Face Generation

27 min read

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

Get the Data

We’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 test our neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

data_dir = './data'

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 we’re aware, the MNIST dataset contains images of handwritten digits. We can view the first number of examples by changing show_n_images.

show_n_images = 25

%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')

png

CelebA

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

show_n_images = 25

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'))

png

Preprocess the Data

Since the project’s main focus is on building the GANs. 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](https://en.wikipedia.org/wiki/Channel_(digital_image%29) while the CelebA images have [3 color channels (RGB color channel)](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images).

Build the Neural Network

We’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 we have the correct version of TensorFlow and access to a GPU

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: 1.1.0
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)

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)
    """

    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

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables 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 discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: 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)
    """

    alpha = 0.2

    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, 32, 5, strides=2, padding="same")
        x1 = tf.maximum(alpha*x1, x1)

        x2 = tf.layers.conv2d(x1, 64, 5, strides=2, padding="same")
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(alpha*x2, x2)

        x3 = tf.layers.conv2d(x2, 128, 5, strides=2, padding="same")
        x3 = tf.layers.batch_normalization(x3, training=True)
        x3 = tf.maximum(alpha*x3, x3)

        x4 = tf.layers.conv2d(x3, 256, 5, strides=2, padding="same")
        x4 = tf.layers.batch_normalization(x4, training=True)
        x4 = tf.maximum(alpha*x4, x4)

        x4 = tf.reshape(x3, (-1, 2*2*256))
        logits = tf.layers.dense(x4, 1)
        out = tf.sigmoid(logits)

    return out, logits

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables 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.

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
    """

    alpha = 0.2

    with tf.variable_scope('generator', reuse= not is_train):
        h1 = tf.layers.dense(z, units=4*4*512)
        h1 = tf.reshape(h1, (-1, 4, 4, 512))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = tf.maximum( alpha * h1, h1)

        h2 = tf.layers.conv2d_transpose(h1, filters=128, kernel_size=4, strides=1, padding='valid')
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.maximum(alpha * h2, h2)

        h3 = tf.layers.conv2d_transpose(h2, filters=64, kernel_size=5, strides=2, padding='same')
        h3 = tf.layers.batch_normalization(h3, training=is_train)
        h3 = tf.maximum(alpha * h3, h3)

        h4= tf.layers.conv2d_transpose(h3, filters=32, kernel_size=5, strides=2, padding='same')
        h4 = tf.layers.batch_normalization(h4, training=is_train)
        h4 = tf.maximum(alpha * h4, h4)

        logits = tf.layers.conv2d_transpose(h4, filters=out_channel_dim, kernel_size=3, strides=1, padding='same')
        out = tf.tanh(logits)

    return out

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)

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
    gen_model = generator(input_z, out_channel_dim)
    disc_model_real, disc_logits_real = discriminator(input_real)
    disc_model_fake, disc_logits_fake = discriminator(gen_model, reuse=True)

    disc_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_logits_real,
                                                                           labels=tf.ones_like(disc_model_real)*(1-0.1)))
    disc_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_logits_fake,
                                                                           labels=tf.zeros_like(disc_model_fake)))
    gen_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_logits_fake,
                                                                     labels=tf.ones_like(disc_model_fake)))

    disc_loss = disc_loss_real + disc_loss_fake

    return disc_loss, gen_loss

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).

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)
    """

    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

Neural Network Training

Show Output

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

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 we 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.

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")
    """

    print_every = 10
    show_every = 100
    step = 0

    samples, width, height, channels = data_shape

    input_real, input_z, lr = model_inputs(width, height, channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, channels)
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)

    saver = tf.train.Saver()

    steps = 0

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model


                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                batch_images = batch_images * 2.0

                # Run optimizers
                sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})

                steps += 1
                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_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_real: batch_images, input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 16, input_z, channels, data_image_mode)

MNIST

Test our 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.

batch_size = 64
z_dim = 128
learning_rate = 0.0001
beta1 = 0.4

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)

Epoch 1/2... Discriminator Loss: 2.0867... Generator Loss: 0.2324
Epoch 1/2... Discriminator Loss: 1.5094... Generator Loss: 0.5193
Epoch 1/2... Discriminator Loss: 1.0356... Generator Loss: 0.9100
Epoch 1/2... Discriminator Loss: 0.9495... Generator Loss: 0.9795
Epoch 1/2... Discriminator Loss: 1.1186... Generator Loss: 0.8746
Epoch 1/2... Discriminator Loss: 1.6295... Generator Loss: 0.6214
Epoch 1/2... Discriminator Loss: 1.8456... Generator Loss: 0.5919
Epoch 1/2... Discriminator Loss: 1.4133... Generator Loss: 0.9339
Epoch 1/2... Discriminator Loss: 1.6277... Generator Loss: 0.6748
Epoch 1/2... Discriminator Loss: 1.2511... Generator Loss: 1.0289

png

Epoch 1/2... Discriminator Loss: 1.0522... Generator Loss: 1.2071
Epoch 1/2... Discriminator Loss: 1.1059... Generator Loss: 1.0716
Epoch 1/2... Discriminator Loss: 0.7196... Generator Loss: 1.5074
Epoch 1/2... Discriminator Loss: 0.8322... Generator Loss: 1.3778
Epoch 1/2... Discriminator Loss: 0.7403... Generator Loss: 1.5193
Epoch 1/2... Discriminator Loss: 0.7883... Generator Loss: 1.4761
Epoch 1/2... Discriminator Loss: 0.8295... Generator Loss: 1.3749
Epoch 1/2... Discriminator Loss: 1.0960... Generator Loss: 1.0644
Epoch 1/2... Discriminator Loss: 1.1658... Generator Loss: 1.0452
Epoch 1/2... Discriminator Loss: 1.2194... Generator Loss: 0.9498

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Epoch 1/2... Discriminator Loss: 1.2080... Generator Loss: 1.0334
Epoch 1/2... Discriminator Loss: 1.1472... Generator Loss: 1.1309
Epoch 1/2... Discriminator Loss: 1.1982... Generator Loss: 1.0109
Epoch 1/2... Discriminator Loss: 1.2863... Generator Loss: 0.9914
Epoch 1/2... Discriminator Loss: 1.1951... Generator Loss: 1.0701
Epoch 1/2... Discriminator Loss: 1.3705... Generator Loss: 0.7629
Epoch 1/2... Discriminator Loss: 1.2540... Generator Loss: 1.0033
Epoch 1/2... Discriminator Loss: 1.2792... Generator Loss: 0.8744
Epoch 1/2... Discriminator Loss: 1.2811... Generator Loss: 1.0062
Epoch 1/2... Discriminator Loss: 1.4031... Generator Loss: 1.0596

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Epoch 1/2... Discriminator Loss: 1.2331... Generator Loss: 0.9807
Epoch 1/2... Discriminator Loss: 1.2764... Generator Loss: 0.6834
Epoch 1/2... Discriminator Loss: 1.2258... Generator Loss: 0.8944
Epoch 1/2... Discriminator Loss: 1.1946... Generator Loss: 0.7654
Epoch 1/2... Discriminator Loss: 1.2413... Generator Loss: 1.1821
Epoch 1/2... Discriminator Loss: 1.1961... Generator Loss: 1.0457
Epoch 1/2... Discriminator Loss: 1.1864... Generator Loss: 1.1887
Epoch 1/2... Discriminator Loss: 1.2148... Generator Loss: 0.8252
Epoch 1/2... Discriminator Loss: 1.2167... Generator Loss: 1.0156
Epoch 1/2... Discriminator Loss: 1.3174... Generator Loss: 0.5959

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Epoch 1/2... Discriminator Loss: 1.1696... Generator Loss: 1.0757
Epoch 1/2... Discriminator Loss: 1.3124... Generator Loss: 0.6242
Epoch 1/2... Discriminator Loss: 1.2202... Generator Loss: 0.9363
Epoch 1/2... Discriminator Loss: 1.2088... Generator Loss: 0.7008
Epoch 1/2... Discriminator Loss: 1.1439... Generator Loss: 0.9151
Epoch 1/2... Discriminator Loss: 1.3049... Generator Loss: 0.6306
Epoch 1/2... Discriminator Loss: 1.1609... Generator Loss: 0.8051
Epoch 1/2... Discriminator Loss: 1.1810... Generator Loss: 0.9045
Epoch 1/2... Discriminator Loss: 1.1866... Generator Loss: 1.2970
Epoch 1/2... Discriminator Loss: 1.1802... Generator Loss: 1.1859

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Epoch 1/2... Discriminator Loss: 1.1831... Generator Loss: 0.7298
Epoch 1/2... Discriminator Loss: 1.1400... Generator Loss: 1.0396
Epoch 1/2... Discriminator Loss: 1.0721... Generator Loss: 1.1058
Epoch 1/2... Discriminator Loss: 1.2129... Generator Loss: 0.6734
Epoch 1/2... Discriminator Loss: 1.1377... Generator Loss: 0.8990
Epoch 1/2... Discriminator Loss: 1.3203... Generator Loss: 0.5271
Epoch 1/2... Discriminator Loss: 1.1493... Generator Loss: 1.1018
Epoch 1/2... Discriminator Loss: 1.0622... Generator Loss: 1.1183
Epoch 1/2... Discriminator Loss: 1.1419... Generator Loss: 0.8327
Epoch 1/2... Discriminator Loss: 1.0768... Generator Loss: 0.9751

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Epoch 1/2... Discriminator Loss: 1.1085... Generator Loss: 0.7544
Epoch 1/2... Discriminator Loss: 1.1810... Generator Loss: 1.4955
Epoch 1/2... Discriminator Loss: 1.0576... Generator Loss: 0.9837
Epoch 1/2... Discriminator Loss: 1.0756... Generator Loss: 1.1582
Epoch 1/2... Discriminator Loss: 1.0922... Generator Loss: 1.0645
Epoch 1/2... Discriminator Loss: 1.1661... Generator Loss: 0.7769
Epoch 1/2... Discriminator Loss: 1.2407... Generator Loss: 0.5921
Epoch 1/2... Discriminator Loss: 1.0693... Generator Loss: 1.1128
Epoch 1/2... Discriminator Loss: 1.1366... Generator Loss: 1.1153
Epoch 1/2... Discriminator Loss: 1.2939... Generator Loss: 1.6455

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Epoch 1/2... Discriminator Loss: 1.1344... Generator Loss: 0.8429
Epoch 1/2... Discriminator Loss: 0.9981... Generator Loss: 1.1148
Epoch 1/2... Discriminator Loss: 1.1439... Generator Loss: 0.7655
Epoch 1/2... Discriminator Loss: 1.1887... Generator Loss: 0.7730
Epoch 1/2... Discriminator Loss: 1.1581... Generator Loss: 0.7955
Epoch 1/2... Discriminator Loss: 1.1732... Generator Loss: 1.3147
Epoch 1/2... Discriminator Loss: 1.1468... Generator Loss: 1.0593
Epoch 1/2... Discriminator Loss: 1.2321... Generator Loss: 0.6411
Epoch 1/2... Discriminator Loss: 1.2414... Generator Loss: 0.6540
Epoch 1/2... Discriminator Loss: 1.1789... Generator Loss: 1.0781

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Epoch 1/2... Discriminator Loss: 1.1860... Generator Loss: 1.4194
Epoch 1/2... Discriminator Loss: 1.1472... Generator Loss: 0.8351
Epoch 1/2... Discriminator Loss: 1.1374... Generator Loss: 0.9677
Epoch 1/2... Discriminator Loss: 1.1041... Generator Loss: 0.8191
Epoch 1/2... Discriminator Loss: 1.1570... Generator Loss: 0.7028
Epoch 1/2... Discriminator Loss: 1.2045... Generator Loss: 0.7549
Epoch 1/2... Discriminator Loss: 1.2028... Generator Loss: 0.7856
Epoch 1/2... Discriminator Loss: 1.2868... Generator Loss: 1.4184
Epoch 1/2... Discriminator Loss: 1.1845... Generator Loss: 0.8564
Epoch 1/2... Discriminator Loss: 1.3979... Generator Loss: 0.5206

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Epoch 1/2... Discriminator Loss: 1.2247... Generator Loss: 1.0396
Epoch 1/2... Discriminator Loss: 1.2053... Generator Loss: 1.1057
Epoch 1/2... Discriminator Loss: 1.2552... Generator Loss: 0.6563
Epoch 2/2... Discriminator Loss: 1.2044... Generator Loss: 1.0764
Epoch 2/2... Discriminator Loss: 1.2413... Generator Loss: 1.0572
Epoch 2/2... Discriminator Loss: 1.1445... Generator Loss: 1.0412
Epoch 2/2... Discriminator Loss: 1.1758... Generator Loss: 0.8125
Epoch 2/2... Discriminator Loss: 1.2081... Generator Loss: 0.6821
Epoch 2/2... Discriminator Loss: 1.2141... Generator Loss: 0.8098
Epoch 2/2... Discriminator Loss: 1.3289... Generator Loss: 0.6118

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Epoch 2/2... Discriminator Loss: 1.3209... Generator Loss: 1.3094
Epoch 2/2... Discriminator Loss: 1.2298... Generator Loss: 0.7401
Epoch 2/2... Discriminator Loss: 1.2435... Generator Loss: 0.9291
Epoch 2/2... Discriminator Loss: 1.1978... Generator Loss: 1.0000
Epoch 2/2... Discriminator Loss: 1.1949... Generator Loss: 0.9605
Epoch 2/2... Discriminator Loss: 1.2492... Generator Loss: 0.8094
Epoch 2/2... Discriminator Loss: 1.2571... Generator Loss: 0.8651
Epoch 2/2... Discriminator Loss: 1.2837... Generator Loss: 1.0014
Epoch 2/2... Discriminator Loss: 1.2181... Generator Loss: 0.8379
Epoch 2/2... Discriminator Loss: 1.3743... Generator Loss: 0.5693

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Epoch 2/2... Discriminator Loss: 1.2555... Generator Loss: 0.7658
Epoch 2/2... Discriminator Loss: 1.2038... Generator Loss: 0.8254
Epoch 2/2... Discriminator Loss: 1.3464... Generator Loss: 0.9879
Epoch 2/2... Discriminator Loss: 1.2209... Generator Loss: 0.7307
Epoch 2/2... Discriminator Loss: 1.2993... Generator Loss: 0.6874
Epoch 2/2... Discriminator Loss: 1.1857... Generator Loss: 0.8730
Epoch 2/2... Discriminator Loss: 1.2955... Generator Loss: 0.9850
Epoch 2/2... Discriminator Loss: 1.3440... Generator Loss: 1.0402
Epoch 2/2... Discriminator Loss: 1.3281... Generator Loss: 0.6175
Epoch 2/2... Discriminator Loss: 1.1911... Generator Loss: 0.8710

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Epoch 2/2... Discriminator Loss: 1.3056... Generator Loss: 0.6775
Epoch 2/2... Discriminator Loss: 1.2098... Generator Loss: 0.9500
Epoch 2/2... Discriminator Loss: 1.3130... Generator Loss: 0.7307
Epoch 2/2... Discriminator Loss: 1.2803... Generator Loss: 1.0129
Epoch 2/2... Discriminator Loss: 1.3080... Generator Loss: 1.0621
Epoch 2/2... Discriminator Loss: 1.2776... Generator Loss: 1.0465
Epoch 2/2... Discriminator Loss: 1.2663... Generator Loss: 0.6722
Epoch 2/2... Discriminator Loss: 1.2557... Generator Loss: 0.8396
Epoch 2/2... Discriminator Loss: 1.3579... Generator Loss: 0.5681
Epoch 2/2... Discriminator Loss: 1.2776... Generator Loss: 0.6637

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Epoch 2/2... Discriminator Loss: 1.3308... Generator Loss: 0.7690
Epoch 2/2... Discriminator Loss: 1.3306... Generator Loss: 0.5899
Epoch 2/2... Discriminator Loss: 1.2653... Generator Loss: 0.8439
Epoch 2/2... Discriminator Loss: 1.2762... Generator Loss: 1.0469
Epoch 2/2... Discriminator Loss: 1.2430... Generator Loss: 0.7680
Epoch 2/2... Discriminator Loss: 1.2945... Generator Loss: 0.6913
Epoch 2/2... Discriminator Loss: 1.3199... Generator Loss: 1.1280
Epoch 2/2... Discriminator Loss: 1.2628... Generator Loss: 0.6944
Epoch 2/2... Discriminator Loss: 1.2870... Generator Loss: 0.7167
Epoch 2/2... Discriminator Loss: 1.2928... Generator Loss: 1.1388

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Epoch 2/2... Discriminator Loss: 1.1898... Generator Loss: 1.0141
Epoch 2/2... Discriminator Loss: 1.2056... Generator Loss: 0.8590
Epoch 2/2... Discriminator Loss: 1.2736... Generator Loss: 0.7476
Epoch 2/2... Discriminator Loss: 1.3113... Generator Loss: 0.7062
Epoch 2/2... Discriminator Loss: 1.2659... Generator Loss: 0.8806
Epoch 2/2... Discriminator Loss: 1.2707... Generator Loss: 0.7873
Epoch 2/2... Discriminator Loss: 1.2009... Generator Loss: 0.9228
Epoch 2/2... Discriminator Loss: 1.2752... Generator Loss: 0.7757
Epoch 2/2... Discriminator Loss: 1.3504... Generator Loss: 0.8774
Epoch 2/2... Discriminator Loss: 1.2314... Generator Loss: 0.9217

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Epoch 2/2... Discriminator Loss: 1.3306... Generator Loss: 0.8571
Epoch 2/2... Discriminator Loss: 1.3156... Generator Loss: 0.6177
Epoch 2/2... Discriminator Loss: 1.2483... Generator Loss: 0.8452
Epoch 2/2... Discriminator Loss: 1.2862... Generator Loss: 0.6832
Epoch 2/2... Discriminator Loss: 1.2899... Generator Loss: 0.6720
Epoch 2/2... Discriminator Loss: 1.3249... Generator Loss: 0.6026
Epoch 2/2... Discriminator Loss: 1.3809... Generator Loss: 0.5495
Epoch 2/2... Discriminator Loss: 1.3165... Generator Loss: 0.6890
Epoch 2/2... Discriminator Loss: 1.2753... Generator Loss: 1.0544
Epoch 2/2... Discriminator Loss: 1.2969... Generator Loss: 0.7528

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Epoch 2/2... Discriminator Loss: 1.2779... Generator Loss: 0.8854
Epoch 2/2... Discriminator Loss: 1.3638... Generator Loss: 0.6127
Epoch 2/2... Discriminator Loss: 1.2186... Generator Loss: 0.9226
Epoch 2/2... Discriminator Loss: 1.2312... Generator Loss: 1.1079
Epoch 2/2... Discriminator Loss: 1.2646... Generator Loss: 0.7210
Epoch 2/2... Discriminator Loss: 1.2953... Generator Loss: 1.2433
Epoch 2/2... Discriminator Loss: 1.1991... Generator Loss: 0.9160
Epoch 2/2... Discriminator Loss: 1.2403... Generator Loss: 0.7974
Epoch 2/2... Discriminator Loss: 1.2443... Generator Loss: 0.7666
Epoch 2/2... Discriminator Loss: 1.1691... Generator Loss: 0.9141

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Epoch 2/2... Discriminator Loss: 1.2296... Generator Loss: 0.8136
Epoch 2/2... Discriminator Loss: 1.2693... Generator Loss: 0.6582
Epoch 2/2... Discriminator Loss: 1.2373... Generator Loss: 0.8782
Epoch 2/2... Discriminator Loss: 1.2765... Generator Loss: 1.0020
Epoch 2/2... Discriminator Loss: 1.3004... Generator Loss: 0.6388
Epoch 2/2... Discriminator Loss: 1.2955... Generator Loss: 1.1928
Epoch 2/2... Discriminator Loss: 1.2272... Generator Loss: 0.7862
Epoch 2/2... Discriminator Loss: 1.2759... Generator Loss: 1.0464
Epoch 2/2... Discriminator Loss: 1.2225... Generator Loss: 0.8203
Epoch 2/2... Discriminator Loss: 1.2413... Generator Loss: 1.0729

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Epoch 2/2... Discriminator Loss: 1.2402... Generator Loss: 0.8544
Epoch 2/2... Discriminator Loss: 1.2417... Generator Loss: 0.7286
Epoch 2/2... Discriminator Loss: 1.2518... Generator Loss: 1.0633
Epoch 2/2... Discriminator Loss: 1.2325... Generator Loss: 0.8128
Epoch 2/2... Discriminator Loss: 1.2957... Generator Loss: 1.2616
Epoch 2/2... Discriminator Loss: 1.2729... Generator Loss: 0.6427
Epoch 2/2... Discriminator Loss: 1.1950... Generator Loss: 0.8738

CelebA

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

batch_size = 64
z_dim = 100
learning_rate = 0.0003
beta1 = 0.3

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)

Epoch 1/1... Discriminator Loss: 2.3149... Generator Loss: 0.2816
Epoch 1/1... Discriminator Loss: 1.2416... Generator Loss: 0.7742
Epoch 1/1... Discriminator Loss: 0.8748... Generator Loss: 1.3591
Epoch 1/1... Discriminator Loss: 1.4324... Generator Loss: 2.4702
Epoch 1/1... Discriminator Loss: 1.1738... Generator Loss: 0.7172
Epoch 1/1... Discriminator Loss: 1.0136... Generator Loss: 2.1460
Epoch 1/1... Discriminator Loss: 1.1662... Generator Loss: 1.0521
Epoch 1/1... Discriminator Loss: 1.1394... Generator Loss: 0.9716
Epoch 1/1... Discriminator Loss: 1.1296... Generator Loss: 0.8982
Epoch 1/1... Discriminator Loss: 1.2313... Generator Loss: 1.0092

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Epoch 1/1... Discriminator Loss: 1.1925... Generator Loss: 1.5360
Epoch 1/1... Discriminator Loss: 1.3641... Generator Loss: 0.9303
Epoch 1/1... Discriminator Loss: 1.2386... Generator Loss: 0.8219
Epoch 1/1... Discriminator Loss: 1.3989... Generator Loss: 0.7562
Epoch 1/1... Discriminator Loss: 1.3945... Generator Loss: 0.7217
Epoch 1/1... Discriminator Loss: 1.3891... Generator Loss: 0.6059
Epoch 1/1... Discriminator Loss: 1.4276... Generator Loss: 0.7372
Epoch 1/1... Discriminator Loss: 1.3764... Generator Loss: 0.6309
Epoch 1/1... Discriminator Loss: 1.3884... Generator Loss: 1.0268
Epoch 1/1... Discriminator Loss: 1.2752... Generator Loss: 0.7784

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Epoch 1/1... Discriminator Loss: 1.1962... Generator Loss: 1.5991
Epoch 1/1... Discriminator Loss: 1.2140... Generator Loss: 2.2475
Epoch 1/1... Discriminator Loss: 1.2710... Generator Loss: 0.6464
Epoch 1/1... Discriminator Loss: 1.5610... Generator Loss: 1.7737
Epoch 1/1... Discriminator Loss: 1.3079... Generator Loss: 0.6572
Epoch 1/1... Discriminator Loss: 1.7443... Generator Loss: 0.3808
Epoch 1/1... Discriminator Loss: 1.0157... Generator Loss: 1.8892
Epoch 1/1... Discriminator Loss: 1.3779... Generator Loss: 2.2072
Epoch 1/1... Discriminator Loss: 1.3951... Generator Loss: 1.3022
Epoch 1/1... Discriminator Loss: 1.0371... Generator Loss: 1.2426

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Epoch 1/1... Discriminator Loss: 1.4993... Generator Loss: 0.4867
Epoch 1/1... Discriminator Loss: 0.9911... Generator Loss: 1.3262
Epoch 1/1... Discriminator Loss: 1.3073... Generator Loss: 0.8244
Epoch 1/1... Discriminator Loss: 1.1086... Generator Loss: 1.3685
Epoch 1/1... Discriminator Loss: 1.6336... Generator Loss: 0.4269
Epoch 1/1... Discriminator Loss: 1.3890... Generator Loss: 0.7221
Epoch 1/1... Discriminator Loss: 1.2874... Generator Loss: 2.3135
Epoch 1/1... Discriminator Loss: 1.2192... Generator Loss: 0.9024
Epoch 1/1... Discriminator Loss: 1.6617... Generator Loss: 0.4317
Epoch 1/1... Discriminator Loss: 1.5095... Generator Loss: 0.5994

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Epoch 1/1... Discriminator Loss: 1.3813... Generator Loss: 1.0992
Epoch 1/1... Discriminator Loss: 1.3092... Generator Loss: 0.6902
Epoch 1/1... Discriminator Loss: 1.1585... Generator Loss: 0.9146
Epoch 1/1... Discriminator Loss: 1.3528... Generator Loss: 0.8420
Epoch 1/1... Discriminator Loss: 1.3202... Generator Loss: 0.7359
Epoch 1/1... Discriminator Loss: 1.3768... Generator Loss: 0.7122
Epoch 1/1... Discriminator Loss: 1.4131... Generator Loss: 0.6578
Epoch 1/1... Discriminator Loss: 1.3587... Generator Loss: 0.8800
Epoch 1/1... Discriminator Loss: 1.1875... Generator Loss: 0.9812
Epoch 1/1... Discriminator Loss: 1.3291... Generator Loss: 0.7910

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Epoch 1/1... Discriminator Loss: 1.3816... Generator Loss: 0.8396
Epoch 1/1... Discriminator Loss: 1.2179... Generator Loss: 1.8638
Epoch 1/1... Discriminator Loss: 1.3999... Generator Loss: 0.7043
Epoch 1/1... Discriminator Loss: 1.4113... Generator Loss: 0.7141
Epoch 1/1... Discriminator Loss: 1.3655... Generator Loss: 0.6968
Epoch 1/1... Discriminator Loss: 1.5068... Generator Loss: 1.1586
Epoch 1/1... Discriminator Loss: 1.4112... Generator Loss: 0.8485
Epoch 1/1... Discriminator Loss: 1.2713... Generator Loss: 0.8209
Epoch 1/1... Discriminator Loss: 1.4052... Generator Loss: 0.6998
Epoch 1/1... Discriminator Loss: 1.4269... Generator Loss: 1.2699

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Epoch 1/1... Discriminator Loss: 1.4125... Generator Loss: 0.6185
Epoch 1/1... Discriminator Loss: 1.3750... Generator Loss: 0.8218
Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 1.1242
Epoch 1/1... Discriminator Loss: 1.4407... Generator Loss: 0.6538
Epoch 1/1... Discriminator Loss: 1.3310... Generator Loss: 0.8178
Epoch 1/1... Discriminator Loss: 1.2202... Generator Loss: 0.9430
Epoch 1/1... Discriminator Loss: 1.2181... Generator Loss: 0.7861
Epoch 1/1... Discriminator Loss: 1.3697... Generator Loss: 0.7326
Epoch 1/1... Discriminator Loss: 1.2229... Generator Loss: 0.9424
Epoch 1/1... Discriminator Loss: 1.1598... Generator Loss: 1.0535

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Epoch 1/1... Discriminator Loss: 1.2089... Generator Loss: 1.0640
Epoch 1/1... Discriminator Loss: 1.1913... Generator Loss: 0.8731
Epoch 1/1... Discriminator Loss: 1.3132... Generator Loss: 0.7522
Epoch 1/1... Discriminator Loss: 1.5001... Generator Loss: 0.5009
Epoch 1/1... Discriminator Loss: 1.2221... Generator Loss: 1.2435
Epoch 1/1... Discriminator Loss: 1.4104... Generator Loss: 0.6448
Epoch 1/1... Discriminator Loss: 1.4843... Generator Loss: 0.5003
Epoch 1/1... Discriminator Loss: 1.3965... Generator Loss: 0.7937
Epoch 1/1... Discriminator Loss: 1.2389... Generator Loss: 0.8937
Epoch 1/1... Discriminator Loss: 1.1637... Generator Loss: 1.0135

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Epoch 1/1... Discriminator Loss: 1.5302... Generator Loss: 0.6909
Epoch 1/1... Discriminator Loss: 1.3750... Generator Loss: 1.0790
Epoch 1/1... Discriminator Loss: 1.4153... Generator Loss: 0.6359
Epoch 1/1... Discriminator Loss: 1.2187... Generator Loss: 0.9002
Epoch 1/1... Discriminator Loss: 1.3082... Generator Loss: 0.8110
Epoch 1/1... Discriminator Loss: 1.5445... Generator Loss: 0.4746
Epoch 1/1... Discriminator Loss: 1.3988... Generator Loss: 0.8564
Epoch 1/1... Discriminator Loss: 1.4058... Generator Loss: 0.9279
Epoch 1/1... Discriminator Loss: 1.1958... Generator Loss: 0.8365
Epoch 1/1... Discriminator Loss: 1.0999... Generator Loss: 0.9948

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Epoch 1/1... Discriminator Loss: 1.2857... Generator Loss: 1.0030
Epoch 1/1... Discriminator Loss: 1.4372... Generator Loss: 0.6809
Epoch 1/1... Discriminator Loss: 1.3551... Generator Loss: 0.9058
Epoch 1/1... Discriminator Loss: 1.3624... Generator Loss: 0.7823
Epoch 1/1... Discriminator Loss: 1.3621... Generator Loss: 0.7567
Epoch 1/1... Discriminator Loss: 1.3296... Generator Loss: 0.8264
Epoch 1/1... Discriminator Loss: 1.3879... Generator Loss: 0.7329
Epoch 1/1... Discriminator Loss: 1.4125... Generator Loss: 0.8207
Epoch 1/1... Discriminator Loss: 1.3492... Generator Loss: 0.7753
Epoch 1/1... Discriminator Loss: 1.3706... Generator Loss: 0.7321

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Epoch 1/1... Discriminator Loss: 0.9864... Generator Loss: 1.3506
Epoch 1/1... Discriminator Loss: 1.3990... Generator Loss: 0.7291
Epoch 1/1... Discriminator Loss: 1.3076... Generator Loss: 0.9040
Epoch 1/1... Discriminator Loss: 1.3990... Generator Loss: 0.6924
Epoch 1/1... Discriminator Loss: 1.3367... Generator Loss: 0.7349
Epoch 1/1... Discriminator Loss: 1.3990... Generator Loss: 0.6318
Epoch 1/1... Discriminator Loss: 1.2399... Generator Loss: 0.8559
Epoch 1/1... Discriminator Loss: 1.4255... Generator Loss: 0.6898
Epoch 1/1... Discriminator Loss: 1.4534... Generator Loss: 1.0882
Epoch 1/1... Discriminator Loss: 1.0179... Generator Loss: 1.3672

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Epoch 1/1... Discriminator Loss: 1.4213... Generator Loss: 0.6941
Epoch 1/1... Discriminator Loss: 1.1932... Generator Loss: 1.0422
Epoch 1/1... Discriminator Loss: 1.2666... Generator Loss: 0.9393
Epoch 1/1... Discriminator Loss: 1.2826... Generator Loss: 0.9103
Epoch 1/1... Discriminator Loss: 1.1003... Generator Loss: 1.0246
Epoch 1/1... Discriminator Loss: 1.3026... Generator Loss: 0.8595
Epoch 1/1... Discriminator Loss: 1.5127... Generator Loss: 0.5016
Epoch 1/1... Discriminator Loss: 1.3469... Generator Loss: 0.8147
Epoch 1/1... Discriminator Loss: 1.3403... Generator Loss: 0.9104
Epoch 1/1... Discriminator Loss: 1.4491... Generator Loss: 0.6292

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Epoch 1/1... Discriminator Loss: 1.3977... Generator Loss: 0.7109
Epoch 1/1... Discriminator Loss: 1.3887... Generator Loss: 0.7925
Epoch 1/1... Discriminator Loss: 1.2540... Generator Loss: 0.8163
Epoch 1/1... Discriminator Loss: 1.3751... Generator Loss: 0.7604
Epoch 1/1... Discriminator Loss: 1.4031... Generator Loss: 0.7976
Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 1.0501
Epoch 1/1... Discriminator Loss: 1.3806... Generator Loss: 0.8318
Epoch 1/1... Discriminator Loss: 1.3547... Generator Loss: 0.9260
Epoch 1/1... Discriminator Loss: 1.1292... Generator Loss: 1.0131
Epoch 1/1... Discriminator Loss: 1.3749... Generator Loss: 0.8000

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Epoch 1/1... Discriminator Loss: 1.3617... Generator Loss: 0.7539
Epoch 1/1... Discriminator Loss: 1.1532... Generator Loss: 1.1888
Epoch 1/1... Discriminator Loss: 1.4558... Generator Loss: 0.6205
Epoch 1/1... Discriminator Loss: 1.6047... Generator Loss: 0.7907
Epoch 1/1... Discriminator Loss: 1.3141... Generator Loss: 0.8944
Epoch 1/1... Discriminator Loss: 1.3552... Generator Loss: 0.8042
Epoch 1/1... Discriminator Loss: 1.3849... Generator Loss: 0.7067
Epoch 1/1... Discriminator Loss: 1.2351... Generator Loss: 0.9189
Epoch 1/1... Discriminator Loss: 1.3557... Generator Loss: 0.6968
Epoch 1/1... Discriminator Loss: 1.3785... Generator Loss: 0.7279

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Epoch 1/1... Discriminator Loss: 1.3492... Generator Loss: 0.8392
Epoch 1/1... Discriminator Loss: 1.2809... Generator Loss: 0.8491
Epoch 1/1... Discriminator Loss: 1.3917... Generator Loss: 0.7274
Epoch 1/1... Discriminator Loss: 1.5575... Generator Loss: 0.6136
Epoch 1/1... Discriminator Loss: 1.2944... Generator Loss: 0.6731
Epoch 1/1... Discriminator Loss: 1.2865... Generator Loss: 0.7802
Epoch 1/1... Discriminator Loss: 1.4422... Generator Loss: 0.6142
Epoch 1/1... Discriminator Loss: 0.9473... Generator Loss: 1.3395
Epoch 1/1... Discriminator Loss: 1.2625... Generator Loss: 0.9060
Epoch 1/1... Discriminator Loss: 1.4201... Generator Loss: 0.6008

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Epoch 1/1... Discriminator Loss: 1.4472... Generator Loss: 0.6684
Epoch 1/1... Discriminator Loss: 1.2699... Generator Loss: 0.8177
Epoch 1/1... Discriminator Loss: 1.1282... Generator Loss: 1.0019
Epoch 1/1... Discriminator Loss: 1.2274... Generator Loss: 0.9027
Epoch 1/1... Discriminator Loss: 1.3671... Generator Loss: 0.8393
Epoch 1/1... Discriminator Loss: 1.3623... Generator Loss: 0.7299
Epoch 1/1... Discriminator Loss: 1.1020... Generator Loss: 1.0048
Epoch 1/1... Discriminator Loss: 1.3621... Generator Loss: 0.7084
Epoch 1/1... Discriminator Loss: 1.3290... Generator Loss: 0.8587
Epoch 1/1... Discriminator Loss: 1.2966... Generator Loss: 0.7183

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Epoch 1/1... Discriminator Loss: 1.4234... Generator Loss: 0.7774
Epoch 1/1... Discriminator Loss: 1.4385... Generator Loss: 0.7451
Epoch 1/1... Discriminator Loss: 1.3204... Generator Loss: 0.9087
Epoch 1/1... Discriminator Loss: 1.3274... Generator Loss: 0.8213
Epoch 1/1... Discriminator Loss: 1.1239... Generator Loss: 1.1774
Epoch 1/1... Discriminator Loss: 1.4184... Generator Loss: 0.6825
Epoch 1/1... Discriminator Loss: 1.4437... Generator Loss: 0.6565
Epoch 1/1... Discriminator Loss: 1.2447... Generator Loss: 0.9294
Epoch 1/1... Discriminator Loss: 1.3252... Generator Loss: 0.8418
Epoch 1/1... Discriminator Loss: 1.2688... Generator Loss: 0.9125

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Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.7188
Epoch 1/1... Discriminator Loss: 1.3419... Generator Loss: 0.8352
Epoch 1/1... Discriminator Loss: 1.3294... Generator Loss: 0.9335
Epoch 1/1... Discriminator Loss: 1.1950... Generator Loss: 0.9622
Epoch 1/1... Discriminator Loss: 1.3473... Generator Loss: 0.7522
Epoch 1/1... Discriminator Loss: 1.3788... Generator Loss: 0.6587
Epoch 1/1... Discriminator Loss: 1.1851... Generator Loss: 0.8257
Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.7985
Epoch 1/1... Discriminator Loss: 1.3686... Generator Loss: 0.7144
Epoch 1/1... Discriminator Loss: 1.3770... Generator Loss: 0.8312

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Epoch 1/1... Discriminator Loss: 1.2954... Generator Loss: 0.8060
Epoch 1/1... Discriminator Loss: 1.3128... Generator Loss: 0.8790
Epoch 1/1... Discriminator Loss: 1.3055... Generator Loss: 0.8695
Epoch 1/1... Discriminator Loss: 1.1582... Generator Loss: 0.9782
Epoch 1/1... Discriminator Loss: 1.3420... Generator Loss: 0.9030
Epoch 1/1... Discriminator Loss: 1.1851... Generator Loss: 0.9982
Epoch 1/1... Discriminator Loss: 1.3686... Generator Loss: 0.7583
Epoch 1/1... Discriminator Loss: 1.3648... Generator Loss: 0.7411
Epoch 1/1... Discriminator Loss: 1.2680... Generator Loss: 0.9324
Epoch 1/1... Discriminator Loss: 1.3726... Generator Loss: 0.7018

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Epoch 1/1... Discriminator Loss: 1.3153... Generator Loss: 0.7678
Epoch 1/1... Discriminator Loss: 1.3920... Generator Loss: 0.7677
Epoch 1/1... Discriminator Loss: 1.3415... Generator Loss: 0.8016
Epoch 1/1... Discriminator Loss: 1.2949... Generator Loss: 0.7652
Epoch 1/1... Discriminator Loss: 1.2062... Generator Loss: 0.9255
Epoch 1/1... Discriminator Loss: 1.4185... Generator Loss: 0.7812
Epoch 1/1... Discriminator Loss: 1.4017... Generator Loss: 0.9312
Epoch 1/1... Discriminator Loss: 1.3149... Generator Loss: 1.1798
Epoch 1/1... Discriminator Loss: 1.4739... Generator Loss: 0.6627
Epoch 1/1... Discriminator Loss: 1.3684... Generator Loss: 0.7918

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Epoch 1/1... Discriminator Loss: 1.4715... Generator Loss: 0.7140
Epoch 1/1... Discriminator Loss: 1.2843... Generator Loss: 0.7990
Epoch 1/1... Discriminator Loss: 1.2161... Generator Loss: 0.8644
Epoch 1/1... Discriminator Loss: 1.2104... Generator Loss: 0.8849
Epoch 1/1... Discriminator Loss: 1.5012... Generator Loss: 0.6990
Epoch 1/1... Discriminator Loss: 1.3124... Generator Loss: 0.8760
Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 0.8102
Epoch 1/1... Discriminator Loss: 1.0677... Generator Loss: 1.0095
Epoch 1/1... Discriminator Loss: 1.3556... Generator Loss: 0.6629
Epoch 1/1... Discriminator Loss: 1.4408... Generator Loss: 0.5373

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Epoch 1/1... Discriminator Loss: 1.3078... Generator Loss: 0.7650
Epoch 1/1... Discriminator Loss: 1.0900... Generator Loss: 1.0246
Epoch 1/1... Discriminator Loss: 1.4812... Generator Loss: 1.0133
Epoch 1/1... Discriminator Loss: 1.3468... Generator Loss: 0.6327
Epoch 1/1... Discriminator Loss: 1.3533... Generator Loss: 0.9688
Epoch 1/1... Discriminator Loss: 1.3225... Generator Loss: 0.7116
Epoch 1/1... Discriminator Loss: 1.5624... Generator Loss: 0.4262
Epoch 1/1... Discriminator Loss: 1.4535... Generator Loss: 0.7086
Epoch 1/1... Discriminator Loss: 1.3774... Generator Loss: 0.7772
Epoch 1/1... Discriminator Loss: 1.3708... Generator Loss: 0.7432

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Epoch 1/1... Discriminator Loss: 1.3522... Generator Loss: 0.7890
Epoch 1/1... Discriminator Loss: 1.2590... Generator Loss: 0.8186
Epoch 1/1... Discriminator Loss: 1.4569... Generator Loss: 0.6861
Epoch 1/1... Discriminator Loss: 1.3049... Generator Loss: 0.7738
Epoch 1/1... Discriminator Loss: 1.4734... Generator Loss: 0.5271
Epoch 1/1... Discriminator Loss: 1.1704... Generator Loss: 1.0307
Epoch 1/1... Discriminator Loss: 1.2443... Generator Loss: 0.8466
Epoch 1/1... Discriminator Loss: 1.3109... Generator Loss: 0.8697
Epoch 1/1... Discriminator Loss: 1.2564... Generator Loss: 0.8187
Epoch 1/1... Discriminator Loss: 1.2972... Generator Loss: 0.8461

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Epoch 1/1... Discriminator Loss: 1.1784... Generator Loss: 0.9402
Epoch 1/1... Discriminator Loss: 1.4024... Generator Loss: 0.7195
Epoch 1/1... Discriminator Loss: 1.3197... Generator Loss: 0.6825
Epoch 1/1... Discriminator Loss: 1.4874... Generator Loss: 0.4925
Epoch 1/1... Discriminator Loss: 1.4779... Generator Loss: 0.9170
Epoch 1/1... Discriminator Loss: 1.7313... Generator Loss: 0.3356
Epoch 1/1... Discriminator Loss: 1.2387... Generator Loss: 0.9218
Epoch 1/1... Discriminator Loss: 1.1896... Generator Loss: 0.8645
Epoch 1/1... Discriminator Loss: 1.3893... Generator Loss: 0.8077
Epoch 1/1... Discriminator Loss: 1.2284... Generator Loss: 0.8447

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Epoch 1/1... Discriminator Loss: 1.3455... Generator Loss: 0.7420
Epoch 1/1... Discriminator Loss: 1.4410... Generator Loss: 0.7239
Epoch 1/1... Discriminator Loss: 1.3469... Generator Loss: 0.7676
Epoch 1/1... Discriminator Loss: 1.3781... Generator Loss: 0.6891
Epoch 1/1... Discriminator Loss: 1.2879... Generator Loss: 0.8008
Epoch 1/1... Discriminator Loss: 1.3583... Generator Loss: 0.7410
Epoch 1/1... Discriminator Loss: 1.3640... Generator Loss: 0.8243
Epoch 1/1... Discriminator Loss: 1.1539... Generator Loss: 0.9609
Epoch 1/1... Discriminator Loss: 1.2718... Generator Loss: 0.8644
Epoch 1/1... Discriminator Loss: 1.2479... Generator Loss: 0.8401

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Epoch 1/1... Discriminator Loss: 1.4485... Generator Loss: 0.5275
Epoch 1/1... Discriminator Loss: 1.4431... Generator Loss: 0.7631
Epoch 1/1... Discriminator Loss: 1.2541... Generator Loss: 0.9157
Epoch 1/1... Discriminator Loss: 1.2299... Generator Loss: 0.8804
Epoch 1/1... Discriminator Loss: 1.4235... Generator Loss: 0.5314
Epoch 1/1... Discriminator Loss: 1.4212... Generator Loss: 0.7266
Epoch 1/1... Discriminator Loss: 1.2835... Generator Loss: 0.7007
Epoch 1/1... Discriminator Loss: 1.3568... Generator Loss: 0.7339
Epoch 1/1... Discriminator Loss: 1.3212... Generator Loss: 0.7385
Epoch 1/1... Discriminator Loss: 1.1365... Generator Loss: 1.0084

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Epoch 1/1... Discriminator Loss: 1.4486... Generator Loss: 1.0020
Epoch 1/1... Discriminator Loss: 1.3057... Generator Loss: 0.7669
Epoch 1/1... Discriminator Loss: 1.5529... Generator Loss: 0.4767
Epoch 1/1... Discriminator Loss: 1.4000... Generator Loss: 0.6269
Epoch 1/1... Discriminator Loss: 1.3090... Generator Loss: 0.8894
Epoch 1/1... Discriminator Loss: 1.2235... Generator Loss: 0.9030
Epoch 1/1... Discriminator Loss: 1.2490... Generator Loss: 0.8701
Epoch 1/1... Discriminator Loss: 1.3437... Generator Loss: 0.5722
Epoch 1/1... Discriminator Loss: 1.2430... Generator Loss: 0.9822
Epoch 1/1... Discriminator Loss: 1.3213... Generator Loss: 0.6595

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Epoch 1/1... Discriminator Loss: 1.1092... Generator Loss: 0.9101
Epoch 1/1... Discriminator Loss: 1.2494... Generator Loss: 0.9528
Epoch 1/1... Discriminator Loss: 1.2662... Generator Loss: 0.8350
Epoch 1/1... Discriminator Loss: 1.3351... Generator Loss: 0.7881
Epoch 1/1... Discriminator Loss: 1.3444... Generator Loss: 0.7316
Epoch 1/1... Discriminator Loss: 1.3791... Generator Loss: 0.5329
Epoch 1/1... Discriminator Loss: 1.3428... Generator Loss: 0.7200
Epoch 1/1... Discriminator Loss: 1.2052... Generator Loss: 0.9178
Epoch 1/1... Discriminator Loss: 1.3815... Generator Loss: 0.7848
Epoch 1/1... Discriminator Loss: 1.3434... Generator Loss: 0.8321

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Epoch 1/1... Discriminator Loss: 1.3740... Generator Loss: 0.6343
Epoch 1/1... Discriminator Loss: 1.1297... Generator Loss: 1.0645
Epoch 1/1... Discriminator Loss: 1.2523... Generator Loss: 0.8868
Epoch 1/1... Discriminator Loss: 1.2764... Generator Loss: 0.7321
Epoch 1/1... Discriminator Loss: 1.3993... Generator Loss: 0.5500
Epoch 1/1... Discriminator Loss: 1.3122... Generator Loss: 0.8831
Epoch 1/1... Discriminator Loss: 1.3974... Generator Loss: 0.8300
Epoch 1/1... Discriminator Loss: 1.4547... Generator Loss: 0.7671
Epoch 1/1... Discriminator Loss: 1.3008... Generator Loss: 0.8082
Epoch 1/1... Discriminator Loss: 1.3335... Generator Loss: 0.6352

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Epoch 1/1... Discriminator Loss: 1.3042... Generator Loss: 0.7183
Epoch 1/1... Discriminator Loss: 1.3713... Generator Loss: 0.5554
Epoch 1/1... Discriminator Loss: 1.4189... Generator Loss: 0.8150
Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.6503
Epoch 1/1... Discriminator Loss: 1.3552... Generator Loss: 0.8267
Epoch 1/1... Discriminator Loss: 1.4232... Generator Loss: 0.4965
Epoch 1/1... Discriminator Loss: 1.2116... Generator Loss: 1.0363
Epoch 1/1... Discriminator Loss: 1.3030... Generator Loss: 0.7540
Epoch 1/1... Discriminator Loss: 1.2195... Generator Loss: 0.9634
Epoch 1/1... Discriminator Loss: 1.2943... Generator Loss: 0.7414

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Epoch 1/1... Discriminator Loss: 1.2517... Generator Loss: 0.7875
Epoch 1/1... Discriminator Loss: 1.3333... Generator Loss: 0.9016
Epoch 1/1... Discriminator Loss: 1.1542... Generator Loss: 0.9181
Epoch 1/1... Discriminator Loss: 1.3371... Generator Loss: 1.0411
Epoch 1/1... Discriminator Loss: 1.2682... Generator Loss: 0.8428
Epoch 1/1... Discriminator Loss: 1.3626... Generator Loss: 0.6750
Epoch 1/1... Discriminator Loss: 1.1461... Generator Loss: 0.9491
Epoch 1/1... Discriminator Loss: 1.0990... Generator Loss: 0.7883
Epoch 1/1... Discriminator Loss: 1.4364... Generator Loss: 0.7612
Epoch 1/1... Discriminator Loss: 1.4027... Generator Loss: 0.8417

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