In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
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".
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data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
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"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
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.
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show_n_images = 25
"""
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"""
%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')
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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.
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show_n_images = 25
"""
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"""
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'))
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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).
You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputsdiscriminatorgeneratormodel_lossmodel_opttrainThis will check to make sure you have the correct version of TensorFlow and access to a GPU
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"""
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()))
Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width, image_height, and image_channels.z_dim.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
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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
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
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).
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def discriminator(images, reuse=False, alpha=0.1):
"""
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)
"""
debug = False
training = True
with tf.variable_scope('discriminator', reuse=reuse):
random_normal_init = tf.random_normal_initializer(mean=0, stddev=0.02)
# Input layer is 28x28x3
# Radford & Metz suggest not doing normalization on the
# generator output layer or discriminator input layer
conv1 = tf.layers.conv2d(inputs=images,
filters=64,
kernel_size=5, # 5 means 5x5
strides=2, # 2 means 2x2,
padding='same',
kernel_initializer=random_normal_init,
activation=None)
# leaky relu activation
conv1 = tf.maximum(alpha * conv1, conv1)
if debug:
print("Expected shape: 14x14x64. conv1.shape: ", conv1.shape)
conv2 = tf.layers.conv2d(inputs=conv1,
filters=128,
kernel_size=5, # 5 means 5x5
strides=2, # 2 means 2x2,
padding='same',
kernel_initializer=random_normal_init,
activation=None)
# batch normalization
conv2 = tf.layers.batch_normalization(conv2,
training=training)
# leaky relu activation
conv2 = tf.maximum(alpha * conv2, conv2)
if debug:
print("Expected shape: 7x7x128. conv2.shape: ", conv2.shape)
conv3 = tf.layers.conv2d(inputs=conv2,
filters=256,
kernel_size=5, # 5 means 5x5
strides=2, # 2 means 2x2,
padding='same',
kernel_initializer=random_normal_init,
activation=None)
# batch normalization
conv3 = tf.layers.batch_normalization(conv3,
training=training)
# leaky relu activation
conv3 = tf.maximum(alpha * conv3, conv3)
if debug:
print("Expected shape: 4x4x256. conv3.shape: ", conv3.shape)
flatten = tf.contrib.layers.flatten(conv3)
# Only looking for one probability of a real image
logits = tf.layers.dense(inputs=flatten,
units=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)
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.
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def generator(z, out_channel_dim, is_train=True, alpha=0.1):
"""
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
"""
debug = False
with tf.variable_scope('generator', reuse=not is_train):
random_normal_init = tf.random_normal_initializer(mean=0, stddev=0.02)
# First fully connected layer
# has linear activation that will be turned into a leaky relu later
fullyconn = tf.layers.dense(inputs=z,
kernel_initializer=random_normal_init,
units=4*4*256)
# reshape to fit conv
fullyconn = tf.reshape(tensor=fullyconn,
shape=(-1, 4, 4, 256))
# normalize
fullyconn = tf.layers.batch_normalization(fullyconn,
training=is_train)
# leaky relu activation
fullyconn = tf.maximum(alpha * fullyconn, fullyconn)
if debug:
print("Expected shape: 4x4x256. fullyconn.shape: ", fullyconn.shape)
# convolutional transpose
conv1 = tf.layers.conv2d_transpose(inputs=fullyconn,
filters=128,
kernel_size=4, # 5 means 5x5
strides=1, # 2 means 2x2,
padding='valid',
kernel_initializer=random_normal_init)
# batch normalization
conv1 = tf.layers.batch_normalization(conv1,
training=is_train)
# leaky relu activation
conv1 = tf.maximum(alpha * conv1, conv1)
if debug:
print("Expected shape: 14x14x256. conv1.shape: ", conv1.shape)
# convolutional transpose
conv2 = tf.layers.conv2d_transpose(inputs=conv1,
filters=64,
kernel_size=5, # 5 means 5x5
strides=2, # 2 means 2x2,
padding='same',
kernel_initializer=random_normal_init)
# batch normalization
conv2 = tf.layers.batch_normalization(conv2,
training=is_train)
# leaky relu activation
conv2 = tf.maximum(alpha * conv2, conv2)
if debug:
print("Expected shape: 28x28x128. conv2.shape: ", conv2.shape)
# Output transpose layer, 28x28x3
logits = tf.layers.conv2d_transpose(inputs=conv2,
filters=out_channel_dim,
kernel_size=5,
strides=2,
padding='same',
kernel_initializer=random_normal_init)
# Radford & Metz suggest not doing normalization on the
# generator output layer or discriminator input layer
if debug:
print("Expected shape: 28x28x3. logits.shape: ", logits.shape)
#logits = tf.image.resize_images(logits, [28,28])
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
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def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
"""
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)
"""
# Source: DCGAN Exercise
g_model = generator(input_z, out_channel_dim, is_train=True, alpha=alpha)
d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
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"""
tests.test_model_loss(model_loss)
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).
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def model_opt(d_loss, g_loss, learning_rate, beta1=0.5):
"""
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)
"""
# Separate out variables for discriminator vs generator
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')]
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
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"""
tests.test_model_opt(model_opt, tf)
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"""
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"""
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()
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.
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# Source: DCGAN notebook
class GAN:
def __init__(self, real_size, z_size, learning_rate, alpha=0.1, beta1=0.5):
real_width = real_size[1]
real_height = real_size[2]
real_depth = real_size[3]
self.input_real, self.input_z, self.learning_rate = model_inputs(real_width, real_height, real_depth, z_size)
self.d_loss, self.g_loss = model_loss(self.input_real, self.input_z, real_depth, alpha)
self.d_opt, self.g_opt = model_opt(self.d_loss, self.g_loss, learning_rate, beta1)
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def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, print_every=50, show_every=100, figsize=(5,5)):
"""
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")
"""
debug = False
net = GAN(data_shape, z_dim, learning_rate, 0.2, beta1)
data_min = -1.0
data_max = 1.0
saver = tf.train.Saver()
# Random Noise for sampling from generator
sample_z = np.random.uniform(data_min, data_max, size=(72, z_dim))
samples, losses = [], []
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):
steps += 1
batch_images = batch_images * 2
# Training noise for generator
batch_z = np.random.uniform(data_min, data_max, size=(batch_size, z_dim))
# optimize discriminator
if debug:
print("optimizing discriminator...")
d_session = sess.run(net.d_opt, feed_dict={net.input_real: batch_images, net.input_z: batch_z, net.learning_rate: learning_rate})
# optimize generator
if debug:
print("optimizing generator...")
g_session = sess.run(net.g_opt, feed_dict={net.input_real: batch_images, net.input_z: batch_z, net.learning_rate: learning_rate})
# optimize generator
if debug:
print("optimizing generator...")
g_session = sess.run(net.g_opt, feed_dict={net.input_real: batch_images, net.input_z: batch_z, net.learning_rate: learning_rate})
if steps % print_every == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = net.d_loss.eval({net.input_z: batch_z, net.input_real: batch_images})
train_loss_g = net.g_loss.eval({net.input_z: batch_z})
print("Epoch {}/{} Step {}...".format(epoch_i+1, epoch_count, steps),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if steps % show_every == 0:
show_generator_output(sess, 10, net.input_z, data_shape[3], data_image_mode)
return losses, samples
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batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
tf.reset_default_graph()
"""
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"""
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)
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batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
tf.reset_default_graph()
"""
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"""
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)
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.
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