Generate faces using deep learning

In this project, Generative Adversarial Networks are used to generate new images of faces. Here is the code for this project which was a part of Udacity’s Deep Learning Nanodegree.

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.

data_dir = '/data'
!pip install matplotlib==2.0.2
# 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)

Collecting matplotlib==2.0.2
  Downloading https://files.pythonhosted.org/packages/60/d4/6b6d8a7a6bc69a1602ab372f6fc6e88ef88a8a96398a1a25edbac636295b/matplotlib-2.0.2-cp36-cp36m-manylinux1_x86_64.whl (14.6MB)
K    100% |████████████████████████████████| 14.6MB 35kB/s  eta 0:00:01
?25hRequirement already satisfied: python-dateutil in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
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Requirement already satisfied: numpy>=1.7.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: pytz in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Installing collected packages: matplotlib
  Found existing installation: matplotlib 2.1.0
    Uninstalling matplotlib-2.1.0:
      Successfully uninstalled matplotlib-2.1.0
Successfully installed matplotlib-2.0.2
33mYou are using pip version 9.0.1, however version 18.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.0m
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.

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')

<matplotlib.image.AxesImage at 0x7face40e7320>

png

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.

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'))

<matplotlib.image.AxesImage at 0x7facdf6db7b8>

png

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](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

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

"""
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: 1.3.0
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)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    l_r = tf.placeholder(tf.float32, name='l_r')
    return input_real, input_z, l_r


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

ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
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==================================
Tests Passed

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)
    """
    # TODO: Implement Function
    alpha = 0.12 # between 0.6 and 0.18
    keep_prob = 0.5

    # https://github.com/udacity/deep-learning/blob/master/dcgan-svhn/DCGAN.ipynb
    # Using xavier weight initalization on each conv2d layer - https://prateekvjoshi.com/2016/03/29/understanding-xavier-initialization-in-deep-neural-networks/

    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x64

        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        d2 = tf.nn.dropout(relu2, keep_prob=keep_prob)
        # 7x7x128

        x3 = tf.layers.conv2d(d2, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        d3 = tf.nn.dropout(relu3, keep_prob=keep_prob)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        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 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
    """
    # TODO: Implement Function
    alpha = 0.12 # between 0.6 and 0.18
    keep_prob = 0.5

    # https://github.com/udacity/deep-learning/blob/master/dcgan-svhn/DCGAN.ipynb
    # Generator bigger than discriminator
    # Make sure there are enough parameters to learn the concepts of the input images
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        # Working backwards, this is 7x7xanything
        x1 = tf.layers.dense(z, 7*7*1024)

        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 1024))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        x1 = tf.nn.dropout(x1, keep_prob=keep_prob)
        # 7x7x1024 now

        x2 = tf.layers.conv2d_transpose(x1, 512, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        x2 = tf.nn.dropout(x2, keep_prob=keep_prob)
        # 14x14x512 now

        x3 = tf.layers.conv2d_transpose(x2, 256, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        x3 = tf.nn.dropout(x3, keep_prob=keep_prob)
        # 28x28x256 now

        # Use strides=1 to preserve 28x28
        x4 = tf.layers.conv2d_transpose(x3, 128, 5, strides=1, padding='same')
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = tf.maximum(alpha * x4, x4)
        x4 = tf.nn.dropout(x4, keep_prob=keep_prob)
        # 28x28x128 now

        # Output layer
        # Use strides=1 to preserve 28x28
        logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 5, strides=1, padding='same')
        # 28x28x3 now

        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)

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

    # https://arxiv.org/abs/1606.03498
    # prevents discriminator from being too strong
    smooth = 0.2

    # https://github.com/udacity/deep-learning/blob/master/dcgan-svhn/DCGAN.ipynb
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake) * (1 - smooth)))
    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).

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
    # https://github.com/udacity/deep-learning/blob/master/dcgan-svhn/DCGAN.ipynb
    # 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.

"""
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.

def plot_losses(d_loss_vec, g_loss_vec):
    print(d_loss_vec, g_loss_vec)
    Discriminator_loss, = pyplot.plot(d_loss_vec, color='b', label='Discriminator loss')
    Genereator_loss, = pyplot.plot(g_loss_vec, color='r', label='Generator loss')
    pyplot.legend(handles=[ Discriminator_loss, Genereator_loss])


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
    # https://github.com/udacity/deep-learning/blob/master/dcgan-svhn/DCGAN.ipynb
    steps = 0
    _, image_width, image_height, image_channels = data_shape
    input_real, input_z, l_r = model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, l_r, beta1)
    d_loss_vec = []
    g_loss_vec = []

    try:
        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
                    batch_images *= 2 # Scale from [-0.5, 0.5] to [-1, 1]
                    steps += 1

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

                    # Run optimizers
                    _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, l_r: learning_rate})

                    # Optimize twice for generator so discriminator loss does not go to 0
                    _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, l_r: learning_rate})
                    _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, l_r: learning_rate})

                    # Store losses every 10 steps for plotting
                    if steps % 10 == 0:
                        train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                        train_loss_g = g_loss.eval({input_z: batch_z})
                        d_loss_vec.append(train_loss_d)
                        g_loss_vec.append(train_loss_g)

                    # Display loss and generator output every 100 steps
                    if steps % 100 == 0:
                        print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                              "Discriminator Loss: {:.4f}...".format(train_loss_d),
                              "Generator Loss: {:.4f}".format(train_loss_g))

                        show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
        plot_losses(d_loss_vec, g_loss_vec)

    except KeyboardInterrupt:
        plot_losses(d_loss_vec, g_loss_vec)
        raise

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.

# if batch size is too small use small learning rate as gradients will be more unstable
batch_size = 32 # start experimenting between 16 and 32
z_dim = 128 # 128 - 256
learning_rate = 0.0003 # between 0.0002 and 0.0008
beta1 = 0.4 # between 0.2 and 0.5


"""
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)

Epoch 1/2... Discriminator Loss: 1.7631... Generator Loss: 0.4857

png

Epoch 1/2... Discriminator Loss: 1.3473... Generator Loss: 1.0257

png

Epoch 1/2... Discriminator Loss: 1.3331... Generator Loss: 1.0466

png

Epoch 1/2... Discriminator Loss: 1.4012... Generator Loss: 1.2983

png

Epoch 1/2... Discriminator Loss: 1.3757... Generator Loss: 1.1439

png

Epoch 1/2... Discriminator Loss: 1.3038... Generator Loss: 1.0628

png

Epoch 1/2... Discriminator Loss: 1.3721... Generator Loss: 0.7614

png

Epoch 1/2... Discriminator Loss: 1.4599... Generator Loss: 0.7005

png

Epoch 1/2... Discriminator Loss: 1.3150... Generator Loss: 1.0910

png

Epoch 1/2... Discriminator Loss: 1.3130... Generator Loss: 0.7493

png

Epoch 1/2... Discriminator Loss: 1.2568... Generator Loss: 0.8532

png

Epoch 1/2... Discriminator Loss: 1.2844... Generator Loss: 0.7904

png

Epoch 1/2... Discriminator Loss: 1.2928... Generator Loss: 0.9467

png

Epoch 1/2... Discriminator Loss: 1.3944... Generator Loss: 0.8512

png

Epoch 1/2... Discriminator Loss: 1.2517... Generator Loss: 0.8836

png

Epoch 1/2... Discriminator Loss: 1.3599... Generator Loss: 0.7999

png

Epoch 1/2... Discriminator Loss: 1.2829... Generator Loss: 0.9189

png

Epoch 1/2... Discriminator Loss: 1.4230... Generator Loss: 1.1281

png

Epoch 2/2... Discriminator Loss: 1.3789... Generator Loss: 0.7287

png

Epoch 2/2... Discriminator Loss: 1.2732... Generator Loss: 1.0550

png

Epoch 2/2... Discriminator Loss: 1.3485... Generator Loss: 0.6742

png

Epoch 2/2... Discriminator Loss: 1.3004... Generator Loss: 0.7185

png

Epoch 2/2... Discriminator Loss: 1.5820... Generator Loss: 0.8377

png

Epoch 2/2... Discriminator Loss: 1.3394... Generator Loss: 0.9154

png

Epoch 2/2... Discriminator Loss: 1.2455... Generator Loss: 1.0773

png

Epoch 2/2... Discriminator Loss: 1.1567... Generator Loss: 1.0234

png

Epoch 2/2... Discriminator Loss: 1.3719... Generator Loss: 0.6979

png

Epoch 2/2... Discriminator Loss: 1.2983... Generator Loss: 0.8884

png

Epoch 2/2... Discriminator Loss: 1.1768... Generator Loss: 1.1571

png

Epoch 2/2... Discriminator Loss: 1.2278... Generator Loss: 1.0748

png

Epoch 2/2... Discriminator Loss: 1.3660... Generator Loss: 1.1076

png

Epoch 2/2... Discriminator Loss: 1.2347... Generator Loss: 1.5720

png

Epoch 2/2... Discriminator Loss: 1.1725... Generator Loss: 1.1581

png

Epoch 2/2... Discriminator Loss: 1.1790... Generator Loss: 1.0179

png

Epoch 2/2... Discriminator Loss: 1.1842... Generator Loss: 1.1422

png

Epoch 2/2... Discriminator Loss: 1.2875... Generator Loss: 1.2989

png

Epoch 2/2... Discriminator Loss: 1.2739... Generator Loss: 0.7897

png

---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

<ipython-input-12-e750998d6ebc> in <module>()
     14 with tf.Graph().as_default():
     15     train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
---> 16           mnist_dataset.shape, mnist_dataset.image_mode)


<ipython-input-11-127fb15aaba0> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     50                     show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
     51
---> 52     Discriminator_loss, = plt.plot(d_loss_vec, color='b', label='Discriminator loss')
     53     Genereator_loss, = plt.plot(g_loss_vec, color='r', label='Generator loss')
     54     plt.legend(handles=[ Discriminator_loss, Genereator_loss])


NameError: name 'plt' is not defined

Plotting failed above because of a typo but fixed it for below.

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.

# if batch size is too small use small learning rate as gradients will be more unstable
batch_size = 32 # start experimenting between 16 and 32
z_dim = 128 # 128 - 256
learning_rate = 0.0003 # between 0.0002 and 0.0008
beta1 = 0.4 # between 0.2 and 0.5


"""
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)

Epoch 1/1... Discriminator Loss: 1.4314... Generator Loss: 0.8973

png

Epoch 1/1... Discriminator Loss: 1.6832... Generator Loss: 1.4495

png

Epoch 1/1... Discriminator Loss: 1.4655... Generator Loss: 0.7879

png

Epoch 1/1... Discriminator Loss: 1.6592... Generator Loss: 0.6088

png

Epoch 1/1... Discriminator Loss: 1.3957... Generator Loss: 0.7341

png

Epoch 1/1... Discriminator Loss: 1.4306... Generator Loss: 0.9456

png

Epoch 1/1... Discriminator Loss: 1.4630... Generator Loss: 0.7972

png

Epoch 1/1... Discriminator Loss: 1.3862... Generator Loss: 0.8995

png

Epoch 1/1... Discriminator Loss: 1.4574... Generator Loss: 0.7624

png

Epoch 1/1... Discriminator Loss: 1.4875... Generator Loss: 0.7489

png

Epoch 1/1... Discriminator Loss: 1.3838... Generator Loss: 0.8543

png

Epoch 1/1... Discriminator Loss: 1.3450... Generator Loss: 0.8888

png

Epoch 1/1... Discriminator Loss: 1.3483... Generator Loss: 0.8978

png

Epoch 1/1... Discriminator Loss: 1.3623... Generator Loss: 0.8631

png

Epoch 1/1... Discriminator Loss: 1.4707... Generator Loss: 0.7636

png

Epoch 1/1... Discriminator Loss: 1.4155... Generator Loss: 0.8877

png

Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 0.8720

png

[0.68147719, 1.0819838, 1.7858406, 1.2155141, 0.89022237, 0.84738535, 0.92325556, 0.77138579, 1.3210111, 1.4313657, 1.0422797, 1.0290053, 1.0530705, 1.3202217, 1.3480206, 1.8708965, 1.4621553, 1.2714307, 1.684045, 1.6832254, 1.4807202, 1.7142226, 1.7021042, 1.2245331, 1.7188144, 1.316097, 1.5010058, 1.3194613, 1.5687752, 1.4654884, 1.4800744, 1.8908931, 1.3160625, 1.6250725, 1.3586822, 1.2754176, 1.5521997, 1.3508202, 1.501712, 1.6592243, 1.5546122, 1.4520644, 1.2715205, 1.5537705, 1.480042, 1.4518548, 1.6001701, 1.3779323, 1.4202495, 1.3956763, 1.5855815, 1.5098754, 1.5925955, 1.6071342, 1.3609899, 1.3971056, 1.4317162, 1.5298216, 1.4867867, 1.4305775, 1.4616724, 1.4394225, 1.2894374, 1.4362776, 1.4443917, 1.4505243, 1.3482745, 1.3224802, 1.3685384, 1.4629893, 1.3737476, 1.3450725, 1.4742806, 1.4334176, 1.4813404, 1.5029886, 1.451731, 1.4808697, 1.4941374, 1.3861592, 1.3457894, 1.5035008, 1.4697601, 1.4285893, 1.487663, 1.5893745, 1.4142377, 1.4513512, 1.2983847, 1.4573878, 1.4092219, 1.3877552, 1.3486496, 1.3831811, 1.3036151, 1.4286606, 1.3385046, 1.4415159, 1.5598974, 1.4874668, 1.4479517, 1.3656049, 1.3736329, 1.3979753, 1.4343805, 1.4379029, 1.4221631, 1.3712252, 1.4546287, 1.383817, 1.4173993, 1.3601048, 1.5564328, 1.3361673, 1.4419196, 1.3407216, 1.362898, 1.3970554, 1.3879319, 1.3450241, 1.4549968, 1.4194081, 1.4276979, 1.4240012, 1.3705657, 1.4404526, 1.3968577, 1.4244807, 1.4252303, 1.3483338, 1.3364701, 1.415686, 1.4352133, 1.4212173, 1.4018812, 1.4202421, 1.3396565, 1.3775671, 1.3364468, 1.3623281, 1.4114624, 1.414068, 1.4177138, 1.4019026, 1.4450707, 1.3350688, 1.4596324, 1.3436275, 1.3302505, 1.4706819, 1.3697618, 1.3559172, 1.4464192, 1.4040041, 1.3859793, 1.499069, 1.3856345, 1.3864915, 1.394897, 1.4154856, 1.4342654, 1.367192, 1.3934625, 1.380867, 1.4183216, 1.4041283, 1.4320245, 1.4526056, 1.4222769, 1.3786578, 1.4176793, 1.3338244] [3.2877154, 3.9759407, 3.4762933, 1.334399, 3.2206836, 2.6775563, 1.4898778, 3.1408005, 4.0187631, 0.89726716, 1.1738485, 1.613856, 2.2237346, 0.93764627, 1.0464046, 0.31233621, 0.66646242, 0.6764065, 0.65342462, 1.449497, 0.68167531, 0.57621861, 0.5420779, 1.5298237, 0.81673568, 1.3508437, 0.60702336, 1.2036, 1.1070168, 0.78788453, 0.67673254, 0.41578835, 1.2106292, 1.3365574, 0.74985659, 1.2144343, 0.76333201, 0.94938147, 0.7668609, 0.60877711, 0.59438539, 0.82391471, 0.89319003, 0.54527152, 0.75529563, 1.0432415, 1.0310619, 0.9794808, 0.80917203, 0.73409951, 0.52926612, 0.68931258, 0.57676733, 0.62740672, 0.91939139, 0.93085289, 0.92197132, 0.80965841, 0.77671766, 0.94557607, 0.7431066, 0.91878217, 0.94347858, 0.90827632, 0.82958782, 0.88061172, 0.89867175, 0.93994582, 0.9113459, 0.79722816, 0.8552857, 0.94621694, 0.86014509, 1.0044515, 1.0988641, 0.75033009, 0.81931031, 0.71832824, 0.79323006, 0.89945543, 1.0809531, 0.83180475, 0.79254234, 0.85471511, 0.84175581, 0.83655787, 0.86363673, 0.88708544, 0.92407441, 0.76240879, 0.9369626, 0.90794194, 0.69591165, 0.92309684, 0.86004496, 0.93940949, 0.98220688, 0.97762132, 0.91421109, 0.7489388, 0.80698597, 0.89822346, 0.87588042, 0.80344999, 0.8996793, 0.77042067, 0.85332507, 0.92013097, 0.8718791, 0.85426486, 0.81752002, 0.92031324, 0.83040059, 0.90491545, 0.77678025, 0.9110527, 0.90340942, 0.86839342, 0.79522294, 0.88882613, 0.82974255, 1.0025598, 0.842013, 0.83546555, 0.79540908, 0.75956017, 0.90113741, 0.85573292, 0.8933937, 0.89780647, 0.96056551, 0.80634958, 0.78192079, 0.82015526, 0.97150236, 0.88953227, 0.84416449, 0.8650372, 0.95992661, 0.86306643, 0.88035041, 0.85304284, 0.93279982, 0.7670083, 0.81185579, 0.93112969, 0.81715357, 0.84220701, 0.86217219, 0.76364315, 0.88889796, 0.80347973, 0.79993761, 0.85243833, 0.79449844, 0.82080579, 0.85526407, 0.87938797, 0.82882649, 0.88773739, 0.92977786, 0.86451054, 0.91142541, 0.84175867, 0.86512423, 0.87868571, 0.80349869, 0.96302319, 0.83065277, 0.87196136, 0.87738603, 0.98455834]



---------------------------------------------------------------------------

KeyboardInterrupt                         Traceback (most recent call last)

<ipython-input-24-8e342fea6a49> in <module>()
     14 with tf.Graph().as_default():
     15     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 16           celeba_dataset.shape, celeba_dataset.image_mode)


<ipython-input-22-6490965b47bf> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     45                     # Optimize twice for generator so discriminator loss does not go to 0
     46                     _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, l_r: learning_rate})
---> 47                     _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, l_r: learning_rate})
     48
     49                     # Store losses every 10 steps for plotting


/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    893     try:
    894       result = self._run(None, fetches, feed_dict, options_ptr,
--> 895                          run_metadata_ptr)
    896       if run_metadata:
    897         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)


/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1122     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1123       results = self._do_run(handle, final_targets, final_fetches,
-> 1124                              feed_dict_tensor, options, run_metadata)
   1125     else:
   1126       results = []


/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1319     if handle is None:
   1320       return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1321                            options, run_metadata)
   1322     else:
   1323       return self._do_call(_prun_fn, self._session, handle, feeds, fetches)


/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1325   def _do_call(self, fn, *args):
   1326     try:
-> 1327       return fn(*args)
   1328     except errors.OpError as e:
   1329       message = compat.as_text(e.message)


/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1304           return tf_session.TF_Run(session, options,
   1305                                    feed_dict, fetch_list, target_list,
-> 1306                                    status, run_metadata)
   1307
   1308     def _prun_fn(session, handle, feed_dict, fetch_list):


KeyboardInterrupt:

png

Results

Some of the images of digits generated were very realistic but the images of the faces are less so.
The Generator loss is below the Discriminator loss as seen above which is a step in the right direction.