生成二次元动漫头像DCGAN代码实现

写在前面

此份代码可以在pycharm上运行，前提是已经安装tensorflow2.0gpu版本

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

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        # Restrict TensorFlow to only use the fourth GPU
        tf.config.experimental.set_visible_devices(gpus[0], 'GPU')

        # Currently, memory growth needs to be the same across GPUs
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        # Memory growth must be set before GPUs have been initialized
        print(e)

image_path = glob.glob('E:\\deep_learning\\kaggle\\animeFaces\\data\\*.png')


# image_path = glob.glob('E:\\deep_learning\\kaggle\\train\\train\\*.jpg')


def load_preprosess_image(path):
    image = tf.io.read_file(path)
    # 对图片进行解码
    image = tf.image.decode_png(image, channels=3)

    image = tf.cast(image, tf.float32)
    image = (image / 127.5) - 1
    return image


image_ds = tf.data.Dataset.from_tensor_slices(image_path)
AUTOTUNE = tf.data.experimental.AUTOTUNE

image_ds = image_ds.map(load_preprosess_image, num_parallel_calls=AUTOTUNE)

BATCH_SIZE = 32
# BATCH_SIZE = 64
image_count = len(image_path)

image_ds = image_ds.shuffle(image_count).batch(BATCH_SIZE)
image_ds = image_ds.prefetch(AUTOTUNE)


def generator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(8 * 8 * 256, use_bias=False, input_shape=(100,)))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Reshape((8, 8, 256)))

    model.add(tf.keras.layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Conv2DTranspose(32, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(
        tf.keras.layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))

    return model


generator = generator_model()
noise = tf.random.normal([1, 100])
generated_image = generator(noise, training=False)


# plt.imshow((generated_image[0, :, :, :3] + 1) / 2)


def discriminator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Conv2D(32, (5, 5), strides=(2, 2), padding='same', input_shape=[64, 64, 3]))
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dropout(0.3))

    model.add(tf.keras.layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same'))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Conv2D(256, (5, 5), strides=(2, 2), padding='same'))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.GlobalAveragePooling2D())

    model.add(tf.keras.layers.Dense(1024))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())
    return model


discriminator = discriminator_model()
decision = discriminator(generated_image)
# print(decision)

cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)


def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss


def generator_loss(fake_output):
    return cross_entropy(tf.ones_like(fake_output), fake_output)


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

# epochs = 800
EPOCHS = 300
noise_dim = 100
num_examples_to_generate = 4

seed = tf.random.normal([num_examples_to_generate, noise_dim])


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

        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)

        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)

    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))


def generate_and_save_images(model, epoch, test_input):
    predictions = model(test_input, training=False)
    fig = plt.figure(figsize=(6, 6))

    for i in range(predictions.shape[0]):
        plt.subplot(2, 2, i + 1)
        plt.imshow((predictions[i, :, :, :] + 1) / 2)
        plt.axis('off')

    plt.show()


def train(dataset, epochs):
    for epoch in range(epochs):
        for image_batch in dataset:
            train_step(image_batch)
            print('.', end='')
        print()
        if epoch % 10 == 0:
            generate_and_save_images(generator, epoch + 1, seed)

    generate_and_save_images(generator, epochs, seed)


train(image_ds, EPOCHS)