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 0x7f59bd9ab390>

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 0x7f59bd8d3f28>

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
Default GPU Device: /gpu:0

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
    real_image = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels))
    z_input = tf.placeholder(tf.float32, shape=(None, z_dim))
    learning_rate = tf.placeholder(tf.float32)

    return (real_image, z_input, 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]:

    
tf.reset_default_graph()

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
    alpha = .2
    with tf.variable_scope('discriminator', reuse=reuse):
        # first convolution
        conv1 = tf.layers.conv2d(images, 128, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * conv1, conv1)
        assert conv1.get_shape()[1:] == (14, 14, 128), conv1.get_shape()
        
        conv2 = tf.layers.conv2d(relu1, 256, 5, strides=2, padding='same')
        conv2 = tf.layers.batch_normalization(conv2, training=True)
        relu2 = tf.maximum(alpha * conv2, conv2)
        assert conv2.get_shape()[1:] == (7, 7, 256), conv2.get_shape()
        
        conv3 = tf.layers.conv2d(relu2, 512, 5, strides=2, padding='same')
        conv3 = tf.layers.batch_normalization(conv3, training=True)
        relu3 = tf.maximum(alpha * conv3, conv3)
        assert conv3.get_shape()[1:] == (4, 4, 512), conv3.get_shape()

        # Flatten it
        flat = tf.contrib.layers.flatten(relu3)
        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 [7]:

    
tf.reset_default_graph()
def generator(z, out_channel_dim, is_train=True):
    """
    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
    """
    # TODO: Implement Function
    
    alpha = .2
    
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        conv1 = tf.layers.dense(z, 2 * 2 * 512)
        
        # first deconvolution
        conv1 = tf.reshape(conv1, (-1, 2, 2, 512))
        conv1 = tf.layers.batch_normalization(conv1, training=is_train)
        conv1 = tf.maximum(alpha * conv1, conv1)
        assert conv1.get_shape()[1:] == (2, 2, 512), conv1.get_shape()
        
        # second deconvolution
        conv2 = tf.layers.conv2d_transpose(conv1, 256, 5, strides=2, padding='valid')
        conv2 = tf.layers.batch_normalization(conv2, training=is_train)
        conv2 = tf.maximum(alpha * conv2, conv2)
        assert conv2.get_shape()[1:] == (7, 7, 256), conv2.get_shape()
        
        # third deconvolution
        conv3 = tf.layers.conv2d_transpose(conv2, 128, 5, strides=2, padding='same')
        conv3 = tf.layers.batch_normalization(conv3, training=is_train)
        conv3 = tf.maximum(alpha * conv3, conv3)
        assert conv3.get_shape()[1:] == (14, 14, 128), conv3.get_shape()
        
        # Output layer
        logits = tf.layers.conv2d_transpose(conv3, out_channel_dim, 5, strides=2, padding='same')
        assert logits.get_shape()[1:] == (28, 28, out_channel_dim), logits.get_shape()
        
        out = tf.tanh(logits)
        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 [8]:

    
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
    smooth = .9
    
    # Build the model
    g_model = generator(input_z, out_channel_dim, is_train=True)
    # g_model is the generator output

    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    
    # Calculate losses
    d_loss_real = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                              labels=tf.ones_like(d_logits_real) * smooth))
    d_loss_fake = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                              labels=tf.zeros_like(d_logits_real)))
    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(
                 tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                         labels=tf.ones_like(d_logits_fake) * smooth))
    
    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 [9]:

    
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)
    """
    # TODO: Implement Function
    
    # Get the trainable_variables, split into G and D parts
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    with tf.control_dependencies(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 [10]:

    
"""
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 [11]:

    
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
    _, image_width, image_height, image_channels = data_shape
    
    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    
    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())
        for epoch_i in range(epoch_count):
            d_losses = []
            g_losses = []
            for b, batch_images in enumerate(get_batches(batch_size)):
                # TODO: Train Model
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _, dis_loss = sess.run([d_train_opt, d_loss], feed_dict={input_real: batch_images * 2, input_z: batch_z, lr: learning_rate})
                _, gen_loss = sess.run([g_train_opt, g_loss], feed_dict={input_real: batch_images * 2, input_z: batch_z, lr: learning_rate})
                
                d_losses.append(dis_loss)
                g_losses.append(gen_loss)
                
                print('Batch: {} - Discriminator Loss: {} - Generator Loss: {}'.format(b, dis_loss, gen_loss))
                
                if b % 100 == 0:
                    show_generator_output(sess, 25, input_z, image_channels, data_image_mode)

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 [12]:

    
batch_size = 32
z_dim = 200
learning_rate = .0005
beta1 = 0.2

tf.reset_default_graph()


"""
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)









    



Batch: 0 - Discriminator Loss: 1.6170847415924072 - Generator Loss: 14.120157241821289






    












    



Batch: 1 - Discriminator Loss: 28.620208740234375 - Generator Loss: 19.34637451171875
Batch: 2 - Discriminator Loss: 0.8477677702903748 - Generator Loss: 16.154464721679688
Batch: 3 - Discriminator Loss: 4.885144233703613 - Generator Loss: 24.63570785522461
Batch: 4 - Discriminator Loss: 1.7336760759353638 - Generator Loss: 15.45213508605957
Batch: 5 - Discriminator Loss: 10.096382141113281 - Generator Loss: 31.823387145996094
Batch: 6 - Discriminator Loss: 7.60203742980957 - Generator Loss: 23.240652084350586
Batch: 7 - Discriminator Loss: 1.0592362880706787 - Generator Loss: 5.544050216674805
Batch: 8 - Discriminator Loss: 17.150920867919922 - Generator Loss: 21.574899673461914
Batch: 9 - Discriminator Loss: 2.1871538162231445 - Generator Loss: 16.10971450805664
Batch: 10 - Discriminator Loss: 0.9652273058891296 - Generator Loss: 3.179990768432617
Batch: 11 - Discriminator Loss: 9.366466522216797 - Generator Loss: 21.299413681030273
Batch: 12 - Discriminator Loss: 5.01315975189209 - Generator Loss: 15.454124450683594
Batch: 13 - Discriminator Loss: 0.65251225233078 - Generator Loss: 7.70668363571167
Batch: 14 - Discriminator Loss: 2.2937986850738525 - Generator Loss: 18.202898025512695
Batch: 15 - Discriminator Loss: 2.645291566848755 - Generator Loss: 11.21938419342041
Batch: 16 - Discriminator Loss: 0.711071789264679 - Generator Loss: 6.162172317504883
Batch: 17 - Discriminator Loss: 4.016200065612793 - Generator Loss: 21.846179962158203
Batch: 18 - Discriminator Loss: 4.681707382202148 - Generator Loss: 15.102295875549316
Batch: 19 - Discriminator Loss: 0.6569504737854004 - Generator Loss: 4.4057793617248535
Batch: 20 - Discriminator Loss: 13.45695686340332 - Generator Loss: 16.000988006591797
Batch: 21 - Discriminator Loss: 4.040962219238281 - Generator Loss: 10.780073165893555
Batch: 22 - Discriminator Loss: 1.604905605316162 - Generator Loss: 0.9935852885246277
Batch: 23 - Discriminator Loss: 6.071826934814453 - Generator Loss: 13.415265083312988
Batch: 24 - Discriminator Loss: 3.3164663314819336 - Generator Loss: 6.870555877685547
Batch: 25 - Discriminator Loss: 1.1940665245056152 - Generator Loss: 7.277880668640137
Batch: 26 - Discriminator Loss: 1.0170304775238037 - Generator Loss: 2.3790500164031982
Batch: 27 - Discriminator Loss: 10.689434051513672 - Generator Loss: 13.78216552734375
Batch: 28 - Discriminator Loss: 3.8314695358276367 - Generator Loss: 7.863905906677246
Batch: 29 - Discriminator Loss: 0.9508937001228333 - Generator Loss: 1.7519134283065796
Batch: 30 - Discriminator Loss: 1.8370282649993896 - Generator Loss: 8.58896255493164
Batch: 31 - Discriminator Loss: 1.035489797592163 - Generator Loss: 4.689337730407715
Batch: 32 - Discriminator Loss: 0.9322935342788696 - Generator Loss: 4.287055969238281
Batch: 33 - Discriminator Loss: 0.9252155423164368 - Generator Loss: 2.8142266273498535
Batch: 34 - Discriminator Loss: 1.9886964559555054 - Generator Loss: 9.72783088684082
Batch: 35 - Discriminator Loss: 3.572885513305664 - Generator Loss: 3.170588731765747
Batch: 36 - Discriminator Loss: 1.0942473411560059 - Generator Loss: 4.60964298248291
Batch: 37 - Discriminator Loss: 1.3726730346679688 - Generator Loss: 0.9804157614707947
Batch: 38 - Discriminator Loss: 1.9947032928466797 - Generator Loss: 8.812600135803223
Batch: 39 - Discriminator Loss: 2.764009714126587 - Generator Loss: 4.515249729156494
Batch: 40 - Discriminator Loss: 0.5218504071235657 - Generator Loss: 2.317723274230957
Batch: 41 - Discriminator Loss: 1.8616344928741455 - Generator Loss: 8.908952713012695
Batch: 42 - Discriminator Loss: 2.197211980819702 - Generator Loss: 4.44005823135376
Batch: 43 - Discriminator Loss: 0.7716200947761536 - Generator Loss: 2.2395143508911133
Batch: 44 - Discriminator Loss: 1.4132899045944214 - Generator Loss: 8.03964614868164
Batch: 45 - Discriminator Loss: 2.358680248260498 - Generator Loss: 2.5180740356445312
Batch: 46 - Discriminator Loss: 2.4191699028015137 - Generator Loss: 10.225797653198242
Batch: 47 - Discriminator Loss: 3.0433571338653564 - Generator Loss: 4.290903091430664
Batch: 48 - Discriminator Loss: 0.8422069549560547 - Generator Loss: 3.2548165321350098
Batch: 49 - Discriminator Loss: 0.9249687790870667 - Generator Loss: 3.3445792198181152
Batch: 50 - Discriminator Loss: 1.0237666368484497 - Generator Loss: 2.1879382133483887
Batch: 51 - Discriminator Loss: 1.6274458169937134 - Generator Loss: 5.754100799560547
Batch: 52 - Discriminator Loss: 2.053518772125244 - Generator Loss: 1.2840838432312012
Batch: 53 - Discriminator Loss: 1.3745139837265015 - Generator Loss: 4.521118640899658
Batch: 54 - Discriminator Loss: 1.2035589218139648 - Generator Loss: 1.6647992134094238
Batch: 55 - Discriminator Loss: 0.933725893497467 - Generator Loss: 3.67857027053833
Batch: 56 - Discriminator Loss: 0.8579779863357544 - Generator Loss: 1.5074102878570557
Batch: 57 - Discriminator Loss: 1.6743292808532715 - Generator Loss: 4.602657794952393
Batch: 58 - Discriminator Loss: 2.1313953399658203 - Generator Loss: 0.8678112030029297
Batch: 59 - Discriminator Loss: 1.675424575805664 - Generator Loss: 4.365609169006348
Batch: 60 - Discriminator Loss: 1.2835962772369385 - Generator Loss: 1.5729522705078125
Batch: 61 - Discriminator Loss: 1.1509251594543457 - Generator Loss: 4.221901893615723
Batch: 62 - Discriminator Loss: 1.2384010553359985 - Generator Loss: 1.2190568447113037
Batch: 63 - Discriminator Loss: 1.5321937799453735 - Generator Loss: 6.003326892852783
Batch: 64 - Discriminator Loss: 1.3269312381744385 - Generator Loss: 2.332425355911255
Batch: 65 - Discriminator Loss: 1.0669952630996704 - Generator Loss: 4.676226615905762
Batch: 66 - Discriminator Loss: 0.8867290019989014 - Generator Loss: 2.4661924839019775
Batch: 67 - Discriminator Loss: 0.7540684938430786 - Generator Loss: 3.20713210105896
Batch: 68 - Discriminator Loss: 0.6810909509658813 - Generator Loss: 2.688908100128174
Batch: 69 - Discriminator Loss: 0.8605963587760925 - Generator Loss: 4.018683433532715
Batch: 70 - Discriminator Loss: 1.239061951637268 - Generator Loss: 0.9986866116523743
Batch: 71 - Discriminator Loss: 1.360122561454773 - Generator Loss: 6.860632419586182
Batch: 72 - Discriminator Loss: 1.7506675720214844 - Generator Loss: 2.9335858821868896
Batch: 73 - Discriminator Loss: 0.6492373943328857 - Generator Loss: 2.760955810546875
Batch: 74 - Discriminator Loss: 0.620682954788208 - Generator Loss: 2.6723527908325195
Batch: 75 - Discriminator Loss: 0.6589236259460449 - Generator Loss: 2.8940539360046387
Batch: 76 - Discriminator Loss: 0.549280047416687 - Generator Loss: 3.1327667236328125
Batch: 77 - Discriminator Loss: 0.7283729314804077 - Generator Loss: 2.0975799560546875
Batch: 78 - Discriminator Loss: 0.7656179666519165 - Generator Loss: 4.552961826324463
Batch: 79 - Discriminator Loss: 0.9419084191322327 - Generator Loss: 2.1083827018737793
Batch: 80 - Discriminator Loss: 0.9794126749038696 - Generator Loss: 6.189618110656738
Batch: 81 - Discriminator Loss: 0.5622014403343201 - Generator Loss: 4.947207927703857
Batch: 82 - Discriminator Loss: 0.4837947189807892 - Generator Loss: 3.2289295196533203
Batch: 83 - Discriminator Loss: 0.5613327026367188 - Generator Loss: 3.744133472442627
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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 [13]:

    
batch_size = 128
z_dim = 200
learning_rate = 0.0001
beta1 = 0.2


"""
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)









    



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Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.