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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1:  Face Generation
In this project, you'll use generative adversarial networks to generate new images of faces.
Get the Data
You'll be using two datasets in this project
Step3:  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.
Step5:  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.
Step7:  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
Step10:  Input
Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders
Step13:  Discriminator
Implement discriminator to create a discriminator neural network that discriminates on images.  This function should be able to reuse the variabes in the neural network.  Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused.  The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
Step16:  Generator
Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network.  Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.
Step19:  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
Step22:  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).
Step25:  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.
Step27:  Train
Implement train to build and train the GANs.  Use the following functions you implemented
Step28:  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.
Step30:  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.
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Python Code:
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)
Explanation: Face Generation
In this project, you'll use generative adversarial networks to generate new images of faces.
Get the Data
You'll be using two datasets in this project:
- MNIST
- CelebA
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA.  Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
End of explanation
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')
Explanation: 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.
End of explanation
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'))
Explanation: 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.
End of explanation
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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()))
Explanation: 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
End of explanation
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)
    
    real_input = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name='real_input_image')
    tensor_z = tf.placeholder(tf.float32, [None, z_dim], name='tensor_z')
    lr = tf.placeholder(tf.float32, [], name='lr')
    return real_input, tensor_z, lr
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tests.test_model_inputs(model_inputs)
tf.reset_default_graph()
Explanation: 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)
End of explanation
def discriminator(images, reuse=False):
    
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
               
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        flat = tf.reshape(relu3, (-1, 4 * 4 * 256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
                
        return out, logits
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tests.test_discriminator(discriminator, tf)
Explanation: Discriminator
Implement discriminator to create a discriminator neural network that discriminates on images.  This function should be able to reuse the variabes in the neural network.  Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused.  The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
End of explanation
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
    
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*64)
        
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 64))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(0.1 * x1, x1)
        # 7x7x64 now
        
        x2 = tf.layers.conv2d_transpose(x1, 32, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(0.1 * x2, x2)
        # 14x14x32 now
        
        x3 = tf.layers.conv2d_transpose(x2, 16, 5, strides=1, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(0.1 * x3, x3)
        # 14x14x16 now
        
        x4 = tf.layers.conv2d_transpose(x3, 8, 5, strides=2, padding='same')
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = tf.maximum(0.1 * x4, x4)
        # 28x28x8 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 5, strides=1, padding='same')
        # 28x28xout_channel_dim now
        
        out = tf.tanh(logits)
        
        
        return out
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tests.test_generator(generator, tf)
Explanation: Generator
Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network.  Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.
End of explanation
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
    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)
        
    # Calculate losses
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real)*0.9))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_real)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss
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tests.test_model_loss(model_loss)
Explanation: 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)
End of explanation
def model_opt(d_loss, g_loss, learning_rate, beta1):
    
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    
    # TODO: Implement Function
    # Get 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   
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tests.test_model_opt(model_opt, tf)
Explanation: 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).
End of explanation
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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()
Explanation: 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.
End of explanation
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 
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], len(data_image_mode), z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    lr = learning_rate
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
    steps = 0
    # Refers to NKreutz's code on Github
    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):
                batch_images = batch_images*2
                steps += 1
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                batch_z = batch_z.reshape(batch_size, z_dim)
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                for i in range(5):    # it was not training without executing g_train multiple times
                    _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images})
                if steps % 200 == 0:
                    show_generator_output(sess, 16, input_z, data_shape[3], data_image_mode)                       
                    train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({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))    
Explanation: 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.
End of explanation
batch_size =64
z_dim = 64
learning_rate = 0.001
beta1 = 0.1
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)
Explanation: 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.
End of explanation
batch_size = 128
z_dim = 64
learning_rate = 0.001
beta1 = 0.1
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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)
Explanation: 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.
End of explanation
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