dlnd_face_generation


Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".


In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)


Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.


In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')


Out[2]:
<matplotlib.image.AxesImage at 0x17190f117f0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.


In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))


Out[3]:
<matplotlib.image.AxesImage at 0x17190fba400>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU


In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))


TensorFlow Version: 1.0.0
C:\Users\Andreas\AppData\Local\conda\conda\envs\dl\lib\site-packages\ipykernel\__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)


In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
        
    inputs_real   = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z      = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, None, name='learning_rate')

    return inputs_real, inputs_z, learning_rate



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)


Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).


In [6]:
def default_gan_activation(x,alpha=0.2):
    #return tf.nn.elu(x)
    return tf.maximum(alpha * x, x)

In [7]:
def discriminator_valid_padding(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    x = images
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(x, 64, 6, strides=2, padding='valid')
        relu1 = default_gan_activation(x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 4, strides=2, padding='valid')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = default_gan_activation(bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 3, strides=2, padding='valid')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = default_gan_activation(bn3)
        
        relu3_size = int(relu3.shape[1]*relu3.shape[2]*relu3.shape[3])

        flat = tf.reshape(relu3, (-1, relu3_size))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator_valid_padding, tf)


Tests Passed

In [8]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    x = images
    d = 0
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(x, 64, 5, strides=2, padding='same')
        x1 = tf.layers.dropout(x1,d)
        relu1 = default_gan_activation(x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        x2 = tf.layers.dropout(x2,d)
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = default_gan_activation(bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        x3 = tf.layers.dropout(x3,d)
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = default_gan_activation(bn3)
        
        relu3_size = int(relu3.shape[1]*relu3.shape[2]*relu3.shape[3])

        flat = tf.reshape(relu3, (-1, relu3_size))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)


Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.


In [9]:
def generator_valid_padding(z, out_channel_dim, is_train=True,debug = False):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    if(debug):
        print('-----verify generator inputs:-----')
        [print(i) for i in zip(['z', 'out_channel_dim', 'is_train'],[z, out_channel_dim, is_train])]
    
    # TODO: Implement Function
    (z, output_dim, reuse, training) = (z, out_channel_dim, not(is_train), is_train)
    
    default_padding = 'same'
    default_kernel_size = 5

    with tf.variable_scope('generator', reuse=reuse):
        a=2
        x1 = tf.layers.dense(z, a*a*512)
        x1 = tf.reshape(x1, (-1, a, a, 512))
        x1 = tf.layers.batch_normalization(x1, training=training)
        x1 = default_gan_activation(x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 3, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=training)
        x2 = default_gan_activation(x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 4, strides=2, padding='valid')
        x3 = tf.layers.batch_normalization(x3, training=training)
        x3 = default_gan_activation(x3)
        
        logits = tf.layers.conv2d_transpose(x3, output_dim, 6, strides=2,  padding='valid')
        
        out = tf.tanh(logits)
        
        if(debug):
            print('-----verify generator processing:-----')
            [print(i) for i in zip(['x1','x2','x3'],[x1,x2,x3])]
            
            print('-----verify generator outputs:-----')
            print(out)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator_valid_padding, tf)


Tests Passed

In [10]:
def generator(z, out_channel_dim, is_train=True,debug = False):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    if(debug):
        print('-----verify generator inputs:-----')
        [print(i) for i in zip(['z', 'out_channel_dim', 'is_train'],[z, out_channel_dim, is_train])]
    
    # TODO: Implement Function
    (z, output_dim, reuse, training) = (z, out_channel_dim, not(is_train), is_train)
    
    default_padding = 'same'
    default_kernel_size = 5

    with tf.variable_scope('generator', reuse=reuse):
        a=2
        x1 = tf.layers.dense(z, a*a*512)
        x1 = tf.reshape(x1, (-1, a, a, 512))
        x1 = tf.layers.batch_normalization(x1, training=training)
        x1 = default_gan_activation(x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, default_kernel_size, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=training)
        x2 = default_gan_activation(x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, default_kernel_size, strides=2, padding=default_padding)
        x3 = tf.layers.batch_normalization(x3, training=training)
        x3 = default_gan_activation(x3)
        
        logits = tf.layers.conv2d_transpose(x3, output_dim, default_kernel_size, strides=2, padding=default_padding)
        
        out = tf.tanh(logits)
        
        if(debug):
            print('-----verify generator processing:-----')
            [print(i) for i in zip(['x1','x2','x3'],[x1,x2,x3])]
            
            print('-----verify generator outputs:-----')
            print(out)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)


Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)

In [11]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    output_dim = out_channel_dim

    g_model = generator(input_z, output_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    smoothing = 1

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)


Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).


In [12]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """

    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)


Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.


In [13]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.


In [14]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    print('-----verify train inputs:-----')
    [print(i) for i in zip(['epoch_count', 'batch_size', 'z_dim', 'learning_rate', 'beta1', 'get_batches', 'data_shape', 'data_image_mode'],\
                           [epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode])]
    
    
    print_every = max(batch_size//16,10)
    show_every  = print_every*10
    n_images = show_n_images
    
    filename_suffix = '_' + data_image_mode + '_' + str(z_dim)
    saver_path  = './checkpoints_generator'+filename_suffix+'.ckpt'
    sample_path = './samples'+filename_suffix+'.pkl'
    
    tf.reset_default_graph()

    # MODEL  INPUT
    input_real, input_z, input_learning_rate =  model_inputs(*data_shape[1:], z_dim)

    # MODEL
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[-1])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        print('variables initialized')
        saver = tf.train.Saver()
        
        try:
            saver.restore(sess, saver_path)
            print('variables restored')
        except:
            pass
        
        steps = 1
        samples, losses = [], []
        for e in range(epoch_count):
            #for x, y in dataset.batches(batch_size):
            for batch_x in get_batches(min(steps,batch_size)): ## incriasing batchsize to optimise trainingspeed (and time) over iterations, partly simular to a learning rate declay
                steps += 1
                #print(steps,end='')

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim)) 
                batch_x *= 2

                # Run optimizers
                for _ in range(1):
                    _ = sess.run(d_train_opt, feed_dict={input_real         : batch_x,
                                                         input_z            : batch_z,
                                                         input_learning_rate: learning_rate})
                
                for _ in range(3):
                    _ = sess.run(g_train_opt, feed_dict={input_real         : batch_x,
                                                         input_z            : batch_z,
                                                         input_learning_rate: learning_rate})
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_x})
                    train_loss_g = g_loss.eval({input_z: batch_z})
 
                    print("Epoch {}/{}...".format(e+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
 
                if steps % show_every == 0:
                    show_generator_output(sess, n_images, input_z, data_shape[-1], data_image_mode)
 
                    saver.save(sess, saver_path)

    with open(sample_path, 'wb') as f:
        pkl.dump(samples, f)
    
    fig, ax = pyplot.subplots()
    losses = np.array(losses)
    pyplot.plot(losses.T[0], label='Discriminator', alpha=0.5)
    pyplot.plot(losses.T[1], label='Generator', alpha=0.5)
    pyplot.title("Training Losses")
    pyplot.legend()

    return losses, samples

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.


In [ ]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 =  .5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)


-----verify train inputs:-----
('epoch_count', 2)
('batch_size', 64)
('z_dim', 100)
('learning_rate', 0.0002)
('beta1', 0.5)
('get_batches', <bound method Dataset.get_batches of <helper.Dataset object at 0x000002ECED7D7630>>)
('data_shape', (60000, 28, 28, 1))
('data_image_mode', 'L')
variables initialized
variables restored
Epoch 1/2... Discriminator Loss: 0.4460... Generator Loss: 1.3485
Epoch 1/2... Discriminator Loss: 0.5952... Generator Loss: 0.8811
Epoch 1/2... Discriminator Loss: 0.4999... Generator Loss: 1.1816
Epoch 1/2... Discriminator Loss: 0.9845... Generator Loss: 0.6933
Epoch 1/2... Discriminator Loss: 0.9770... Generator Loss: 0.8147
Epoch 1/2... Discriminator Loss: 0.5781... Generator Loss: 1.1745
Epoch 1/2... Discriminator Loss: 0.5527... Generator Loss: 1.0867
Epoch 1/2... Discriminator Loss: 0.8988... Generator Loss: 0.6637
Epoch 1/2... Discriminator Loss: 0.3945... Generator Loss: 1.4081
Epoch 1/2... Discriminator Loss: 0.2316... Generator Loss: 1.9002
Epoch 1/2... Discriminator Loss: 0.2582... Generator Loss: 1.6952
Epoch 1/2... Discriminator Loss: 0.3361... Generator Loss: 1.6519
Epoch 1/2... Discriminator Loss: 0.7431... Generator Loss: 0.8715
Epoch 1/2... Discriminator Loss: 0.3465... Generator Loss: 1.4844
Epoch 1/2... Discriminator Loss: 0.2201... Generator Loss: 1.6906
Epoch 1/2... Discriminator Loss: 0.5183... Generator Loss: 1.1318
Epoch 1/2... Discriminator Loss: 0.1344... Generator Loss: 2.2027
Epoch 1/2... Discriminator Loss: 0.2128... Generator Loss: 1.7603
Epoch 1/2... Discriminator Loss: 1.3391... Generator Loss: 0.6491
Epoch 1/2... Discriminator Loss: 0.8356... Generator Loss: 0.8932
Epoch 1/2... Discriminator Loss: 0.3958... Generator Loss: 1.5585
Epoch 1/2... Discriminator Loss: 0.5801... Generator Loss: 1.1614
Epoch 1/2... Discriminator Loss: 1.0166... Generator Loss: 0.7229
Epoch 1/2... Discriminator Loss: 0.7335... Generator Loss: 0.7751
Epoch 1/2... Discriminator Loss: 1.1895... Generator Loss: 0.7712
Epoch 1/2... Discriminator Loss: 0.2204... Generator Loss: 1.6533
Epoch 1/2... Discriminator Loss: 0.5450... Generator Loss: 1.1360
Epoch 1/2... Discriminator Loss: 1.8619... Generator Loss: 0.3574
Epoch 1/2... Discriminator Loss: 0.7364... Generator Loss: 0.9263
Epoch 1/2... Discriminator Loss: 0.7899... Generator Loss: 0.6889
Epoch 1/2... Discriminator Loss: 0.4411... Generator Loss: 1.6165
Epoch 1/2... Discriminator Loss: 0.2852... Generator Loss: 1.5059
Epoch 1/2... Discriminator Loss: 0.3327... Generator Loss: 1.5242
Epoch 1/2... Discriminator Loss: 0.9836... Generator Loss: 0.8414
Epoch 1/2... Discriminator Loss: 0.5755... Generator Loss: 1.3811
Epoch 1/2... Discriminator Loss: 0.6227... Generator Loss: 0.9368
Epoch 1/2... Discriminator Loss: 0.9782... Generator Loss: 0.9018
Epoch 1/2... Discriminator Loss: 0.7383... Generator Loss: 1.2219
Epoch 1/2... Discriminator Loss: 0.4999... Generator Loss: 1.2672
Epoch 1/2... Discriminator Loss: 0.5176... Generator Loss: 1.0058
Epoch 1/2... Discriminator Loss: 0.3284... Generator Loss: 1.5936
Epoch 1/2... Discriminator Loss: 0.8157... Generator Loss: 1.2253
Epoch 1/2... Discriminator Loss: 0.3304... Generator Loss: 1.8545
Epoch 1/2... Discriminator Loss: 0.9737... Generator Loss: 0.5871
Epoch 1/2... Discriminator Loss: 0.7554... Generator Loss: 1.1039
Epoch 1/2... Discriminator Loss: 0.3258... Generator Loss: 1.2929
Epoch 1/2... Discriminator Loss: 0.3922... Generator Loss: 1.7230
Epoch 1/2... Discriminator Loss: 0.1248... Generator Loss: 2.1741
Epoch 1/2... Discriminator Loss: 0.3111... Generator Loss: 1.4019
Epoch 1/2... Discriminator Loss: 0.2229... Generator Loss: 1.7852
Epoch 1/2... Discriminator Loss: 1.1143... Generator Loss: 0.5066
Epoch 1/2... Discriminator Loss: 0.5834... Generator Loss: 1.1037
Epoch 1/2... Discriminator Loss: 0.1870... Generator Loss: 1.8656
Epoch 1/2... Discriminator Loss: 0.4020... Generator Loss: 1.2969
Epoch 1/2... Discriminator Loss: 0.5873... Generator Loss: 0.8637
Epoch 1/2... Discriminator Loss: 0.3664... Generator Loss: 1.5741
Epoch 1/2... Discriminator Loss: 0.2276... Generator Loss: 1.7721
Epoch 1/2... Discriminator Loss: 0.4694... Generator Loss: 1.2540
Epoch 1/2... Discriminator Loss: 0.6662... Generator Loss: 1.2246
Epoch 1/2... Discriminator Loss: 0.3860... Generator Loss: 1.3536
Epoch 1/2... Discriminator Loss: 0.9716... Generator Loss: 0.7203
Epoch 1/2... Discriminator Loss: 0.3464... Generator Loss: 1.6634
Epoch 1/2... Discriminator Loss: 0.1844... Generator Loss: 1.8245
Epoch 1/2... Discriminator Loss: 1.9195... Generator Loss: 0.3035
Epoch 1/2... Discriminator Loss: 1.1382... Generator Loss: 0.5228
Epoch 1/2... Discriminator Loss: 1.1138... Generator Loss: 0.5805
Epoch 1/2... Discriminator Loss: 0.2895... Generator Loss: 1.3945
Epoch 1/2... Discriminator Loss: 0.4755... Generator Loss: 1.3775
Epoch 1/2... Discriminator Loss: 0.3964... Generator Loss: 1.3681
Epoch 1/2... Discriminator Loss: 0.5735... Generator Loss: 1.1277
Epoch 1/2... Discriminator Loss: 0.4073... Generator Loss: 1.2676
Epoch 1/2... Discriminator Loss: 1.1094... Generator Loss: 0.6955
Epoch 1/2... Discriminator Loss: 0.6634... Generator Loss: 1.2271
Epoch 1/2... Discriminator Loss: 0.6146... Generator Loss: 0.9147
Epoch 1/2... Discriminator Loss: 0.5355... Generator Loss: 1.2869
Epoch 1/2... Discriminator Loss: 0.4826... Generator Loss: 1.1761
Epoch 1/2... Discriminator Loss: 0.3435... Generator Loss: 1.4711
Epoch 1/2... Discriminator Loss: 0.5451... Generator Loss: 1.0745
Epoch 1/2... Discriminator Loss: 0.4188... Generator Loss: 1.2521
Epoch 1/2... Discriminator Loss: 0.8456... Generator Loss: 0.8686
Epoch 1/2... Discriminator Loss: 0.9882... Generator Loss: 1.0378
Epoch 1/2... Discriminator Loss: 1.8289... Generator Loss: 0.5607
Epoch 1/2... Discriminator Loss: 0.4271... Generator Loss: 1.1081
Epoch 1/2... Discriminator Loss: 0.8994... Generator Loss: 0.6271
Epoch 1/2... Discriminator Loss: 0.3250... Generator Loss: 1.6777
Epoch 1/2... Discriminator Loss: 0.6032... Generator Loss: 0.9973
Epoch 1/2... Discriminator Loss: 0.5670... Generator Loss: 0.9150
Epoch 1/2... Discriminator Loss: 0.6917... Generator Loss: 0.9261
Epoch 1/2... Discriminator Loss: 1.0388... Generator Loss: 0.7386
Epoch 1/2... Discriminator Loss: 0.2531... Generator Loss: 1.7433
Epoch 1/2... Discriminator Loss: 0.3878... Generator Loss: 1.4438
Epoch 1/2... Discriminator Loss: 0.4023... Generator Loss: 1.2448
Epoch 1/2... Discriminator Loss: 0.5537... Generator Loss: 1.1832
Epoch 1/2... Discriminator Loss: 0.5816... Generator Loss: 0.9263
Epoch 1/2... Discriminator Loss: 0.4481... Generator Loss: 1.5903
Epoch 1/2... Discriminator Loss: 0.7230... Generator Loss: 1.0985
Epoch 1/2... Discriminator Loss: 1.1759... Generator Loss: 0.9636
Epoch 1/2... Discriminator Loss: 0.4235... Generator Loss: 1.5561
Epoch 1/2... Discriminator Loss: 0.6021... Generator Loss: 1.2525
Epoch 1/2... Discriminator Loss: 0.4885... Generator Loss: 1.2401
Epoch 1/2... Discriminator Loss: 0.7092... Generator Loss: 0.8027
Epoch 1/2... Discriminator Loss: 0.5054... Generator Loss: 1.0666
Epoch 1/2... Discriminator Loss: 0.7981... Generator Loss: 0.8360
Epoch 1/2... Discriminator Loss: 0.6760... Generator Loss: 1.0216
Epoch 1/2... Discriminator Loss: 0.2941... Generator Loss: 1.6002
Epoch 1/2... Discriminator Loss: 0.6656... Generator Loss: 1.4945
Epoch 1/2... Discriminator Loss: 0.7323... Generator Loss: 0.7202
Epoch 1/2... Discriminator Loss: 0.5358... Generator Loss: 1.0973
Epoch 1/2... Discriminator Loss: 0.8389... Generator Loss: 1.2926
Epoch 1/2... Discriminator Loss: 2.4662... Generator Loss: 1.1720
Epoch 1/2... Discriminator Loss: 1.2047... Generator Loss: 0.7606
Epoch 1/2... Discriminator Loss: 0.9062... Generator Loss: 0.8497
Epoch 1/2... Discriminator Loss: 0.8980... Generator Loss: 0.7455
Epoch 1/2... Discriminator Loss: 0.5646... Generator Loss: 1.1451
Epoch 1/2... Discriminator Loss: 0.1846... Generator Loss: 1.9701
Epoch 1/2... Discriminator Loss: 0.4466... Generator Loss: 1.6173
Epoch 1/2... Discriminator Loss: 0.9112... Generator Loss: 0.7808
Epoch 1/2... Discriminator Loss: 1.7853... Generator Loss: 0.7515
Epoch 1/2... Discriminator Loss: 0.7253... Generator Loss: 1.1837
Epoch 1/2... Discriminator Loss: 0.2971... Generator Loss: 1.7829
Epoch 1/2... Discriminator Loss: 0.5114... Generator Loss: 1.3268
Epoch 1/2... Discriminator Loss: 0.7570... Generator Loss: 0.9061
Epoch 1/2... Discriminator Loss: 0.1559... Generator Loss: 2.1220
Epoch 1/2... Discriminator Loss: 0.3404... Generator Loss: 1.3612

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.


In [ ]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 =  .5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)


-----verify train inputs:-----
('epoch_count', 1)
('batch_size', 64)
('z_dim', 100)
('learning_rate', 0.0002)
('beta1', 0.5)
('get_batches', <bound method Dataset.get_batches of <helper.Dataset object at 0x0000023D861E1F98>>)
('data_shape', (202599, 28, 28, 3))
('data_image_mode', 'RGB')
variables initialized
variables restored
Epoch 1/1... Discriminator Loss: 1.0029... Generator Loss: 0.5329
Epoch 1/1... Discriminator Loss: 0.7697... Generator Loss: 0.6359
Epoch 1/1... Discriminator Loss: 0.1687... Generator Loss: 1.9035
Epoch 1/1... Discriminator Loss: 0.2816... Generator Loss: 1.8822
Epoch 1/1... Discriminator Loss: 0.1509... Generator Loss: 2.0611
Epoch 1/1... Discriminator Loss: 0.3295... Generator Loss: 1.6052
Epoch 1/1... Discriminator Loss: 0.1468... Generator Loss: 2.0200
Epoch 1/1... Discriminator Loss: 0.2414... Generator Loss: 1.6707
Epoch 1/1... Discriminator Loss: 0.2588... Generator Loss: 2.2352
Epoch 1/1... Discriminator Loss: 0.1206... Generator Loss: 2.2712
Epoch 1/1... Discriminator Loss: 0.1821... Generator Loss: 1.9599
Epoch 1/1... Discriminator Loss: 0.1889... Generator Loss: 1.8430
Epoch 1/1... Discriminator Loss: 0.5983... Generator Loss: 0.9915
Epoch 1/1... Discriminator Loss: 0.3328... Generator Loss: 1.2829
Epoch 1/1... Discriminator Loss: 0.1515... Generator Loss: 2.0225
Epoch 1/1... Discriminator Loss: 0.3968... Generator Loss: 1.4970
Epoch 1/1... Discriminator Loss: 1.3830... Generator Loss: 0.7812
Epoch 1/1... Discriminator Loss: 0.2394... Generator Loss: 1.6307
Epoch 1/1... Discriminator Loss: 0.1699... Generator Loss: 1.8683
Epoch 1/1... Discriminator Loss: 0.4371... Generator Loss: 1.0871
Epoch 1/1... Discriminator Loss: 0.4553... Generator Loss: 1.2122
Epoch 1/1... Discriminator Loss: 0.3013... Generator Loss: 1.5197
Epoch 1/1... Discriminator Loss: 0.1890... Generator Loss: 1.8837
Epoch 1/1... Discriminator Loss: 0.1786... Generator Loss: 1.8983
Epoch 1/1... Discriminator Loss: 0.1634... Generator Loss: 2.0791
Epoch 1/1... Discriminator Loss: 0.1391... Generator Loss: 2.1667
Epoch 1/1... Discriminator Loss: 0.0922... Generator Loss: 2.5403
Epoch 1/1... Discriminator Loss: 0.1007... Generator Loss: 2.4312
Epoch 1/1... Discriminator Loss: 0.2860... Generator Loss: 1.8082
Epoch 1/1... Discriminator Loss: 0.6944... Generator Loss: 0.7287