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+from __future__ import print_function, division
+from keras.datasets import mnist
+from keras.layers import Input, Dense, Reshape, Flatten, Dropout
+from keras.layers import BatchNormalization, Activation, ZeroPadding2D
+from keras.layers.advanced_activations import LeakyReLU
+from keras.layers.convolutional import UpSampling2D, Conv2D
+from keras.models import Sequential, Model
+from keras.optimizers import Adam
+
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+import sys
+
+import numpy as np
+
+class DCGAN():
+ def __init__(self):
+ # Input shape
+ self.img_rows = 28
+ self.img_cols = 28
+ self.channels = 1
+ self.img_shape = (self.img_rows, self.img_cols, self.channels)
+ self.latent_dim = 100
+
+ optimizer = Adam(0.002, 0.5)
+
+ # Build and compile the discriminator
+ self.discriminator = self.build_discriminator()
+ self.discriminator.compile(loss='binary_crossentropy',
+ optimizer=optimizer,
+ metrics=['accuracy'])
+
+ # Build the generator
+ self.generator = self.build_generator()
+
+ # The generator takes noise as input and generates imgs
+ z = Input(shape=(self.latent_dim,))
+ img = self.generator(z)
+
+ # For the combined model we will only train the generator
+ self.discriminator.trainable = False
+
+ # The discriminator takes generated images as input and determines validity
+ valid = self.discriminator(img)
+
+ # The combined model (stacked generator and discriminator)
+ # Trains the generator to fool the discriminator
+ self.combined = Model(z, valid)
+ self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)
+
+ def build_generator(self):
+
+ model = Sequential()
+
+ model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
+ model.add(Reshape((7, 7, 128)))
+ model.add(UpSampling2D())
+ model.add(Conv2D(128, kernel_size=3, padding="same"))
+ model.add(BatchNormalization())
+ model.add(Activation("relu"))
+ model.add(UpSampling2D())
+ model.add(Conv2D(64, kernel_size=3, padding="same"))
+ model.add(BatchNormalization())
+ model.add(Activation("relu"))
+ model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
+ model.add(Activation("tanh"))
+
+ #model.summary()
+
+ noise = Input(shape=(self.latent_dim,))
+ img = model(noise)
+
+ return Model(noise, img)
+
+ def build_discriminator(self):
+
+ model = Sequential()
+
+ model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
+ model.add(LeakyReLU(alpha=0.2))
+ model.add(Dropout(0.25))
+ model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
+ model.add(ZeroPadding2D(padding=((0,1),(0,1))))
+ model.add(BatchNormalization())
+ model.add(LeakyReLU(alpha=0.2))
+ model.add(Dropout(0.25))
+ model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
+ model.add(BatchNormalization())
+ model.add(LeakyReLU(alpha=0.2))
+ model.add(Dropout(0.25))
+ model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
+ model.add(BatchNormalization())
+ model.add(LeakyReLU(alpha=0.2))
+ model.add(Dropout(0.25))
+ model.add(Flatten())
+ model.add(Dense(1, activation='sigmoid'))
+
+ #model.summary()
+
+ img = Input(shape=self.img_shape)
+ validity = model(img)
+
+ return Model(img, validity)
+
+ def train(self, epochs, batch_size=128, save_interval=50):
+
+ # Load the dataset
+ (X_train, _), (_, _) = mnist.load_data()
+
+ # Rescale -1 to 1
+ X_train = X_train / 127.5 - 1.
+ X_train = np.expand_dims(X_train, axis=3)
+
+ # Adversarial ground truths
+ valid = np.ones((batch_size, 1))
+ fake = np.zeros((batch_size, 1))
+
+ xaxis = np.arange(epochs)
+ loss = np.zeros((2,epochs))
+ for epoch in range(epochs):
+
+ # ---------------------
+ # Train Discriminator
+ # ---------------------
+
+ # Select a random half of images
+ idx = np.random.randint(0, X_train.shape[0], batch_size)
+ imgs = X_train[idx]
+
+ # Sample noise and generate a batch of new images
+ noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
+ gen_imgs = self.generator.predict(noise)
+
+ # Train the discriminator (real classified as ones and generated as zeros)
+ d_loss_real = self.discriminator.train_on_batch(imgs, valid)
+ d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
+ d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
+
+ # ---------------------
+ # Train Generator
+ # ---------------------
+
+ # Train the generator (wants discriminator to mistake images as real)
+ g_loss = self.combined.train_on_batch(noise, valid)
+
+ # Plot the progress
+ #print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
+ if epoch % 500 == 0:
+ print(epoch)
+ loss[0][epoch] = d_loss[0]
+ loss[1][epoch] = g_loss
+ # If at save interval => save generated image samples
+ if epoch % save_interval == 0:
+ self.save_imgs(epoch)
+ plt.plot(xaxis,loss[0])
+ plt.plot(xaxis,loss[1])
+ plt.legend(('Discriminator', 'Generator'), loc='best')
+ plt.xlabel('Epoch')
+ plt.ylabel('Binary Crossentropy Loss')
+
+ def save_imgs(self, epoch):
+ r, c = 10, 10
+ noise = np.random.normal(0, 1, (r * c, self.latent_dim))
+ gen_imgs = self.generator.predict(noise)
+
+ # Rescale images 0 - 1
+ gen_imgs = 0.5 * gen_imgs + 0.5
+
+ fig, axs = plt.subplots(r, c)
+ gs = gridspec.GridSpec(r, c)
+ gs.update(wspace=0.05, hspace=0.05)
+ cnt = 0
+ for i in range(r):
+ for j in range(c):
+ axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
+ axs[i,j].axis('off')
+ cnt += 1
+ fig.savefig("images/mnist_%d.png" % epoch)
+ plt.close()
+
+'''
+if __name__ == '__main__':
+ dcgan = DCGAN()
+ dcgan.train(epochs=4000, batch_size=32, save_interval=50)
+''' \ No newline at end of file