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 os import numpy as np class GAN(): def __init__(self): # --------------------------------- # # 行28，列28，也就是mnist的shape # --------------------------------- # self.img_rows = 28 self.img_cols = 28 self.channels = 1 # 28,28,1 self.img_shape = (self.img_rows, self.img_cols, self.channels) self.latent_dim = 100 # adam最佳化器 optimizer = Adam(0.0002, 0.5) self.discriminator = self.build_discriminator() self.discriminator.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) self.generator = self.build_generator() gan_input = Input(shape=(self.latent_dim,)) img = self.generator(gan_input) # 在訓練generate的時候不訓練discriminator self.discriminator.trainable = False # 對生成的假圖片進行預測 validity = self.discriminator(img) self.combined = Model(gan_input, validity) self.combined.compile(loss='binary_crossentropy', optimizer=optimizer) def build_generator(self): # --------------------------------- # # 生成器，輸入一串隨機數字 # --------------------------------- # 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)) noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img) def build_discriminator(self): # ----------------------------------- # # 評價器，對輸入進來的圖片進行評價 # ----------------------------------- # model = Sequential() # 輸入一張圖片 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)) # 判斷真偽 model.add(Dense(1, activation='sigmoid')) img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity) def train(self, epochs, batch_size=128, sample_interval=50): # 獲得資料 (X_train, _), (_, _) = mnist.load_data() # 進行標準化 X_train = X_train / 127.5 - 1. X_train = np.expand_dims(X_train, axis=3) # 建立標籤 valid = np.ones((batch_size, 1)) fake = np.zeros((batch_size, 1)) for epoch in range(epochs): # --------------------------- # # 隨機選取batch_size個圖片 # 對discriminator進行訓練 # --------------------------- # idx = np.random.randint(0, X_train.shape[0], batch_size) imgs = X_train[idx] noise = np.random.normal(0, 1, (batch_size, self.latent_dim)) gen_imgs = self.generator.predict(noise) d_loss_real = self.discriminator.train_on_batch(imgs, valid) d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake) d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) # --------------------------- # # 訓練generator # --------------------------- # noise = np.random.normal(0, 1, (batch_size, self.latent_dim)) g_loss = self.combined.train_on_batch(noise, valid) print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss)) 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) 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__': if not os.path.exists("./images"): os.makedirs("./images") gan = GAN() gan.train(epochs=3000, batch_size=256, sample_interval=200)
執行結果
