GAN对抗网络(原始版).zip

#基于Kera环境实现的 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 sys import numpy as np 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) # 生成原始噪点数据大小 self.latent_dim = 100 optimizer = Adam(0.0002, 0.5) # Build and compile the discriminator # 1、建立判别器训练参数 # 选择损失，优化器，以及衡量准确率 self.discriminator = self.build_discriminator() self.discriminator.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) # Build the generator # 2、联合建立生成器训练参数，指定生成器损失 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 # 合并模型的损失，并且之后只训练生成器，判别器不训练 self.discriminator.trainable = False # The discriminator takes generated images as input and determines validity validity = self.discriminator(img) # The combined model (stacked generator and discriminator) # Trains the generator to fool the discriminator # 训练生成器欺骗判别器 self.combined = Model(z, validity) self.combined.compile(loss='binary_crossentropy', optimizer=optimizer) def build_generator(self): model = Sequential() model.add(Dense(256, input_dim=self.latent_dim)) 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=(self.latent_dim,)) img = model(noise) return Model(noise, img) def build_discriminator(self): model = Sequential() model.add(Flatten(input_shape=self.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=self.img_shape) validity = model(img) return Model(img, validity) def train(self, epochs, batch_size=128, sample_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 # 正负样本的目标值建立 valid = np.ones((batch_size, 1)) fake = np.zeros((batch_size, 1)) for epoch in range(epochs): # --------------------- # Train Discriminator # --------------------- # Select a random batch of images # 1、训练判别器 # 选择随机的一些真实样本 idx = np.random.randint(0, X_train.shape[0], batch_size) imgs = X_train[idx] # 生成器产生假样本 noise = np.random.normal(0, 1, (batch_size, self.latent_dim)) # Generate a batch of new images gen_imgs = self.generator.predict(noise) # Train the discriminator # 训练判别器过程 d_loss_real = self.discriminator.train_on_batch(imgs, valid) d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake) # 计算平均两部分损失 d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) # --------------------- # Train Generator # --------------------- # 2、训练生成器，停止判别器 noise = np.random.normal(0, 1, (batch_size, self.latent_dim)) # 合并训练，并停止训练判别器 # 用目标值为1去训练，目的使得生成器生成的样本越来越接近真是样本 # Train the generator (to have the discriminator label samples as valid) 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 at save interval => save generated image samples if epoch % sample_interval == 0: self.sample_images(epoch) def sample_images(self, epoch): r, c = 5, 5 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) 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/%d.png" % epoch) plt.close() if __name__ == '__main__': gan = GAN() gan.train(epochs=30000, batch_size=32, sample_interval=200)