add model to MNIST.py

MNIST_GAN/MNIST_GAN.py

 import numpy as np
-from keras.layers import Input
-from keras.models import Model
+from keras.layers import Input, Activation, BatchNormalization, Conv2D, Dense, Dropout, Flatten, Reshape, UpSampling2D
+from keras.layers.advanced_activations import LeakyReLU
+from keras.regularizers import l1_l2
+from keras.models import Sequential, Model
 from keras.optimizers import Adam
 import mnist
 import matplotlib.pyplot as plt
 import keras.backend.tensorflow_backend as KTF
-from gan import build_generator, build_discriminator, plot_images, make_trainable, get_session
+from gan import plot_images, make_trainable, get_session
 
 
 log_dir = "."
@@ -20,27 +22,50 @@ X_test = data.test_images.reshape(-1, 28, 28, 1) / 255.
 idx = np.random.choice(len(X_train), 16)
 plot_images(X_train[idx], fname=log_dir + '/real_images.png')
 
-# --------------------------------------------------
-# Set up generator, discriminator and GAN (stacked generator + discriminator)
-# Feel free to modify eg. :
-# - the provided models (see gan.py)
-# - the learning rate
-# - the batchsize
-# --------------------------------------------------
-
 # Set up generator
 print('\nGenerator')
 latent_dim = 100
-generator = build_generator(latent_dim)
+generator = Sequential(name='generator')
+generator.add(Dense(7 * 7 * 128, input_shape=(latent_dim,)))
+generator.add(BatchNormalization())
+generator.add(Activation('relu'))
+generator.add(Reshape([7, 7, 128]))
+generator.add(UpSampling2D(size=(2, 2)))
+generator.add(Conv2D(128, (5, 5), padding='same'))
+generator.add(BatchNormalization())
+generator.add(Activation('relu'))
+generator.add(UpSampling2D(size=(2, 2)))
+generator.add(Conv2D(64, (5, 5), padding='same'))
+generator.add(BatchNormalization())
+generator.add(Activation('relu'))
+generator.add(Conv2D(1, (5, 5), padding='same', activation='sigmoid'))
+
 print(generator.summary())
 
 # Set up discriminator
 print('\nDiscriminator')
-discriminator = build_discriminator()
+drop_rate = 0.25
+discriminator = Sequential(name='discriminator')
+discriminator.add(Conv2D(32, (5, 5), padding='same', strides=(2, 2), activation='relu', input_shape=(28, 28, 1)))
+discriminator.add(LeakyReLU(0.2))
+discriminator.add(Dropout(drop_rate))
+discriminator.add(Conv2D(64, (5, 5), padding='same', strides=(2, 2), activation='relu'))
+discriminator.add(LeakyReLU(0.2))
+discriminator.add(Dropout(drop_rate))
+discriminator.add(Conv2D(128, (5, 5), padding='same', strides=(2, 2), activation='relu'))
+discriminator.add(LeakyReLU(0.2))
+discriminator.add(Dropout(drop_rate))
+discriminator.add(Flatten())
+discriminator.add(Dense(256, activity_regularizer=l1_l2(1e-5)))
+discriminator.add(LeakyReLU(0.2))
+discriminator.add(Dropout(drop_rate))
+discriminator.add(Dense(2, activation='softmax'))
+
 print(discriminator.summary())
 d_opt = Adam(lr=2e-4, beta_1=0.5, decay=0.0005)
 discriminator.compile(loss='binary_crossentropy', optimizer=d_opt, metrics=['accuracy'])
 
+
 # Set up GAN by stacking the discriminator on top of the generator
 print('\nGenerative Adversarial Network')
 gan_input = Input(shape=[latent_dim])
@@ -52,10 +77,8 @@ make_trainable(discriminator, False)  # freezes the discriminator when training
 GAN.compile(loss='binary_crossentropy', optimizer=g_opt)
 # Compile saves the trainable status of the model --> After the model is compiled, updating using make_trainable will have no effect
 
-# --------------------------------------------------
-# Pretrain the discriminator:
-# --------------------------------------------------
 
+# Pretrain the discriminator:
 # - Create a dataset of 10000 real train images and 10000 fake images.
 ntrain = 10000
 no = np.random.choice(60000, size=ntrain, replace='False')

MNIST_GAN/gan.py

 import numpy as np
 import matplotlib.pyplot as plt
-from keras.layers import Activation, BatchNormalization, Conv2D, Dense, Dropout, Flatten, Reshape, UpSampling2D
-from keras.layers.advanced_activations import LeakyReLU
-from keras.regularizers import l1_l2
-from keras.models import Sequential
 import tensorflow as tf
 
 
@@ -34,41 +30,3 @@ def make_trainable(model, trainable):
     model.trainable = trainable
     for l in model.layers:
         l.trainable = trainable
-
-
-def build_generator(latent_dim):
-    generator = Sequential(name='generator')
-    generator.add(Dense(7 * 7 * 128, input_shape=(latent_dim,)))
-    generator.add(BatchNormalization())
-    generator.add(Activation('relu'))
-    generator.add(Reshape([7, 7, 128]))
-    generator.add(UpSampling2D(size=(2, 2)))
-    generator.add(Conv2D(128, (5, 5), padding='same'))
-    generator.add(BatchNormalization())
-    generator.add(Activation('relu'))
-    generator.add(UpSampling2D(size=(2, 2)))
-    generator.add(Conv2D(64, (5, 5), padding='same'))
-    generator.add(BatchNormalization())
-    generator.add(Activation('relu'))
-    generator.add(Conv2D(1, (5, 5), padding='same', activation='sigmoid'))
-    return generator
-
-
-def build_discriminator(drop_rate=0.25):
-    """ Discriminator network """
-    discriminator = Sequential(name='discriminator')
-    discriminator.add(Conv2D(32, (5, 5), padding='same', strides=(2, 2), activation='relu', input_shape=(28, 28, 1)))
-    discriminator.add(LeakyReLU(0.2))
-    discriminator.add(Dropout(drop_rate))
-    discriminator.add(Conv2D(64, (5, 5), padding='same', strides=(2, 2), activation='relu'))
-    discriminator.add(LeakyReLU(0.2))
-    discriminator.add(Dropout(drop_rate))
-    discriminator.add(Conv2D(128, (5, 5), padding='same', strides=(2, 2), activation='relu'))
-    discriminator.add(LeakyReLU(0.2))
-    discriminator.add(Dropout(drop_rate))
-    discriminator.add(Flatten())
-    discriminator.add(Dense(256, activity_regularizer=l1_l2(1e-5)))
-    discriminator.add(LeakyReLU(0.2))
-    discriminator.add(Dropout(drop_rate))
-    discriminator.add(Dense(2, activation='softmax'))
-    return discriminator