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- class GAN():
- def __init__(self):
- self.img_rows = 28
- self.img_cols = 28
- self.channels = 1
- self.img_shape = (self.img_rows, self.img_cols, self.channels)
- optimizer = Adam(0.0002, 0.5)
- # Build and compile the discriminator
- self.discriminator = self.build_discriminator()
- self.discriminator.compile(loss='binary_crossentropy',
- optimizer=optimizer,
- metrics=['accuracy'])
- # Build and compile the generator
- self.generator = self.build_generator()
- self.generator.compile(loss='binary_crossentropy', optimizer=optimizer)
- # The generator takes noise as input and generated imgs
- z = Input(shape=(100,))
- img = self.generator(z)
- # For the combined model we will only train the generator
- self.discriminator.trainable = False
- # The valid takes generated images as input and determines validity
- valid = self.discriminator(img)
- # The combined model (stacked generator and discriminator) takes
- # noise as input => generates images => determines validity
- self.combined = Model(z, valid)
- self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)
- def build_generator(self):
- noise_shape = (100,)
- model = Sequential()
- model.add(Dense(256, input_shape=noise_shape))
- model.add(LeakyReLU(alpha=0.2))
- model.add(BatchNormalization(momentum=0.8))
- model.add(Dense(512))
- model.add(LeakyReLU(alpha=0.2))
- model.add(BatchNormalization(momentum=0.8))
- model.add(Dense(1024))
- model.add(LeakyReLU(alpha=0.2))
- model.add(BatchNormalization(momentum=0.8))
- model.add(Dense(np.prod(self.img_shape), activation='tanh'))
- model.add(Reshape(self.img_shape))
- model.summary()
- noise = Input(shape=noise_shape)
- img = model(noise)
- return Model(noise, img)
- def build_discriminator(self):
- img_shape = (self.img_rows, self.img_cols, self.channels)
- model = Sequential()
- model.add(Flatten(input_shape=img_shape))
- model.add(Dense(512))
- model.add(LeakyReLU(alpha=0.2))
- model.add(Dense(256))
- model.add(LeakyReLU(alpha=0.2))
- model.add(Dense(1, activation='sigmoid'))
- model.summary()
- img = Input(shape=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.astype(np.float32) - 127.5) / 127.5
- X_train = np.expand_dims(X_train, axis=3)
- half_batch = int(batch_size / 2)
- for epoch in range(epochs):
- # ---------------------
- # Train Discriminator
- # ---------------------
- # Select a random half batch of images
- idx = np.random.randint(0, X_train.shape[0], half_batch)
- imgs = X_train[idx]
- noise = np.random.normal(0, 1, (half_batch, 100))
- # Generate a half batch of new images
- gen_imgs = self.generator.predict(noise)
- # Train the discriminator
- d_loss_real = self.discriminator.train_on_batch(imgs, np.ones((half_batch, 1)))
- d_loss_fake = self.discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1)))
- d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
- # ---------------------
- # Train Generator
- # ---------------------
- noise = np.random.normal(0, 1, (batch_size, 100))
- # The generator wants the discriminator to label the generated samples
- # as valid (ones)
- valid_y = np.array([1] * batch_size)
- # Train the generator
- g_loss = self.combined.train_on_batch(noise, valid_y)
- # Plot the progress
- print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
- # If at save interval => save generated image samples
- if epoch % save_interval == 0:
- self.save_imgs(epoch)
- def save_imgs(self, epoch):
- r, c = 5, 5
- noise = np.random.normal(0, 1, (r * c, 100))
- gen_imgs = self.generator.predict(noise)
- # Rescale images 0 - 1
- gen_imgs = 0.5 * gen_imgs + 0.5
- fig, axs = plt.subplots(r, c)
- 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("gan/images/mnist_%d.png" % epoch)
- plt.close()
- if __name__ == '__main__':
- gan = GAN()
- gan.train(epochs=30000, batch_size=32, save_interval=200)
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