AAEGan 简介与代码实战

1.介绍

  AAE的全名是对抗自动编码器（Adversarial Autoencoders），很明显，它是结合了gan的思想和自动编码器的思想， 更加详细的内容参见论文：Adversarial Autoencoders

 

2.模型结构

  图片上部分为自动编码器结构，包括两大部分，编码结构（encoder）和解码结构（decoder），下部分为判别器，目的就是使得q(z | x) 不断向p(z)（固定噪声分布，比如高斯分布）靠近

 

3.模型特点

      结合gan和自动编码器，个人觉得创新点不是很大，就是一个简单的结合

 

 4.代码实现keras

class AdversarialAutoencoder(): 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) self.latent_dim = 10 optimizer = Adam(0.0002, 0.5) # Build and compile the discriminator self.discriminator = self.build_discriminator() self.discriminator.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) # Build the encoder / decoder self.encoder = self.build_encoder() self.decoder = self.build_decoder() img = Input(shape=self.img_shape) # The generator takes the image, encodes it and reconstructs it # from the encoding encoded_repr = self.encoder(img) reconstructed_img = self.decoder(encoded_repr) # For the adversarial_autoencoder model we will only train the generator self.discriminator.trainable = False # The discriminator determines validity of the encoding validity = self.discriminator(encoded_repr) # The adversarial_autoencoder model (stacked generator and discriminator) self.adversarial_autoencoder = Model(img, [reconstructed_img, validity]) self.adversarial_autoencoder.compile(loss=['mse', 'binary_crossentropy'], loss_weights=[0.999, 0.001], optimizer=optimizer) def build_encoder(self): # Encoder img = Input(shape=self.img_shape) h = Flatten()(img) h = Dense(512)(h) h = LeakyReLU(alpha=0.2)(h) h = Dense(512)(h) h = LeakyReLU(alpha=0.2)(h) mu = Dense(self.latent_dim)(h) log_var = Dense(self.latent_dim)(h) latent_repr = merge([mu, log_var], mode=lambda p: p[0] + K.random_normal(K.shape(p[0])) * K.exp(p[1] / 2), output_shape=lambda p: p[0]) return Model(img, latent_repr) def build_decoder(self): model = Sequential() model.add(Dense(512, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() z = Input(shape=(self.latent_dim,)) img = model(z) return Model(z, img) def build_discriminator(self): model = Sequential() model.add(Dense(512, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(1, activation="sigmoid")) model.summary() encoded_repr = Input(shape=(self.latent_dim, )) validity = model(encoded_repr) return Model(encoded_repr, 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.astype(np.float32) - 127.5) / 127.5 X_train = np.expand_dims(X_train, axis=3) # Adversarial ground truths 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 idx = np.random.randint(0, X_train.shape[0], batch_size) imgs = X_train[idx] latent_fake = self.encoder.predict(imgs) latent_real = np.random.normal(size=(batch_size, self.latent_dim)) # Train the discriminator d_loss_real = self.discriminator.train_on_batch(latent_real, valid) d_loss_fake = self.discriminator.train_on_batch(latent_fake, fake) d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) # --------------------- # Train Generator # --------------------- # Train the generator g_loss = self.adversarial_autoencoder.train_on_batch(imgs, [imgs, valid]) # Plot the progress print ("%d [D loss: %f, acc: %.2f%%] [G loss: %f, mse: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss[0], g_loss[1])) # 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 z = np.random.normal(size=(r*c, self.latent_dim)) gen_imgs = self.decoder.predict(z) 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/mnist_%d.png" % epoch) plt.close()