GAN生成对抗网络-INFOGAN原理与基本实现-可解释的生成对抗网络-06

代码

import tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras import layersimport matplotlib.pyplot as plt%matplotlib inlineimport numpy as npimport glob

gpu = tf.config.experimental.list_physical_devices(device_type='GPU')tf.config.experimental.set_memory_growth(gpu[0], True)

import tensorflow.keras.datasets.mnist as mnist

(train_image, train_label), (_, _) = mnist.load_data()

train_image = train_image / 127.5 - 1

train_image = np.expand_dims(train_image, -1)

dataset = tf.data.Dataset.from_tensor_slices((train_image, train_label))

BATCH_SIZE = 256image_count = train_image.shape[0]noise_dim = 30con_dim = 30

dataset = dataset.shuffle(image_count).batch(BATCH_SIZE)

def generator_model():noise_seed = layers.Input(shape=((noise_dim,)))con_seed = layers.Input(shape=((con_dim,)))label = layers.Input(shape=(()))x = layers.Embedding(10, 30, input_length=1)(label)x = layers.Flatten()(x)x = layers.concatenate([noise_seed, con_seed, x])x = layers.Dense(3*3*128, use_bias=False)(x)x = layers.Reshape((3, 3, 128))(x)x = layers.BatchNormalization()(x)x = layers.ReLU()(x)x = layers.Conv2DTranspose(64, (3, 3), strides=(2, 2), use_bias=False)(x)x = layers.BatchNormalization()(x)x = layers.ReLU()(x)# 7*7x = layers.Conv2DTranspose(32, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)x = layers.BatchNormalization()(x)x = layers.ReLU()(x) # 14*14x = layers.Conv2DTranspose(1, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)x = layers.Activation('tanh')(x)model = tf.keras.Model(inputs=[noise_seed, con_seed, label], outputs=x) return model

def discriminator_model():image = tf.keras.Input(shape=((28,28,1)))x = layers.Conv2D(32, (3, 3), strides=(2, 2), padding='same', use_bias=False)(image)x = layers.BatchNormalization()(x)x = layers.LeakyReLU()(x)x = layers.Dropout(0.5)(x)x = layers.Conv2D(32*2, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)x = layers.BatchNormalization()(x)x = layers.LeakyReLU()(x)x = layers.Dropout(0.5)(x)x = layers.Conv2D(32*4, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)x = layers.BatchNormalization()(x)x = layers.LeakyReLU()(x)x = layers.Dropout(0.5)(x)x = layers.Flatten()(x)x1 = layers.Dense(1)(x)x2 = layers.Dense(10)(x)x3 = layers.Dense(con_dim, activation='sigmoid')(x)model = tf.keras.Model(inputs=image, outputs=[x1, x2, x3])return model

generator = generator_model()

discriminator = discriminator_model()

binary_cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)category_cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

def discriminator_loss(real_output, real_cat_out, fake_output, label, con_out, cond_in):real_loss = binary_cross_entropy(tf.ones_like(real_output), real_output)fake_loss = binary_cross_entropy(tf.zeros_like(fake_output), fake_output)cat_loss = category_cross_entropy(label, real_cat_out)con_loss = tf.reduce_mean(tf.square(con_out - cond_in))total_loss = real_loss + fake_loss + cat_loss + con_lossreturn total_loss

def generator_loss(fake_output, fake_cat_out, label, con_out, cond_in):fake_loss = binary_cross_entropy(tf.ones_like(fake_output), fake_output)cat_loss = category_cross_entropy(label, fake_cat_out)con_loss = tf.reduce_mean(tf.square(con_out - cond_in))return fake_loss + cat_loss + con_loss

generator_optimizer = tf.keras.optimizers.Adam(1e-5)discriminator_optimizer = tf.keras.optimizers.Adam(1e-5)

@tf.functiondef train_step(images, labels):batchsize = labels.shape[0]noise = tf.random.normal([batchsize, noise_dim])cond = tf.random.uniform([batchsize, noise_dim])with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:generated_images = generator((noise, cond, labels), training=True)real_output, real_cat_out, _ = discriminator(images, training=True)fake_output, fake_cat_out, con_out = discriminator(generated_images, training=True)gen_loss = generator_loss(fake_output, fake_cat_out, labels, con_out, cond)disc_loss = discriminator_loss(real_output, real_cat_out, fake_output, labels, con_out, cond)gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

num = 10noise_seed = tf.random.normal([num, noise_dim])cat_seed = np.random.randint(0, 10, size=(num, 1))print(cat_seed.T)

def generate_and_save_images(model, test_noise_input, test_cat_input, epoch):print('Epoch:', epoch+1)# Notice `training` is set to False.# This is so all layers run in inference mode (batchnorm).cond_seed = tf.random.uniform([num, con_dim])predictions = model((test_noise_input, cond_seed, test_cat_input), training=False)predictions = tf.squeeze(predictions)fig = plt.figure(figsize=(10, 1))for i in range(predictions.shape[0]):plt.subplot(1, 10, i+1)plt.imshow((predictions[i, :, :] + 1)/2, cmap='gray')plt.axis('off')# plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))plt.show()

def train(dataset, epochs):for epoch in range(epochs):for image_batch, label_batch in dataset:train_step(image_batch, label_batch)if epoch%10 == 0:generate_and_save_images(generator,noise_seed,cat_seed,epoch)generate_and_save_images(generator,noise_seed,cat_seed,epoch)

EPOCHS = 200

train(dataset, EPOCHS)

generator.save('generate_infogan.h5')

num = 10noise_seed = tf.random.normal([num, noise_dim])cat_seed = np.arange(10).reshape(-1, 1)print(cat_seed.T)