0.0017641587, 0.0018111296, 0.001762848]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.051732, 1.0, 0.0, 0.0019136391, 0.002789294, 0.001912815, 0.0027897446]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0323495, 1.0, 0.0, 0.001785082, 0.0011559231, 0.0017835672, 0.0011557844]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.028925, 1.0, 0.0, 0.0012141174, 0.0014154542, 0.001213834, 0.0014153314]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0276675, 1.0, 0.0, 0.000841372, 0.0016738812, 0.00084116287, 0.0016739264]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0384481, 1.0, 0.0, 0.0018537523, 0.0016415133, 0.0018539086, 0.0016414701]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0487977, 1.0, 0.0, 0.002014786, 0.0024214634, 0.0020139944, 0.002421291]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0296644, 1.0, 0.0, 0.0012104779, 0.0014863557, 0.0012096965, 0.0014863025]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0349165, 1.0, 0.0, 0.0016535863, 0.0015206309, 0.001654048, 0.0015203243]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.036685, 1.0, 0.0, 0.0009618592, 0.0023731515, 0.00096178154, 0.002373104]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0355731, 1.0, 0.0, 0.0013644323, 0.0018696131, 0.0013632348, 0.0018692717]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0340847, 1.0, 0.0, 0.0014045865, 0.001694194, 0.0014031216, 0.0016938865]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0337023, 1.0, 0.0, 0.0015331735, 0.0015308426, 0.001531374, 0.0015308205]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0257596, 1.0, 0.0, 0.0008390617, 0.0015027198, 0.00083916914, 0.0015025771]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0306852, 1.0, 0.0, 0.0015458791, 0.0012436466, 0.0015463049, 0.0012435724]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0360981, 1.0, 0.0, 0.0016660475, 0.0016157202, 0.001665246, 0.0016152919]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0414839, 1.0, 0.0, 0.0022597748, 0.0015114568, 0.0022603767, 0.00151121]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0375749, 1.0, 0.0, 0.001430898, 0.0019851008, 0.0014298606, 0.0019850156]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0417494, 1.0, 0.0, 0.0010503076, 0.0027451282, 0.0010498818, 0.0027451809]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0258551, 1.0, 0.0, 0.0013561319, 0.0009944281, 0.0013550234, 0.000994331]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0523013, 1.0, 0.0, 0.0023083407, 0.0024465197, 0.0023068315, 0.0024457972]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.032638, 1.0, 0.0, 0.001549351, 0.0014177291, 0.0015493558, 0.0014177745]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0346316, 1.0, 0.0, 0.0013071328, 0.0018412094, 0.0013071129, 0.001841111]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.041532, 1.0, 0.0, 0.001523871, 0.0022518486, 0.0015228252, 0.0022520307]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.028479, 1.0, 0.0, 0.0010448277, 0.0015442612, 0.001044441, 0.0015437659]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0315155, 1.0, 0.0, 0.001834064, 0.0010309431, 0.0018345779, 0.0010308553]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0381064, 1.0, 0.0, 0.0019869113, 0.001477388, 0.0019861404, 0.0014774107]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.034826, 1.0, 0.0, 0.0017020436, 0.0014639122, 0.0017025933, 0.0014638344]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.042026, 1.0, 0.0, 0.001586461, 0.00223403, 0.0015871191, 0.0022339872]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0407809, 1.0, 0.0, 0.0011878312, 0.0025195857, 0.0011875437, 0.0025192322]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0330613, 1.0, 0.0, 0.0018371753, 0.0011684576, 0.0018367576, 0.0011682123]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0391, 1.0, 0.0, 0.001397529, 0.0021570742, 0.0013968443, 0.0021570912]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0373979, 1.0, 0.0, 0.002017763, 0.0013821428, 0.0020168233, 0.0013819342]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0408185, 1.0, 0.0, 0.0012437827, 0.002466993, 0.0012437911, 0.002466903]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0361403, 1.0, 0.0, 0.0018134188, 0.0014720778, 0.0018133104, 0.0014719495]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0315073, 1.0, 0.0, 0.0012298273, 0.0016344588, 0.0012301949, 0.0016342683]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.041343, 1.0, 0.0, 0.0020325808, 0.0017258831, 0.002033105, 0.0017252432]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0276055, 1.0, 0.0, 0.0010705204, 0.0014390945, 0.0010703566, 0.0014389867]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0285578, 1.0, 0.0, 0.0014510801, 0.0011453121, 0.0014489468, 0.0011449091]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0272852, 1.0, 0.0, 0.0012072078, 0.001273346, 0.0012065026, 0.0012731744]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0278757, 1.0, 0.0, 0.0015307462, 0.0010034046, 0.0015308578, 0.0010032689]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.034238, 1.0, 0.0, 0.001807664, 0.0013048039, 0.0018085621, 0.0013047545]
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G_Loss: [1.0392399, 1.0, 0.0, 0.0024157208, 0.001151545, 0.0024157483, 0.0011514518]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0549954, 1.0, 0.0, 0.0017824322, 0.0032170543, 0.0017834529, 0.0032170494]
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D_Loss: [0.5 0.5]
G_Loss: [1.0622886, 1.0, 0.0, 0.003495268, 0.0021671574, 0.0034973715, 0.0021671]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0395288, 1.0, 0.0, 0.0017276886, 0.0018658262, 0.0017280008, 0.0018656205]
Epoch: 7
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0324637, 1.0, 0.0, 0.0011298468, 0.0018213957, 0.0011302207, 0.0018210947]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0507885, 1.0, 0.0, 0.0018780823, 0.002739029, 0.0018781571, 0.0027392583]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0310246, 1.0, 0.0, 0.0016194793, 0.0012010266, 0.0016186056, 0.0012009349]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.054724, 1.0, 0.0, 0.0031646658, 0.0018101479, 0.0031658532, 0.0018100364]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0251697, 1.0, 0.0, 0.001111531, 0.0011766166, 0.0011116812, 0.0011765917]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0409944, 1.0, 0.0, 0.00094579405, 0.0027809623, 0.0009458489, 0.002780978]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0616328, 1.0, 0.0, 0.0012019197, 0.0044010556, 0.0012019884, 0.004401035]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0336665, 1.0, 0.0, 0.0010984209, 0.0019622594, 0.0010975474, 0.0019621786]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0367225, 1.0, 0.0, 0.0018860224, 0.00145254, 0.0018846381, 0.0014523787]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0329287, 1.0, 0.0, 0.0012144552, 0.0017790673, 0.0012144786, 0.0017789596]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0466609, 1.0, 0.0, 0.001638793, 0.0026032939, 0.001636711, 0.0026033004]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0399873, 1.0, 0.0, 0.0019731624, 0.0016620944, 0.0019726807, 0.001662066]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0404195, 1.0, 0.0, 0.0012496831, 0.0024248217, 0.0012496902, 0.0024247072]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0309374, 1.0, 0.0, 0.0019608266, 0.00085164513, 0.0019610962, 0.00085158687]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0279093, 1.0, 0.0, 0.0013178838, 0.0012193662, 0.001317494, 0.0012192343]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.025984, 1.0, 0.0, 0.0013680032, 0.0009942651, 0.0013671985, 0.0009941813]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0295124, 1.0, 0.0, 0.0014158125, 0.0012670665, 0.001416503, 0.001267144]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0392146, 1.0, 0.0, 0.0012686509, 0.0022963583, 0.0012683193, 0.00229635]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0358166, 1.0, 0.0, 0.0014116568, 0.0018444424, 0.0014114861, 0.0018441285]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0537504, 1.0, 0.0, 0.0016489069, 0.0032375476, 0.0016485497, 0.0032373779]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.041497, 1.0, 0.0, 0.00161804, 0.0021544113, 0.0016181103, 0.0021543573]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0346092, 1.0, 0.0, 0.0015751215, 0.0015712813, 0.0015739028, 0.0015711423]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.031014, 1.0, 0.0, 0.0014295543, 0.0013899785, 0.0014286289, 0.0013899351]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.031491, 1.0, 0.0, 0.00096260174, 0.0019002118, 0.0009629648, 0.0018999681]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.041421, 1.0, 0.0, 0.0019752379, 0.00179041, 0.0019741752, 0.0017903764]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0414479, 1.0, 0.0, 0.0014589217, 0.0023090816, 0.0014587734, 0.002309083]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.032917, 1.0, 0.0, 0.002054419, 0.00093805994, 0.0020542385, 0.0009380261]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.035932, 1.0, 0.0, 0.0016460373, 0.0016205313, 0.001645736, 0.0016205122]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0308107, 1.0, 0.0, 0.0017286878, 0.0010722528, 0.0017291453, 0.0010721653]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0563548, 1.0, 0.0, 0.0027064565, 0.0024167558, 0.0027059237, 0.0024167378]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.033414, 1.0, 0.0, 0.0014508227, 0.0015868673, 0.0014502308, 0.0015869265]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0317265, 1.0, 0.0, 0.001342026, 0.0015421824, 0.0013422093, 0.0015421516]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0364507, 1.0, 0.0, 0.0010284671, 0.0022852588, 0.0010283631, 0.0022851373]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.027779, 1.0, 0.0, 0.0012228994, 0.0013025433, 0.0012224407, 0.0013021608]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0302236, 1.0, 0.0, 0.0013561406, 0.0013914853, 0.0013562588, 0.001391228]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0376129, 1.0, 0.0, 0.0016304762, 0.0017889237, 0.0016303341, 0.0017885957]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.026108, 1.0, 0.0, 0.001192605, 0.0011809543, 0.0011915156, 0.0011809139]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0287591, 1.0, 0.0, 0.0016131885, 0.0010013238, 0.0016129867, 0.0010010158]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0239812, 1.0, 0.0, 0.0015219103, 0.0006581908, 0.0015220575, 0.0006582075]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0302597, 1.0, 0.0, 0.0021784487, 0.00057239155, 0.002178785, 0.00057238515]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0572778, 1.0, 0.0, 0.0017794722, 0.0034277479, 0.0017779443, 0.0034277895]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.059503, 1.0, 0.0, 0.0029180283, 0.0024913433, 0.0029179594, 0.0024912546]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.052295, 1.0, 0.0, 0.0016062055, 0.0031478908, 0.0016059532, 0.003148095]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.041878, 1.0, 0.0, 0.0010355923, 0.0027714465, 0.0010360866, 0.0027715259]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0246335, 1.0, 0.0, 0.0010352478, 0.0012042272, 0.0010351564, 0.0012035486]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0317502, 1.0, 0.0, 0.001295256, 0.0015911143, 0.0012956173, 0.0015910098]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0405524, 1.0, 0.0, 0.0022209412, 0.0014656861, 0.0022205343, 0.001465572]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0310684, 1.0, 0.0, 0.0008832108, 0.0019412352, 0.0008828038, 0.0019411966]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0429932, 1.0, 0.0, 0.0017942332, 0.0021143025, 0.0017939003, 0.0021139649]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0269719, 1.0, 0.0, 0.0008468559, 0.0016051901, 0.00084641716, 0.001605117]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0240732, 1.0, 0.0, 0.0007137174, 0.0014747862, 0.0007139792, 0.0014743116]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0382079, 1.0, 0.0, 0.0016866531, 0.0017868157, 0.0016864756, 0.0017867015]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0428034, 1.0, 0.0, 0.0014112077, 0.002479996, 0.0014113258, 0.0024799872]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0393157, 1.0, 0.0, 0.0016957857, 0.0018783397, 0.0016963151, 0.0018780007]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0342734, 1.0, 0.0, 0.0019508197, 0.001164853, 0.001952053, 0.0011645835]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0398456, 1.0, 0.0, 0.0016098573, 0.0020124856, 0.00160966, 0.0020123417]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.035115, 1.0, 0.0, 0.001374054, 0.0018182297, 0.001374233, 0.0018179803]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0371807, 1.0, 0.0, 0.0016851876, 0.00169491, 0.0016848694, 0.0016947541]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0329586, 1.0, 0.0, 0.0017670068, 0.0012292302, 0.0017671175, 0.0012291705]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.034103, 1.0, 0.0, 0.0016133275, 0.0014870702, 0.001612165, 0.0014868921]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0339932, 1.0, 0.0, 0.0017484541, 0.0013418235, 0.0017489138, 0.0013415733]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0320743, 1.0, 0.0, 0.0013862199, 0.0015295696, 0.0013868865, 0.0015294858]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0436674, 1.0, 0.0, 0.0016286976, 0.0023411051, 0.0016288618, 0.0023405345]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0421889, 1.0, 0.0, 0.0016949625, 0.0021404545, 0.001694425, 0.0021403283]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0417441, 1.0, 0.0, 0.001825039, 0.0019700038, 0.0018246477, 0.001969155]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0479931, 1.0, 0.0, 0.0019524014, 0.002410732, 0.0019510302, 0.0024107844]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0312567, 1.0, 0.0, 0.0013980579, 0.0014434825, 0.0013984347, 0.001442756]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0320653, 1.0, 0.0, 0.0010509293, 0.0018641495, 0.0010504259, 0.0018640441]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0489259, 1.0, 0.0, 0.0030565085, 0.0013912196, 0.0030573178, 0.0013911955]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0422288, 1.0, 0.0, 0.0013691115, 0.0024699625, 0.00136843, 0.0024697506]
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D_Loss: [0.5 0.5]
G_Loss: [1.0390718, 1.0, 0.0, 0.002037317, 0.0015146604, 0.0020375769, 0.0015144099]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0679628, 1.0, 0.0, 0.0033544104, 0.002824171, 0.0033527694, 0.0028242196]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0273529, 1.0, 0.0, 0.0011750873, 0.0013115674, 0.0011749808, 0.0013115166]
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D_Loss: [0.5 0.5]
G_Loss: [1.0343639, 1.0, 0.0, 0.0011019559, 0.0020220883, 0.0011013906, 0.0020220056]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0330106, 1.0, 0.0, 0.0015045895, 0.0014965604, 0.0015025521, 0.0014965298]
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D_Loss: [0.5 0.5]
G_Loss: [1.039125, 1.0, 0.0, 0.001815367, 0.0017416216, 0.0018149368, 0.0017400405]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0525457, 1.0, 0.0, 0.00191548, 0.0028614313, 0.0019146472, 0.002861943]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0323375, 1.0, 0.0, 0.0017859223, 0.0011539956, 0.0017845209, 0.0011537807]
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D_Loss: [0.5 0.5]
G_Loss: [1.0288779, 1.0, 0.0, 0.0012052855, 0.0014200073, 0.0012050029, 0.0014198932]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0276532, 1.0, 0.0, 0.00084655406, 0.0016673966, 0.00084633566, 0.0016674355]
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D_Loss: [0.5 0.5]
G_Loss: [1.0382296, 1.0, 0.0, 0.0018362972, 0.001639106, 0.001836502, 0.0016390599]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0490547, 1.0, 0.0, 0.0020321822, 0.0024274315, 0.0020312886, 0.0024272471]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0297241, 1.0, 0.0, 0.0012048845, 0.001497394, 0.001203953, 0.0014973402]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0348735, 1.0, 0.0, 0.0016490193, 0.0015212935, 0.001649379, 0.0015209843]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0370286, 1.0, 0.0, 0.0009607809, 0.002405453, 0.0009607898, 0.0024053925]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0350347, 1.0, 0.0, 0.0013627573, 0.0018223749, 0.0013614027, 0.0018219863]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0338292, 1.0, 0.0, 0.0013919866, 0.0016835523, 0.0013905833, 0.0016831888]
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D_Loss: [0.5 0.5]
G_Loss: [1.0338455, 1.0, 0.0, 0.001536235, 0.0015407668, 0.0015346874, 0.0015407407]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0253861, 1.0, 0.0, 0.0008347912, 0.0014730432, 0.00083487347, 0.0014729118]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0303192, 1.0, 0.0, 0.0015383142, 0.0012179457, 0.0015387828, 0.00121786]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0362928, 1.0, 0.0, 0.0016741846, 0.0016252665, 0.0016734882, 0.0016247941]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0414069, 1.0, 0.0, 0.0022595944, 0.0015046534, 0.0022600517, 0.0015043323]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.037353, 1.0, 0.0, 0.0014255117, 0.0019703242, 0.00142431, 0.0019702518]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0416893, 1.0, 0.0, 0.0010434221, 0.0027465483, 0.0010430072, 0.0027466083]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0256511, 1.0, 0.0, 0.0013443364, 0.0009876945, 0.0013431896, 0.0009875984]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0522478, 1.0, 0.0, 0.002308029, 0.0024419674, 0.002306609, 0.002441179]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0326208, 1.0, 0.0, 0.0015564976, 0.0014090347, 0.0015564839, 0.0014090779]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0345117, 1.0, 0.0, 0.0013029104, 0.0018345205, 0.0013029608, 0.0018344261]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0415024, 1.0, 0.0, 0.0015177346, 0.0022552942, 0.0015165574, 0.0022554838]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0284611, 1.0, 0.0, 0.0010434492, 0.001544007, 0.0010430019, 0.0015435002]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0316491, 1.0, 0.0, 0.0018540577, 0.0010230851, 0.0018544861, 0.0010229965]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.037405, 1.0, 0.0, 0.0019212121, 0.001479309, 0.0019204281, 0.0014793321]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0344152, 1.0, 0.0, 0.0016746538, 0.0014539605, 0.0016752419, 0.0014538737]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.042141, 1.0, 0.0, 0.001602111, 0.0022288032, 0.0016029727, 0.002228769]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0409002, 1.0, 0.0, 0.0011899228, 0.0025283496, 0.0011895846, 0.002528011]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0330578, 1.0, 0.0, 0.0018280944, 0.001177245, 0.0018275303, 0.0011769915]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0389047, 1.0, 0.0, 0.0014084959, 0.0021283473, 0.0014078696, 0.0021283515]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0373527, 1.0, 0.0, 0.0020191236, 0.0013766867, 0.0020181302, 0.0013764563]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0401633, 1.0, 0.0, 0.0012350169, 0.0024161888, 0.0012350429, 0.0024160952]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0357589, 1.0, 0.0, 0.0017892494, 0.0014615732, 0.0017890414, 0.0014614637]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0313314, 1.0, 0.0, 0.0012311018, 0.0016172021, 0.0012314382, 0.0016169851]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0413069, 1.0, 0.0, 0.00202354, 0.0017316389, 0.0020240066, 0.0017310549]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0275006, 1.0, 0.0, 0.00106777, 0.0014323264, 0.0010675759, 0.001432228]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0284303, 1.0, 0.0, 0.0014506371, 0.0011341682, 0.0014484539, 0.0011337795]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0272763, 1.0, 0.0, 0.0011996848, 0.0012800524, 0.0011990047, 0.0012798884]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0277303, 1.0, 0.0, 0.0015244628, 0.000996482, 0.0015245681, 0.0009963503]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0341834, 1.0, 0.0, 0.0018073877, 0.0013000921, 0.001808363, 0.0013000423]
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D_Loss: [0.5 0.5]
G_Loss: [1.0393369, 1.0, 0.0, 0.002421009, 0.0011550805, 0.002421014, 0.0011549921]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.055113, 1.0, 0.0, 0.0017992283, 0.003210961, 0.0018002368, 0.0032109558]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0621262, 1.0, 0.0, 0.0034844452, 0.0021631918, 0.003486656, 0.0021631417]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0394561, 1.0, 0.0, 0.0017200344, 0.0018668722, 0.0017203048, 0.0018666824]
Epoch: 8
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.032553, 1.0, 0.0, 0.0011377493, 0.0018216118, 0.0011381079, 0.0018212885]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0505737, 1.0, 0.0, 0.0018668526, 0.0027307204, 0.0018670205, 0.0027309142]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.03123, 1.0, 0.0, 0.0016417201, 0.0011974713, 0.0016407976, 0.0011973665]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0538192, 1.0, 0.0, 0.0030898086, 0.0018027325, 0.0030912599, 0.0018026002]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0252905, 1.0, 0.0, 0.0011125009, 0.0011866271, 0.0011126414, 0.001186592]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0409449, 1.0, 0.0, 0.0009458693, 0.002776403, 0.0009458146, 0.0027764344]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0616767, 1.0, 0.0, 0.0011968288, 0.0044101486, 0.0011968805, 0.004410134]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0338519, 1.0, 0.0, 0.0011005476, 0.001976973, 0.001099735, 0.001976889]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.035814, 1.0, 0.0, 0.0018724641, 0.0013835197, 0.0018708931, 0.0013833388]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0328888, 1.0, 0.0, 0.00120981, 0.0017800895, 0.0012098139, 0.001779936]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0462354, 1.0, 0.0, 0.001640435, 0.0025629778, 0.0016382368, 0.0025629895]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0405216, 1.0, 0.0, 0.0019667274, 0.0017170899, 0.0019664122, 0.0017170617]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.040152, 1.0, 0.0, 0.0012451975, 0.0024049967, 0.0012451846, 0.0024048674]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0308325, 1.0, 0.0, 0.0019529593, 0.0008499685, 0.0019532447, 0.000849907]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0278461, 1.0, 0.0, 0.001320089, 0.0012114149, 0.0013197332, 0.0012113086]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0258142, 1.0, 0.0, 0.0013604467, 0.000986364, 0.0013597355, 0.0009862932]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0293807, 1.0, 0.0, 0.0014075296, 0.0012633653, 0.001408193, 0.0012634424]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0391563, 1.0, 0.0, 0.001265032, 0.0022946426, 0.0012647586, 0.0022946447]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0358461, 1.0, 0.0, 0.0014093528, 0.001849426, 0.0014092205, 0.0018491199]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0533023, 1.0, 0.0, 0.001645802, 0.0031998986, 0.0016455646, 0.003199684]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0411031, 1.0, 0.0, 0.0016138082, 0.002122828, 0.0016139946, 0.0021227864]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0344405, 1.0, 0.0, 0.0015696681, 0.0015614197, 0.0015683926, 0.0015612643]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.030941, 1.0, 0.0, 0.0014270784, 0.0013858473, 0.0014260106, 0.0013857826]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0310729, 1.0, 0.0, 0.0009541862, 0.0018706067, 0.00095456827, 0.001870374]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0414016, 1.0, 0.0, 0.0019777052, 0.0017861887, 0.0019765792, 0.001786145]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0412673, 1.0, 0.0, 0.0014445463, 0.0023070388, 0.0014444103, 0.0023070124]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0327992, 1.0, 0.0, 0.0020434952, 0.0009382754, 0.0020433161, 0.000938184]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.035845, 1.0, 0.0, 0.0016423918, 0.0016162728, 0.0016421233, 0.0016162276]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0308973, 1.0, 0.0, 0.0017348782, 0.0010739288, 0.0017353378, 0.0010737951]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0564039, 1.0, 0.0, 0.0027045729, 0.002423102, 0.0027040194, 0.0024230815]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.033492, 1.0, 0.0, 0.001450009, 0.0015947621, 0.0014494768, 0.0015947944]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.031699, 1.0, 0.0, 0.0013384447, 0.0015432623, 0.001338688, 0.0015431906]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0361491, 1.0, 0.0, 0.0010247709, 0.0022615436, 0.0010246162, 0.002261421]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0276306, 1.0, 0.0, 0.0012189958, 0.0012929596, 0.0012185804, 0.001292524]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0303857, 1.0, 0.0, 0.0013496648, 0.0014126946, 0.0013497691, 0.001412434]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0375513, 1.0, 0.0, 0.0016260576, 0.0017877332, 0.0016259435, 0.0017874136]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0260104, 1.0, 0.0, 0.001182488, 0.0011822004, 0.0011813894, 0.001182157]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0286548, 1.0, 0.0, 0.0016042894, 0.0010007105, 0.0016042858, 0.001000408]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0240611, 1.0, 0.0, 0.001529126, 0.0006582476, 0.0015291438, 0.00065825065]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0302109, 1.0, 0.0, 0.0021773102, 0.0005691215, 0.0021775302, 0.0005691218]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.057113, 1.0, 0.0, 0.001765723, 0.0034265039, 0.0017641687, 0.0034265588]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0595244, 1.0, 0.0, 0.002921178, 0.0024901463, 0.002921128, 0.0024900553]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0520704, 1.0, 0.0, 0.001583895, 0.0031497716, 0.0015837058, 0.0031499453]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0419314, 1.0, 0.0, 0.0010326323, 0.0027792593, 0.001033149, 0.0027793383]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0247422, 1.0, 0.0, 0.0010444598, 0.0012048953, 0.0010443435, 0.0012042115]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0315133, 1.0, 0.0, 0.001286963, 0.0015778709, 0.0012873235, 0.0015777738]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0407144, 1.0, 0.0, 0.0022444122, 0.0014569275, 0.002244079, 0.0014568161]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0312508, 1.0, 0.0, 0.00091321464, 0.0019278147, 0.00091283163, 0.0019277681]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0422697, 1.0, 0.0, 0.0017477295, 0.0020950434, 0.0017472096, 0.0020947119]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.02672, 1.0, 0.0, 0.0008384531, 0.0015906757, 0.0008380988, 0.001590591]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0237794, 1.0, 0.0, 0.00068981404, 0.0014719582, 0.0006900453, 0.0014715025]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0382059, 1.0, 0.0, 0.0016916189, 0.001781672, 0.0016914747, 0.0017815573]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0427002, 1.0, 0.0, 0.0013977767, 0.0024840673, 0.0013977799, 0.002484022]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0395069, 1.0, 0.0, 0.0017228769, 0.001868651, 0.0017233236, 0.001868308]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0340747, 1.0, 0.0, 0.0019408311, 0.0011567654, 0.0019420206, 0.0011565152]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0398594, 1.0, 0.0, 0.0016154781, 0.0020081378, 0.0016152341, 0.0020079848]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0350637, 1.0, 0.0, 0.0013717972, 0.0018158295, 0.0013719182, 0.0018155978]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0371912, 1.0, 0.0, 0.0016936428, 0.001687425, 0.0016932179, 0.0016872586]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0324441, 1.0, 0.0, 0.0017240959, 0.0012253618, 0.0017241754, 0.0012253071]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.034126, 1.0, 0.0, 0.0016237893, 0.0014786993, 0.0016226596, 0.0014785397]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0340093, 1.0, 0.0, 0.0017482217, 0.0013435237, 0.0017486522, 0.0013432645]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0319461, 1.0, 0.0, 0.001382995, 0.0015211306, 0.0013836604, 0.001521056]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0435971, 1.0, 0.0, 0.001627891, 0.0023355298, 0.0016280249, 0.0023349582]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0420858, 1.0, 0.0, 0.0016803747, 0.0021456573, 0.0016798694, 0.0021455432]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.04168, 1.0, 0.0, 0.0018373076, 0.0019518939, 0.0018367859, 0.0019511457]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0477668, 1.0, 0.0, 0.0019398484, 0.0024027135, 0.0019384343, 0.0024027964]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0312222, 1.0, 0.0, 0.001398149, 0.0014402645, 0.0013985478, 0.0014395651]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0320046, 1.0, 0.0, 0.0010412146, 0.0018683589, 0.0010406589, 0.001868261]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0486971, 1.0, 0.0, 0.0030497836, 0.0013771611, 0.0030505164, 0.0013771366]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0422931, 1.0, 0.0, 0.001376196, 0.0024687075, 0.001375499, 0.0024684777]
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D_Loss: [0.5 0.5]
G_Loss: [1.0390846, 1.0, 0.0, 0.002034712, 0.0015184397, 0.0020349587, 0.0015181876]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0679867, 1.0, 0.0, 0.0033632764, 0.0028174915, 0.0033615977, 0.002817524]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0272498, 1.0, 0.0, 0.0011709232, 0.0013063594, 0.0011707946, 0.0013063152]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0343153, 1.0, 0.0, 0.0011007495, 0.0020188848, 0.001100187, 0.0020188037]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0329403, 1.0, 0.0, 0.0015037237, 0.0014910283, 0.0015017824, 0.0014909923]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.038921, 1.0, 0.0, 0.0018124192, 0.0017260567, 0.0018119472, 0.0017242173]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0529693, 1.0, 0.0, 0.0019088917, 0.002906546, 0.0019079682, 0.0029070661]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0323374, 1.0, 0.0, 0.0017850756, 0.0011548253, 0.0017837811, 0.0011546034]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.02886, 1.0, 0.0, 0.0012043363, 0.001419344, 0.0012039766, 0.0014192434]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0275617, 1.0, 0.0, 0.0008435382, 0.0016620765, 0.0008433666, 0.0016621158]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0380954, 1.0, 0.0, 0.0018266663, 0.0016365232, 0.001826838, 0.0016364927]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0492148, 1.0, 0.0, 0.002045306, 0.002428854, 0.00204452, 0.0024286811]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0297265, 1.0, 0.0, 0.0012015142, 0.0015009707, 0.0012006508, 0.001500922]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.034803, 1.0, 0.0, 0.0016427079, 0.0015212067, 0.0016430405, 0.0015209066]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0371817, 1.0, 0.0, 0.00095751777, 0.0024226597, 0.0009575173, 0.0024225845]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0347749, 1.0, 0.0, 0.001362697, 0.0017988466, 0.0013611683, 0.0017984386]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0336857, 1.0, 0.0, 0.0013836143, 0.0016788876, 0.0013821002, 0.0016785299]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0340297, 1.0, 0.0, 0.0015414862, 0.0015522789, 0.0015398405, 0.0015522566]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0250872, 1.0, 0.0, 0.0008259286, 0.0014547389, 0.000825985, 0.0014545952]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0301216, 1.0, 0.0, 0.0015359921, 0.0012022913, 0.0015364356, 0.0012021896]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0365049, 1.0, 0.0, 0.0016833981, 0.0016353403, 0.0016826966, 0.0016348318]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0415087, 1.0, 0.0, 0.0022717575, 0.0015017509, 0.0022722487, 0.001501369]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.037324, 1.0, 0.0, 0.0014285723, 0.00196464, 0.0014273359, 0.00196458]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.04169, 1.0, 0.0, 0.0010441369, 0.0027458903, 0.0010436657, 0.002745963]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0255257, 1.0, 0.0, 0.0013375267, 0.0009831074, 0.0013363445, 0.0009830156]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0522057, 1.0, 0.0, 0.002307025, 0.0024391424, 0.0023057444, 0.0024382775]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0324686, 1.0, 0.0, 0.0015487359, 0.0014029467, 0.0015487187, 0.0014029896]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0344249, 1.0, 0.0, 0.0012990701, 0.0018304776, 0.0012989966, 0.0018303592]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0415907, 1.0, 0.0, 0.0015188938, 0.00226216, 0.0015177425, 0.0022623567]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0283597, 1.0, 0.0, 0.0010343692, 0.001543882, 0.0010339102, 0.0015433012]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.031447, 1.0, 0.0, 0.0018394608, 0.0010193506, 0.0018397174, 0.0010192565]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0374843, 1.0, 0.0, 0.0019289752, 0.0014787487, 0.001928369, 0.0014788012]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0342577, 1.0, 0.0, 0.001664816, 0.0014494774, 0.0016653028, 0.001449385]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.042059, 1.0, 0.0, 0.0015979132, 0.002225577, 0.0015985086, 0.0022255336]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0409247, 1.0, 0.0, 0.0011867168, 0.002533772, 0.001186356, 0.0025334167]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.033075, 1.0, 0.0, 0.001825141, 0.0011817738, 0.0018243955, 0.001181516]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0387962, 1.0, 0.0, 0.001411723, 0.0021152624, 0.0014110672, 0.002115231]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0373225, 1.0, 0.0, 0.0020184233, 0.0013746332, 0.0020174412, 0.0013743893]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0398353, 1.0, 0.0, 0.0012296207, 0.002391794, 0.0012295494, 0.0023916843]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0355451, 1.0, 0.0, 0.0017767875, 0.0014546277, 0.0017765057, 0.0014544846]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0313035, 1.0, 0.0, 0.0012321332, 0.0016136253, 0.0012324628, 0.0016134183]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0412711, 1.0, 0.0, 0.0020170866, 0.0017348512, 0.0020174955, 0.0017342239]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0274144, 1.0, 0.0, 0.001067718, 0.0014245235, 0.0010674978, 0.0014244076]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0281911, 1.0, 0.0, 0.0014350838, 0.0011279681, 0.001432949, 0.0011275457]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.027231, 1.0, 0.0, 0.0011934513, 0.0012821748, 0.0011927246, 0.0012820103]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0276097, 1.0, 0.0, 0.0015184704, 0.000991503, 0.0015185736, 0.0009913717]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0341007, 1.0, 0.0, 0.0018020209, 0.0012979577, 0.0018029492, 0.0012978951]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.0393313, 1.0, 0.0, 0.002419326, 0.0011562449, 0.0024193474, 0.0011561566]
Batch: 118
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G_Loss: [1.0551006, 1.0, 0.0, 0.0017997972, 0.003209257, 0.0018007355, 0.003209251]
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D_Loss: [0.5 0.5]
G_Loss: [1.0620557, 1.0, 0.0, 0.003479459, 0.0021617925, 0.0034815378, 0.0021617583]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0395218, 1.0, 0.0, 0.0017242065, 0.0018686743, 0.0017245143, 0.001868492]
Epoch: 9
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0324439, 1.0, 0.0, 0.00113142, 0.0018180229, 0.0011317637, 0.001817708]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0504963, 1.0, 0.0, 0.0018611061, 0.002729437, 0.0018612624, 0.0027296375]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0312511, 1.0, 0.0, 0.0016486682, 0.0011924442, 0.0016477563, 0.0011923446]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0532835, 1.0, 0.0, 0.0030443112, 0.0017995326, 0.0030456195, 0.0017993938]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0253448, 1.0, 0.0, 0.0011142329, 0.0011898221, 0.0011143874, 0.0011897809]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0410131, 1.0, 0.0, 0.0009518546, 0.002776618, 0.0009518181, 0.0027766558]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.061671, 1.0, 0.0, 0.0011930398, 0.0044134203, 0.0011930766, 0.004413414]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0338416, 1.0, 0.0, 0.0010963196, 0.0019802514, 0.0010956002, 0.0019801767]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0356133, 1.0, 0.0, 0.001879421, 0.0013582896, 0.0018780805, 0.0013581112]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0327486, 1.0, 0.0, 0.001201427, 0.0017757553, 0.0012013089, 0.0017755567]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0460857, 1.0, 0.0, 0.0016416349, 0.0025481663, 0.0016394367, 0.0025481882]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0406803, 1.0, 0.0, 0.0019646804, 0.0017335783, 0.0019642687, 0.0017335414]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0400118, 1.0, 0.0, 0.0012410161, 0.0023964243, 0.0012409987, 0.0023963174]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0307848, 1.0, 0.0, 0.0019536777, 0.00084493426, 0.0019539585, 0.0008448862]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0277684, 1.0, 0.0, 0.0013161125, 0.0012083253, 0.0013157539, 0.0012082288]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0257007, 1.0, 0.0, 0.0013563908, 0.000980109, 0.0013556131, 0.0009800431]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0293313, 1.0, 0.0, 0.0014070538, 0.0012593766, 0.0014076708, 0.0012594572]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0391176, 1.0, 0.0, 0.0012633337, 0.0022928347, 0.0012630493, 0.0022928407]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0358037, 1.0, 0.0, 0.0014066768, 0.0018482481, 0.0014064821, 0.0018479426]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0532659, 1.0, 0.0, 0.0016433587, 0.0031990283, 0.0016431352, 0.0031988323]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.041024, 1.0, 0.0, 0.0016095636, 0.002119875, 0.0016096248, 0.002119881]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0344162, 1.0, 0.0, 0.0015677643, 0.0015610943, 0.0015666271, 0.0015609227]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.030837, 1.0, 0.0, 0.0014295245, 0.0013739495, 0.0014285308, 0.0013739001]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0310428, 1.0, 0.0, 0.0009458584, 0.0018762089, 0.0009462215, 0.001876038]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0413183, 1.0, 0.0, 0.0019704981, 0.0017858164, 0.001969501, 0.0017857729]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0412296, 1.0, 0.0, 0.0014434846, 0.0023046788, 0.0014433849, 0.002304704]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0327524, 1.0, 0.0, 0.0020409166, 0.00093659875, 0.0020407315, 0.0009365232]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0357757, 1.0, 0.0, 0.001636948, 0.0016154202, 0.001636673, 0.0016153965]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0309821, 1.0, 0.0, 0.0017434708, 0.0010730538, 0.0017438708, 0.0010729792]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0563595, 1.0, 0.0, 0.0026992192, 0.0024244273, 0.0026985684, 0.0024244376]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0334203, 1.0, 0.0, 0.001443751, 0.0015944979, 0.0014432485, 0.0015945485]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0316284, 1.0, 0.0, 0.0013323696, 0.001542936, 0.0013325573, 0.001542866]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0360456, 1.0, 0.0, 0.0010233612, 0.0022535278, 0.0010231856, 0.0022533867]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0275317, 1.0, 0.0, 0.0012159836, 0.0012869895, 0.0012155033, 0.0012865535]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.03047, 1.0, 0.0, 0.0013468217, 0.0014231885, 0.0013468609, 0.0014229533]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0375127, 1.0, 0.0, 0.0016251364, 0.0017851582, 0.001624993, 0.0017848375]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0259677, 1.0, 0.0, 0.0011759399, 0.0011848575, 0.0011748886, 0.001184818]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0285019, 1.0, 0.0, 0.0015936475, 0.0009974615, 0.0015935574, 0.0009971465]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0241766, 1.0, 0.0, 0.0015379688, 0.0006598936, 0.00153803, 0.00065989164]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0300634, 1.0, 0.0, 0.002168708, 0.00056430313, 0.002169001, 0.0005642998]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0570606, 1.0, 0.0, 0.001761439, 0.0034260193, 0.0017600509, 0.0034260661]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0596396, 1.0, 0.0, 0.0029299576, 0.002491829, 0.002929897, 0.0024917321]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0518633, 1.0, 0.0, 0.0015569987, 0.0031578531, 0.0015567645, 0.00315801]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0419626, 1.0, 0.0, 0.001021303, 0.002793426, 0.001021784, 0.0027935002]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0250034, 1.0, 0.0, 0.001061747, 0.001211353, 0.0010616439, 0.0012107283]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0311738, 1.0, 0.0, 0.001278413, 0.0015555499, 0.001278736, 0.0015554556]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0410908, 1.0, 0.0, 0.0022774725, 0.0014580961, 0.002277153, 0.0014580059]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0315338, 1.0, 0.0, 0.0009457872, 0.0019209769, 0.0009453809, 0.0019209475]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0417162, 1.0, 0.0, 0.0017069695, 0.0020855004, 0.0017063632, 0.002085161]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0266389, 1.0, 0.0, 0.000837123, 0.0015846326, 0.0008367671, 0.001584546]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0236765, 1.0, 0.0, 0.0006848186, 0.0014676022, 0.00068504596, 0.0014671793]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0382222, 1.0, 0.0, 0.0016974417, 0.0017773351, 0.0016972015, 0.0017772191]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0425165, 1.0, 0.0, 0.0013790817, 0.0024860436, 0.001379226, 0.0024859484]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0401725, 1.0, 0.0, 0.0017840788, 0.0018679607, 0.0017846159, 0.001867558]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0339272, 1.0, 0.0, 0.0019306407, 0.0011535528, 0.0019319844, 0.0011532889]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0400025, 1.0, 0.0, 0.0016287093, 0.0020079094, 0.0016284604, 0.0020077582]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0348612, 1.0, 0.0, 0.0013567153, 0.0018124935, 0.0013568251, 0.0018122551]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0371702, 1.0, 0.0, 0.0016990951, 0.0016800807, 0.0016986706, 0.001679935]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.031881, 1.0, 0.0, 0.0016756479, 0.0012226169, 0.0016756984, 0.0012225336]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0341643, 1.0, 0.0, 0.0016362757, 0.0014696668, 0.0016352753, 0.0014694902]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0339592, 1.0, 0.0, 0.0017433097, 0.0013438857, 0.0017437111, 0.0013436442]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0317287, 1.0, 0.0, 0.0013763316, 0.001508039, 0.0013771032, 0.0015079724]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0435132, 1.0, 0.0, 0.0016288823, 0.0023269071, 0.0016289509, 0.0023262836]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0418929, 1.0, 0.0, 0.0016612469, 0.0021472592, 0.0016607032, 0.0021471526]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0416486, 1.0, 0.0, 0.0018393921, 0.001946982, 0.0018387374, 0.0019461974]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0476629, 1.0, 0.0, 0.0019281089, 0.0024050106, 0.0019265842, 0.0024050714]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0311251, 1.0, 0.0, 0.0013941182, 0.0014354648, 0.001394494, 0.0014346988]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0320103, 1.0, 0.0, 0.0010340377, 0.0018760468, 0.0010335234, 0.0018759524]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0484585, 1.0, 0.0, 0.0030388117, 0.0013664449, 0.0030394976, 0.0013664144]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0424092, 1.0, 0.0, 0.0013874876, 0.002467989, 0.0013866483, 0.0024677617]
Batch: 70
D_Loss: [0.5 0.5]
G_Loss: [1.0391345, 1.0, 0.0, 0.0020351287, 0.001522548, 0.002035359, 0.0015222998]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0678704, 1.0, 0.0, 0.0033569685, 0.0028131898, 0.0033555678, 0.002813217]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.027288, 1.0, 0.0, 0.0011772981, 0.0013034383, 0.0011771519, 0.0013034118]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0342704, 1.0, 0.0, 0.0010972028, 0.0020183413, 0.0010967029, 0.0020182712]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0329014, 1.0, 0.0, 0.0015018356, 0.0014893741, 0.0014998727, 0.0014893446]
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D_Loss: [0.5 0.5]
G_Loss: [1.0388218, 1.0, 0.0, 0.001811872, 0.0017175862, 0.0018114187, 0.001715777]
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D_Loss: [0.5 0.5]
G_Loss: [1.0532429, 1.0, 0.0, 0.0019123172, 0.0029279622, 0.0019115929, 0.0029285315]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.03227, 1.0, 0.0, 0.0017801609, 0.0011536046, 0.0017788965, 0.0011534404]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0288913, 1.0, 0.0, 0.0012097913, 0.0014167263, 0.0012095432, 0.0014166217]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.02762, 1.0, 0.0, 0.00085430604, 0.0016566121, 0.0008541467, 0.0016566472]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0377566, 1.0, 0.0, 0.0018018212, 0.0016305834, 0.001801933, 0.001630517]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0496285, 1.0, 0.0, 0.0020859444, 0.0024258127, 0.0020852159, 0.0024256182]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0296805, 1.0, 0.0, 0.0012001318, 0.0014981695, 0.001199404, 0.0014981244]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0348551, 1.0, 0.0, 0.0016506852, 0.0015179475, 0.0016510248, 0.001517633]
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D_Loss: [0.5 0.5]
G_Loss: [1.0372484, 1.0, 0.0, 0.00096442504, 0.002421793, 0.00096439465, 0.002421714]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0345548, 1.0, 0.0, 0.001361062, 0.0017804562, 0.0013597393, 0.0017800049]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.033734, 1.0, 0.0, 0.0013850564, 0.0016818186, 0.0013836935, 0.0016815162]
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D_Loss: [0.5 0.5]
G_Loss: [1.0341469, 1.0, 0.0, 0.0015424807, 0.001561912, 0.0015410159, 0.0015618941]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.025091, 1.0, 0.0, 0.0008321741, 0.0014488315, 0.00083225034, 0.0014487174]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0300235, 1.0, 0.0, 0.001531628, 0.001197756, 0.0015319254, 0.0011976503]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.036612, 1.0, 0.0, 0.0016905544, 0.0016379213, 0.0016898387, 0.0016374815]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0414885, 1.0, 0.0, 0.0022725435, 0.0014991404, 0.002272941, 0.001498756]
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D_Loss: [0.5 0.5]
G_Loss: [1.0372123, 1.0, 0.0, 0.0014196933, 0.0019633328, 0.0014186755, 0.0019632936]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0416677, 1.0, 0.0, 0.001043898, 0.0027441, 0.0010434056, 0.0027441862]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0252297, 1.0, 0.0, 0.0013108756, 0.000982847, 0.0013097711, 0.0009827534]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0522387, 1.0, 0.0, 0.0023085438, 0.0024406384, 0.0023070541, 0.0024397653]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0324405, 1.0, 0.0, 0.0015477394, 0.0014013838, 0.0015477668, 0.0014014171]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0343361, 1.0, 0.0, 0.0012941586, 0.0018273056, 0.0012941188, 0.0018272001]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0416539, 1.0, 0.0, 0.0015223539, 0.0022644557, 0.0015212242, 0.0022646585]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0283715, 1.0, 0.0, 0.001036667, 0.0015426541, 0.0010362124, 0.0015421306]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.031607, 1.0, 0.0, 0.0018543506, 0.001018998, 0.001854624, 0.0010189003]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0369732, 1.0, 0.0, 0.0018853473, 0.0014759208, 0.0018845779, 0.0014759912]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0340499, 1.0, 0.0, 0.0016473803, 0.00144803, 0.001647817, 0.0014479253]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0422118, 1.0, 0.0, 0.0016130344, 0.002224355, 0.0016136509, 0.0022243042]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0409505, 1.0, 0.0, 0.001185001, 0.0025378333, 0.0011847073, 0.0025375183]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0330518, 1.0, 0.0, 0.0018198695, 0.0011849158, 0.0018192847, 0.0011846647]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0387527, 1.0, 0.0, 0.0014161945, 0.0021068226, 0.0014157685, 0.0021068081]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0372826, 1.0, 0.0, 0.0020180861, 0.0013713457, 0.0020172033, 0.0013710756]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.039641, 1.0, 0.0, 0.001221318, 0.0023824247, 0.0012213257, 0.0023822894]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0353653, 1.0, 0.0, 0.001762579, 0.0014524903, 0.0017623098, 0.0014523249]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0312408, 1.0, 0.0, 0.0012308678, 0.0016091911, 0.0012312223, 0.0016089753]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0412427, 1.0, 0.0, 0.0020093028, 0.001740055, 0.0020096179, 0.0017394777]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0274051, 1.0, 0.0, 0.0010715857, 0.0014198255, 0.0010713565, 0.0014197193]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0280427, 1.0, 0.0, 0.0014242753, 0.0011252784, 0.0014223261, 0.0011248459]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0271119, 1.0, 0.0, 0.0011812365, 0.0012835611, 0.0011805267, 0.0012833974]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0275955, 1.0, 0.0, 0.0015187638, 0.0009899226, 0.0015188395, 0.0009897994]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0341179, 1.0, 0.0, 0.0018059757, 0.0012955798, 0.0018068596, 0.0012955228]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.0393779, 1.0, 0.0, 0.002421848, 0.0011579728, 0.0024218576, 0.0011578923]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.055084, 1.0, 0.0, 0.0017992216, 0.003208339, 0.0018001049, 0.0032083308]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0622047, 1.0, 0.0, 0.0034954343, 0.0021593438, 0.0034975917, 0.0021593124]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0395396, 1.0, 0.0, 0.0017254717, 0.0018690241, 0.0017257485, 0.0018688447]
Epoch: 10
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0323281, 1.0, 0.0, 0.001124511, 0.001814421, 0.0011247884, 0.0018140612]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0504589, 1.0, 0.0, 0.001859524, 0.0027276105, 0.0018598007, 0.0027278005]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.031292, 1.0, 0.0, 0.0016548524, 0.0011899834, 0.0016538263, 0.0011898843]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0528562, 1.0, 0.0, 0.0030071675, 0.0017978078, 0.0030087149, 0.0017976491]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0254263, 1.0, 0.0, 0.0011219222, 0.0011895557, 0.0011220247, 0.0011894947]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0411372, 1.0, 0.0, 0.0009606509, 0.0027791152, 0.00096047856, 0.002779171]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0617807, 1.0, 0.0, 0.0011921944, 0.004424229, 0.0011922051, 0.0044242274]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0339004, 1.0, 0.0, 0.001091185, 0.0019907444, 0.0010904064, 0.0019906613]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.035418, 1.0, 0.0, 0.0018887031, 0.0013312737, 0.0018871687, 0.0013310732]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0326613, 1.0, 0.0, 0.0011962533, 0.0017729918, 0.001196149, 0.0017727658]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0458897, 1.0, 0.0, 0.0016349837, 0.0025370163, 0.0016328017, 0.002537016]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0409417, 1.0, 0.0, 0.0019663437, 0.0017556796, 0.0019659034, 0.0017556377]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0398617, 1.0, 0.0, 0.0012333571, 0.0023904594, 0.0012333256, 0.0023903064]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0306587, 1.0, 0.0, 0.001946439, 0.00084069284, 0.0019467542, 0.00084061257]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0279248, 1.0, 0.0, 0.001330405, 0.0012082551, 0.0013299877, 0.0012081431]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0256726, 1.0, 0.0, 0.0013552678, 0.0009786815, 0.0013544597, 0.0009786062]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0291489, 1.0, 0.0, 0.0013926416, 0.0012571979, 0.0013932901, 0.0012572889]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0391272, 1.0, 0.0, 0.0012638409, 0.002293206, 0.001263496, 0.0022932063]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0357649, 1.0, 0.0, 0.0014057236, 0.0018456802, 0.001405492, 0.0018453619]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0532699, 1.0, 0.0, 0.0016411826, 0.0032015813, 0.0016409612, 0.0032013953]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0409255, 1.0, 0.0, 0.0016074312, 0.002113055, 0.0016075291, 0.0021130745]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0343753, 1.0, 0.0, 0.0015645998, 0.0015605604, 0.001563462, 0.0015603853]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0307472, 1.0, 0.0, 0.0014252996, 0.0013700011, 0.0014242505, 0.0013699654]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0309067, 1.0, 0.0, 0.00094759197, 0.0018620861, 0.00094795914, 0.0018619053]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0413009, 1.0, 0.0, 0.0019720993, 0.0017826173, 0.0019710688, 0.0017825696]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0410603, 1.0, 0.0, 0.0014329292, 0.0022998494, 0.0014327979, 0.0022998443]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0326711, 1.0, 0.0, 0.002033887, 0.00093623006, 0.0020337312, 0.00093611]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0357426, 1.0, 0.0, 0.0016343896, 0.0016149755, 0.0016340569, 0.001614928]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0309962, 1.0, 0.0, 0.0017441657, 0.0010736522, 0.0017444657, 0.0010735723]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0564606, 1.0, 0.0, 0.0027050802, 0.0024277552, 0.0027044108, 0.002427761]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0334976, 1.0, 0.0, 0.0014471479, 0.001598126, 0.0014466243, 0.0015981636]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0316288, 1.0, 0.0, 0.0013308268, 0.0015445041, 0.0013311025, 0.0015444302]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0359943, 1.0, 0.0, 0.001023562, 0.0022486849, 0.001023312, 0.0022485415]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0274299, 1.0, 0.0, 0.0012101731, 0.0012835445, 0.0012096041, 0.0012830986]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0305015, 1.0, 0.0, 0.0013467374, 0.0014261346, 0.0013468167, 0.00142589]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0374721, 1.0, 0.0, 0.0016207139, 0.001785897, 0.0016205696, 0.0017855683]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0259283, 1.0, 0.0, 0.0011751957, 0.0011820155, 0.0011741084, 0.001181973]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0284619, 1.0, 0.0, 0.0015925106, 0.0009949805, 0.0015923507, 0.0009946579]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0242023, 1.0, 0.0, 0.0015447333, 0.00065547077, 0.0015447896, 0.0006554575]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0300467, 1.0, 0.0, 0.0021655955, 0.0005658937, 0.0021659024, 0.0005659015]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0570239, 1.0, 0.0, 0.0017554049, 0.0034287386, 0.00175366, 0.0034288124]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0597216, 1.0, 0.0, 0.0029379684, 0.0024912814, 0.002937959, 0.0024911754]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0516275, 1.0, 0.0, 0.0015428972, 0.0031505115, 0.0015426882, 0.0031506554]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0417714, 1.0, 0.0, 0.001012768, 0.0027845781, 0.0010133749, 0.0027846522]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.025018, 1.0, 0.0, 0.0010703845, 0.0012040648, 0.0010701863, 0.0012033989]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0312786, 1.0, 0.0, 0.0012735031, 0.0015700038, 0.0012737231, 0.0015698853]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0410273, 1.0, 0.0, 0.0022790446, 0.001450752, 0.002278723, 0.001450675]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0315151, 1.0, 0.0, 0.000947178, 0.0019178751, 0.0009467453, 0.0019178621]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0416824, 1.0, 0.0, 0.0017070026, 0.0020824014, 0.0017063085, 0.0020820377]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0264946, 1.0, 0.0, 0.0008309409, 0.0015777003, 0.0008306274, 0.0015776101]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0236408, 1.0, 0.0, 0.00068238366, 0.0014668123, 0.00068245025, 0.0014664021]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0382248, 1.0, 0.0, 0.0017054975, 0.0017694964, 0.0017054233, 0.0017693688]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.042595, 1.0, 0.0, 0.0013813579, 0.0024909284, 0.0013814468, 0.002490812]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0401672, 1.0, 0.0, 0.0017876294, 0.0018639545, 0.0017878801, 0.0018635092]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0337806, 1.0, 0.0, 0.0019238971, 0.0011469729, 0.001925185, 0.0011466872]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399861, 1.0, 0.0, 0.0016281907, 0.0020069443, 0.0016278884, 0.002006803]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0347992, 1.0, 0.0, 0.0013555312, 0.0018080459, 0.0013556147, 0.0018077795]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0371982, 1.0, 0.0, 0.0017027828, 0.001678912, 0.0017023941, 0.0016787786]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0318362, 1.0, 0.0, 0.0016726253, 0.0012215814, 0.00167262, 0.0012214768]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0342093, 1.0, 0.0, 0.0016456784, 0.0014643709, 0.0016445508, 0.0014641965]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.03402, 1.0, 0.0, 0.0017485488, 0.0013441439, 0.0017490487, 0.0013439077]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0316861, 1.0, 0.0, 0.0013772073, 0.0015032643, 0.0013780113, 0.0015032006]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0434645, 1.0, 0.0, 0.0016285852, 0.002322794, 0.0016285498, 0.0023221453]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0418916, 1.0, 0.0, 0.0016605414, 0.0021478527, 0.0016599582, 0.0021477283]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.041791, 1.0, 0.0, 0.0018543592, 0.0019449702, 0.001853424, 0.0019441718]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0475641, 1.0, 0.0, 0.0019134625, 0.0024107038, 0.0019116974, 0.0024107406]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0311637, 1.0, 0.0, 0.0014018489, 0.0014312634, 0.0014021979, 0.0014303576]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0320442, 1.0, 0.0, 0.0010261943, 0.001886966, 0.001025785, 0.0018868647]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0483892, 1.0, 0.0, 0.003040085, 0.0013588794, 0.003040708, 0.0013588379]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0424515, 1.0, 0.0, 0.0013931736, 0.002466164, 0.0013922477, 0.0024658784]
Batch: 70
D_Loss: [0.5 0.5]
G_Loss: [1.0391772, 1.0, 0.0, 0.002035463, 0.0015261027, 0.0020356388, 0.0015258369]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0679647, 1.0, 0.0, 0.0033656035, 0.0028131478, 0.0033640158, 0.002813172]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0272827, 1.0, 0.0, 0.0011769935, 0.0013032819, 0.0011768762, 0.0013032489]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0342201, 1.0, 0.0, 0.0010954603, 0.0020155092, 0.0010948884, 0.0020154372]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0329167, 1.0, 0.0, 0.0015032071, 0.0014894117, 0.0015011276, 0.0014893867]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.0386869, 1.0, 0.0, 0.001808669, 0.0017085498, 0.0018082187, 0.0017064556]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0531139, 1.0, 0.0, 0.0019109984, 0.0029175663, 0.0019101906, 0.0029181219]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0323534, 1.0, 0.0, 0.0017883562, 0.0011529862, 0.0017872327, 0.0011528626]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.028915, 1.0, 0.0, 0.0012133231, 0.001415363, 0.0012129813, 0.0014152665]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0276526, 1.0, 0.0, 0.0008563771, 0.0016574976, 0.00085624156, 0.001657546]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0376128, 1.0, 0.0, 0.0017895212, 0.0016298183, 0.0017896469, 0.0016297665]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0498927, 1.0, 0.0, 0.002112751, 0.0024230354, 0.002112084, 0.0024228173]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0295817, 1.0, 0.0, 0.0011978735, 0.0014914337, 0.0011970578, 0.001491386]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0348347, 1.0, 0.0, 0.0016517846, 0.0015150114, 0.0016520902, 0.0015146647]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0371393, 1.0, 0.0, 0.0009669867, 0.002409313, 0.00096696685, 0.0024092544]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.034662, 1.0, 0.0, 0.0013640465, 0.001787239, 0.0013624509, 0.0017867665]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0336739, 1.0, 0.0, 0.0013806471, 0.0016807949, 0.0013790536, 0.0016804591]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0340511, 1.0, 0.0, 0.0015391663, 0.0015565394, 0.001537479, 0.0015565208]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0251037, 1.0, 0.0, 0.00083390676, 0.0014482505, 0.0008339372, 0.001448126]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0299464, 1.0, 0.0, 0.0015269408, 0.0011954377, 0.0015273071, 0.0011953068]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0367848, 1.0, 0.0, 0.0017012835, 0.0016429061, 0.0017005156, 0.0016423812]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0415006, 1.0, 0.0, 0.0022753081, 0.0014974428, 0.002275892, 0.0014969882]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0372391, 1.0, 0.0, 0.0014214161, 0.0019641109, 0.0014197222, 0.001964084]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0416528, 1.0, 0.0, 0.0010412559, 0.0027454048, 0.0010406682, 0.0027455108]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0251284, 1.0, 0.0, 0.001301845, 0.0009826735, 0.0013005972, 0.0009825737]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.052259, 1.0, 0.0, 0.0023095089, 0.002441506, 0.002308261, 0.0024405688]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0324306, 1.0, 0.0, 0.0015470188, 0.0014012274, 0.0015469918, 0.0014012607]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0343298, 1.0, 0.0, 0.0012945246, 0.0018263827, 0.0012943917, 0.001826264]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0416703, 1.0, 0.0, 0.0015214226, 0.0022668678, 0.0015203115, 0.002267092]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0283796, 1.0, 0.0, 0.0010370783, 0.0015429803, 0.0010365756, 0.001542432]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0316832, 1.0, 0.0, 0.0018606514, 0.0010196401, 0.0018607702, 0.0010195303]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0367198, 1.0, 0.0, 0.0018658221, 0.0014724184, 0.0018650072, 0.0014724918]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0340375, 1.0, 0.0, 0.0016456663, 0.0014486206, 0.0016461061, 0.0014484918]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0422624, 1.0, 0.0, 0.0016170508, 0.002224939, 0.0016176957, 0.0022248782]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0409765, 1.0, 0.0, 0.0011838595, 0.002541338, 0.001183528, 0.0025410238]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.033056, 1.0, 0.0, 0.0018157845, 0.0011893918, 0.0018151987, 0.0011891384]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0387954, 1.0, 0.0, 0.001424105, 0.0021027797, 0.0014237717, 0.0021027387]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0372795, 1.0, 0.0, 0.0020218426, 0.0013673062, 0.0020210152, 0.0013670052]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0395162, 1.0, 0.0, 0.0012186526, 0.0023737377, 0.0012186326, 0.0023736134]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0352459, 1.0, 0.0, 0.0017552088, 0.0014489962, 0.0017549973, 0.0014487879]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0312365, 1.0, 0.0, 0.0012309533, 0.0016087163, 0.0012312636, 0.001608453]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0411242, 1.0, 0.0, 0.0020029247, 0.0017356918, 0.0020031251, 0.0017349626]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.027441, 1.0, 0.0, 0.0010739206, 0.0014207606, 0.0010736018, 0.0014206038]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0280051, 1.0, 0.0, 0.001415649, 0.0011305118, 0.0014136247, 0.0011300349]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0269908, 1.0, 0.0, 0.001171489, 0.0012822918, 0.0011708193, 0.0012821113]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0276172, 1.0, 0.0, 0.0015199195, 0.0009907327, 0.0015200117, 0.0009905931]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0341551, 1.0, 0.0, 0.0018088522, 0.0012960847, 0.0018097495, 0.0012960047]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.039419, 1.0, 0.0, 0.0024235854, 0.001159976, 0.0024235803, 0.0011598791]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0550655, 1.0, 0.0, 0.001798322, 0.0032075495, 0.0017991816, 0.0032075532]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0622963, 1.0, 0.0, 0.003504951, 0.00215815, 0.003506959, 0.0021581103]
Batch: 120
D_Loss: [0.5 0.5]






    




W0822 13:05:44.663225 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.688777 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.707403 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).






    




G_Loss: [1.0395207, 1.0, 0.0, 0.0017244318, 0.0018683599, 0.0017247358, 0.001868157]






    




W0822 13:05:44.726286 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.747255 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.765892 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.837347 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.854864 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.872599 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.891363 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.910731 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:05:44.929961 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).






    




Epoch: 11
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0323367, 1.0, 0.0, 0.0011241152, 0.0018155951, 0.001124394, 0.0018151808]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0503628, 1.0, 0.0, 0.0018500803, 0.0027283058, 0.001850457, 0.002728514]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0312318, 1.0, 0.0, 0.0016515641, 0.0011877902, 0.0016505847, 0.0011876842]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0526011, 1.0, 0.0, 0.0029847133, 0.001797103, 0.0029860102, 0.0017969267]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0254378, 1.0, 0.0, 0.0011237985, 0.0011887223, 0.0011239868, 0.0011886555]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.04115, 1.0, 0.0, 0.00096145994, 0.0027794505, 0.0009614271, 0.0027794938]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0618309, 1.0, 0.0, 0.0011945376, 0.004426442, 0.0011946002, 0.0044264365]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0339528, 1.0, 0.0, 0.0010948367, 0.0019918424, 0.0010941806, 0.001991741]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0352837, 1.0, 0.0, 0.0018823864, 0.0013253622, 0.0018811417, 0.0013251161]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0325928, 1.0, 0.0, 0.001193433, 0.0017695928, 0.0011932442, 0.0017693006]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0459387, 1.0, 0.0, 0.0016430512, 0.0025333862, 0.0016409806, 0.002533331]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0409789, 1.0, 0.0, 0.0019650105, 0.0017603971, 0.0019644913, 0.0017603482]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0398563, 1.0, 0.0, 0.0012317761, 0.002391544, 0.0012317654, 0.0023913246]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0306146, 1.0, 0.0, 0.0019502258, 0.0008329125, 0.0019504044, 0.0008327899]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0278572, 1.0, 0.0, 0.0013229538, 0.0012095734, 0.0013226029, 0.0012094376]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0256124, 1.0, 0.0, 0.0013513099, 0.0009771731, 0.001350531, 0.0009770879]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0291504, 1.0, 0.0, 0.0013964527, 0.0012535241, 0.0013970059, 0.0012536319]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0390815, 1.0, 0.0, 0.0012611935, 0.0022916913, 0.0012608815, 0.0022916775]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0356854, 1.0, 0.0, 0.0014040777, 0.0018401194, 0.0014037343, 0.001839764]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0533812, 1.0, 0.0, 0.0016389363, 0.0032139225, 0.0016388382, 0.003213745]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0408405, 1.0, 0.0, 0.0016051515, 0.0021076202, 0.0016051807, 0.0021076542]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0343695, 1.0, 0.0, 0.0015657765, 0.0015588452, 0.0015646787, 0.00155865]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.030734, 1.0, 0.0, 0.0014263052, 0.0013677701, 0.0014253322, 0.0013677485]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.030753, 1.0, 0.0, 0.00094601145, 0.0018497031, 0.0009463716, 0.0018495363]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0411596, 1.0, 0.0, 0.001967278, 0.0017746023, 0.0019663046, 0.0017745531]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0410502, 1.0, 0.0, 0.0014272587, 0.002304604, 0.0014270826, 0.002304586]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0326457, 1.0, 0.0, 0.0020299717, 0.0009378608, 0.0020297982, 0.00093773176]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0357407, 1.0, 0.0, 0.0016334985, 0.0016156968, 0.0016332241, 0.0016156484]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0309975, 1.0, 0.0, 0.0017442014, 0.0010737269, 0.0017445728, 0.001073651]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0565068, 1.0, 0.0, 0.0027060725, 0.00243095, 0.0027055466, 0.0024309661]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0335611, 1.0, 0.0, 0.0014482795, 0.0016027761, 0.0014477682, 0.0016028096]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0316498, 1.0, 0.0, 0.0013298015, 0.0015474313, 0.0013301087, 0.0015473482]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0358492, 1.0, 0.0, 0.0010238667, 0.002235189, 0.0010236499, 0.002235056]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0273771, 1.0, 0.0, 0.0012088493, 0.0012800747, 0.0012082891, 0.0012795706]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0305208, 1.0, 0.0, 0.0013432361, 0.0014314093, 0.0013432561, 0.0014311505]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0374768, 1.0, 0.0, 0.0016206247, 0.0017863839, 0.001620487, 0.0017860571]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.025887, 1.0, 0.0, 0.0011720724, 0.0011813934, 0.0011709844, 0.0011813489]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0284808, 1.0, 0.0, 0.0015945237, 0.0009946988, 0.0015943318, 0.0009943608]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0242063, 1.0, 0.0, 0.001546591, 0.0006539728, 0.0015466863, 0.0006539606]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0300008, 1.0, 0.0, 0.0021633091, 0.0005640001, 0.002163655, 0.0005640091]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0569484, 1.0, 0.0, 0.0017493027, 0.0034279549, 0.0017478431, 0.003428021]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0596284, 1.0, 0.0, 0.0029300237, 0.002490765, 0.0029299534, 0.0024906397]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0519603, 1.0, 0.0, 0.0015712148, 0.0031524496, 0.0015710248, 0.0031526173]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0418161, 1.0, 0.0, 0.0010142459, 0.0027871658, 0.0010147793, 0.00278724]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0250661, 1.0, 0.0, 0.0010722729, 0.0012065526, 0.0010720936, 0.0012058143]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0312382, 1.0, 0.0, 0.0012745999, 0.0015652336, 0.0012748619, 0.0015651244]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0409294, 1.0, 0.0, 0.002269697, 0.0014511984, 0.0022694024, 0.0014511201]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0313928, 1.0, 0.0, 0.00093904405, 0.0019148804, 0.0009386196, 0.0019148644]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0417317, 1.0, 0.0, 0.0017164878, 0.0020773949, 0.0017159237, 0.0020770184]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0264509, 1.0, 0.0, 0.0008306788, 0.0015739801, 0.0008303555, 0.0015738854]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0234834, 1.0, 0.0, 0.0006694563, 0.0014654221, 0.0006695849, 0.001464962]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0382384, 1.0, 0.0, 0.0017052942, 0.001770944, 0.0017051275, 0.0017708022]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0426133, 1.0, 0.0, 0.0013837926, 0.0024901384, 0.0013838619, 0.0024900145]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0400281, 1.0, 0.0, 0.0017754594, 0.0018634717, 0.0017758657, 0.001862994]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0336918, 1.0, 0.0, 0.0019223378, 0.0011404637, 0.0019236985, 0.0011401761]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399668, 1.0, 0.0, 0.0016270105, 0.0020063599, 0.001626766, 0.0020062518]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0348059, 1.0, 0.0, 0.0013576493, 0.001806534, 0.0013577425, 0.0018062503]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0372316, 1.0, 0.0, 0.0017037175, 0.0016810317, 0.0017033075, 0.0016808908]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0318253, 1.0, 0.0, 0.0016719571, 0.0012212485, 0.0016719708, 0.0012211658]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0342718, 1.0, 0.0, 0.001651462, 0.001464254, 0.0016504985, 0.0014640903]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0340409, 1.0, 0.0, 0.0017538104, 0.0013408118, 0.0017541659, 0.0013405979]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0316879, 1.0, 0.0, 0.0013764875, 0.0015041651, 0.0013772183, 0.0015041055]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0434883, 1.0, 0.0, 0.0016283198, 0.002325226, 0.0016283307, 0.0023245858]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0419443, 1.0, 0.0, 0.0016621866, 0.0021509873, 0.0016616343, 0.0021508508]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0417616, 1.0, 0.0, 0.0018625229, 0.0019341323, 0.0018617483, 0.0019333637]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0474747, 1.0, 0.0, 0.0019048523, 0.002411157, 0.0019034126, 0.0024111827]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0311613, 1.0, 0.0, 0.0014035819, 0.0014293151, 0.0014039078, 0.0014283289]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0319352, 1.0, 0.0, 0.0010228488, 0.0018804037, 0.0010223082, 0.0018802817]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0484107, 1.0, 0.0, 0.0030392348, 0.001361674, 0.0030398727, 0.0013616271]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0424701, 1.0, 0.0, 0.0013979883, 0.0024630234, 0.0013972141, 0.0024627303]
Batch: 70
D_Loss: [0.5 0.5]
G_Loss: [1.0391904, 1.0, 0.0, 0.0020342157, 0.0015285598, 0.0020344346, 0.0015282803]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0680645, 1.0, 0.0, 0.0033780918, 0.0028097492, 0.0033762543, 0.0028097718]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0271482, 1.0, 0.0, 0.0011698867, 0.0012981535, 0.0011697779, 0.0012981191]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0341114, 1.0, 0.0, 0.0010941883, 0.002006908, 0.0010936817, 0.002006827]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.032817, 1.0, 0.0, 0.001498675, 0.0014848701, 0.0014967266, 0.0014848409]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.0386829, 1.0, 0.0, 0.0018053416, 0.0017115471, 0.0018048419, 0.0017092072]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0532146, 1.0, 0.0, 0.0019051428, 0.0029325872, 0.0019039673, 0.00293325]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0322618, 1.0, 0.0, 0.0017862476, 0.0011467696, 0.0017851193, 0.0011465575]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0288622, 1.0, 0.0, 0.0012176784, 0.001406204, 0.0012173473, 0.001406091]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.027604, 1.0, 0.0, 0.0008524457, 0.0016570194, 0.00085233746, 0.0016570631]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0376023, 1.0, 0.0, 0.001789174, 0.0016292054, 0.0017892893, 0.001629135]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0501205, 1.0, 0.0, 0.0021322633, 0.0024242168, 0.0021316526, 0.0024239612]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0295638, 1.0, 0.0, 0.0011964676, 0.0014912207, 0.0011957011, 0.0014911642]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0349053, 1.0, 0.0, 0.0016575903, 0.0015156261, 0.0016578797, 0.0015152558]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0370905, 1.0, 0.0, 0.000968389, 0.0024035028, 0.00096838153, 0.0024034055]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0348382, 1.0, 0.0, 0.0013633508, 0.001803945, 0.0013617771, 0.0018034596]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0335083, 1.0, 0.0, 0.001377302, 0.001669084, 0.0013757634, 0.0016686823]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.034083, 1.0, 0.0, 0.0015483373, 0.0015502714, 0.0015467147, 0.0015502421]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0250909, 1.0, 0.0, 0.0008276855, 0.0014533252, 0.00082772766, 0.0014531821]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0300019, 1.0, 0.0, 0.001530272, 0.0011971559, 0.0015305117, 0.0011970063]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0369562, 1.0, 0.0, 0.0017113802, 0.0016484035, 0.0017106461, 0.001647756]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0415838, 1.0, 0.0, 0.00228605, 0.001494302, 0.0022865243, 0.0014937483]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0373693, 1.0, 0.0, 0.001435728, 0.0019616047, 0.0014343441, 0.001961551]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0416721, 1.0, 0.0, 0.0010385198, 0.0027498826, 0.0010380205, 0.0027499907]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0252323, 1.0, 0.0, 0.0013118285, 0.0009821363, 0.0013105882, 0.0009820266]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0522727, 1.0, 0.0, 0.0023085745, 0.0024437124, 0.0023072953, 0.002442649]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0324935, 1.0, 0.0, 0.0015550313, 0.0013989215, 0.0015550256, 0.0013989605]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0343374, 1.0, 0.0, 0.0012959186, 0.0018256886, 0.0012957308, 0.0018255344]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0418372, 1.0, 0.0, 0.0015288198, 0.0022746467, 0.0015276595, 0.002274899]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0283438, 1.0, 0.0, 0.0010339868, 0.0015428137, 0.0010335029, 0.0015421951]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0316055, 1.0, 0.0, 0.0018551488, 0.0010180814, 0.0018552805, 0.0010179565]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0369488, 1.0, 0.0, 0.0018877308, 0.0014712877, 0.0018872516, 0.0014713479]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0340042, 1.0, 0.0, 0.0016417835, 0.001449476, 0.00164226, 0.0014493335]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0422082, 1.0, 0.0, 0.0016093815, 0.0022276626, 0.001609986, 0.0022276144]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0410159, 1.0, 0.0, 0.0011829378, 0.00254585, 0.0011825422, 0.0025454916]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0329851, 1.0, 0.0, 0.0018103325, 0.0011884046, 0.0018094528, 0.0011881252]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0388821, 1.0, 0.0, 0.0014302635, 0.0021045303, 0.0014297321, 0.002104488]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0372465, 1.0, 0.0, 0.0020227036, 0.0013634465, 0.0020218021, 0.0013631068]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0394688, 1.0, 0.0, 0.0012204057, 0.0023676907, 0.0012203606, 0.0023675526]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0351853, 1.0, 0.0, 0.0017483862, 0.0014503268, 0.0017481763, 0.0014501022]
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G_Loss: [1.03115, 1.0, 0.0, 0.0012287338, 0.001603093, 0.0012289302, 0.0016028014]
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D_Loss: [0.5 0.5]
G_Loss: [1.0412239, 1.0, 0.0, 0.0020021475, 0.0017455344, 0.0020024113, 0.001744752]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.027418, 1.0, 0.0, 0.0010767886, 0.0014158064, 0.0010764957, 0.0014156211]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0278081, 1.0, 0.0, 0.0014068652, 0.0011213713, 0.001405045, 0.0011208579]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0269545, 1.0, 0.0, 0.001164819, 0.0012856687, 0.0011642542, 0.0012854822]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0275568, 1.0, 0.0, 0.0015197261, 0.0009854473, 0.001519802, 0.0009853032]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0341727, 1.0, 0.0, 0.0018093591, 0.0012971733, 0.0018101365, 0.0012970921]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.0393889, 1.0, 0.0, 0.0024241568, 0.0011566657, 0.002424172, 0.0011565677]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0550456, 1.0, 0.0, 0.0017952684, 0.0032088014, 0.0017960744, 0.0032088114]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0625811, 1.0, 0.0, 0.00352718, 0.002161853, 0.0035289517, 0.0021618302]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0396342, 1.0, 0.0, 0.0017308495, 0.0018722616, 0.0017310887, 0.001872032]
Epoch: 12
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0322598, 1.0, 0.0, 0.0011198395, 0.0018128885, 0.001120087, 0.0018124282]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0503893, 1.0, 0.0, 0.0018490104, 0.0027318003, 0.0018491406, 0.0027319973]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0313398, 1.0, 0.0, 0.0016636832, 0.0011854826, 0.0016626946, 0.0011853718]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0522501, 1.0, 0.0, 0.002955145, 0.0017947778, 0.0029562963, 0.0017945593]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0255921, 1.0, 0.0, 0.0011349194, 0.0011916235, 0.0011351663, 0.0011915343]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0412933, 1.0, 0.0, 0.0009722909, 0.0027816454, 0.0009722056, 0.0027817045]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0619395, 1.0, 0.0, 0.0011967098, 0.004434155, 0.0011967157, 0.004434155]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0339878, 1.0, 0.0, 0.0010893322, 0.0020005254, 0.0010887287, 0.0020004169]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0352515, 1.0, 0.0, 0.0018976391, 0.0013071636, 0.0018964962, 0.0013068786]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0325302, 1.0, 0.0, 0.0011891802, 0.0017681655, 0.0011890002, 0.0017677927]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0458735, 1.0, 0.0, 0.0016421725, 0.0025283357, 0.0016402307, 0.002528283]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0410993, 1.0, 0.0, 0.0019671987, 0.0017691484, 0.0019667353, 0.0017690955]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0398601, 1.0, 0.0, 0.0012344642, 0.0023892168, 0.0012344546, 0.0023889693]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0305709, 1.0, 0.0, 0.0019477874, 0.0008313742, 0.0019479955, 0.0008312345]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0279328, 1.0, 0.0, 0.0013303411, 0.0012090469, 0.00133, 0.0012089121]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0255995, 1.0, 0.0, 0.0013509946, 0.0009763025, 0.0013502815, 0.00097621424]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.029031, 1.0, 0.0, 0.0013871347, 0.0012520028, 0.0013876369, 0.0012521031]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0390912, 1.0, 0.0, 0.0012633707, 0.0022904212, 0.001263057, 0.002290396]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0356503, 1.0, 0.0, 0.0014044071, 0.0018365975, 0.0014040751, 0.001836179]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0535171, 1.0, 0.0, 0.0016379151, 0.0032273054, 0.0016378628, 0.0032271165]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0408206, 1.0, 0.0, 0.0016032603, 0.002107705, 0.0016032215, 0.0021077124]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0342971, 1.0, 0.0, 0.0015622894, 0.00155575, 0.0015612186, 0.0015555352]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0307493, 1.0, 0.0, 0.0014304855, 0.0013649908, 0.0014295562, 0.001364903]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0307204, 1.0, 0.0, 0.0009474328, 0.001845317, 0.0009478205, 0.0018450882]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0410788, 1.0, 0.0, 0.001964277, 0.0017702521, 0.0019634352, 0.0017701918]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0411072, 1.0, 0.0, 0.0014270052, 0.0023100097, 0.0014268814, 0.0023099994]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0326824, 1.0, 0.0, 0.0020313908, 0.0009397787, 0.0020312257, 0.00093960075]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0357639, 1.0, 0.0, 0.0016335995, 0.001617705, 0.0016333163, 0.001617594]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0310783, 1.0, 0.0, 0.001750379, 0.001074913, 0.0017506945, 0.0010747638]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.056543, 1.0, 0.0, 0.0027083852, 0.0024319433, 0.0027077375, 0.0024319491]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0335373, 1.0, 0.0, 0.0014475638, 0.0016013298, 0.001447054, 0.0016013358]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0316349, 1.0, 0.0, 0.0013277059, 0.0015481808, 0.0013279683, 0.0015480093]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0358385, 1.0, 0.0, 0.0010247192, 0.0022333625, 0.0010244942, 0.0022331837]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0273486, 1.0, 0.0, 0.001206194, 0.0012801485, 0.0012056712, 0.0012795243]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0305355, 1.0, 0.0, 0.0013444446, 0.0014315317, 0.0013444773, 0.0014312046]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0374681, 1.0, 0.0, 0.0016203473, 0.0017858725, 0.0016202513, 0.0017854791]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0258951, 1.0, 0.0, 0.0011744491, 0.0011797473, 0.001173496, 0.0011797051]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0284617, 1.0, 0.0, 0.0015954152, 0.0009920567, 0.0015952883, 0.0009916654]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0241714, 1.0, 0.0, 0.0015438814, 0.0006534999, 0.0015439427, 0.00065348984]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0298698, 1.0, 0.0, 0.0021564094, 0.00055900915, 0.0021566674, 0.00055900397]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0569289, 1.0, 0.0, 0.0017498797, 0.0034255804, 0.0017483983, 0.0034256647]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0597357, 1.0, 0.0, 0.0029381744, 0.0024923524, 0.0029380936, 0.002492225]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.051959, 1.0, 0.0, 0.0015602547, 0.0031632888, 0.0015600391, 0.003163468]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0419225, 1.0, 0.0, 0.0010086319, 0.0028024586, 0.001009068, 0.0028025447]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.025182, 1.0, 0.0, 0.0010776313, 0.0012117317, 0.0010775329, 0.0012109082]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0309743, 1.0, 0.0, 0.0012676964, 0.0015481402, 0.0012679477, 0.0015480353]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0410583, 1.0, 0.0, 0.002280614, 0.0014519928, 0.0022803857, 0.0014518964]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0314364, 1.0, 0.0, 0.00094654865, 0.0019113575, 0.0009461213, 0.0019113322]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.041595, 1.0, 0.0, 0.0017144505, 0.0020669766, 0.0017140565, 0.002066505]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0263451, 1.0, 0.0, 0.00082486565, 0.0015701733, 0.00082452304, 0.0015700685]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0235792, 1.0, 0.0, 0.00067663484, 0.0014669554, 0.0006768231, 0.0014664843]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.038159, 1.0, 0.0, 0.0017093482, 0.0017596834, 0.0017091023, 0.0017595303]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0427678, 1.0, 0.0, 0.0013834782, 0.0025045085, 0.0013835788, 0.002504368]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0399755, 1.0, 0.0, 0.0017866716, 0.0018474835, 0.0017871193, 0.0018468987]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0335411, 1.0, 0.0, 0.0019167198, 0.0011323891, 0.001917856, 0.0011320846]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399394, 1.0, 0.0, 0.0016287846, 0.002002108, 0.0016285162, 0.0020019505]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.034786, 1.0, 0.0, 0.0013609754, 0.0018013946, 0.0013611579, 0.0018010833]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.037446, 1.0, 0.0, 0.0017108255, 0.0016934325, 0.0017103088, 0.0016932922]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0318396, 1.0, 0.0, 0.0016760343, 0.0012184854, 0.0016760351, 0.0012183662]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0343025, 1.0, 0.0, 0.0016522987, 0.0014661901, 0.001651496, 0.001465987]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0339794, 1.0, 0.0, 0.0017594968, 0.0013295357, 0.0017598051, 0.0013292842]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0317249, 1.0, 0.0, 0.001379326, 0.0015047041, 0.0013800104, 0.0015046487]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0435373, 1.0, 0.0, 0.001631492, 0.002326496, 0.0016316068, 0.00232576]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0420926, 1.0, 0.0, 0.0016707785, 0.0021558753, 0.0016702423, 0.0021557356]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0418032, 1.0, 0.0, 0.0018688731, 0.0019315677, 0.0018681462, 0.001930693]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0474918, 1.0, 0.0, 0.0018997716, 0.0024178145, 0.0018982057, 0.0024178093]
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G_Loss: [1.0312588, 1.0, 0.0, 0.0014151029, 0.0014266998, 0.0014154159, 0.0014255343]
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G_Loss: [1.0320085, 1.0, 0.0, 0.0010195752, 0.001890339, 0.0010192066, 0.0018902048]
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G_Loss: [1.048393, 1.0, 0.0, 0.0030440972, 0.0013552147, 0.003044678, 0.001355161]
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G_Loss: [1.0425326, 1.0, 0.0, 0.0014031556, 0.002463553, 0.001402298, 0.0024632055]
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G_Loss: [1.0392503, 1.0, 0.0, 0.0020375792, 0.0015306245, 0.0020378958, 0.0015303197]
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D_Loss: [0.5 0.5]
G_Loss: [1.0681744, 1.0, 0.0, 0.003388579, 0.0028092563, 0.0033867569, 0.0028092675]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0272175, 1.0, 0.0, 0.0011767185, 0.0012976177, 0.0011766144, 0.0012975826]
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D_Loss: [0.5 0.5]
G_Loss: [1.0340794, 1.0, 0.0, 0.0010935534, 0.002004635, 0.0010930416, 0.0020045503]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0328437, 1.0, 0.0, 0.0014993816, 0.0014865814, 0.0014976196, 0.0014865589]
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D_Loss: [0.5 0.5]
G_Loss: [1.0386486, 1.0, 0.0, 0.0018102836, 0.0017034813, 0.0018098608, 0.0017011305]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0530132, 1.0, 0.0, 0.0019035081, 0.0029158853, 0.001902756, 0.0029165468]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0323844, 1.0, 0.0, 0.0018009346, 0.0011432116, 0.0017998897, 0.0011430343]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0288466, 1.0, 0.0, 0.0012206733, 0.0014017862, 0.0012203215, 0.001401664]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0276686, 1.0, 0.0, 0.0008588275, 0.0016564985, 0.00085873157, 0.00165655]
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D_Loss: [0.5 0.5]
G_Loss: [1.0373843, 1.0, 0.0, 0.0017720647, 0.0016264993, 0.0017721764, 0.0016264099]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.05052, 1.0, 0.0, 0.0021712645, 0.0024215335, 0.0021706931, 0.002421239]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0294982, 1.0, 0.0, 0.0011971663, 0.0014845652, 0.0011963851, 0.0014845057]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0349444, 1.0, 0.0, 0.0016622887, 0.0015144974, 0.0016624928, 0.0015140786]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0369774, 1.0, 0.0, 0.00097129267, 0.002390305, 0.0009712784, 0.0023901968]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0349406, 1.0, 0.0, 0.0013636108, 0.001812988, 0.0013622013, 0.0018124776]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.033543, 1.0, 0.0, 0.0013761722, 0.0016733292, 0.0013749315, 0.0016729257]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0340883, 1.0, 0.0, 0.001550395, 0.0015486651, 0.0015489818, 0.0015486337]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0251708, 1.0, 0.0, 0.00083225785, 0.001455998, 0.00083229237, 0.0014558616]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0299679, 1.0, 0.0, 0.0015260257, 0.0011983179, 0.0015263286, 0.0011981626]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.037053, 1.0, 0.0, 0.001717377, 0.0016511923, 0.0017166866, 0.0016505283]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0415728, 1.0, 0.0, 0.0022875287, 0.0014918286, 0.0022879436, 0.0014912344]
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D_Loss: [0.5 0.5]
G_Loss: [1.0373123, 1.0, 0.0, 0.0014310749, 0.0019610822, 0.0014297472, 0.0019610464]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0416936, 1.0, 0.0, 0.0010362451, 0.002754106, 0.0010357611, 0.0027542277]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0251259, 1.0, 0.0, 0.0013002299, 0.0009840338, 0.0012992041, 0.0009839224]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0523212, 1.0, 0.0, 0.0023102663, 0.0024464098, 0.002309246, 0.002445268]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0323906, 1.0, 0.0, 0.0015465785, 0.0013980202, 0.0015466, 0.0013980573]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0343072, 1.0, 0.0, 0.0012925202, 0.0018263548, 0.0012923734, 0.0018261945]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.04191, 1.0, 0.0, 0.001532235, 0.0022778483, 0.0015311022, 0.002278116]
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D_Loss: [0.5 0.5]
G_Loss: [1.0283513, 1.0, 0.0, 0.0010360176, 0.0015414865, 0.001035527, 0.0015408238]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0317677, 1.0, 0.0, 0.0018693353, 0.001018642, 0.0018694772, 0.0010185196]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0364772, 1.0, 0.0, 0.0018485645, 0.0014675765, 0.0018482247, 0.0014676332]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0339174, 1.0, 0.0, 0.0016321852, 0.0014511896, 0.0016325833, 0.0014510553]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0424056, 1.0, 0.0, 0.0016244684, 0.0022305646, 0.0016248755, 0.002230528]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0410008, 1.0, 0.0, 0.0011793062, 0.0025480965, 0.0011790971, 0.0025477065]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0329291, 1.0, 0.0, 0.0018060234, 0.001187607, 0.0018054165, 0.0011873161]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0389458, 1.0, 0.0, 0.001435783, 0.0021048023, 0.0014351741, 0.0021047476]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0372012, 1.0, 0.0, 0.0020200936, 0.0013619306, 0.0020192803, 0.0013615761]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0395455, 1.0, 0.0, 0.0012279751, 0.0023671025, 0.0012278678, 0.0023669451]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0350372, 1.0, 0.0, 0.0017418212, 0.001443414, 0.001741589, 0.0014431922]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.031065, 1.0, 0.0, 0.0012228314, 0.0016012493, 0.0012231334, 0.0016009742]
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G_Loss: [1.0411683, 1.0, 0.0, 0.001996861, 0.0017457615, 0.00199695, 0.0017450652]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0274836, 1.0, 0.0, 0.0010784748, 0.0014200837, 0.0010781428, 0.0014199265]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0278425, 1.0, 0.0, 0.0014055455, 0.0011257906, 0.0014038044, 0.0011252959]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0268382, 1.0, 0.0, 0.0011574005, 0.001282494, 0.0011568719, 0.0012823094]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0275668, 1.0, 0.0, 0.0015226747, 0.0009834035, 0.0015227222, 0.0009832669]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0342203, 1.0, 0.0, 0.0018131885, 0.0012976975, 0.0018139054, 0.0012976135]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.0394483, 1.0, 0.0, 0.0024290415, 0.0011571757, 0.0024290187, 0.0011570773]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0551319, 1.0, 0.0, 0.0018020467, 0.003209876, 0.0018028443, 0.0032098826]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0625298, 1.0, 0.0, 0.003523067, 0.0021613187, 0.0035246697, 0.0021612858]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0396577, 1.0, 0.0, 0.0017354385, 0.001869818, 0.001735624, 0.0018695795]
Epoch: 13
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0321978, 1.0, 0.0, 0.0011141397, 0.0018129607, 0.0011144129, 0.0018124796]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0502799, 1.0, 0.0, 0.0018397161, 0.002731163, 0.0018396699, 0.0027313333]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0313462, 1.0, 0.0, 0.0016613491, 0.0011884249, 0.0016602231, 0.0011883226]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0519907, 1.0, 0.0, 0.0029316219, 0.0017947451, 0.0029326158, 0.0017945122]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0255972, 1.0, 0.0, 0.0011405637, 0.0011864394, 0.0011407918, 0.0011863541]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0414006, 1.0, 0.0, 0.0009781185, 0.0027855607, 0.0009781141, 0.0027856203]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.062035, 1.0, 0.0, 0.0012023309, 0.004437206, 0.0012023662, 0.0044372026]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0340332, 1.0, 0.0, 0.0010924052, 0.0020015752, 0.0010917895, 0.0020014658]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.035343, 1.0, 0.0, 0.0019016849, 0.0013114591, 0.0019004915, 0.0013111739]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0324681, 1.0, 0.0, 0.0011877588, 0.0017639403, 0.0011875306, 0.0017635536]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0459112, 1.0, 0.0, 0.0016435432, 0.002530397, 0.0016415226, 0.0025303233]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0411265, 1.0, 0.0, 0.0019717212, 0.0017671096, 0.0019711652, 0.0017670654]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0398711, 1.0, 0.0, 0.0012316653, 0.0023929907, 0.0012316713, 0.002392738]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0305204, 1.0, 0.0, 0.001949372, 0.00082521426, 0.001949508, 0.0008250601]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0280105, 1.0, 0.0, 0.0013354516, 0.0012109939, 0.0013351298, 0.0012108351]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0255977, 1.0, 0.0, 0.0013500849, 0.0009770688, 0.0013493123, 0.0009769796]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.02905, 1.0, 0.0, 0.0013914423, 0.0012494035, 0.0013919405, 0.0012495217]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0390925, 1.0, 0.0, 0.0012670215, 0.0022868803, 0.0012666731, 0.002286861]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0355847, 1.0, 0.0, 0.0014072589, 0.0018277836, 0.0014069221, 0.001827365]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0537802, 1.0, 0.0, 0.0016369617, 0.0032521687, 0.001636922, 0.003252008]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0408067, 1.0, 0.0, 0.0016002376, 0.002109449, 0.0016002124, 0.0021094824]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0343403, 1.0, 0.0, 0.001566573, 0.0015553865, 0.0015655651, 0.0015551242]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.030706, 1.0, 0.0, 0.001430782, 0.001360774, 0.0014298328, 0.0013607026]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0307518, 1.0, 0.0, 0.0009514183, 0.001844191, 0.00095179287, 0.0018439812]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0409182, 1.0, 0.0, 0.0019568321, 0.0017630958, 0.0019560345, 0.0017630258]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0410813, 1.0, 0.0, 0.001429672, 0.002304998, 0.0014295696, 0.002305027]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0326781, 1.0, 0.0, 0.002033514, 0.00093724864, 0.0020333135, 0.0009370903]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0358224, 1.0, 0.0, 0.0016336867, 0.0016229231, 0.0016334015, 0.0016228305]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.031121, 1.0, 0.0, 0.0017527302, 0.0010764343, 0.0017530076, 0.0010763272]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0566133, 1.0, 0.0, 0.0027093438, 0.0024373876, 0.0027087023, 0.0024374058]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0335428, 1.0, 0.0, 0.001446899, 0.0016024804, 0.0014463795, 0.0016024947]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0316643, 1.0, 0.0, 0.0013263879, 0.0015521895, 0.0013265723, 0.0015520167]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0358915, 1.0, 0.0, 0.0010259323, 0.0022369719, 0.0010257002, 0.002236786]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0272925, 1.0, 0.0, 0.0012048194, 0.0012764261, 0.0012041843, 0.0012758686]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0306011, 1.0, 0.0, 0.0013457295, 0.0014362234, 0.0013457149, 0.0014359311]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0374415, 1.0, 0.0, 0.0016213044, 0.0017825009, 0.0016212126, 0.0017821261]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0259141, 1.0, 0.0, 0.0011742333, 0.0011816826, 0.0011731993, 0.0011816404]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0284768, 1.0, 0.0, 0.0015975316, 0.0009913233, 0.0015973295, 0.0009909461]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.024229, 1.0, 0.0, 0.0015504411, 0.00065219146, 0.0015505101, 0.00065218005]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0298705, 1.0, 0.0, 0.002155386, 0.0005600909, 0.0021556094, 0.0005600919]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0569229, 1.0, 0.0, 0.0017473876, 0.0034275511, 0.0017458316, 0.0034276322]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0597758, 1.0, 0.0, 0.002941207, 0.0024929717, 0.002941109, 0.0024928525]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0518705, 1.0, 0.0, 0.0015551514, 0.0031603468, 0.0015549235, 0.0031605063]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0417713, 1.0, 0.0, 0.0009995138, 0.0027978322, 0.0009999605, 0.0027979086]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0253062, 1.0, 0.0, 0.0010921138, 0.0012085491, 0.0010919985, 0.0012077328]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0309616, 1.0, 0.0, 0.0012600508, 0.0015546355, 0.0012602569, 0.0015545187]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.041291, 1.0, 0.0, 0.0022995947, 0.0014541537, 0.0022994117, 0.001454067]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0315752, 1.0, 0.0, 0.00095887063, 0.0019116391, 0.00095843687, 0.0019116122]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0415097, 1.0, 0.0, 0.0017029422, 0.0020707685, 0.0017023488, 0.002070287]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0262756, 1.0, 0.0, 0.0008229684, 0.0015657507, 0.0008226086, 0.0015656468]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0235784, 1.0, 0.0, 0.000682601, 0.0014609195, 0.0006827185, 0.0014604782]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0382894, 1.0, 0.0, 0.0017168878, 0.0017640162, 0.001716596, 0.00176387]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0427378, 1.0, 0.0, 0.0013854185, 0.0024998419, 0.0013855391, 0.0024996903]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.040233, 1.0, 0.0, 0.0018046264, 0.0018529267, 0.001805089, 0.0018524171]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0334593, 1.0, 0.0, 0.0019134735, 0.0011282099, 0.0019145019, 0.0011279462]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399574, 1.0, 0.0, 0.0016319221, 0.0020006062, 0.0016316194, 0.0020004897]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.034768, 1.0, 0.0, 0.001360541, 0.0018002046, 0.0013606391, 0.0017998959]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0374539, 1.0, 0.0, 0.0017105525, 0.0016944048, 0.0017099064, 0.0016942676]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0318531, 1.0, 0.0, 0.0016761692, 0.0012195853, 0.0016761343, 0.0012194732]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.034414, 1.0, 0.0, 0.0016630896, 0.001465556, 0.0016622434, 0.0014653462]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0340511, 1.0, 0.0, 0.0017669974, 0.0013285559, 0.0017673124, 0.0013283059]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0317298, 1.0, 0.0, 0.0013836864, 0.0015007814, 0.0013843745, 0.0015007278]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0435661, 1.0, 0.0, 0.001634778, 0.0023258622, 0.0016347748, 0.0023250857]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.042214, 1.0, 0.0, 0.0016776777, 0.0021600248, 0.0016771611, 0.0021598677]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0417039, 1.0, 0.0, 0.0018698524, 0.0019215628, 0.0018691416, 0.0019206428]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0474712, 1.0, 0.0, 0.0018936143, 0.0024220678, 0.001892179, 0.00242208]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0312817, 1.0, 0.0, 0.0014175243, 0.001426345, 0.0014178848, 0.0014251182]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0319883, 1.0, 0.0, 0.0010175579, 0.0018905151, 0.00101711, 0.0018903806]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0484838, 1.0, 0.0, 0.0030504018, 0.0013571897, 0.0030509795, 0.0013571312]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0425621, 1.0, 0.0, 0.0014082321, 0.0024611456, 0.0014075115, 0.0024607892]
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D_Loss: [0.5 0.5]
G_Loss: [1.03926, 1.0, 0.0, 0.002037727, 0.0015313738, 0.0020379683, 0.0015310573]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0683494, 1.0, 0.0, 0.0034024464, 0.0028112833, 0.003400669, 0.0028112882]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0272623, 1.0, 0.0, 0.0011798672, 0.0012985431, 0.0011797426, 0.001298513]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0340416, 1.0, 0.0, 0.0010940381, 0.0020007088, 0.0010935449, 0.0020006201]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0328313, 1.0, 0.0, 0.0015003355, 0.0014844985, 0.0014984198, 0.0014844683]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.038636, 1.0, 0.0, 0.001811973, 0.0017006611, 0.0018114763, 0.0016981725]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0528849, 1.0, 0.0, 0.001903228, 0.002904528, 0.00190219, 0.0029051974]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0322953, 1.0, 0.0, 0.0018006512, 0.0011353905, 0.0017996645, 0.001135197]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0288444, 1.0, 0.0, 0.0012300375, 0.0013922228, 0.0012296364, 0.0013920907]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.027758, 1.0, 0.0, 0.0008652839, 0.0016581805, 0.00086519116, 0.0016582285]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0372492, 1.0, 0.0, 0.0017613695, 0.0016249233, 0.0017615211, 0.0016248429]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0509759, 1.0, 0.0, 0.0022135763, 0.0024206846, 0.0022129256, 0.00242037]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0294646, 1.0, 0.0, 0.001198534, 0.0014801512, 0.0011976811, 0.0014800845]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0350418, 1.0, 0.0, 0.0016745438, 0.0015110956, 0.0016747345, 0.0015106619]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0368776, 1.0, 0.0, 0.0009781838, 0.0023743433, 0.0009782021, 0.0023742258]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0352231, 1.0, 0.0, 0.0013677287, 0.0018345721, 0.0013661124, 0.0018340768]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0335068, 1.0, 0.0, 0.0013789295, 0.0016672924, 0.0013775916, 0.0016669282]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0341179, 1.0, 0.0, 0.0015540568, 0.0015476989, 0.0015526518, 0.0015476686]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0252823, 1.0, 0.0, 0.00083422265, 0.0014641717, 0.0008342577, 0.0014640513]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.03005, 1.0, 0.0, 0.0015275832, 0.0012042255, 0.001527867, 0.0012040716]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0371375, 1.0, 0.0, 0.0017270342, 0.0016492356, 0.0017263114, 0.0016485965]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0416023, 1.0, 0.0, 0.0022923674, 0.0014896742, 0.002292798, 0.0014890996]
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D_Loss: [0.5 0.5]
G_Loss: [1.0373869, 1.0, 0.0, 0.0014350277, 0.0019639141, 0.0014336278, 0.001963883]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0417595, 1.0, 0.0, 0.0010383471, 0.0027580038, 0.0010378265, 0.0027581241]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0250608, 1.0, 0.0, 0.0012928926, 0.000985452, 0.0012918604, 0.000985343]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0523542, 1.0, 0.0, 0.00231014, 0.0024495197, 0.0023091151, 0.0024484596]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0323541, 1.0, 0.0, 0.0015425652, 0.001398714, 0.0015425542, 0.001398742]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.034355, 1.0, 0.0, 0.0012955798, 0.0018276466, 0.0012953061, 0.001827504]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0419888, 1.0, 0.0, 0.001535327, 0.0022819238, 0.0015341378, 0.0022821983]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0283725, 1.0, 0.0, 0.0010389958, 0.0015404199, 0.0010385235, 0.0015397782]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0316861, 1.0, 0.0, 0.0018602409, 0.0010203253, 0.0018602669, 0.0010201945]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0367851, 1.0, 0.0, 0.0018779915, 0.0014661481, 0.0018774958, 0.001466218]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0339707, 1.0, 0.0, 0.0016359965, 0.0014522359, 0.0016364236, 0.0014520857]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0423354, 1.0, 0.0, 0.0016153587, 0.0022332608, 0.0016159413, 0.002233216]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0410258, 1.0, 0.0, 0.0011792379, 0.002550433, 0.0011790241, 0.0025500641]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0328817, 1.0, 0.0, 0.0018029255, 0.0011863941, 0.0018024001, 0.0011861181]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.039009, 1.0, 0.0, 0.0014390845, 0.002107235, 0.001438627, 0.0021071625]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.037209, 1.0, 0.0, 0.0020228415, 0.0013598904, 0.0020221034, 0.0013595307]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0395385, 1.0, 0.0, 0.0012245374, 0.0023698895, 0.0012243905, 0.0023697456]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0350375, 1.0, 0.0, 0.0017387934, 0.0014464897, 0.0017384512, 0.0014462601]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0310601, 1.0, 0.0, 0.0012274496, 0.0015962038, 0.0012277239, 0.0015959099]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0411966, 1.0, 0.0, 0.0020040213, 0.0017411639, 0.0020043047, 0.001740428]
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G_Loss: [1.0275632, 1.0, 0.0, 0.0010835263, 0.0014222576, 0.0010832337, 0.0014220958]
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G_Loss: [1.0278192, 1.0, 0.0, 0.0014005401, 0.0011286817, 0.0013987165, 0.0011281833]
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G_Loss: [1.0267563, 1.0, 0.0, 0.0011529232, 0.0012795336, 0.001152373, 0.0012793423]
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D_Loss: [0.5 0.5]
G_Loss: [1.0275848, 1.0, 0.0, 0.0015231551, 0.0009845505, 0.0015232235, 0.0009844124]
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D_Loss: [0.5 0.5]
G_Loss: [1.0342716, 1.0, 0.0, 0.0018167517, 0.0012987795, 0.0018175539, 0.0012986907]
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D_Loss: [0.5 0.5]
G_Loss: [1.0394759, 1.0, 0.0, 0.0024325345, 0.0011562018, 0.0024325252, 0.0011560996]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0551493, 1.0, 0.0, 0.0018021881, 0.0032113134, 0.0018029931, 0.0032113255]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0627463, 1.0, 0.0, 0.0035425974, 0.0021614376, 0.0035444736, 0.0021614174]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0396508, 1.0, 0.0, 0.0017361005, 0.0018685054, 0.0017363448, 0.0018682521]
Epoch: 14
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0321941, 1.0, 0.0, 0.0011154518, 0.0018113153, 0.0011157116, 0.0018107973]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0504589, 1.0, 0.0, 0.0018478418, 0.0027392842, 0.0018480584, 0.0027395035]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0315194, 1.0, 0.0, 0.0016787895, 0.0011867145, 0.0016777454, 0.0011866204]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0516967, 1.0, 0.0, 0.0029052906, 0.0017943373, 0.0029063318, 0.0017940906]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0256425, 1.0, 0.0, 0.0011511268, 0.0011799891, 0.0011513287, 0.0011798785]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0415194, 1.0, 0.0, 0.0009868672, 0.0027876408, 0.000986706, 0.0027877032]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0620079, 1.0, 0.0, 0.00120189, 0.0044351975, 0.001201868, 0.0044351975]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0338774, 1.0, 0.0, 0.0010846349, 0.0019951942, 0.0010839341, 0.001995084]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0356749, 1.0, 0.0, 0.0019175031, 0.0013258271, 0.001916075, 0.0013255522]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0324038, 1.0, 0.0, 0.001185385, 0.0017604819, 0.0011852297, 0.0017600536]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0459859, 1.0, 0.0, 0.0016388424, 0.002541892, 0.0016368452, 0.002541833]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0408955, 1.0, 0.0, 0.0019727377, 0.0017450843, 0.0019723377, 0.0017450248]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0399653, 1.0, 0.0, 0.0012319426, 0.0024012965, 0.0012319157, 0.002401032]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0304161, 1.0, 0.0, 0.0019447922, 0.00082030054, 0.0019450164, 0.00082012836]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0282258, 1.0, 0.0, 0.001352097, 0.0012139226, 0.0013517786, 0.0012137671]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0256166, 1.0, 0.0, 0.0013497601, 0.0009790945, 0.0013490701, 0.0009790097]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0288881, 1.0, 0.0, 0.0013792767, 0.0012468677, 0.0013797639, 0.0012469853]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0390751, 1.0, 0.0, 0.0012674171, 0.0022848956, 0.0012671346, 0.0022848714]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0355015, 1.0, 0.0, 0.0014071601, 0.0018203151, 0.0014069357, 0.0018198633]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0540836, 1.0, 0.0, 0.0016372106, 0.0032795062, 0.0016371417, 0.003279321]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0409471, 1.0, 0.0, 0.0016014157, 0.002121044, 0.0016014843, 0.002121061]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0343494, 1.0, 0.0, 0.0015640662, 0.0015587357, 0.0015629958, 0.001558485]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.030642, 1.0, 0.0, 0.0014325852, 0.0013531687, 0.001431487, 0.0013530541]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0307329, 1.0, 0.0, 0.0009537543, 0.0018401348, 0.00095406384, 0.0018398681]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0408357, 1.0, 0.0, 0.0019558128, 0.0017566211, 0.0019548568, 0.0017565533]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0410458, 1.0, 0.0, 0.0014219478, 0.0023095028, 0.0014218306, 0.0023094954]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0326861, 1.0, 0.0, 0.0020288483, 0.0009426602, 0.0020286436, 0.00094244786]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0359038, 1.0, 0.0, 0.0016372708, 0.0016267411, 0.0016370424, 0.0016266274]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0311649, 1.0, 0.0, 0.0017563603, 0.001076796, 0.0017566661, 0.0010766583]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0567633, 1.0, 0.0, 0.002721131, 0.0024392088, 0.0027205655, 0.0024392358]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0336275, 1.0, 0.0, 0.0014542799, 0.0016028124, 0.0014537502, 0.0016028206]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0317172, 1.0, 0.0, 0.0013279675, 0.0015554249, 0.0013281686, 0.0015552402]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0359098, 1.0, 0.0, 0.0010293822, 0.0022351749, 0.0010291503, 0.0022349982]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0273068, 1.0, 0.0, 0.0012010864, 0.0012814513, 0.0012005207, 0.0012808591]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0305074, 1.0, 0.0, 0.0013469012, 0.001426535, 0.0013468931, 0.0014262106]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0374471, 1.0, 0.0, 0.0016187595, 0.0017855717, 0.0016186455, 0.0017851567]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0259395, 1.0, 0.0, 0.0011814398, 0.0011767963, 0.0011803987, 0.0011767504]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0285219, 1.0, 0.0, 0.0016041294, 0.0009888241, 0.0016039929, 0.0009884068]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0241101, 1.0, 0.0, 0.0015425193, 0.00064929534, 0.001542582, 0.00064928073]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.029822, 1.0, 0.0, 0.0021507852, 0.0005602867, 0.0021510562, 0.0005602853]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.056893, 1.0, 0.0, 0.0017429416, 0.0034292608, 0.0017414857, 0.003429354]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0598004, 1.0, 0.0, 0.0029444047, 0.0024920157, 0.002944346, 0.0024918811]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0516697, 1.0, 0.0, 0.0015421066, 0.003155143, 0.0015418672, 0.003155341]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0415622, 1.0, 0.0, 0.0009905125, 0.0027878173, 0.0009909933, 0.0027878957]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0253597, 1.0, 0.0, 0.001106085, 0.0011994451, 0.0011059566, 0.0011985671]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0311402, 1.0, 0.0, 0.0012554808, 0.0015754405, 0.001255639, 0.0015753246]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0413263, 1.0, 0.0, 0.0023073843, 0.0014495817, 0.002307124, 0.0014495079]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.031644, 1.0, 0.0, 0.00096378266, 0.0019129888, 0.00096334453, 0.0019129718]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0415134, 1.0, 0.0, 0.001701926, 0.0020721233, 0.0017013925, 0.002071606]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0262552, 1.0, 0.0, 0.0008225811, 0.0015643036, 0.0008222528, 0.0015641942]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.023455, 1.0, 0.0, 0.0006739165, 0.0014583962, 0.00067399465, 0.0014579298]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0382532, 1.0, 0.0, 0.0017177596, 0.001759844, 0.0017175166, 0.0017596796]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0427517, 1.0, 0.0, 0.0013815191, 0.00250501, 0.0013816252, 0.0025048587]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0404121, 1.0, 0.0, 0.0018222916, 0.001851543, 0.0018227354, 0.0018509605]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.033382, 1.0, 0.0, 0.001911517, 0.0011231531, 0.0019125007, 0.0011228571]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399693, 1.0, 0.0, 0.0016364419, 0.0019971752, 0.0016361093, 0.0019970366]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0347961, 1.0, 0.0, 0.0013620902, 0.0018012152, 0.001362227, 0.001800904]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0375121, 1.0, 0.0, 0.001713551, 0.0016967079, 0.0017129527, 0.001696558]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0318407, 1.0, 0.0, 0.0016752654, 0.0012193505, 0.0016752728, 0.0012192391]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0344243, 1.0, 0.0, 0.0016647691, 0.0014647933, 0.0016640013, 0.0014645668]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0341355, 1.0, 0.0, 0.0017776062, 0.0013256238, 0.0017778757, 0.0013253171]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0318376, 1.0, 0.0, 0.0013912464, 0.0015030191, 0.0013920036, 0.0015029573]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0436383, 1.0, 0.0, 0.0016393438, 0.002327852, 0.0016393138, 0.0023270324]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0421277, 1.0, 0.0, 0.0016683224, 0.0021615275, 0.0016678004, 0.0021613466]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0417315, 1.0, 0.0, 0.0018776925, 0.0019162366, 0.0018769004, 0.0019152733]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0474566, 1.0, 0.0, 0.0018841674, 0.002430209, 0.0018827186, 0.0024301922]
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D_Loss: [0.5 0.5]
G_Loss: [1.0313245, 1.0, 0.0, 0.0014227796, 0.0014250011, 0.0014230434, 0.001423714]
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D_Loss: [0.5 0.5]
G_Loss: [1.0319793, 1.0, 0.0, 0.0010161339, 0.001891138, 0.0010157152, 0.0018909895]
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D_Loss: [0.5 0.5]
G_Loss: [1.0484813, 1.0, 0.0, 0.0030495455, 0.001357803, 0.0030501229, 0.0013577428]
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D_Loss: [0.5 0.5]
G_Loss: [1.0426188, 1.0, 0.0, 0.0014138416, 0.002460695, 0.0014130056, 0.0024603214]
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D_Loss: [0.5 0.5]
G_Loss: [1.0392888, 1.0, 0.0, 0.0020408207, 0.0015308938, 0.0020410535, 0.0015305553]
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D_Loss: [0.5 0.5]
G_Loss: [1.0683606, 1.0, 0.0, 0.003402547, 0.0028122084, 0.0034009144, 0.002812206]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0272409, 1.0, 0.0, 0.0011764176, 0.001300052, 0.0011762695, 0.0013000038]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0340078, 1.0, 0.0, 0.001093207, 0.0019984627, 0.0010926748, 0.0019983698]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0328449, 1.0, 0.0, 0.0014993288, 0.0014867561, 0.0014973076, 0.0014867403]
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G_Loss: [1.0385256, 1.0, 0.0, 0.0018065916, 0.0016960076, 0.00180608, 0.0016935667]
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G_Loss: [1.0524541, 1.0, 0.0, 0.00189416, 0.002874431, 0.0018931864, 0.002875025]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0323309, 1.0, 0.0, 0.0018078589, 0.0011314137, 0.0018068292, 0.0011312519]
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D_Loss: [0.5 0.5]
G_Loss: [1.0288769, 1.0, 0.0, 0.0012411175, 0.0013840911, 0.0012407954, 0.0013839696]
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D_Loss: [0.5 0.5]
G_Loss: [1.0277851, 1.0, 0.0, 0.0008638017, 0.0016621188, 0.0008637256, 0.0016621815]
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D_Loss: [0.5 0.5]
G_Loss: [1.0371921, 1.0, 0.0, 0.0017558008, 0.0016252941, 0.0017558602, 0.0016252666]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.051241, 1.0, 0.0, 0.002240918, 0.0024174477, 0.00224025, 0.002417129]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0293753, 1.0, 0.0, 0.0011989676, 0.001471604, 0.0011980942, 0.0014715332]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0350755, 1.0, 0.0, 0.0016783655, 0.0015103382, 0.0016786125, 0.0015098837]
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D_Loss: [0.5 0.5]
G_Loss: [1.0367017, 1.0, 0.0, 0.00098079, 0.0023557367, 0.0009807636, 0.0023556137]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0354683, 1.0, 0.0, 0.0013680798, 0.0018564917, 0.001366501, 0.001856047]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0335257, 1.0, 0.0, 0.0013782718, 0.0016696773, 0.0013768851, 0.0016692962]
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D_Loss: [0.5 0.5]
G_Loss: [1.0341101, 1.0, 0.0, 0.0015559128, 0.0015451435, 0.0015544707, 0.0015451077]
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D_Loss: [0.5 0.5]
G_Loss: [1.025357, 1.0, 0.0, 0.00083267083, 0.001472532, 0.0008326998, 0.0014724063]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0301471, 1.0, 0.0, 0.0015291966, 0.001211446, 0.0015294394, 0.0012112751]
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D_Loss: [0.5 0.5]
G_Loss: [1.0373045, 1.0, 0.0, 0.0017380947, 0.0016533462, 0.0017373157, 0.0016527105]
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D_Loss: [0.5 0.5]
G_Loss: [1.0416715, 1.0, 0.0, 0.00230046, 0.001487874, 0.0023008916, 0.0014872679]
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D_Loss: [0.5 0.5]
G_Loss: [1.0374838, 1.0, 0.0, 0.0014436181, 0.0019641393, 0.0014421954, 0.0019641006]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0417907, 1.0, 0.0, 0.001039807, 0.0027593877, 0.0010392576, 0.0027595172]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0251021, 1.0, 0.0, 0.0012935551, 0.0009885646, 0.0012925256, 0.00098845]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0524006, 1.0, 0.0, 0.00231059, 0.0024533162, 0.0023094718, 0.0024521616]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0324022, 1.0, 0.0, 0.0015441374, 0.0014015073, 0.0015441357, 0.0014015323]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0343385, 1.0, 0.0, 0.0012935752, 0.001828151, 0.0012933013, 0.0018279986]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0421493, 1.0, 0.0, 0.0015452025, 0.0022866372, 0.0015440286, 0.0022869091]
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D_Loss: [0.5 0.5]
G_Loss: [1.0283502, 1.0, 0.0, 0.0010388321, 0.0015385638, 0.0010383509, 0.0015378792]
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D_Loss: [0.5 0.5]
G_Loss: [1.0317321, 1.0, 0.0, 0.0018609263, 0.0010238303, 0.001860911, 0.0010236972]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0367506, 1.0, 0.0, 0.0018796672, 0.0014613332, 0.00187908, 0.0014613818]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0340155, 1.0, 0.0, 0.0016355976, 0.0014566984, 0.0016359864, 0.0014565377]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0423952, 1.0, 0.0, 0.001616639, 0.0022374294, 0.0016171363, 0.0022373733]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0410026, 1.0, 0.0, 0.0011775792, 0.0025499985, 0.0011773615, 0.0025496096]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0328197, 1.0, 0.0, 0.0017993189, 0.0011843849, 0.0017986735, 0.0011840854]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0391572, 1.0, 0.0, 0.0014456464, 0.00211414, 0.0014451514, 0.0021140552]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0371921, 1.0, 0.0, 0.0020237118, 0.0013575063, 0.002022922, 0.0013571166]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0396427, 1.0, 0.0, 0.0012245784, 0.002379337, 0.0012244305, 0.0023791865]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0350951, 1.0, 0.0, 0.0017441437, 0.0014463828, 0.0017438518, 0.0014461161]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.030945, 1.0, 0.0, 0.001215697, 0.001597487, 0.0012159126, 0.0015971949]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0410798, 1.0, 0.0, 0.001996106, 0.0017384791, 0.0019963318, 0.00173764]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.027598, 1.0, 0.0, 0.0010877275, 0.0014212416, 0.0010873436, 0.0014210386]
Batch: 113
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G_Loss: [1.0277313, 1.0, 0.0, 0.0013923242, 0.0011289215, 0.00139047, 0.0011283662]
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G_Loss: [1.0266224, 1.0, 0.0, 0.0011457098, 0.0012745885, 0.0011451793, 0.0012743887]
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G_Loss: [1.0275905, 1.0, 0.0, 0.0015245726, 0.0009836694, 0.0015246366, 0.000983515]
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D_Loss: [0.5 0.5]
G_Loss: [1.034297, 1.0, 0.0, 0.001818096, 0.0012997504, 0.0018188849, 0.0012996495]
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G_Loss: [1.0394534, 1.0, 0.0, 0.0024308646, 0.0011558004, 0.0024308693, 0.0011556867]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0551454, 1.0, 0.0, 0.0017987301, 0.0032144214, 0.0017994577, 0.0032144429]
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D_Loss: [0.5 0.5]
G_Loss: [1.0628722, 1.0, 0.0, 0.0035545512, 0.0021609385, 0.0035563488, 0.0021609287]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0396048, 1.0, 0.0, 0.0017352393, 0.0018652051, 0.0017354846, 0.0018649391]
Epoch: 15
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0321105, 1.0, 0.0, 0.0011119596, 0.0018072056, 0.0011122173, 0.0018066311]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0505114, 1.0, 0.0, 0.0018476234, 0.0027442677, 0.0018480232, 0.0027444917]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0315822, 1.0, 0.0, 0.0016828037, 0.001188418, 0.00168172, 0.0011883292]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0515862, 1.0, 0.0, 0.0028948144, 0.0017947687, 0.0028958223, 0.0017945023]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0256658, 1.0, 0.0, 0.0011592447, 0.0011739924, 0.0011595606, 0.0011738762]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0416638, 1.0, 0.0, 0.0009916401, 0.0027959803, 0.000991463, 0.0027960557]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0621234, 1.0, 0.0, 0.0012045322, 0.004443058, 0.0012044631, 0.0044430625]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0338895, 1.0, 0.0, 0.0010812117, 0.0019997284, 0.00108049, 0.0019996082]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0357839, 1.0, 0.0, 0.0019244745, 0.0013287368, 0.0019231825, 0.0013284483]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0323385, 1.0, 0.0, 0.0011836065, 0.0017563093, 0.0011834905, 0.0017558606]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0460453, 1.0, 0.0, 0.0016391017, 0.0025470098, 0.0016371738, 0.0025469463]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0408942, 1.0, 0.0, 0.0019753268, 0.0017423588, 0.0019749394, 0.001742311]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0399625, 1.0, 0.0, 0.001228485, 0.0024044984, 0.0012284718, 0.002404264]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0303572, 1.0, 0.0, 0.0019435752, 0.000816163, 0.0019438129, 0.0008160005]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0283579, 1.0, 0.0, 0.0013620465, 0.001215983, 0.0013617272, 0.0012158179]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0256333, 1.0, 0.0, 0.0013496222, 0.0009807389, 0.0013490744, 0.0009806589]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0288258, 1.0, 0.0, 0.0013768724, 0.0012436064, 0.0013773247, 0.0012437365]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0390619, 1.0, 0.0, 0.0012702872, 0.002280817, 0.0012700089, 0.0022808085]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0354236, 1.0, 0.0, 0.0014080596, 0.0018123421, 0.0014078021, 0.0018119016]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0543147, 1.0, 0.0, 0.0016352437, 0.003302477, 0.0016352166, 0.0033023143]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0409731, 1.0, 0.0, 0.001599923, 0.0021249007, 0.0015999675, 0.0021249047]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0343469, 1.0, 0.0, 0.0015648251, 0.0015577488, 0.001563797, 0.0015574906]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0306653, 1.0, 0.0, 0.0014352524, 0.0013526096, 0.0014341439, 0.0013525188]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0308838, 1.0, 0.0, 0.000961989, 0.0018456338, 0.00096227066, 0.0018453858]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0406921, 1.0, 0.0, 0.0019499601, 0.0017494026, 0.0019490713, 0.0017493328]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0410033, 1.0, 0.0, 0.0014230121, 0.002304579, 0.0014228644, 0.002304574]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0327151, 1.0, 0.0, 0.0020334176, 0.00094072754, 0.0020331857, 0.00094049517]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0359598, 1.0, 0.0, 0.0016364097, 0.0016327044, 0.0016361398, 0.0016325797]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0311878, 1.0, 0.0, 0.001758028, 0.0010772126, 0.0017583314, 0.0010770678]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.056915, 1.0, 0.0, 0.0027277672, 0.002446378, 0.002727136, 0.0024464044]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0336794, 1.0, 0.0, 0.0014580116, 0.0016037868, 0.0014575282, 0.0016037992]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0318006, 1.0, 0.0, 0.0013281924, 0.001562776, 0.0013283516, 0.0015625751]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0358474, 1.0, 0.0, 0.0010319899, 0.0022268961, 0.001031783, 0.0022267024]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0272716, 1.0, 0.0, 0.0011995842, 0.0012797768, 0.0011990566, 0.0012791713]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0305403, 1.0, 0.0, 0.0013484546, 0.0014279671, 0.0013484256, 0.001427662]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0374359, 1.0, 0.0, 0.0016211044, 0.0017821968, 0.0016210052, 0.0017818243]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0259702, 1.0, 0.0, 0.0011820183, 0.0011790087, 0.0011809599, 0.0011789625]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0285364, 1.0, 0.0, 0.0016050846, 0.0009891693, 0.0016049854, 0.000988793]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0241029, 1.0, 0.0, 0.0015465587, 0.00064460956, 0.0015465966, 0.00064458005]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0298817, 1.0, 0.0, 0.002146333, 0.0005701719, 0.0021465844, 0.0005701955]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0569273, 1.0, 0.0, 0.0017435276, 0.0034318056, 0.0017419518, 0.0034319146]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0598687, 1.0, 0.0, 0.0029518758, 0.0024907575, 0.002951786, 0.0024906227]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0513604, 1.0, 0.0, 0.001533364, 0.0031357775, 0.0015330762, 0.0031359517]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0412374, 1.0, 0.0, 0.0009902938, 0.002758503, 0.000990776, 0.0027585772]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0252799, 1.0, 0.0, 0.0011129617, 0.0011852948, 0.0011128187, 0.0011844565]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0315347, 1.0, 0.0, 0.0012488745, 0.0016179248, 0.0012489939, 0.0016177977]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0415133, 1.0, 0.0, 0.0023240477, 0.0014499181, 0.0023238994, 0.0014498687]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0318522, 1.0, 0.0, 0.00097622303, 0.0019194664, 0.00097572664, 0.0019194728]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0415076, 1.0, 0.0, 0.0016873401, 0.00208619, 0.0016867118, 0.002085711]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0262692, 1.0, 0.0, 0.00082295726, 0.0015651839, 0.0008226108, 0.001565093]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0234455, 1.0, 0.0, 0.0006791913, 0.001452245, 0.00067923113, 0.0014518799]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0383859, 1.0, 0.0, 0.001721714, 0.0017679404, 0.0017214646, 0.0017677798]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0426496, 1.0, 0.0, 0.0013792919, 0.0024979617, 0.0013793893, 0.002497783]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0407475, 1.0, 0.0, 0.0018409856, 0.0018633399, 0.0018415143, 0.0018627888]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0332874, 1.0, 0.0, 0.0019067836, 0.0011192627, 0.0019078103, 0.0011190306]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399991, 1.0, 0.0, 0.0016421893, 0.0019941353, 0.001641866, 0.0019940138]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0348173, 1.0, 0.0, 0.001359974, 0.0018052536, 0.0013600485, 0.0018049891]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0375323, 1.0, 0.0, 0.0017178457, 0.0016942575, 0.0017171785, 0.0016941405]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0316842, 1.0, 0.0, 0.0016602259, 0.001220157, 0.0016602196, 0.001220051]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.034479, 1.0, 0.0, 0.0016705729, 0.001463957, 0.001669891, 0.0014637612]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0343252, 1.0, 0.0, 0.0017918362, 0.0013286436, 0.0017920726, 0.0013284059]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0318933, 1.0, 0.0, 0.0013959003, 0.00150341, 0.0013967501, 0.0015033606]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0436451, 1.0, 0.0, 0.0016385037, 0.0023293132, 0.0016383128, 0.0023285756]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0422807, 1.0, 0.0, 0.0016788611, 0.0021648959, 0.0016782912, 0.002164752]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0417755, 1.0, 0.0, 0.0018869485, 0.0019109778, 0.0018860631, 0.0019101566]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0474229, 1.0, 0.0, 0.0018736387, 0.0024376728, 0.0018720897, 0.0024376593]
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D_Loss: [0.5 0.5]
G_Loss: [1.0314587, 1.0, 0.0, 0.0014376929, 0.0014222573, 0.0014380892, 0.0014210811]
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D_Loss: [0.5 0.5]
G_Loss: [1.0319887, 1.0, 0.0, 0.0010145032, 0.001893616, 0.0010140431, 0.0018934997]
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D_Loss: [0.5 0.5]
G_Loss: [1.0485437, 1.0, 0.0, 0.0030529257, 0.0013600981, 0.0030534824, 0.0013600462]
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D_Loss: [0.5 0.5]
G_Loss: [1.0426966, 1.0, 0.0, 0.0014235962, 0.0024580234, 0.0014227, 0.0024576923]
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D_Loss: [0.5 0.5]
G_Loss: [1.0393574, 1.0, 0.0, 0.0020440875, 0.0015338702, 0.0020442922, 0.0015335832]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0684727, 1.0, 0.0, 0.0034104679, 0.00281451, 0.0034084732, 0.0028145025]
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D_Loss: [0.5 0.5]
G_Loss: [1.0273523, 1.0, 0.0, 0.0011867617, 0.0012998234, 0.0011866366, 0.0012997993]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.03398, 1.0, 0.0, 0.0010936371, 0.0019955065, 0.0010931098, 0.0019954257]
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D_Loss: [0.5 0.5]
G_Loss: [1.0328245, 1.0, 0.0, 0.0014983467, 0.0014858937, 0.00149634, 0.0014858829]
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D_Loss: [0.5 0.5]
G_Loss: [1.0385778, 1.0, 0.0, 0.0018105576, 0.0016967907, 0.0018099999, 0.0016943542]
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G_Loss: [1.0525404, 1.0, 0.0, 0.001897804, 0.002878646, 0.001896662, 0.0028793598]
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D_Loss: [0.5 0.5]
G_Loss: [1.0322778, 1.0, 0.0, 0.0018068786, 0.0011275825, 0.0018059224, 0.0011273943]
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D_Loss: [0.5 0.5]
G_Loss: [1.0288397, 1.0, 0.0, 0.0012460476, 0.0013757927, 0.0012455675, 0.0013756542]
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D_Loss: [0.5 0.5]
G_Loss: [1.0279009, 1.0, 0.0, 0.0008706496, 0.001665802, 0.00087055133, 0.001665869]
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D_Loss: [0.5 0.5]
G_Loss: [1.0370742, 1.0, 0.0, 0.0017429565, 0.0016274157, 0.0017430678, 0.0016273828]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0516614, 1.0, 0.0, 0.0022773948, 0.0024191702, 0.0022767805, 0.0024188352]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0293264, 1.0, 0.0, 0.001197653, 0.0014684789, 0.0011966927, 0.0014684121]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0351497, 1.0, 0.0, 0.0016867649, 0.0015086944, 0.0016868737, 0.0015082434]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0366232, 1.0, 0.0, 0.0009887025, 0.0023406988, 0.0009887299, 0.0023405587]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0357728, 1.0, 0.0, 0.0013677466, 0.0018845471, 0.0013657061, 0.0018841646]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0334896, 1.0, 0.0, 0.0013785028, 0.0016661852, 0.0013768142, 0.0016658584]
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D_Loss: [0.5 0.5]
G_Loss: [1.0340744, 1.0, 0.0, 0.0015573886, 0.0015404292, 0.0015557858, 0.0015403938]
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D_Loss: [0.5 0.5]
G_Loss: [1.0255069, 1.0, 0.0, 0.00084030733, 0.0014785017, 0.0008403225, 0.0014784001]
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D_Loss: [0.5 0.5]
G_Loss: [1.0301735, 1.0, 0.0, 0.0015302895, 0.001212735, 0.0015306394, 0.0012125679]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0373696, 1.0, 0.0, 0.0017423745, 0.0016549989, 0.001741587, 0.0016543751]
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D_Loss: [0.5 0.5]
G_Loss: [1.0416274, 1.0, 0.0, 0.0023011933, 0.0014831161, 0.0023018122, 0.0014825059]
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D_Loss: [0.5 0.5]
G_Loss: [1.0374575, 1.0, 0.0, 0.0014395776, 0.001965826, 0.001437668, 0.0019658096]
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D_Loss: [0.5 0.5]
G_Loss: [1.0418748, 1.0, 0.0, 0.0010430674, 0.0027637747, 0.0010424443, 0.0027639172]
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D_Loss: [0.5 0.5]
G_Loss: [1.024959, 1.0, 0.0, 0.0012779087, 0.0009911996, 0.0012767834, 0.0009910983]
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D_Loss: [0.5 0.5]
G_Loss: [1.052469, 1.0, 0.0, 0.0023146789, 0.0024554203, 0.00231366, 0.002454395]
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D_Loss: [0.5 0.5]
G_Loss: [1.0323102, 1.0, 0.0, 0.0015347127, 0.0014025832, 0.0015346957, 0.0014026101]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0342975, 1.0, 0.0, 0.0012884373, 0.0018295627, 0.001288097, 0.001829423]
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D_Loss: [0.5 0.5]
G_Loss: [1.0422207, 1.0, 0.0, 0.0015512294, 0.0022871078, 0.0015498581, 0.0022873806]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0283511, 1.0, 0.0, 0.0010424796, 0.0015349945, 0.0010419233, 0.0015343353]
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D_Loss: [0.5 0.5]
G_Loss: [1.0319082, 1.0, 0.0, 0.0018755768, 0.0010251722, 0.0018755388, 0.0010250432]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0363747, 1.0, 0.0, 0.0018472408, 0.0014595571, 0.0018470869, 0.0014596251]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0339193, 1.0, 0.0, 0.00162538, 0.0014581545, 0.0016258515, 0.0014579997]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.042565, 1.0, 0.0, 0.001629618, 0.0022398809, 0.0016302136, 0.0022398275]
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D_Loss: [0.5 0.5]
G_Loss: [1.0410097, 1.0, 0.0, 0.0011748625, 0.0025533282, 0.0011747106, 0.0025529554]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0326881, 1.0, 0.0, 0.0017928708, 0.0011788585, 0.0017921899, 0.0011785734]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0394245, 1.0, 0.0, 0.0014558991, 0.0021282332, 0.0014550674, 0.0021281524]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.037189, 1.0, 0.0, 0.0020221127, 0.001358832, 0.002021219, 0.0013584362]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0396377, 1.0, 0.0, 0.0012249382, 0.0023785294, 0.0012247596, 0.0023783832]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.035007, 1.0, 0.0, 0.001740407, 0.0014420887, 0.0017401674, 0.0014418494]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0307498, 1.0, 0.0, 0.0012106701, 0.0015847669, 0.0012109153, 0.0015844969]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.041106, 1.0, 0.0, 0.001991646, 0.0017453157, 0.001991812, 0.0017445676]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0276883, 1.0, 0.0, 0.0010928249, 0.0014243405, 0.0010924025, 0.0014241742]
Batch: 113
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G_Loss: [1.027635, 1.0, 0.0, 0.0013845407, 0.0011279299, 0.0013827733, 0.0011274003]
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G_Loss: [1.0265123, 1.0, 0.0, 0.001136425, 0.0012738425, 0.0011359528, 0.001273659]
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G_Loss: [1.0276031, 1.0, 0.0, 0.0015296523, 0.000979736, 0.0015296962, 0.000979602]
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G_Loss: [1.03442, 1.0, 0.0, 0.0018257865, 0.0013032465, 0.0018265033, 0.0013031552]
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G_Loss: [1.0395067, 1.0, 0.0, 0.002437087, 0.0011544509, 0.0024370428, 0.0011543476]
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D_Loss: [0.5 0.5]
G_Loss: [1.0551809, 1.0, 0.0, 0.0018018524, 0.0032145216, 0.0018025436, 0.003214539]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0628972, 1.0, 0.0, 0.0035551856, 0.0021626, 0.00355674, 0.002162586]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0396178, 1.0, 0.0, 0.0017359763, 0.0018656491, 0.0017361789, 0.0018653949]
Epoch: 16
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0321045, 1.0, 0.0, 0.0011080045, 0.0018106141, 0.0011082822, 0.0018100918]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0505351, 1.0, 0.0, 0.0018522465, 0.002741808, 0.0018523575, 0.00274204]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0316536, 1.0, 0.0, 0.001688923, 0.0011887896, 0.0016878038, 0.0011887224]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0513704, 1.0, 0.0, 0.0028775209, 0.0017924644, 0.002878311, 0.0017922278]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0258101, 1.0, 0.0, 0.0011709151, 0.0011754531, 0.0011712204, 0.0011753237]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0417725, 1.0, 0.0, 0.0009992861, 0.0027982164, 0.0009991808, 0.0027983]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.062231, 1.0, 0.0, 0.0012081937, 0.00444918, 0.0012081118, 0.0044491845]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0339745, 1.0, 0.0, 0.0010812026, 0.002007464, 0.0010805532, 0.002007353]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0357419, 1.0, 0.0, 0.0019327112, 0.0013166913, 0.0019314142, 0.0013164021]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.032321, 1.0, 0.0, 0.0011823425, 0.0017559837, 0.0011820386, 0.0017555576]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0459328, 1.0, 0.0, 0.0016385303, 0.0025373714, 0.0016365381, 0.0025372957]
Batch: 11
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G_Loss: [1.0411211, 1.0, 0.0, 0.0019789166, 0.0017594111, 0.0019783678, 0.0017593619]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0399508, 1.0, 0.0, 0.0012262927, 0.0024056202, 0.0012262687, 0.002405399]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.030262, 1.0, 0.0, 0.0019408579, 0.00081023434, 0.0019410679, 0.0008100786]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0285676, 1.0, 0.0, 0.0013791751, 0.0012179196, 0.0013788751, 0.0012177571]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0256301, 1.0, 0.0, 0.0013518147, 0.0009782664, 0.001351106, 0.0009781912]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0286977, 1.0, 0.0, 0.001365795, 0.0012430478, 0.0013662336, 0.0012431692]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0391254, 1.0, 0.0, 0.0012751699, 0.0022817252, 0.0012748505, 0.0022817166]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0354698, 1.0, 0.0, 0.001409523, 0.0018150837, 0.0014090459, 0.0018146891]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0542681, 1.0, 0.0, 0.0016339675, 0.0032995134, 0.0016339547, 0.0032993082]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0409756, 1.0, 0.0, 0.0015991939, 0.002125857, 0.001599147, 0.0021258933]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0342709, 1.0, 0.0, 0.0015630621, 0.0015525882, 0.0015620261, 0.0015523492]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0307039, 1.0, 0.0, 0.0014386242, 0.0013527365, 0.0014374899, 0.0013526627]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0309328, 1.0, 0.0, 0.00097085454, 0.0018412056, 0.0009711764, 0.0018409655]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0405333, 1.0, 0.0, 0.0019407793, 0.0017441503, 0.0019400439, 0.0017440912]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0410907, 1.0, 0.0, 0.0014236874, 0.0023118476, 0.001423501, 0.002311844]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.032775, 1.0, 0.0, 0.0020320476, 0.00094755006, 0.0020318076, 0.00094733795]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0360931, 1.0, 0.0, 0.0016422378, 0.0016390022, 0.001641928, 0.0016389079]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0312802, 1.0, 0.0, 0.0017627499, 0.001080883, 0.0017630529, 0.0010807619]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0571489, 1.0, 0.0, 0.002741011, 0.0024544247, 0.0027401908, 0.002454444]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0338159, 1.0, 0.0, 0.0014663108, 0.0016079019, 0.0014658493, 0.0016079145]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0318881, 1.0, 0.0, 0.0013293158, 0.0015696, 0.0013295426, 0.0015694153]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.035794, 1.0, 0.0, 0.0010351858, 0.0022188504, 0.0010349003, 0.0022186702]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.027247, 1.0, 0.0, 0.001195957, 0.0012811462, 0.0011953178, 0.0012806172]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0305998, 1.0, 0.0, 0.0013530164, 0.0014288286, 0.0013529605, 0.0014285236]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.037438, 1.0, 0.0, 0.0016216254, 0.0017818724, 0.0016215462, 0.0017815114]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.026056, 1.0, 0.0, 0.0011887719, 0.0011800674, 0.001187722, 0.001180025]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0286373, 1.0, 0.0, 0.0016151635, 0.0009882733, 0.0016150137, 0.0009879037]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0240753, 1.0, 0.0, 0.0015424078, 0.0006462496, 0.001542466, 0.0006462333]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0297109, 1.0, 0.0, 0.0021406896, 0.00056028366, 0.0021409225, 0.0005602945]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0568984, 1.0, 0.0, 0.0017396961, 0.0034330236, 0.001738091, 0.003433114]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0599221, 1.0, 0.0, 0.0029541035, 0.00249338, 0.0029540136, 0.0024932579]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0515748, 1.0, 0.0, 0.001534604, 0.0031540268, 0.0015343345, 0.0031542098]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0413836, 1.0, 0.0, 0.0009785523, 0.0027835378, 0.0009790357, 0.0027836056]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0255886, 1.0, 0.0, 0.0011298773, 0.0011964547, 0.001129681, 0.0011956498]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0310818, 1.0, 0.0, 0.0012408702, 0.0015847416, 0.0012409717, 0.0015846415]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0417554, 1.0, 0.0, 0.0023420297, 0.0014539349, 0.0023418684, 0.0014538954]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0320075, 1.0, 0.0, 0.0009960074, 0.0019138097, 0.0009955121, 0.0019138106]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0412405, 1.0, 0.0, 0.0016765827, 0.0020726384, 0.0016759699, 0.002072177]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0261549, 1.0, 0.0, 0.0008198054, 0.0015579574, 0.0008194827, 0.0015578703]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0236062, 1.0, 0.0, 0.0006932921, 0.0014527467, 0.0006932983, 0.0014523971]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0384576, 1.0, 0.0, 0.0017347431, 0.0017614213, 0.0017345608, 0.0017612946]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0429014, 1.0, 0.0, 0.0013879789, 0.0025121612, 0.0013880673, 0.0025119893]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0404948, 1.0, 0.0, 0.0018405959, 0.0018407556, 0.0018410732, 0.0018402598]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0331293, 1.0, 0.0, 0.0019038878, 0.001107801, 0.0019048629, 0.0011075502]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399855, 1.0, 0.0, 0.0016475635, 0.001987532, 0.0016471953, 0.00198739]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0348045, 1.0, 0.0, 0.0013603305, 0.001803722, 0.0013604864, 0.0018034453]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0377414, 1.0, 0.0, 0.0017200413, 0.0017110619, 0.0017194257, 0.001710933]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0317658, 1.0, 0.0, 0.0016706527, 0.0012171653, 0.0016706602, 0.0012170499]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0346653, 1.0, 0.0, 0.0016784354, 0.0014730302, 0.0016778415, 0.0014728281]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0343183, 1.0, 0.0, 0.0018063751, 0.0013134746, 0.0018065905, 0.0013132174]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0319371, 1.0, 0.0, 0.0013901838, 0.001513117, 0.0013910036, 0.001513075]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0436426, 1.0, 0.0, 0.0016293549, 0.0023382404, 0.0016292164, 0.0023374374]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0425824, 1.0, 0.0, 0.0017012218, 0.0021699518, 0.0017007107, 0.0021697949]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0417755, 1.0, 0.0, 0.0018944587, 0.0019034588, 0.0018936603, 0.001902638]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0473398, 1.0, 0.0, 0.0018617315, 0.0024420447, 0.0018600646, 0.0024420512]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0318031, 1.0, 0.0, 0.0014700146, 0.0014212527, 0.0014704842, 0.0014200658]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0319952, 1.0, 0.0, 0.0010211058, 0.0018875722, 0.0010209465, 0.001887447]
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D_Loss: [0.5 0.5]
G_Loss: [1.0486326, 1.0, 0.0, 0.0030532149, 0.0013678835, 0.0030538714, 0.0013678384]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0428225, 1.0, 0.0, 0.0014339993, 0.0024590762, 0.001433047, 0.0024587442]
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D_Loss: [0.5 0.5]
G_Loss: [1.0392206, 1.0, 0.0, 0.002033296, 0.0015322135, 0.002033556, 0.001531926]
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D_Loss: [0.5 0.5]
G_Loss: [1.0687588, 1.0, 0.0, 0.0034371242, 0.0028138394, 0.003435342, 0.0028138254]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0273746, 1.0, 0.0, 0.0011904789, 0.0012981449, 0.0011903506, 0.0012981249]
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D_Loss: [0.5 0.5]
G_Loss: [1.0339189, 1.0, 0.0, 0.001095365, 0.001988221, 0.0010948029, 0.0019881427]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0329188, 1.0, 0.0, 0.0015014219, 0.0014913804, 0.0014994765, 0.0014913649]
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D_Loss: [0.5 0.5]
G_Loss: [1.038626, 1.0, 0.0, 0.0018075919, 0.0017041278, 0.0018070567, 0.0017016346]
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D_Loss: [0.5 0.5]
G_Loss: [1.0525653, 1.0, 0.0, 0.0018916915, 0.00288699, 0.0018907494, 0.0028877417]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0323546, 1.0, 0.0, 0.0018187515, 0.0011226701, 0.0018179301, 0.0011224343]
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D_Loss: [0.5 0.5]
G_Loss: [1.0288678, 1.0, 0.0, 0.0012568128, 0.0013675928, 0.0012563297, 0.0013674633]
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D_Loss: [0.5 0.5]
G_Loss: [1.0280018, 1.0, 0.0, 0.0008792699, 0.0016663382, 0.0008791919, 0.0016663892]
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D_Loss: [0.5 0.5]
G_Loss: [1.0368389, 1.0, 0.0, 0.0017239447, 0.001625046, 0.0017240461, 0.0016249928]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0523343, 1.0, 0.0, 0.0023387899, 0.0024189637, 0.002338206, 0.002418638]
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D_Loss: [0.5 0.5]
G_Loss: [1.0293224, 1.0, 0.0, 0.0011994242, 0.0014663283, 0.0011985357, 0.0014662608]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0353159, 1.0, 0.0, 0.0016992951, 0.0015112769, 0.0016993715, 0.0015108166]
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D_Loss: [0.5 0.5]
G_Loss: [1.0366561, 1.0, 0.0, 0.0009992784, 0.002333106, 0.000999274, 0.0023329402]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0361807, 1.0, 0.0, 0.0013707367, 0.0019185946, 0.0013691956, 0.0019182554]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0334332, 1.0, 0.0, 0.0013816804, 0.0016578378, 0.0013804093, 0.0016574861]
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D_Loss: [0.5 0.5]
G_Loss: [1.0340623, 1.0, 0.0, 0.0015654196, 0.0015312661, 0.0015640927, 0.0015312276]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0255892, 1.0, 0.0, 0.00084275485, 0.0014835474, 0.00084278354, 0.0014834569]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0302137, 1.0, 0.0, 0.0015305113, 0.0012161671, 0.001530828, 0.0012160083]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0374767, 1.0, 0.0, 0.0017466822, 0.0016604206, 0.0017459397, 0.0016597712]
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D_Loss: [0.5 0.5]
G_Loss: [1.041652, 1.0, 0.0, 0.00230604, 0.0014805228, 0.0023064115, 0.0014799044]
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D_Loss: [0.5 0.5]
G_Loss: [1.0374342, 1.0, 0.0, 0.001436522, 0.0019666988, 0.0014352314, 0.001966674]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0419765, 1.0, 0.0, 0.0010455396, 0.0027705478, 0.0010449325, 0.0027706933]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0248631, 1.0, 0.0, 0.001265079, 0.0009953023, 0.0012641484, 0.0009951988]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.052609, 1.0, 0.0, 0.0023192582, 0.002463552, 0.002318354, 0.0024624972]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0322745, 1.0, 0.0, 0.0015283035, 0.0014057414, 0.0015283674, 0.0014057632]
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D_Loss: [0.5 0.5]
G_Loss: [1.0343153, 1.0, 0.0, 0.001287149, 0.0018324587, 0.0012869184, 0.001832319]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0422724, 1.0, 0.0, 0.0015513279, 0.002291706, 0.00155006, 0.0022919858]
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D_Loss: [0.5 0.5]
G_Loss: [1.0284101, 1.0, 0.0, 0.001048113, 0.00153472, 0.0010476179, 0.0015340952]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0320156, 1.0, 0.0, 0.0018834756, 0.0010270459, 0.0018835155, 0.0010269224]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0362804, 1.0, 0.0, 0.0018401353, 0.0014581049, 0.001839744, 0.0014581587]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0339266, 1.0, 0.0, 0.0016232242, 0.0014609941, 0.0016235973, 0.001460839]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0426607, 1.0, 0.0, 0.0016345555, 0.0022436534, 0.0016351214, 0.002243608]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0410124, 1.0, 0.0, 0.0011741526, 0.0025542842, 0.0011740436, 0.0025539394]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0325772, 1.0, 0.0, 0.0017871892, 0.0011744371, 0.0017866851, 0.0011741814]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0396025, 1.0, 0.0, 0.0014546381, 0.0021456515, 0.0014541321, 0.002145561]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0371774, 1.0, 0.0, 0.0020262138, 0.0013536515, 0.0020254774, 0.0013532847]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0396798, 1.0, 0.0, 0.0012182005, 0.0023890734, 0.0012181061, 0.0023889085]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0350256, 1.0, 0.0, 0.0017392971, 0.0014448719, 0.001739104, 0.001444658]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0306765, 1.0, 0.0, 0.0012101829, 0.0015785911, 0.001210388, 0.0015783018]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0409403, 1.0, 0.0, 0.001983792, 0.0017380959, 0.0019840524, 0.0017373405]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0277758, 1.0, 0.0, 0.001096759, 0.00142837, 0.0010963731, 0.0014281766]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0275115, 1.0, 0.0, 0.0013693538, 0.0011318763, 0.0013676821, 0.0011313681]
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D_Loss: [0.5 0.5]
G_Loss: [1.0264326, 1.0, 0.0, 0.0011322272, 0.0012708006, 0.0011317672, 0.0012706183]
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D_Loss: [0.5 0.5]
G_Loss: [1.0276093, 1.0, 0.0, 0.0015306463, 0.0009793034, 0.0015306945, 0.0009791702]
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G_Loss: [1.0344521, 1.0, 0.0, 0.0018273649, 0.0013045941, 0.0018280538, 0.0013044996]
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G_Loss: [1.0394742, 1.0, 0.0, 0.002435152, 0.0011534289, 0.0024351277, 0.001153327]
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D_Loss: [0.5 0.5]
G_Loss: [1.0551043, 1.0, 0.0, 0.0017922693, 0.0032171507, 0.0017929622, 0.0032171672]
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D_Loss: [0.5 0.5]
G_Loss: [1.0631325, 1.0, 0.0, 0.0035761124, 0.0021630675, 0.0035777264, 0.0021630595]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0396211, 1.0, 0.0, 0.0017374007, 0.0018645115, 0.001737558, 0.0018642741]
Epoch: 17
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0319678, 1.0, 0.0, 0.0010994493, 0.0018067341, 0.0010997134, 0.0018062199]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0505881, 1.0, 0.0, 0.0018511339, 0.0027477462, 0.0018514422, 0.0027479453]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0316738, 1.0, 0.0, 0.0016891282, 0.0011904249, 0.0016880229, 0.0011903543]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.05134, 1.0, 0.0, 0.002875187, 0.0017920241, 0.00287609, 0.0017917812]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0257584, 1.0, 0.0, 0.0011718641, 0.0011697852, 0.0011721752, 0.0011696611]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0417845, 1.0, 0.0, 0.000997947, 0.0028006602, 0.0009978215, 0.0028007422]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0622171, 1.0, 0.0, 0.0012104914, 0.004445618, 0.0012104483, 0.0044456227]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0338589, 1.0, 0.0, 0.0010799554, 0.0019982005, 0.0010793395, 0.0019980785]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.036109, 1.0, 0.0, 0.0019376971, 0.0013450796, 0.0019364604, 0.001344789]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0322671, 1.0, 0.0, 0.0011820269, 0.001751398, 0.0011818178, 0.0017509644]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0461838, 1.0, 0.0, 0.0016439595, 0.0025547557, 0.0016421324, 0.002554678]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0407703, 1.0, 0.0, 0.001979964, 0.0017264811, 0.0019795222, 0.0017264353]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0401174, 1.0, 0.0, 0.0012279983, 0.002419058, 0.0012279716, 0.0024188533]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0302156, 1.0, 0.0, 0.0019410113, 0.0008058533, 0.0019411711, 0.00080569857]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0287144, 1.0, 0.0, 0.0013874301, 0.0012229986, 0.0013871812, 0.0012228407]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.025689, 1.0, 0.0, 0.0013524974, 0.000982931, 0.001351849, 0.0009828593]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0286931, 1.0, 0.0, 0.0013689116, 0.0012395047, 0.0013692603, 0.0012396346]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0390944, 1.0, 0.0, 0.0012783939, 0.0022756676, 0.0012781131, 0.002275663]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.035331, 1.0, 0.0, 0.0014119982, 0.0017999881, 0.0014116069, 0.0017996152]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0547673, 1.0, 0.0, 0.0016321638, 0.003346686, 0.0016321898, 0.0033465466]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0411955, 1.0, 0.0, 0.0015980122, 0.0021470473, 0.0015978974, 0.0021471034]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0343556, 1.0, 0.0, 0.0015628305, 0.0015605199, 0.0015618947, 0.0015602686]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0305954, 1.0, 0.0, 0.0014380789, 0.0013434199, 0.0014370619, 0.0013433706]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0311371, 1.0, 0.0, 0.0009756249, 0.0018550216, 0.00097593677, 0.0018548148]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0403543, 1.0, 0.0, 0.0019307567, 0.0017378709, 0.0019301055, 0.0017378093]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0410408, 1.0, 0.0, 0.0014230148, 0.002307978, 0.0014228595, 0.0023079868]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0328019, 1.0, 0.0, 0.0020347415, 0.00094729644, 0.002034456, 0.0009470942]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0362226, 1.0, 0.0, 0.0016452703, 0.0016477378, 0.00164501, 0.00164765]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0312712, 1.0, 0.0, 0.0017610742, 0.0010817419, 0.0017613684, 0.0010816667]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0572159, 1.0, 0.0, 0.0027441047, 0.0024574106, 0.002743327, 0.0024574334]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0337828, 1.0, 0.0, 0.0014674838, 0.0016037312, 0.0014670244, 0.0016037474]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0319271, 1.0, 0.0, 0.0013300984, 0.0015723644, 0.0013302541, 0.001572215]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0358392, 1.0, 0.0, 0.0010368833, 0.0022212567, 0.0010366887, 0.0022210884]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0272408, 1.0, 0.0, 0.0011928943, 0.00128365, 0.0011922516, 0.0012832207]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0305601, 1.0, 0.0, 0.0013521214, 0.0014261103, 0.0013519931, 0.001425847]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0374378, 1.0, 0.0, 0.0016231723, 0.0017802906, 0.0016230937, 0.0017799821]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0260738, 1.0, 0.0, 0.0011917099, 0.0011787345, 0.0011906091, 0.0011786971]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0286542, 1.0, 0.0, 0.0016176174, 0.0009873491, 0.001617381, 0.0009870253]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0240453, 1.0, 0.0, 0.0015438016, 0.0006421495, 0.0015437927, 0.00064212433]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0296826, 1.0, 0.0, 0.002133179, 0.0005652244, 0.0021334135, 0.0005652504]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0569079, 1.0, 0.0, 0.0017397493, 0.003433812, 0.00173827, 0.003433906]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0599815, 1.0, 0.0, 0.002959847, 0.0024930367, 0.002959704, 0.0024929242]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0514562, 1.0, 0.0, 0.0015338059, 0.0031440523, 0.0015334899, 0.0031442007]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.041215, 1.0, 0.0, 0.0009799656, 0.0027668094, 0.000980401, 0.0027668725]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0255545, 1.0, 0.0, 0.0011380806, 0.0011851385, 0.0011379605, 0.0011843906]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0314062, 1.0, 0.0, 0.0012377133, 0.0016173981, 0.0012377813, 0.0016173071]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0418365, 1.0, 0.0, 0.0023529746, 0.001450358, 0.0023528715, 0.0014503365]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0321012, 1.0, 0.0, 0.0010006258, 0.0019176936, 0.0010001108, 0.0019177052]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0412824, 1.0, 0.0, 0.0016758414, 0.0020772133, 0.0016751684, 0.0020767935]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0262052, 1.0, 0.0, 0.0008231022, 0.0015592177, 0.0008227924, 0.0015591244]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0234561, 1.0, 0.0, 0.0006820342, 0.0014503605, 0.0006820909, 0.0014500283]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0383, 1.0, 0.0, 0.0017242297, 0.001757622, 0.001723947, 0.0017574851]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.042785, 1.0, 0.0, 0.0013792128, 0.0025103358, 0.0013793232, 0.0025101728]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0407915, 1.0, 0.0, 0.0018612079, 0.0018471086, 0.0018617505, 0.001846633]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0331432, 1.0, 0.0, 0.0019051465, 0.0011078168, 0.0019060903, 0.0011075917]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399984, 1.0, 0.0, 0.0016461154, 0.0019901502, 0.0016457256, 0.001990045]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0348611, 1.0, 0.0, 0.0013673325, 0.0018018757, 0.0013674973, 0.001801654]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0377561, 1.0, 0.0, 0.0017234888, 0.0017089462, 0.0017227913, 0.0017088513]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0317665, 1.0, 0.0, 0.0016715066, 0.0012163691, 0.0016715007, 0.0012162849]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0346446, 1.0, 0.0, 0.0016812724, 0.0014683242, 0.0016805511, 0.001468142]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.034309, 1.0, 0.0, 0.0018052536, 0.0013137443, 0.0018054747, 0.00131354]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0319976, 1.0, 0.0, 0.0014019585, 0.0015068407, 0.0014027175, 0.0015068005]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0437635, 1.0, 0.0, 0.0016414303, 0.0023371454, 0.0016413461, 0.0023364406]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0424886, 1.0, 0.0, 0.001689372, 0.0021732815, 0.0016888579, 0.0021731502]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0418235, 1.0, 0.0, 0.0019037188, 0.001898536, 0.0019029597, 0.0018978959]
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D_Loss: [0.5 0.5]
G_Loss: [1.0472612, 1.0, 0.0, 0.0018512334, 0.002445381, 0.0018498199, 0.0024453788]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0316609, 1.0, 0.0, 0.0014584156, 0.0014199119, 0.0014587189, 0.0014188136]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0319213, 1.0, 0.0, 0.0010145877, 0.0018873983, 0.0010141718, 0.0018872946]
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D_Loss: [0.5 0.5]
G_Loss: [1.0487407, 1.0, 0.0, 0.003056896, 0.0013740427, 0.0030574037, 0.0013739918]
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D_Loss: [0.5 0.5]
G_Loss: [1.0427573, 1.0, 0.0, 0.0014336344, 0.0024534978, 0.0014327994, 0.0024532028]
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D_Loss: [0.5 0.5]
G_Loss: [1.0393082, 1.0, 0.0, 0.0020379035, 0.0015355533, 0.0020382067, 0.0015352926]
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D_Loss: [0.5 0.5]
G_Loss: [1.0688326, 1.0, 0.0, 0.003443348, 0.0028143595, 0.0034412388, 0.002814331]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0273402, 1.0, 0.0, 0.0011894018, 0.0012960779, 0.0011892915, 0.0012960574]
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D_Loss: [0.5 0.5]
G_Loss: [1.0338719, 1.0, 0.0, 0.0010971496, 0.001982163, 0.0010965831, 0.0019820838]
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D_Loss: [0.5 0.5]
G_Loss: [1.0328274, 1.0, 0.0, 0.001497425, 0.0014870543, 0.001495634, 0.0014870465]
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D_Loss: [0.5 0.5]
G_Loss: [1.0386374, 1.0, 0.0, 0.001809758, 0.0017030053, 0.0018091915, 0.0017005152]
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D_Loss: [0.5 0.5]
G_Loss: [1.0519937, 1.0, 0.0, 0.0018909717, 0.0028357725, 0.0018897911, 0.0028364758]
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D_Loss: [0.5 0.5]
G_Loss: [1.032161, 1.0, 0.0, 0.0018059029, 0.0011179124, 0.0018051715, 0.0011176529]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0288684, 1.0, 0.0, 0.0012668355, 0.0013576238, 0.0012663117, 0.0013574881]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0279363, 1.0, 0.0, 0.000871602, 0.0016680737, 0.0008715396, 0.0016681298]
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D_Loss: [0.5 0.5]
G_Loss: [1.0368413, 1.0, 0.0, 0.0017261014, 0.0016231079, 0.0017261928, 0.0016230587]
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D_Loss: [0.5 0.5]
G_Loss: [1.0525274, 1.0, 0.0, 0.002358548, 0.0024167625, 0.0023579614, 0.0024164019]
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D_Loss: [0.5 0.5]
G_Loss: [1.0292487, 1.0, 0.0, 0.0012002053, 0.0014588637, 0.0011993013, 0.0014587907]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0353519, 1.0, 0.0, 0.0017026616, 0.0015111784, 0.0017026758, 0.001510716]
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D_Loss: [0.5 0.5]
G_Loss: [1.0364939, 1.0, 0.0, 0.000999579, 0.002318055, 0.0009995973, 0.0023178859]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0365114, 1.0, 0.0, 0.0013737597, 0.0019456379, 0.0013720519, 0.001945344]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0334039, 1.0, 0.0, 0.0013833416, 0.0016535267, 0.0013819607, 0.001653183]
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D_Loss: [0.5 0.5]
G_Loss: [1.0340935, 1.0, 0.0, 0.0015698344, 0.0015297016, 0.0015684688, 0.0015296672]
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D_Loss: [0.5 0.5]
G_Loss: [1.0256252, 1.0, 0.0, 0.0008366441, 0.0014929349, 0.00083664904, 0.0014928548]
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D_Loss: [0.5 0.5]
G_Loss: [1.0303087, 1.0, 0.0, 0.001534198, 0.0012211234, 0.0015345286, 0.0012209686]
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D_Loss: [0.5 0.5]
G_Loss: [1.0375866, 1.0, 0.0, 0.0017551224, 0.0016619605, 0.0017543877, 0.001661361]
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D_Loss: [0.5 0.5]
G_Loss: [1.0417577, 1.0, 0.0, 0.002314624, 0.001481543, 0.002315021, 0.0014809659]
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D_Loss: [0.5 0.5]
G_Loss: [1.037492, 1.0, 0.0, 0.0014423644, 0.0019661388, 0.0014408331, 0.001966119]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0420737, 1.0, 0.0, 0.0010481076, 0.0027768132, 0.0010474556, 0.0027769636]
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D_Loss: [0.5 0.5]
G_Loss: [1.0248911, 1.0, 0.0, 0.0012654357, 0.0009975008, 0.001264433, 0.0009974035]
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D_Loss: [0.5 0.5]
G_Loss: [1.0526189, 1.0, 0.0, 0.0023176826, 0.0024660279, 0.0023168433, 0.00246504]
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D_Loss: [0.5 0.5]
G_Loss: [1.032279, 1.0, 0.0, 0.0015281612, 0.0014062938, 0.001528214, 0.0014063135]
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D_Loss: [0.5 0.5]
G_Loss: [1.0343465, 1.0, 0.0, 0.0012891704, 0.0018332839, 0.0012888624, 0.0018331398]
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D_Loss: [0.5 0.5]
G_Loss: [1.0423863, 1.0, 0.0, 0.0015584555, 0.0022949306, 0.0015571434, 0.0022952012]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0284159, 1.0, 0.0, 0.0010500506, 0.0015333141, 0.0010495293, 0.0015327209]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0320216, 1.0, 0.0, 0.0018838055, 0.0010272709, 0.0018837574, 0.0010271466]
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D_Loss: [0.5 0.5]
G_Loss: [1.0363623, 1.0, 0.0, 0.0018461613, 0.0014595173, 0.0018459437, 0.0014595778]
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D_Loss: [0.5 0.5]
G_Loss: [1.0339724, 1.0, 0.0, 0.0016259524, 0.0014624218, 0.0016263798, 0.0014622605]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0427084, 1.0, 0.0, 0.0016356503, 0.0022468823, 0.0016362518, 0.0022468297]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0409982, 1.0, 0.0, 0.0011730208, 0.0025541377, 0.0011727645, 0.0025537838]
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D_Loss: [0.5 0.5]
G_Loss: [1.0324454, 1.0, 0.0, 0.0017807491, 0.0011689243, 0.0017800133, 0.0011686704]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0398415, 1.0, 0.0, 0.0014651293, 0.0021569019, 0.0014644682, 0.0021567936]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0372083, 1.0, 0.0, 0.0020272091, 0.0013554636, 0.0020264264, 0.001355109]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0398219, 1.0, 0.0, 0.0012233867, 0.0023968194, 0.0012231452, 0.0023966627]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0350337, 1.0, 0.0, 0.0017416999, 0.0014432326, 0.0017414228, 0.0014430229]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0306069, 1.0, 0.0, 0.001205801, 0.0015766339, 0.0012059875, 0.0015763713]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0408835, 1.0, 0.0, 0.0019809084, 0.0017358239, 0.00198107, 0.0017351641]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0278369, 1.0, 0.0, 0.001100089, 0.0014306012, 0.0010996341, 0.0014304393]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0274612, 1.0, 0.0, 0.0013641962, 0.0011324682, 0.0013625429, 0.0011319651]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0263036, 1.0, 0.0, 0.001122818, 0.0012684842, 0.0011223531, 0.0012683158]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0276532, 1.0, 0.0, 0.0015369091, 0.0009770311, 0.0015369483, 0.0009769036]
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D_Loss: [0.5 0.5]
G_Loss: [1.0345618, 1.0, 0.0, 0.0018334502, 0.0013084777, 0.0018341308, 0.0013083804]
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D_Loss: [0.5 0.5]
G_Loss: [1.0394483, 1.0, 0.0, 0.0024356479, 0.001150568, 0.0024355897, 0.0011504688]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0550654, 1.0, 0.0, 0.0017860603, 0.0032198157, 0.0017866637, 0.0032198327]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0634062, 1.0, 0.0, 0.003598898, 0.0021651702, 0.0036004107, 0.002165168]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0395598, 1.0, 0.0, 0.001731419, 0.0018649481, 0.0017315714, 0.0018647196]
Epoch: 18
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0318947, 1.0, 0.0, 0.001095463, 0.0018040763, 0.0010957618, 0.001803568]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.050767, 1.0, 0.0, 0.0018651758, 0.0027499744, 0.0018653055, 0.0027502002]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0317898, 1.0, 0.0, 0.0016962786, 0.0011938208, 0.0016950497, 0.001193766]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0513055, 1.0, 0.0, 0.0028714538, 0.0017926467, 0.002872161, 0.0017923926]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0258228, 1.0, 0.0, 0.001184451, 0.0011630572, 0.0011847918, 0.0011629193]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0419563, 1.0, 0.0, 0.0010087932, 0.002805408, 0.0010087113, 0.0028054977]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0622308, 1.0, 0.0, 0.001209696, 0.004447656, 0.0012095866, 0.004447667]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0337361, 1.0, 0.0, 0.0010684382, 0.0019985517, 0.0010677543, 0.0019984292]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0364512, 1.0, 0.0, 0.0019551665, 0.0013587164, 0.0019540957, 0.0013584454]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0322332, 1.0, 0.0, 0.0011803166, 0.0017500583, 0.0011799667, 0.0017495444]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0461711, 1.0, 0.0, 0.0016392863, 0.0025582616, 0.0016373743, 0.0025581983]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0407697, 1.0, 0.0, 0.001986374, 0.0017200225, 0.0019857748, 0.001719974]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0401379, 1.0, 0.0, 0.0012235779, 0.0024253335, 0.0012235362, 0.0024251384]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0301453, 1.0, 0.0, 0.0019400464, 0.00080044224, 0.001940181, 0.0008002825]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0289197, 1.0, 0.0, 0.0014027711, 0.0012263241, 0.0014025037, 0.0012261721]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0257171, 1.0, 0.0, 0.0013532536, 0.0009847453, 0.0013525838, 0.0009846706]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.028619, 1.0, 0.0, 0.0013652216, 0.0012364634, 0.0013656246, 0.0012365961]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0391228, 1.0, 0.0, 0.0012809967, 0.0022756527, 0.0012806818, 0.0022756457]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0353131, 1.0, 0.0, 0.0014129765, 0.0017973887, 0.0014124789, 0.0017970081]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0550103, 1.0, 0.0, 0.0016301224, 0.0033708194, 0.0016302043, 0.003370685]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.04131, 1.0, 0.0, 0.0015963734, 0.002159073, 0.001596281, 0.002159128]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0343416, 1.0, 0.0, 0.0015626935, 0.0015593809, 0.0015617202, 0.0015591471]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0306039, 1.0, 0.0, 0.0014401749, 0.0013420944, 0.0014391323, 0.0013420118]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.031268, 1.0, 0.0, 0.0009835975, 0.0018589337, 0.00098393, 0.0018587245]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0401427, 1.0, 0.0, 0.001920064, 0.0017293289, 0.0019193883, 0.0017292685]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0411425, 1.0, 0.0, 0.0014275594, 0.002312689, 0.0014273871, 0.0023126835]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.03295, 1.0, 0.0, 0.0020424156, 0.00095310685, 0.0020421024, 0.0009528968]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0362915, 1.0, 0.0, 0.0016433434, 0.0016559153, 0.0016430521, 0.0016557991]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0313008, 1.0, 0.0, 0.0017616409, 0.0010838689, 0.0017619581, 0.0010837719]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0573884, 1.0, 0.0, 0.0027533157, 0.002463887, 0.0027524163, 0.002463914]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0338184, 1.0, 0.0, 0.0014709709, 0.0016034778, 0.0014705062, 0.0016034963]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0320106, 1.0, 0.0, 0.0013293126, 0.0015807266, 0.001329486, 0.0015805503]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0358104, 1.0, 0.0, 0.0010413495, 0.002214182, 0.0010410874, 0.0022140052]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0272694, 1.0, 0.0, 0.0011898926, 0.001289255, 0.0011891476, 0.0012887825]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0305436, 1.0, 0.0, 0.0013575638, 0.0014191607, 0.0013574467, 0.0014188776]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0374428, 1.0, 0.0, 0.0016235843, 0.0017803456, 0.0016235063, 0.0017800058]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0261053, 1.0, 0.0, 0.001197125, 0.0011761858, 0.0011959788, 0.0011761538]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0286684, 1.0, 0.0, 0.0016209141, 0.0009853777, 0.0016205467, 0.0009850466]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.024023, 1.0, 0.0, 0.0015417028, 0.0006422108, 0.0015417086, 0.00064219296]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.02964, 1.0, 0.0, 0.0021339166, 0.0005605982, 0.0021341871, 0.00056061754]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0569824, 1.0, 0.0, 0.001742685, 0.003437664, 0.001741139, 0.0034377708]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.060047, 1.0, 0.0, 0.0029645504, 0.0024942933, 0.002964424, 0.0024941792]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.051634, 1.0, 0.0, 0.0015360648, 0.0031579551, 0.0015357868, 0.0031581158]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0413692, 1.0, 0.0, 0.0009774615, 0.00278334, 0.0009779304, 0.002783401]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0257277, 1.0, 0.0, 0.0011470162, 0.0011919619, 0.0011468458, 0.0011911736]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0311955, 1.0, 0.0, 0.0012327349, 0.0016032375, 0.0012327088, 0.0016031275]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0419439, 1.0, 0.0, 0.0023589479, 0.0014541371, 0.0023589313, 0.0014541289]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0320544, 1.0, 0.0, 0.0009998567, 0.0019142326, 0.0009992956, 0.0019142498]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0412049, 1.0, 0.0, 0.0016806822, 0.0020653163, 0.0016800631, 0.0020648856]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.026094, 1.0, 0.0, 0.0008167697, 0.0015554419, 0.0008164296, 0.0015553598]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.023472, 1.0, 0.0, 0.00068219507, 0.0014516376, 0.0006822323, 0.0014513425]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0382715, 1.0, 0.0, 0.0017314991, 0.0017477719, 0.0017311472, 0.0017476288]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.043007, 1.0, 0.0, 0.0013830741, 0.0025266525, 0.001383173, 0.0025264695]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0406485, 1.0, 0.0, 0.0018674035, 0.0018279036, 0.0018679667, 0.0018274174]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0330137, 1.0, 0.0, 0.0019044485, 0.0010967427, 0.0019053065, 0.0010965464]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399745, 1.0, 0.0, 0.0016486573, 0.0019854265, 0.001648276, 0.0019853415]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.034822, 1.0, 0.0, 0.0013681003, 0.0017975371, 0.0013682551, 0.001797323]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0379761, 1.0, 0.0, 0.0017241698, 0.00172828, 0.0017234454, 0.0017281924]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0318189, 1.0, 0.0, 0.0016787383, 0.0012138991, 0.0016787136, 0.0012138106]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0346988, 1.0, 0.0, 0.0016793415, 0.0014751835, 0.0016787269, 0.0014749765]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0343051, 1.0, 0.0, 0.0018213685, 0.0012972706, 0.0018215859, 0.0012970658]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0321639, 1.0, 0.0, 0.0014106156, 0.001513317, 0.0014113691, 0.0015132846]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0439, 1.0, 0.0, 0.0016465825, 0.0023444134, 0.0016464419, 0.0023436693]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0427029, 1.0, 0.0, 0.0017058443, 0.0021762948, 0.0017053441, 0.002176166]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0418092, 1.0, 0.0, 0.0019084226, 0.0018925531, 0.0019075967, 0.0018918394]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0472615, 1.0, 0.0, 0.0018439649, 0.002452673, 0.001842506, 0.002452657]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0317653, 1.0, 0.0, 0.0014685441, 0.0014193037, 0.0014688305, 0.0014181717]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.0319438, 1.0, 0.0, 0.0010120864, 0.0018919387, 0.001011683, 0.0018918093]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0488039, 1.0, 0.0, 0.003060603, 0.001376084, 0.0030610473, 0.0013760271]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0428203, 1.0, 0.0, 0.001438838, 0.0024540257, 0.0014379935, 0.0024537058]
Batch: 70
D_Loss: [0.5 0.5]
G_Loss: [1.03934, 1.0, 0.0, 0.0020411226, 0.0015352535, 0.0020413965, 0.0015349742]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0688527, 1.0, 0.0, 0.0034439843, 0.002815554, 0.0034417715, 0.00281553]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0273645, 1.0, 0.0, 0.0011908091, 0.0012968843, 0.0011906859, 0.0012968595]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.033829, 1.0, 0.0, 0.001094724, 0.0019807029, 0.0010941434, 0.001980608]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0328282, 1.0, 0.0, 0.0014930879, 0.0014914747, 0.0014911765, 0.0014914547]
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D_Loss: [0.5 0.5]
G_Loss: [1.0386897, 1.0, 0.0, 0.0018121649, 0.00170537, 0.0018115608, 0.0017028904]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0518726, 1.0, 0.0, 0.001889889, 0.0028258604, 0.0018886569, 0.002826522]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0322162, 1.0, 0.0, 0.0018140483, 0.0011147815, 0.0018133449, 0.0011145722]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0289061, 1.0, 0.0, 0.0012767576, 0.0013511303, 0.0012762529, 0.0013509917]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0280606, 1.0, 0.0, 0.00087989424, 0.0016710735, 0.0008798222, 0.0016711303]
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D_Loss: [0.5 0.5]
G_Loss: [1.0368222, 1.0, 0.0, 0.0017238259, 0.0016236384, 0.0017239265, 0.0016236012]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0528055, 1.0, 0.0, 0.002385019, 0.0024155676, 0.002384545, 0.0024152112]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0291982, 1.0, 0.0, 0.0012007279, 0.0014537573, 0.00119978, 0.0014536869]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0354236, 1.0, 0.0, 0.0017068075, 0.0015135792, 0.0017067498, 0.0015131538]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0363675, 1.0, 0.0, 0.0009958835, 0.0023102658, 0.0009958881, 0.0023101]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0367398, 1.0, 0.0, 0.0013765515, 0.0019635893, 0.0013749257, 0.0019633546]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.033397, 1.0, 0.0, 0.0013846392, 0.0016515965, 0.0013833463, 0.0016512787]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0339407, 1.0, 0.0, 0.0015623919, 0.0015232414, 0.001561021, 0.0015232014]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0256573, 1.0, 0.0, 0.0008354353, 0.0014970526, 0.0008354257, 0.0014969965]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0303434, 1.0, 0.0, 0.0015361203, 0.001222348, 0.0015364853, 0.0012222053]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0377693, 1.0, 0.0, 0.001767524, 0.0016661682, 0.0017668221, 0.0016656221]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0417615, 1.0, 0.0, 0.0023162481, 0.0014802578, 0.0023166817, 0.0014797181]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0374981, 1.0, 0.0, 0.00144961, 0.001959458, 0.0014479985, 0.001959443]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.042114, 1.0, 0.0, 0.0010450617, 0.002783533, 0.0010444243, 0.0027836778]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0249454, 1.0, 0.0, 0.0012665107, 0.0010013478, 0.0012655637, 0.0010012581]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0526872, 1.0, 0.0, 0.002316961, 0.0024729199, 0.0023161848, 0.002472023]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.032259, 1.0, 0.0, 0.0015257637, 0.0014068646, 0.0015258486, 0.0014068829]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0343701, 1.0, 0.0, 0.0012887685, 0.0018358197, 0.0012884259, 0.0018356937]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0424525, 1.0, 0.0, 0.0015618589, 0.0022975542, 0.0015605849, 0.0022978138]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0284005, 1.0, 0.0, 0.0010512784, 0.0015306685, 0.0010508031, 0.0015301185]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0320855, 1.0, 0.0, 0.0018872073, 0.001029681, 0.001887066, 0.0010295644]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0363346, 1.0, 0.0, 0.0018462136, 0.0014569251, 0.0018461758, 0.0014569662]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0339959, 1.0, 0.0, 0.0016254836, 0.0014650378, 0.0016259078, 0.0014648853]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0427468, 1.0, 0.0, 0.0016354017, 0.0022506479, 0.0016358295, 0.0022505994]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0409881, 1.0, 0.0, 0.0011712483, 0.0025550034, 0.0011710068, 0.0025546728]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0323738, 1.0, 0.0, 0.001777595, 0.001165574, 0.0017768269, 0.0011653372]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0399871, 1.0, 0.0, 0.0014721986, 0.002163082, 0.0014714526, 0.0021629771]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0372127, 1.0, 0.0, 0.0020284264, 0.0013546494, 0.0020276802, 0.0013543355]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0399207, 1.0, 0.0, 0.0012263273, 0.0024028686, 0.0012260764, 0.0024027182]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0350485, 1.0, 0.0, 0.001744489, 0.0014417813, 0.0017442604, 0.0014415667]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0304478, 1.0, 0.0, 0.0012012653, 0.0015667297, 0.001201425, 0.0015664762]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0408415, 1.0, 0.0, 0.0019813038, 0.0017315946, 0.001981479, 0.001730963]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0279135, 1.0, 0.0, 0.0011013695, 0.0014362729, 0.0011008838, 0.0014361048]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0274482, 1.0, 0.0, 0.0013589117, 0.0011365694, 0.0013573179, 0.0011360825]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0261818, 1.0, 0.0, 0.001118226, 0.001261988, 0.0011178027, 0.0012618101]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0276749, 1.0, 0.0, 0.0015398192, 0.0009760987, 0.0015398504, 0.0009759794]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0346293, 1.0, 0.0, 0.0018380086, 0.0013100761, 0.0018385855, 0.0013099817]
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D_Loss: [0.5 0.5]
G_Loss: [1.0394907, 1.0, 0.0, 0.0024387268, 0.0011513554, 0.0024386607, 0.0011512511]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0550693, 1.0, 0.0, 0.0017853237, 0.0032209158, 0.0017859532, 0.0032209405]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0635571, 1.0, 0.0, 0.0036137057, 0.0021640847, 0.0036150175, 0.0021641045]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0395579, 1.0, 0.0, 0.0017350784, 0.0018611073, 0.0017351714, 0.001860877]
Epoch: 19
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0318385, 1.0, 0.0, 0.0010897419, 0.001804684, 0.0010900272, 0.0018041921]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0508302, 1.0, 0.0, 0.0018620009, 0.002758901, 0.0018621212, 0.0027591048]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0317793, 1.0, 0.0, 0.0016994614, 0.0011896822, 0.0016983104, 0.0011896298]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0510561, 1.0, 0.0, 0.002849409, 0.0017920245, 0.0028499763, 0.00179179]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0259076, 1.0, 0.0, 0.0011916442, 0.0011635729, 0.001191968, 0.001163447]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0419377, 1.0, 0.0, 0.001009073, 0.0028034416, 0.0010090154, 0.002803526]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0621397, 1.0, 0.0, 0.0012135615, 0.004435513, 0.0012134714, 0.0044355197]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0335401, 1.0, 0.0, 0.0010685282, 0.0019806377, 0.0010679052, 0.0019805275]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0369731, 1.0, 0.0, 0.0019649877, 0.0013963172, 0.0019639295, 0.0013961089]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0321571, 1.0, 0.0, 0.0011804507, 0.0017430116, 0.0011800486, 0.0017425264]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0463659, 1.0, 0.0, 0.001639259, 0.0025759968, 0.001637294, 0.0025759689]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.040453, 1.0, 0.0, 0.0019926245, 0.0016849742, 0.00199205, 0.0016849309]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0402582, 1.0, 0.0, 0.0012244795, 0.0024353797, 0.0012244425, 0.0024352577]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0301019, 1.0, 0.0, 0.0019382918, 0.0007982353, 0.0019384259, 0.0007981092]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0291728, 1.0, 0.0, 0.0014221186, 0.0012299777, 0.0014218717, 0.0012298396]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0258007, 1.0, 0.0, 0.0013554629, 0.0009901271, 0.0013548164, 0.0009900702]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.0285751, 1.0, 0.0, 0.0013621884, 0.001235506, 0.0013624893, 0.0012356339]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0392092, 1.0, 0.0, 0.0012873316, 0.0022771745, 0.0012870643, 0.002277191]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0353719, 1.0, 0.0, 0.0014161513, 0.0017995544, 0.0014156508, 0.001799206]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.0550205, 1.0, 0.0, 0.0016291074, 0.0033727693, 0.0016291449, 0.0033726767]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.0413754, 1.0, 0.0, 0.0015977346, 0.0021636575, 0.0015976084, 0.0021637315]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.0343106, 1.0, 0.0, 0.0015598332, 0.0015594289, 0.0015588484, 0.001559193]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.0306336, 1.0, 0.0, 0.001440465, 0.0013444987, 0.0014394242, 0.001344478]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0313318, 1.0, 0.0, 0.0009947311, 0.0018535967, 0.0009950458, 0.0018534386]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0399609, 1.0, 0.0, 0.0019091995, 0.0017236697, 0.0019085605, 0.0017236102]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0413322, 1.0, 0.0, 0.0014338645, 0.0023236298, 0.0014336967, 0.0023236468]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0330472, 1.0, 0.0, 0.0020458046, 0.00095852546, 0.0020454833, 0.0009583463]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.036425, 1.0, 0.0, 0.0016511742, 0.0016602206, 0.001650891, 0.00166012]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0313706, 1.0, 0.0, 0.0017655774, 0.0010862877, 0.0017659022, 0.0010862414]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.0575547, 1.0, 0.0, 0.0027654022, 0.002466926, 0.0027644713, 0.0024669701]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0338637, 1.0, 0.0, 0.0014772522, 0.0016013071, 0.0014767858, 0.0016013244]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0320781, 1.0, 0.0, 0.0013310028, 0.0015851798, 0.0013311524, 0.001585026]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0358428, 1.0, 0.0, 0.0010455251, 0.0022129589, 0.0010452566, 0.0022127964]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.0273069, 1.0, 0.0, 0.0011904195, 0.0012921196, 0.0011896682, 0.0012917486]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0305833, 1.0, 0.0, 0.0013621014, 0.0014182309, 0.0013619425, 0.001417996]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.03744, 1.0, 0.0, 0.0016247341, 0.0017789318, 0.001624633, 0.0017786289]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0261997, 1.0, 0.0, 0.0012042455, 0.001177662, 0.0012030478, 0.0011776357]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0287533, 1.0, 0.0, 0.0016282147, 0.0009857729, 0.0016277718, 0.0009854715]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0239335, 1.0, 0.0, 0.0015353491, 0.00064042624, 0.0015353861, 0.00064040936]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0296584, 1.0, 0.0, 0.002134035, 0.0005621583, 0.002134269, 0.0005621884]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0569702, 1.0, 0.0, 0.0017403602, 0.003438891, 0.0017388084, 0.0034389931]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.0600908, 1.0, 0.0, 0.002967944, 0.002494888, 0.002967767, 0.0024947913]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.051657, 1.0, 0.0, 0.0015421791, 0.0031539341, 0.0015418633, 0.0031540818]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0412598, 1.0, 0.0, 0.0009755549, 0.0027752952, 0.0009760118, 0.0027753506]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0257581, 1.0, 0.0, 0.0011532649, 0.0011884612, 0.0011531196, 0.0011877924]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0312546, 1.0, 0.0, 0.0012307179, 0.0016106216, 0.001230716, 0.0016105436]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0420976, 1.0, 0.0, 0.0023685228, 0.0014585427, 0.0023684432, 0.0014585705]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0321789, 1.0, 0.0, 0.0010074221, 0.0019179846, 0.001006871, 0.0019180109]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.0412714, 1.0, 0.0, 0.0016784344, 0.002073607, 0.0016777909, 0.0020732423]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0260857, 1.0, 0.0, 0.000816847, 0.0015546201, 0.00081648293, 0.0015545555]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.023402, 1.0, 0.0, 0.00068306876, 0.0014444139, 0.000683089, 0.0014441486]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0383986, 1.0, 0.0, 0.0017395013, 0.0017513233, 0.0017391806, 0.0017511894]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.0429139, 1.0, 0.0, 0.0013861856, 0.002515075, 0.0013862341, 0.0025148992]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0408016, 1.0, 0.0, 0.0018699076, 0.0018393237, 0.001870493, 0.0018388563]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0329745, 1.0, 0.0, 0.0019041451, 0.0010934835, 0.001904908, 0.0010932907]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0399957, 1.0, 0.0, 0.0016523916, 0.001983614, 0.0016519837, 0.0019835255]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0349094, 1.0, 0.0, 0.0013692292, 0.0018043492, 0.0013693736, 0.0018041797]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0379435, 1.0, 0.0, 0.0017261652, 0.0017233089, 0.0017254287, 0.0017232415]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0318606, 1.0, 0.0, 0.0016860387, 0.0012103948, 0.0016859977, 0.0012103354]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.034659, 1.0, 0.0, 0.0016768423, 0.0014740509, 0.001676213, 0.0014739205]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0344186, 1.0, 0.0, 0.0018335907, 0.0012953687, 0.0018338086, 0.0012952463]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.032297, 1.0, 0.0, 0.0014189604, 0.0015170631, 0.0014197312, 0.0015170502]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0439934, 1.0, 0.0, 0.0016498185, 0.0023496514, 0.001649665, 0.0023490393]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.042894, 1.0, 0.0, 0.0017180943, 0.0021814222, 0.0017175989, 0.0021812925]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0418036, 1.0, 0.0, 0.0019152574, 0.0018851978, 0.0019145501, 0.0018845655]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0471005, 1.0, 0.0, 0.0018348198, 0.0024471763, 0.0018334279, 0.0024471814]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.031894, 1.0, 0.0, 0.0014759293, 0.0014235917, 0.0014762004, 0.0014225006]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.031833, 1.0, 0.0, 0.0010117708, 0.0018822053, 0.0010112962, 0.0018820837]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0491012, 1.0, 0.0, 0.0030694352, 0.0013942751, 0.0030699195, 0.0013942219]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0427762, 1.0, 0.0, 0.0014411721, 0.0024476815, 0.001440353, 0.002447376]
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D_Loss: [0.5 0.5]
G_Loss: [1.0392563, 1.0, 0.0, 0.002038894, 0.001529865, 0.0020392167, 0.0015296077]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.069081, 1.0, 0.0, 0.003462045, 0.0028182487, 0.0034598361, 0.0028182142]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0273151, 1.0, 0.0, 0.0011893915, 0.0012938101, 0.0011893054, 0.0012937768]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0337074, 1.0, 0.0, 0.0010954165, 0.0019689584, 0.0010948342, 0.0019688702]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.032811, 1.0, 0.0, 0.0014911201, 0.0014918621, 0.001489412, 0.0014918519]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.0387789, 1.0, 0.0, 0.001813895, 0.0017117185, 0.0018132923, 0.0017094959]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0513315, 1.0, 0.0, 0.0018878744, 0.0027786861, 0.0018867726, 0.0027792563]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0321263, 1.0, 0.0, 0.0018099949, 0.0011106476, 0.0018093237, 0.0011104993]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.028979, 1.0, 0.0, 0.0012841204, 0.0013503863, 0.0012835228, 0.0013502812]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0280465, 1.0, 0.0, 0.0008808447, 0.0016688232, 0.00088077894, 0.001668887]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0367225, 1.0, 0.0, 0.0017189321, 0.0016194762, 0.0017190112, 0.0016194908]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0531421, 1.0, 0.0, 0.0024205884, 0.0024105741, 0.0024200133, 0.002410261]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0291182, 1.0, 0.0, 0.0011993006, 0.0014478927, 0.0011983785, 0.0014478343]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0355629, 1.0, 0.0, 0.0017163986, 0.0015166237, 0.0017163602, 0.0015162457]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0363404, 1.0, 0.0, 0.0009996224, 0.002304066, 0.0009996201, 0.002303937]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0369235, 1.0, 0.0, 0.0013799137, 0.0019769336, 0.0013783318, 0.001976736]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.033359, 1.0, 0.0, 0.0013858003, 0.0016469767, 0.0013845741, 0.0016467671]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0340291, 1.0, 0.0, 0.0015692483, 0.0015244394, 0.0015678776, 0.0015244024]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.0257351, 1.0, 0.0, 0.00083820015, 0.0015013694, 0.0008381904, 0.0015013332]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0303749, 1.0, 0.0, 0.0015377299, 0.0012236172, 0.0015380911, 0.0012234717]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0378717, 1.0, 0.0, 0.0017734267, 0.0016695748, 0.0017726923, 0.0016691075]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.04182, 1.0, 0.0, 0.0023205634, 0.0014812546, 0.002320948, 0.0014807689]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0375712, 1.0, 0.0, 0.0014592179, 0.0019564943, 0.0014575839, 0.001956488]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0420605, 1.0, 0.0, 0.0010354125, 0.0027883183, 0.0010347669, 0.0027884645]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0251015, 1.0, 0.0, 0.0012777119, 0.0010043501, 0.0012767611, 0.0010042635]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0527214, 1.0, 0.0, 0.0023170263, 0.0024759618, 0.002316249, 0.0024752137]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0323392, 1.0, 0.0, 0.0015323975, 0.0014075116, 0.001532496, 0.0014075313]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0344214, 1.0, 0.0, 0.0012912744, 0.0018379972, 0.0012909111, 0.0018378972]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0424843, 1.0, 0.0, 0.0015667406, 0.0022955541, 0.0015654429, 0.0022958065]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0283796, 1.0, 0.0, 0.0010509407, 0.0015291069, 0.0010504379, 0.0015286345]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0320435, 1.0, 0.0, 0.0018828256, 0.0010302484, 0.0018826405, 0.0010301338]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0364894, 1.0, 0.0, 0.0018598347, 0.0014573863, 0.0018597497, 0.001457426]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0340015, 1.0, 0.0, 0.0016266447, 0.0014643706, 0.0016270573, 0.0014642264]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0427376, 1.0, 0.0, 0.0016328357, 0.0022523487, 0.0016333519, 0.0022522956]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0409694, 1.0, 0.0, 0.0011677783, 0.0025567617, 0.0011675118, 0.0025564888]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0322565, 1.0, 0.0, 0.0017738406, 0.0011586539, 0.001773052, 0.0011584433]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.040207, 1.0, 0.0, 0.0014771768, 0.002178084, 0.0014764335, 0.002177987]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0372719, 1.0, 0.0, 0.002027642, 0.0013607936, 0.0020268736, 0.0013605072]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.040071, 1.0, 0.0, 0.0012322166, 0.002410624, 0.0012320087, 0.0024105024]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.035018, 1.0, 0.0, 0.0017471248, 0.0014363634, 0.0017469116, 0.0014361825]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0303859, 1.0, 0.0, 0.0011951553, 0.0015671938, 0.0011952803, 0.0015669514]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0408843, 1.0, 0.0, 0.0019779315, 0.0017388549, 0.0019781305, 0.0017382132]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.0279738, 1.0, 0.0, 0.0011063645, 0.0014367747, 0.0011058392, 0.0014366029]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0272906, 1.0, 0.0, 0.0013486953, 0.0011324561, 0.0013470582, 0.0011319726]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0260751, 1.0, 0.0, 0.0011111599, 0.0012593607, 0.0011107177, 0.0012591775]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0276756, 1.0, 0.0, 0.0015434861, 0.0009724869, 0.0015435154, 0.0009723659]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0346832, 1.0, 0.0, 0.0018395422, 0.0013134403, 0.0018400996, 0.0013133466]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.0394084, 1.0, 0.0, 0.0024343927, 0.001148195, 0.0024343275, 0.0011480905]
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D_Loss: [0.5 0.5]
G_Loss: [1.0549958, 1.0, 0.0, 0.0017752645, 0.003224296, 0.0017758005, 0.0032243198]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.063851, 1.0, 0.0, 0.0036375537, 0.0021669534, 0.003638952, 0.0021669741]
Batch: 120
D_Loss: [0.5 0.5]
G_Loss: [1.0395254, 1.0, 0.0, 0.0017311296, 0.0018620959, 0.0017312134, 0.0018618777]
Epoch: 20
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.031762, 1.0, 0.0, 0.0010857059, 0.0018017681, 0.0010859935, 0.0018013049]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0509679, 1.0, 0.0, 0.0018750399, 0.0027583633, 0.0018752528, 0.0027585758]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0319538, 1.0, 0.0, 0.0017110899, 0.0011939346, 0.0017097273, 0.0011939113]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0510447, 1.0, 0.0, 0.0028514396, 0.0017889555, 0.0028520552, 0.0017887389]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0260189, 1.0, 0.0, 0.0012021405, 0.0011632015, 0.0012024658, 0.001163062]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0420821, 1.0, 0.0, 0.0010185931, 0.0028070416, 0.0010184897, 0.0028071366]
Batch: 6
D_Loss: [0.5 0.5]
G_Loss: [1.0621687, 1.0, 0.0, 0.0012110521, 0.004440671, 0.001210931, 0.004440679]
Batch: 7
D_Loss: [0.5 0.5]
G_Loss: [1.0335295, 1.0, 0.0, 0.0010604453, 0.0019877553, 0.001059826, 0.001987652]
Batch: 8
D_Loss: [0.5 0.5]
G_Loss: [1.0370389, 1.0, 0.0, 0.001973149, 0.0013941401, 0.0019720206, 0.0013939454]
Batch: 9
D_Loss: [0.5 0.5]
G_Loss: [1.0321226, 1.0, 0.0, 0.0011819066, 0.0017384002, 0.0011815382, 0.0017379057]
Batch: 10
D_Loss: [0.5 0.5]
G_Loss: [1.0462992, 1.0, 0.0, 0.0016359723, 0.0025732215, 0.0016340713, 0.0025731921]
Batch: 11
D_Loss: [0.5 0.5]
G_Loss: [1.0405555, 1.0, 0.0, 0.0019933921, 0.0016935196, 0.0019928087, 0.0016934823]
Batch: 12
D_Loss: [0.5 0.5]
G_Loss: [1.0402775, 1.0, 0.0, 0.0012229466, 0.0024386588, 0.0012229021, 0.0024385427]
Batch: 13
D_Loss: [0.5 0.5]
G_Loss: [1.0300137, 1.0, 0.0, 0.0019340577, 0.00079445646, 0.0019342041, 0.0007943441]
Batch: 14
D_Loss: [0.5 0.5]
G_Loss: [1.0293646, 1.0, 0.0, 0.0014370914, 0.0012324471, 0.0014368416, 0.001232326]
Batch: 15
D_Loss: [0.5 0.5]
G_Loss: [1.0258054, 1.0, 0.0, 0.0013564338, 0.0009895694, 0.0013557759, 0.0009895267]
Batch: 16
D_Loss: [0.5 0.5]
G_Loss: [1.02848, 1.0, 0.0, 0.0013558274, 0.0012332343, 0.0013561301, 0.0012333589]
Batch: 17
D_Loss: [0.5 0.5]
G_Loss: [1.0392098, 1.0, 0.0, 0.0012894671, 0.002275081, 0.0012892147, 0.0022751226]
Batch: 18
D_Loss: [0.5 0.5]
G_Loss: [1.0352863, 1.0, 0.0, 0.0014165564, 0.0017913694, 0.0014160194, 0.0017910709]
Batch: 19
D_Loss: [0.5 0.5]
G_Loss: [1.055467, 1.0, 0.0, 0.0016287086, 0.003413749, 0.0016287975, 0.0034137082]
Batch: 20
D_Loss: [0.5 0.5]
G_Loss: [1.041546, 1.0, 0.0, 0.0015972555, 0.0021796497, 0.0015971186, 0.002179734]
Batch: 21
D_Loss: [0.5 0.5]
G_Loss: [1.03433, 1.0, 0.0, 0.001559102, 0.0015619257, 0.0015580619, 0.0015616847]
Batch: 22
D_Loss: [0.5 0.5]
G_Loss: [1.030588, 1.0, 0.0, 0.0014417511, 0.0013390755, 0.0014406508, 0.0013390263]
Batch: 23
D_Loss: [0.5 0.5]
G_Loss: [1.0315473, 1.0, 0.0, 0.0010021295, 0.0018657951, 0.0010023986, 0.0018656636]
Batch: 24
D_Loss: [0.5 0.5]
G_Loss: [1.0399143, 1.0, 0.0, 0.0019097237, 0.0017189134, 0.0019090471, 0.0017188545]
Batch: 25
D_Loss: [0.5 0.5]
G_Loss: [1.0411891, 1.0, 0.0, 0.0014259114, 0.002318562, 0.0014257275, 0.0023185844]
Batch: 26
D_Loss: [0.5 0.5]
G_Loss: [1.0330373, 1.0, 0.0, 0.0020442149, 0.00095922855, 0.0020438428, 0.0009590448]
Batch: 27
D_Loss: [0.5 0.5]
G_Loss: [1.0365442, 1.0, 0.0, 0.0016541538, 0.0016680865, 0.001653871, 0.001667995]
Batch: 28
D_Loss: [0.5 0.5]
G_Loss: [1.0314324, 1.0, 0.0, 0.0017692724, 0.0010881893, 0.0017696342, 0.0010881711]
Batch: 29
D_Loss: [0.5 0.5]
G_Loss: [1.057723, 1.0, 0.0, 0.0027728586, 0.0024747842, 0.0027719026, 0.0024748177]
Batch: 30
D_Loss: [0.5 0.5]
G_Loss: [1.0339209, 1.0, 0.0, 0.001482028, 0.00160173, 0.0014815673, 0.0016017521]
Batch: 31
D_Loss: [0.5 0.5]
G_Loss: [1.0320717, 1.0, 0.0, 0.0013316341, 0.0015839749, 0.0013317859, 0.0015838271]
Batch: 32
D_Loss: [0.5 0.5]
G_Loss: [1.0360016, 1.0, 0.0, 0.0010485788, 0.0022243224, 0.0010482881, 0.002224153]
Batch: 33
D_Loss: [0.5 0.5]
G_Loss: [1.027247, 1.0, 0.0, 0.001185141, 0.0012919588, 0.0011843897, 0.0012915932]
Batch: 34
D_Loss: [0.5 0.5]
G_Loss: [1.0306399, 1.0, 0.0, 0.0013650595, 0.0014204273, 0.0013648896, 0.0014201938]
Batch: 35
D_Loss: [0.5 0.5]
G_Loss: [1.0374326, 1.0, 0.0, 0.0016240941, 0.001778899, 0.0016240051, 0.0017786245]
Batch: 36
D_Loss: [0.5 0.5]
G_Loss: [1.0262914, 1.0, 0.0, 0.0012122078, 0.0011780352, 0.0012109522, 0.0011780094]
Batch: 37
D_Loss: [0.5 0.5]
G_Loss: [1.0287861, 1.0, 0.0, 0.0016337103, 0.0009832574, 0.0016332341, 0.0009829766]
Batch: 38
D_Loss: [0.5 0.5]
G_Loss: [1.0239105, 1.0, 0.0, 0.0015337104, 0.00063997856, 0.0015337075, 0.00063996523]
Batch: 39
D_Loss: [0.5 0.5]
G_Loss: [1.0295863, 1.0, 0.0, 0.0021298989, 0.00055973814, 0.0021301247, 0.00055976014]
Batch: 40
D_Loss: [0.5 0.5]
G_Loss: [1.0569739, 1.0, 0.0, 0.0017407136, 0.0034388648, 0.0017391775, 0.0034389556]
Batch: 41
D_Loss: [0.5 0.5]
G_Loss: [1.060181, 1.0, 0.0, 0.0029747665, 0.0024962542, 0.0029745803, 0.0024961575]
Batch: 42
D_Loss: [0.5 0.5]
G_Loss: [1.0515972, 1.0, 0.0, 0.0015301753, 0.0031605058, 0.0015298761, 0.0031606539]
Batch: 43
D_Loss: [0.5 0.5]
G_Loss: [1.0412954, 1.0, 0.0, 0.0009700396, 0.0027840342, 0.0009705226, 0.0027840827]
Batch: 44
D_Loss: [0.5 0.5]
G_Loss: [1.0258994, 1.0, 0.0, 0.0011617623, 0.0011927957, 0.0011616095, 0.0011921976]
Batch: 45
D_Loss: [0.5 0.5]
G_Loss: [1.0310634, 1.0, 0.0, 0.0012231991, 0.0016007638, 0.0012231154, 0.0016006841]
Batch: 46
D_Loss: [0.5 0.5]
G_Loss: [1.0422815, 1.0, 0.0, 0.0023827879, 0.0014609806, 0.0023827425, 0.0014610023]
Batch: 47
D_Loss: [0.5 0.5]
G_Loss: [1.0322785, 1.0, 0.0, 0.0010188838, 0.0019155806, 0.0010183232, 0.0019156062]
Batch: 48
D_Loss: [0.5 0.5]
G_Loss: [1.041182, 1.0, 0.0, 0.0016750928, 0.002068827, 0.0016743432, 0.002068471]
Batch: 49
D_Loss: [0.5 0.5]
G_Loss: [1.0260246, 1.0, 0.0, 0.00081514916, 0.0015507606, 0.00081478816, 0.0015506956]
Batch: 50
D_Loss: [0.5 0.5]
G_Loss: [1.0234177, 1.0, 0.0, 0.000687354, 0.001441552, 0.0006873333, 0.0014413211]
Batch: 51
D_Loss: [0.5 0.5]
G_Loss: [1.0383681, 1.0, 0.0, 0.0017385986, 0.0017494524, 0.0017382716, 0.001749322]
Batch: 52
D_Loss: [0.5 0.5]
G_Loss: [1.042916, 1.0, 0.0, 0.0013809557, 0.0025205088, 0.0013810284, 0.0025203442]
Batch: 53
D_Loss: [0.5 0.5]
G_Loss: [1.0409602, 1.0, 0.0, 0.0018903227, 0.0018333147, 0.0018909121, 0.0018328668]
Batch: 54
D_Loss: [0.5 0.5]
G_Loss: [1.0328778, 1.0, 0.0, 0.0019032598, 0.0010855824, 0.0019039657, 0.001085365]
Batch: 55
D_Loss: [0.5 0.5]
G_Loss: [1.0400084, 1.0, 0.0, 0.0016553912, 0.0019817872, 0.001655021, 0.0019816873]
Batch: 56
D_Loss: [0.5 0.5]
G_Loss: [1.0349036, 1.0, 0.0, 0.0013705832, 0.0018024824, 0.0013707189, 0.0018023615]
Batch: 57
D_Loss: [0.5 0.5]
G_Loss: [1.0380063, 1.0, 0.0, 0.0017284066, 0.001726777, 0.0017277147, 0.0017267361]
Batch: 58
D_Loss: [0.5 0.5]
G_Loss: [1.0318496, 1.0, 0.0, 0.0016862033, 0.0012092271, 0.0016861574, 0.0012091603]
Batch: 59
D_Loss: [0.5 0.5]
G_Loss: [1.0346175, 1.0, 0.0, 0.0016729087, 0.0014742131, 0.0016722241, 0.0014740685]
Batch: 60
D_Loss: [0.5 0.5]
G_Loss: [1.0345839, 1.0, 0.0, 0.0018567576, 0.0012872231, 0.0018570155, 0.0012871261]
Batch: 61
D_Loss: [0.5 0.5]
G_Loss: [1.0323975, 1.0, 0.0, 0.00142484, 0.0015203131, 0.0014256935, 0.0015203113]
Batch: 62
D_Loss: [0.5 0.5]
G_Loss: [1.0439736, 1.0, 0.0, 0.001642341, 0.0023553243, 0.0016420998, 0.0023547397]
Batch: 63
D_Loss: [0.5 0.5]
G_Loss: [1.0433083, 1.0, 0.0, 0.0017498626, 0.0021873156, 0.0017492762, 0.0021872222]
Batch: 64
D_Loss: [0.5 0.5]
G_Loss: [1.0418314, 1.0, 0.0, 0.0019244165, 0.0018785759, 0.001923477, 0.0018780254]
Batch: 65
D_Loss: [0.5 0.5]
G_Loss: [1.0469831, 1.0, 0.0, 0.0018237126, 0.0024476247, 0.0018221162, 0.0024476335]
Batch: 66
D_Loss: [0.5 0.5]
G_Loss: [1.0321903, 1.0, 0.0, 0.0014991667, 0.0014273042, 0.0014993725, 0.0014262553]
Batch: 67
D_Loss: [0.5 0.5]
G_Loss: [1.031825, 1.0, 0.0, 0.0010129234, 0.0018803156, 0.0010124478, 0.001880195]
Batch: 68
D_Loss: [0.5 0.5]
G_Loss: [1.0492519, 1.0, 0.0, 0.0030741869, 0.0014032396, 0.003074552, 0.001403184]
Batch: 69
D_Loss: [0.5 0.5]
G_Loss: [1.0428371, 1.0, 0.0, 0.0014490404, 0.002445368, 0.0014479943, 0.002445083]
Batch: 70
D_Loss: [0.5 0.5]
G_Loss: [1.0391766, 1.0, 0.0, 0.0020331335, 0.0015283603, 0.0020334956, 0.001528116]
Batch: 71
D_Loss: [0.5 0.5]
G_Loss: [1.0694069, 1.0, 0.0, 0.0034906776, 0.002819276, 0.003488138, 0.002819233]
Batch: 72
D_Loss: [0.5 0.5]
G_Loss: [1.0272957, 1.0, 0.0, 0.0011886966, 0.0012927458, 0.0011886067, 0.0012927193]
Batch: 73
D_Loss: [0.5 0.5]
G_Loss: [1.0336707, 1.0, 0.0, 0.0010970236, 0.001964009, 0.0010963837, 0.001963919]
Batch: 74
D_Loss: [0.5 0.5]
G_Loss: [1.0328513, 1.0, 0.0, 0.0014914175, 0.0014952143, 0.0014897209, 0.0014952146]
Batch: 75
D_Loss: [0.5 0.5]
G_Loss: [1.0388095, 1.0, 0.0, 0.0018143298, 0.0017140484, 0.0018136665, 0.0017120275]
Batch: 76
D_Loss: [0.5 0.5]
G_Loss: [1.0510029, 1.0, 0.0, 0.0018853032, 0.0027513872, 0.001883969, 0.002751897]
Batch: 77
D_Loss: [0.5 0.5]
G_Loss: [1.0321256, 1.0, 0.0, 0.0018128918, 0.0011076981, 0.0018122431, 0.0011075722]
Batch: 78
D_Loss: [0.5 0.5]
G_Loss: [1.0289961, 1.0, 0.0, 0.0012927951, 0.0013432822, 0.0012921154, 0.0013431844]
Batch: 79
D_Loss: [0.5 0.5]
G_Loss: [1.0281091, 1.0, 0.0, 0.0008837995, 0.0016715818, 0.0008837378, 0.0016716465]
Batch: 80
D_Loss: [0.5 0.5]
G_Loss: [1.0366894, 1.0, 0.0, 0.0017149956, 0.0016204057, 0.0017150375, 0.0016204314]
Batch: 81
D_Loss: [0.5 0.5]
G_Loss: [1.0535716, 1.0, 0.0, 0.0024596488, 0.0024105788, 0.0024590655, 0.0024102516]
Batch: 82
D_Loss: [0.5 0.5]
G_Loss: [1.0290697, 1.0, 0.0, 0.0011983076, 0.0014444795, 0.0011973022, 0.0014444215]
Batch: 83
D_Loss: [0.5 0.5]
G_Loss: [1.0356461, 1.0, 0.0, 0.0017217182, 0.0015188815, 0.0017216485, 0.0015185147]
Batch: 84
D_Loss: [0.5 0.5]
G_Loss: [1.0362724, 1.0, 0.0, 0.0010005485, 0.0022969642, 0.0010005354, 0.0022968408]
Batch: 85
D_Loss: [0.5 0.5]
G_Loss: [1.0371575, 1.0, 0.0, 0.0013809197, 0.0019972194, 0.0013791265, 0.0019970436]
Batch: 86
D_Loss: [0.5 0.5]
G_Loss: [1.0334011, 1.0, 0.0, 0.0013879118, 0.0016487059, 0.0013864973, 0.0016485394]
Batch: 87
D_Loss: [0.5 0.5]
G_Loss: [1.0340297, 1.0, 0.0, 0.0015679987, 0.0015257497, 0.0015665668, 0.001525721]
Batch: 88
D_Loss: [0.5 0.5]
G_Loss: [1.025744, 1.0, 0.0, 0.0008372845, 0.0015030759, 0.0008372585, 0.0015030508]
Batch: 89
D_Loss: [0.5 0.5]
G_Loss: [1.0304021, 1.0, 0.0, 0.0015397856, 0.0012240161, 0.001540123, 0.0012238675]
Batch: 90
D_Loss: [0.5 0.5]
G_Loss: [1.0379899, 1.0, 0.0, 0.0017816279, 0.0016721019, 0.0017808506, 0.0016716582]
Batch: 91
D_Loss: [0.5 0.5]
G_Loss: [1.0418786, 1.0, 0.0, 0.0023264585, 0.0014806848, 0.002326909, 0.0014802254]
Batch: 92
D_Loss: [0.5 0.5]
G_Loss: [1.0375973, 1.0, 0.0, 0.0014620431, 0.001956052, 0.0014601757, 0.0019560591]
Batch: 93
D_Loss: [0.5 0.5]
G_Loss: [1.0421395, 1.0, 0.0, 0.001038446, 0.0027924725, 0.0010377064, 0.0027926276]
Batch: 94
D_Loss: [0.5 0.5]
G_Loss: [1.0250717, 1.0, 0.0, 0.0012722824, 0.0010070696, 0.001271293, 0.0010069769]
Batch: 95
D_Loss: [0.5 0.5]
G_Loss: [1.0527713, 1.0, 0.0, 0.0023167618, 0.0024807635, 0.0023160186, 0.0024799774]
Batch: 96
D_Loss: [0.5 0.5]
G_Loss: [1.0322775, 1.0, 0.0, 0.0015260859, 0.0014082303, 0.0015261426, 0.001408247]
Batch: 97
D_Loss: [0.5 0.5]
G_Loss: [1.0344924, 1.0, 0.0, 0.0012941902, 0.0018415228, 0.0012938122, 0.0018414212]
Batch: 98
D_Loss: [0.5 0.5]
G_Loss: [1.0425364, 1.0, 0.0, 0.001568997, 0.0022980485, 0.0015676646, 0.0022982932]
Batch: 99
D_Loss: [0.5 0.5]
G_Loss: [1.0283717, 1.0, 0.0, 0.001053901, 0.0015254244, 0.0010533975, 0.0015249705]
Batch: 100
D_Loss: [0.5 0.5]
G_Loss: [1.0320138, 1.0, 0.0, 0.0018789268, 0.0010314492, 0.0018786575, 0.0010313431]
Batch: 101
D_Loss: [0.5 0.5]
G_Loss: [1.0366875, 1.0, 0.0, 0.0018783172, 0.0014569024, 0.0018783044, 0.0014569508]
Batch: 102
D_Loss: [0.5 0.5]
G_Loss: [1.0340805, 1.0, 0.0, 0.0016309532, 0.0014672492, 0.0016313721, 0.0014671113]
Batch: 103
D_Loss: [0.5 0.5]
G_Loss: [1.0427271, 1.0, 0.0, 0.0016282676, 0.002255972, 0.0016288089, 0.0022559126]
Batch: 104
D_Loss: [0.5 0.5]
G_Loss: [1.0409677, 1.0, 0.0, 0.001166272, 0.0025581075, 0.00116597, 0.0025578653]
Batch: 105
D_Loss: [0.5 0.5]
G_Loss: [1.0321752, 1.0, 0.0, 0.0017714417, 0.0011536679, 0.0017705989, 0.0011534595]
Batch: 106
D_Loss: [0.5 0.5]
G_Loss: [1.0404569, 1.0, 0.0, 0.0014787556, 0.0021992102, 0.0014780327, 0.0021991208]
Batch: 107
D_Loss: [0.5 0.5]
G_Loss: [1.0372792, 1.0, 0.0, 0.0020286213, 0.0013605019, 0.002027859, 0.001360208]
Batch: 108
D_Loss: [0.5 0.5]
G_Loss: [1.0402292, 1.0, 0.0, 0.0012369014, 0.0024203353, 0.0012366995, 0.002420194]
Batch: 109
D_Loss: [0.5 0.5]
G_Loss: [1.0350393, 1.0, 0.0, 0.001748792, 0.0014366355, 0.0017485797, 0.0014364452]
Batch: 110
D_Loss: [0.5 0.5]
G_Loss: [1.0303367, 1.0, 0.0, 0.0011912576, 0.0015666328, 0.0011914144, 0.0015664395]
Batch: 111
D_Loss: [0.5 0.5]
G_Loss: [1.0408204, 1.0, 0.0, 0.0019766313, 0.0017343466, 0.0019767717, 0.0017338158]
Batch: 112
D_Loss: [0.5 0.5]
G_Loss: [1.028091, 1.0, 0.0, 0.0011102355, 0.0014435581, 0.0011096642, 0.001443421]
Batch: 113
D_Loss: [0.5 0.5]
G_Loss: [1.0270739, 1.0, 0.0, 0.0013245367, 0.0011369099, 0.0013229653, 0.0011364755]
Batch: 114
D_Loss: [0.5 0.5]
G_Loss: [1.0259445, 1.0, 0.0, 0.0011068482, 0.0012517942, 0.0011063777, 0.0012516417]
Batch: 115
D_Loss: [0.5 0.5]
G_Loss: [1.0276794, 1.0, 0.0, 0.0015456218, 0.0009706937, 0.0015456406, 0.000970599]
Batch: 116
D_Loss: [0.5 0.5]
G_Loss: [1.0347098, 1.0, 0.0, 0.0018385837, 0.0013168086, 0.0018391602, 0.0013167257]
Batch: 117
D_Loss: [0.5 0.5]
G_Loss: [1.0393384, 1.0, 0.0, 0.0024280108, 0.0011482252, 0.0024279084, 0.001148141]
Batch: 118
D_Loss: [0.5 0.5]
G_Loss: [1.0548131, 1.0, 0.0, 0.0017590927, 0.0032238779, 0.0017595916, 0.003223897]
Batch: 119
D_Loss: [0.5 0.5]
G_Loss: [1.0641576, 1.0, 0.0, 0.0036672824, 0.002165115, 0.0036686016, 0.002165122]
Batch: 120
D_Loss: [0.5 0.5]






    




W0822 13:12:00.810707 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:00.835649 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:00.855693 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).






    




G_Loss: [1.039357, 1.0, 0.0, 0.0017186594, 0.0018592626, 0.0017186969, 0.0018590989]






    




W0822 13:12:00.874701 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:00.895823 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:00.913496 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:00.991259 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:01.010306 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:01.029375 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:01.048117 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:01.066426 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
W0822 13:12:01.084241 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).






    




Epoch: 21
Number of batches: 121
Batch: 0
D_Loss: [0.5 0.5]
G_Loss: [1.0317351, 1.0, 0.0, 0.0010825285, 0.0018024929, 0.0010828323, 0.0018020737]
Batch: 1
D_Loss: [0.5 0.5]
G_Loss: [1.0511647, 1.0, 0.0, 0.0018909018, 0.0027604117, 0.0018909741, 0.002760621]
Batch: 2
D_Loss: [0.5 0.5]
G_Loss: [1.0320256, 1.0, 0.0, 0.0017160284, 0.0011955148, 0.0017146135, 0.0011955099]
Batch: 3
D_Loss: [0.5 0.5]
G_Loss: [1.0510559, 1.0, 0.0, 0.0028536946, 0.0017877315, 0.0028541593, 0.0017875144]
Batch: 4
D_Loss: [0.5 0.5]
G_Loss: [1.0260245, 1.0, 0.0, 0.0012029654, 0.0011628708, 0.0012032909, 0.0011627255]
Batch: 5
D_Loss: [0.5 0.5]
G_Loss: [1.0420709, 1.0, 0.0, 0.0010179211, 0.002806708, 0.0010178435, 0.0028068065]
Batch: 6
D_Loss: [0.5 0.5]

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