Added figures to part2

ch17/ch17_part2.py

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+# coding: utf-8
+
+
+#from google.colab import drive
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+import itertools
+
+# *Python Machine Learning 3rd Edition* by [Sebastian Raschka](https://sebastianraschka.com) & [Vahid Mirjalili](http://vahidmirjalili.com), Packt Publishing Ltd. 2019
+# 
+# Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition
+# 
+# Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/LICENSE.txt)
+
+# Chapter 17: Generative Adversarial Networks (part 2/2)
+# =====
+
+# Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
+
+
+
+
+
+
+
+
+
+# # Improving the quality of synthesized images using a convolutional and Wasserstein GAN
+
+# ## Transposed convolution
+
+
+
+
+
+
+
+
+
+# ## Batch normalization
+
+
+
+
+
+# ## Implementing the generator and discriminator
+
+
+
+
+
+
+
+
+
+#  * **Setting up the Google Colab**
+
+
+
+#! pip install -q tensorflow-gpu==2.0.0-beta1
+
+
+
+
+#drive.mount('/content/drive/')
+
+
+
+
+#import tensorflow as tf
+#print("GPU Available: ", tf.test.is_gpu_available())
+#device_name = tf.test.gpu_device_name()
+#device_name
+
+
+
+
+
+
+
+
+
+
+
+
+
+def make_dcgan_generator(z_size=20, output_size=(28, 28, 1),
+                         n_filters=128, n_blocks=2):
+    size_factor = 2**n_blocks
+    hidden_size = (output_size[0]//size_factor, 
+                   output_size[1]//size_factor)
+
+    model = tf.keras.Sequential([
+        tf.keras.layers.Input(shape=(z_size,)),
+
+        tf.keras.layers.Dense(
+            units=n_filters*np.prod(hidden_size), 
+            use_bias=False),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.LeakyReLU(),
+        tf.keras.layers.Reshape(
+            (hidden_size[0], hidden_size[1], n_filters)),
+
+        tf.keras.layers.Conv2DTranspose(
+            filters=n_filters, kernel_size=(5, 5), strides=(1, 1),
+            padding='same', use_bias=False),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.LeakyReLU()
+    ])
+
+    nf = n_filters
+    for i in range(n_blocks):
+        nf = nf // 2
+        model.add(tf.keras.layers.Conv2DTranspose(
+            filters=nf, kernel_size=(5, 5), strides=(2, 2),
+            padding='same', use_bias=False))
+        model.add(tf.keras.layers.BatchNormalization())
+        model.add(tf.keras.layers.LeakyReLU())
+
+
+    model.add(tf.keras.layers.Conv2DTranspose(
+        filters=output_size[2], kernel_size=(5, 5), strides=(1, 1), 
+        padding='same', use_bias=False, activation='tanh'))
+
+    return model
+
+def make_dcgan_discriminator(input_size=(28, 28, 1),
+                             n_filters=64, n_blocks=2):
+    model = tf.keras.Sequential([
+        tf.keras.layers.Input(shape=input_size),
+        tf.keras.layers.Conv2D(
+            filters=n_filters, kernel_size=5, 
+            strides=(1, 1), padding='same'),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.LeakyReLU()
+    ])
+
+    nf = n_filters
+    for i in range(n_blocks):
+        nf = nf*2
+        model.add(tf.keras.layers.Conv2D(
+            filters=nf, kernel_size=(5, 5), 
+            strides=(2, 2),padding='same'))
+        model.add(tf.keras.layers.BatchNormalization())
+        model.add(tf.keras.layers.LeakyReLU())
+        model.add(tf.keras.layers.Dropout(0.3))
+
+    model.add(tf.keras.layers.Conv2D(
+            filters=1, kernel_size=(7, 7), padding='valid'))
+
+    model.add(tf.keras.layers.Reshape((1,)))
+
+    return model
+
+
+
+
+gen_model = make_dcgan_generator()
+gen_model.summary()
+
+disc_model = make_dcgan_discriminator()
+disc_model.summary()
+
+
+# ## Dissimilarity measures between two distributions 
+
+
+
+
+
+
+
+
+
+# ## Using EM distance in practice for GANs
+
+# ## Gradient penalty 
+
+# ## Implementing WGAN-GP to train the DCGAN model
+
+
+
+
+mnist_bldr = tfds.builder('mnist')
+mnist_bldr.download_and_prepare()
+mnist = mnist_bldr.as_dataset(shuffle_files=False)
+
+def preprocess(ex, mode='uniform'):
+    image = ex['image']
+    image = tf.image.convert_image_dtype(image, tf.float32)
+
+    image = image*2 - 1.0
+    if mode == 'uniform':
+      input_z = tf.random.uniform(shape=(z_size,),
+                                  minval=-1.0, maxval=1.0)
+    elif mode == 'normal':
+      input_z = tf.random.normal(shape=(z_size,))
+    return input_z, image
+
+
+
+
+num_epochs = 100
+batch_size = 64
+image_size = (28, 28)
+z_size = 20
+mode_z = 'uniform'
+gen_hidden_layers = 1
+gen_hidden_size = 100
+disc_hidden_layers = 1
+disc_hidden_size = 100
+
+tf.random.set_seed(1)
+np.random.seed(1)
+
+
+if mode_z == 'uniform':
+    fixed_z = tf.random.uniform(
+        shape=(batch_size, z_size),
+        minval=-1, maxval=1)
+elif mode_z == 'normal':
+    fixed_z = tf.random.normal(
+        shape=(batch_size, z_size))
+
+def create_samples(g_model, input_z):
+    g_output = g_model(input_z, training=False)
+    images = tf.reshape(g_output, (batch_size, *image_size))    
+    return (images+1)/2.0
+
+## Set-up the dataset
+mnist_trainset = mnist['train']
+mnist_trainset = mnist_trainset.map(
+    lambda ex: preprocess(ex, mode=mode_z))
+
+input_z, input_real = next(iter(mnist_trainset))
+
+mnist_trainset = mnist_trainset.shuffle(10000)
+mnist_trainset = mnist_trainset.batch(
+    batch_size, drop_remainder=True)
+
+## Set-up the model
+with tf.device(device_name):
+    gen_model = make_dcgan_generator()
+    gen_model.build(input_shape=(None, z_size))
+
+    disc_model = make_dcgan_discriminator()
+    disc_model.build(input_shape=(None, np.prod(image_size)))
+
+## Loss function and optimizers:
+loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)
+g_optimizer = tf.keras.optimizers.Adam()
+d_optimizer = tf.keras.optimizers.Adam()
+
+avg_epoch_losses = []
+avg_d_vals = []
+epoch_samples = []
+
+start_time = time.time()
+for epoch in range(1, num_epochs+1):
+    losses = []
+    for i,(input_z,input_real) in enumerate(mnist_trainset):
+
+        ## Compute discriminator's real-loss and its gradients:
+        with tf.GradientTape() as d_tape_real:
+            d_logits_real = disc_model(input_real, training=True)
+
+            d_labels_real = tf.ones_like(d_logits_real)# * smoothing_factor
+
+            d_loss_real = loss_fn(y_true=d_labels_real,
+                                  y_pred=d_logits_real)
+        d_grads_real = d_tape_real.gradient(
+               d_loss_real, disc_model.trainable_variables)
+        ## Optimization: Apply the gradients
+        d_optimizer.apply_gradients(
+            grads_and_vars=zip(d_grads_real,
+                               disc_model.trainable_variables))
+
+
+        ## Compute generator's loss and its gradients:
+        with tf.GradientTape() as g_tape:
+            g_output = gen_model(input_z)
+            d_logits_fake = disc_model(g_output, training=True)
+            labels_real = tf.ones_like(d_logits_fake)
+            g_loss = loss_fn(y_true=labels_real,
+                             y_pred=d_logits_fake)
+
+        g_grads = g_tape.gradient(g_loss, gen_model.trainable_variables)
+        g_optimizer.apply_gradients(
+            grads_and_vars=zip(g_grads, gen_model.trainable_variables))
+
+
+        ## Compute discriminator's fake-loss and its gradients:
+        with tf.GradientTape() as d_tape_fake:
+            d_logits_fake = disc_model(g_output.numpy(), training=True)
+            d_labels_fake = tf.zeros_like(d_logits_fake)
+
+            d_loss_fake = loss_fn(y_true=d_labels_fake,
+                                  y_pred=d_logits_fake)
+
+            d_grads_fake = d_tape_fake.gradient(
+                d_loss_fake, disc_model.trainable_variables)
+        ## Optimization: Apply the gradients
+        d_optimizer.apply_gradients(
+            grads_and_vars=zip(d_grads_fake, 
+                               disc_model.trainable_variables))
+
+        d_loss = (d_loss_real + d_loss_fake)/2.0
+        losses.append(
+            (g_loss.numpy(), d_loss.numpy(), 
+             d_loss_real.numpy(), d_loss_fake.numpy()))
+
+
+        d_probs_real = tf.reduce_mean(tf.sigmoid(d_logits_real))
+        d_probs_fake = tf.reduce_mean(tf.sigmoid(d_logits_fake))
+        avg_d_vals.append((d_probs_real.numpy(), d_probs_fake.numpy()))        
+    avg_epoch_losses.append(np.mean(losses, axis=0))
+    print('Epoch {:-3d} | ET {:.2f} min | Avg Losses >>'
+          ' G/D {:.4f}/{:.4f} [D-Real: {:.4f} D-Fake: {:.4f}]'
+          .format(epoch, (time.time() - start_time)/60, 
+                  *list(avg_epoch_losses[-1])))
+    epoch_samples.append(create_samples(
+          gen_model, num_samples=8).numpy())
+
+
+
+
+#import pickle
+#pickle.dump({'all_losses':all_losses, 
+#             'samples':epoch_samples}, 
+#            open('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-WDCGAN-learning.pkl', 'wb'))
+
+#gen_model.save('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-WDCGAN-gan_gen.h5')
+#disc_model.save('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-WDCGAN-gan_disc.h5')
+
+
+
+
+
+
+fig = plt.figure(figsize=(8, 6))
+
+## Plotting the losses
+ax = fig.add_subplot(1, 1, 1)
+g_losses = [item[0] for item in itertools.chain(*all_losses)]
+d_losses = [item[1] for item in itertools.chain(*all_losses)]
+plt.plot(g_losses, label='Generator loss', alpha=0.95)
+plt.plot(d_losses, label='Discriminator loss', alpha=0.95)
+plt.legend(fontsize=20)
+ax.set_xlabel('Iteration', size=15)
+ax.set_ylabel('Loss', size=15)
+
+epochs = np.arange(1, 101)
+epoch2iter = lambda e: e*len(all_losses[-1])
+epoch_ticks = [1, 20, 40, 60, 80, 100]
+newpos   = [epoch2iter(e) for e in epoch_ticks]
+ax2 = ax.twiny()
+ax2.set_xticks(newpos)
+ax2.set_xticklabels(epoch_ticks)
+ax2.xaxis.set_ticks_position('bottom')
+ax2.xaxis.set_label_position('bottom')
+ax2.spines['bottom'].set_position(('outward', 60))
+ax2.set_xlabel('Epoch', size=15)
+ax2.set_xlim(ax.get_xlim())
+ax.tick_params(axis='both', which='major', labelsize=15)
+ax2.tick_params(axis='both', which='major', labelsize=15)
+
+#plt.savefig('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-wdcgan-learning-curve.pdf')
+plt.show()
+
+
+
+
+selected_epochs = [1, 2, 4, 10, 50, 100]
+fig = plt.figure(figsize=(10, 14))
+for i,e in enumerate(selected_epochs):
+    for j in range(5):
+        ax = fig.add_subplot(6, 5, i*5+j+1)
+        ax.set_xticks([])
+        ax.set_yticks([])
+        if j == 0:
+            ax.text(-0.06, 0.5, 'Epoch {}'.format(e),
+                    rotation=90, size=18, color='red',
+                    horizontalalignment='right',
+                    verticalalignment='center', 
+                    transform=ax.transAxes)
+
+        image = epoch_samples[e-1][j]
+        ax.imshow(image, cmap='gray_r')
+
+#plt.savefig('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-wdcgan-samples.pdf')
+plt.show()
+
+
+
+
+
+
+
+# ## Mode collapse
+
+
+
+
+
+
+
+
+