ACGAN

$$ACGAN$$

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• CGAN通过在生成器和判别器中均使用标签信息进行训练， 不仅能产生特定标签的数据。
• ACGAN是条件GAN的另一种实现，既使用标签信息进行训练，同时也重建标签信息。

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生成器的输入包含class和Noise两个部分。
其中class为训练数据标签信息; Noise为随机向量。
然后将两者进行拼接。
生成器的的输出张量为图片: (batch size, channel,Height, Width)。

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判别器的输入为图片(生成图片和真实图片) ;
判别的器的输出为两部分,
部分是源数据真假的判断，形状为:(batch size, 1)，
部分是输入数据的分类结果，形状为: (batch_ size,class num)

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• 对于判别器而言, 既希望分类正确，又希望能正确分辨数据的真假; 对于生成器而言，也希望能够分类正确，但希望判别器 不能正确分辨假数据。

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损失设计

• 判别器

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• 生成器

代码

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import glob

from numba import cuda
cuda.select_device(0)
cuda.close()

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

print('Tensorflow version: {}'.format(tf.__version__))

import tensorflow.keras.datasets.mnist as mnist

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

train_image.shape

train_label.shape

plt.imshow(train_image[5])

train_label[:5]

train_image = train_image / 127.5  - 1

train_image = np.expand_dims(train_image, -1)

train_image.shape

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

AUTOTUNE = tf.data.experimental.AUTOTUNE

dataset

BATCH_SIZE = 256
image_count = train_image.shape[0]
noise_dim = 50

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

def generator_model():
    seed = layers.Input(shape=((noise_dim,)))
    label = layers.Input(shape=(()))
    
    x = layers.Embedding(10, 50, input_length=1)(label)
    x = layers.Flatten()(x)
    x = layers.concatenate([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*7

    x = layers.Conv2DTranspose(32, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)    #   14*14

    x = layers.Conv2DTranspose(1, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)
    x = layers.Activation('tanh')(x)
    
    model = tf.keras.Model(inputs=[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)
    
    model = tf.keras.Model(inputs=image, outputs=[x1, x2])
    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):
    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)
    total_loss = real_loss + fake_loss + cat_loss
    return total_loss

def generator_loss(fake_output, fake_cat_out, label):
    fake_loss = binary_cross_entropy(tf.ones_like(fake_output), fake_output)
    cat_loss = category_cross_entropy(label, fake_cat_out)
    return fake_loss + cat_loss

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

@tf.function
def train_step(images, labels):
    batchsize = labels.shape[0]
    noise = tf.random.normal([batchsize, noise_dim])
    
    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator((noise, labels), training=True)

        real_output, real_cat_out = discriminator(images, training=True)
        fake_output, fake_cat_out = discriminator(generated_images, training=True)
        
        gen_loss = generator_loss(fake_output, fake_cat_out, labels)
        disc_loss = discriminator_loss(real_output, real_cat_out, fake_output, labels)

    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))

noise_dim = 50
num = 10
noise_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).
    predictions = model((test_noise_input, 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_acgan.h5')

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

generate_and_save_images(generator, noise_seed, cat_seed, 1)

cat_seed = np.array([3]*10)
generate_and_save_images(generator, noise_seed, cat_seed, 0)

cat_seed = np.array([6]*10)
generate_and_save_images(generator, noise_seed, cat_seed, 0)