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 = '/home/guy/datasets/'
# 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')
Out[2]:
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
In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
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)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32,[None,image_height,image_width,image_channels])
inputs_z = tf.placeholder(tf.float32,[None,z_dim])
learning_rate = tf.placeholder(tf.float32)
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):
"""
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 # coefficient for leaky relu
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
# layer 1: input = 28x28x3
# x = tf.layers.conv2d(images,64,5,2,padding='same',kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.02)) # now 14x14x64
# x = tf.layers.conv2d(images,64,5,2,padding='same',use_bias=False) # now 14x14x64
x = tf.layers.conv2d(images,32,5,2,padding='same') # now 14x14x64
x = tf.layers.batch_normalization(x,training=True)
x = tf.maximum(alpha*x,x)
# x = tf.nn.dropout(x,0.5)
# x = tf.layers.conv2d(x,128,5,2,padding='same',kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.02)) # now 7x7x128
# x = tf.layers.conv2d(x,128,5,2,padding='same',use_bias=False) # now 7x7x128
x = tf.layers.conv2d(x,64,5,2,padding='same') # now 7x7x128
x = tf.layers.batch_normalization(x,training=True)
x = tf.maximum(alpha * x, x)
# #layer 3: 7x7x128 --> 4x4x256
# # x = tf.layers.conv2d(x,256,5,2,padding='same',kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.02)) # now 4x4x256
x = tf.layers.conv2d(x,128,5,2,padding='same',use_bias=False) # now 4x4x256
x = tf.layers.batch_normalization(x,training=True)
x = tf.maximum(alpha * x, x)
# flattening
x_flat = tf.contrib.layers.flatten(x)
# logits = tf.layers.dense(x_flat,1,kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.02))
logits = tf.layers.dense(x_flat,1)
out = tf.sigmoid(logits)
return logits,out
"""
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"""
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):
"""
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 # coefficient for leaky relu
# reuse of (trained) weights is needed when we want to generate images i.e. during inference
# thus the reuse flag should be opposite to is_train
with tf.variable_scope('generator', reuse=not is_train):
# x = tf.layers.dense(z, 4 * 4 * 256,kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.02))
# x = tf.layers.dense(z, 4 * 4 * 256,use_bias=False)
x = tf.layers.dense(z, 4 * 4 * 128)
x = tf.reshape(x, (-1, 4, 4, 128)) # now the tensor is [N,4,4,256]
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(alpha * x, x)
# x = tf.nn.dropout(x, 0.5)
# Layer 2:
# x = tf.layers.conv2d_transpose(x, 128, 4, 1, padding='valid',kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.02)) # now the tensor is [N,7,7,128]
# x = tf.layers.conv2d_transpose(x, 128, 4, 1, padding='valid',use_bias=False) # now the tensor is [N,7,7,128]
x = tf.layers.conv2d_transpose(x, 64, 4, 1, padding='valid') # now the tensor is [N,7,7,128]
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(alpha * x, x)
# # x = tf.nn.dropout(x, 0.5)
# # Layer 3:
# # x = tf.layers.conv2d_transpose(x, 64, 5, 2, padding='same',kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.02)) # now the tensor is [N,14,14,64]
x = tf.layers.conv2d_transpose(x, 32, 5, 2, padding='same',use_bias=False) # now the tensor is [N,14,14,64]
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(alpha * x, x)
# Layer 4: the last layer to bring it to image size of 28x28
# logits = tf.layers.conv2d_transpose(x, out_channel_dim, 5, 2, padding='same',kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.02))
logits = tf.layers.conv2d_transpose(x, out_channel_dim, 5, 2, padding='same')
out = tf.tanh(logits)
return out
"""
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"""
tests.test_generator(generator, tf)
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def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
smooth=0.0
# define a generator network
input_fake = generator(input_z,out_channel_dim,is_train=True)
# define a discriminator that gets real
d_out_real,d_logits_real = discriminator(input_real)
# define a discriminator that gets fake
d_out_fake,d_logits_fake = discriminator(input_fake,reuse=True)
# define loss_d_real - the loss for real images - should be compared to ones
loss_d_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,labels=tf.ones_like(d_out_real)*(1.0-smooth)))
# define loss_d_fake - the loss for the fake images - should be compared to zeros
loss_d_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,labels=tf.zeros_like(d_out_fake)))
loss_g = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,labels=tf.ones_like(d_out_fake)*(1.0-smooth)))
# define loss_g as the opposite of loss_d_fake. i.e. should be compared to ones
loss_d=(loss_d_real + loss_d_fake)/2
return loss_d, loss_g
"""
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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):
"""
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
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
"""
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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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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
# synchronize training s.t. moving around equilibrium
equilibrium = 0.693
margin = 0.1
input_real, input_z, lr = model_inputs(data_shape[2],data_shape[1],data_shape[3],z_dim)
d_loss, g_loss = model_loss(input_real,input_z,data_shape[3])
d_opt,g_opt = model_opt(d_loss,g_loss,lr,beta1)
# train the model
saver = tf.train.Saver()
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 * 2.0
# Sample random noise for G
batch_z = np.random.uniform(-1,1,size=(batch_size,z_dim))
# batch_z = np.random.normal(0.0, 1.0, size=(batch_size, z_dim))
train_loss_d = d_loss.eval({input_z:batch_z,input_real:batch_images})
train_loss_g = g_loss.eval({input_z:batch_z})
if steps % 10 ==0:
print("Epoch {}/{}...".format(epoch_i + 1, epoch_count),
"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))
gen_update = True
dis_update = True
if train_loss_d > (equilibrium + margin):
gen_update = False
if train_loss_d < (equilibrium - margin):
dis_update = False
if not (gen_update or dis_update):
gen_update = True
dis_update = True
# Run optimizers
if dis_update:
_ = sess.run(d_opt,feed_dict={input_real:batch_images,input_z:batch_z, lr:learning_rate})
if gen_update:
_ = sess.run(g_opt,feed_dict={input_real:batch_images,input_z:batch_z,lr:learning_rate})
if steps % 100 ==0:
show_generator_output(sess,25,input_z,data_shape[3],data_image_mode)
saver.save(sess,'./checkpoints/generator.ckpt')
return losses
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batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
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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 = 32
z_dim = 150
learning_rate = 0.0002
beta1 = 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)
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.