# 使用 Keras 的简单的 GAN 您可以按照 Jupyter 笔记本中的代码`ch-14a_SimpleGAN`。 现在让我们在 Keras 实现相同的模型： 1. 超参数定义与上一节保持一致： ```py # graph hyperparameters g_learning_rate = 0.00001 d_learning_rate = 0.01 n_x = 784 # number of pixels in the MNIST image # number of hidden layers for generator and discriminator g_n_layers = 3 d_n_layers = 1 # neurons in each hidden layer g_n_neurons = [256, 512, 1024] d_n_neurons = [256] ``` 1. 接下来，定义生成器网络： ```py # define generator g_model = Sequential() g_model.add(Dense(units=g_n_neurons[0], input_shape=(n_z,), name='g_0')) g_model.add(LeakyReLU()) for i in range(1,g_n_layers): g_model.add(Dense(units=g_n_neurons[i], name='g_{}'.format(i) )) g_model.add(LeakyReLU()) g_model.add(Dense(units=n_x, activation='tanh',name='g_out')) print('Generator:') g_model.summary() g_model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(lr=g_learning_rate) ) ``` 这就是生成器模型的样子： ```py Generator: _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= g_0 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_1 (LeakyReLU) (None, 256) 0 _________________________________________________________________ g_1 (Dense) (None, 512) 131584 _________________________________________________________________ leaky_re_lu_2 (LeakyReLU) (None, 512) 0 _________________________________________________________________ g_2 (Dense) (None, 1024) 525312 _________________________________________________________________ leaky_re_lu_3 (LeakyReLU) (None, 1024) 0 _________________________________________________________________ g_out (Dense) (None, 784) 803600 ================================================================= Total params: 1,526,288 Trainable params: 1,526,288 Non-trainable params: 0 _________________________________________________________________ ``` 1. 在 Keras 示例中，我们没有定义两个判别器网络，就像我们在 TensorFlow 示例中定义的那样。相反，我们定义一个判别器网络，然后将生成器和判别器网络缝合到 GAN 网络中。然后，GAN 网络仅用于训练生成器参数，判别器网络用于训练判别器参数： ```py # define discriminator d_model = Sequential() d_model.add(Dense(units=d_n_neurons[0], input_shape=(n_x,), name='d_0' )) d_model.add(LeakyReLU()) d_model.add(Dropout(0.3)) for i in range(1,d_n_layers): d_model.add(Dense(units=d_n_neurons[i], name='d_{}'.format(i) )) d_model.add(LeakyReLU()) d_model.add(Dropout(0.3)) d_model.add(Dense(units=1, activation='sigmoid',name='d_out')) print('Discriminator:') d_model.summary() d_model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.SGD(lr=d_learning_rate) ) ``` 这是判别器模型的外观： ```py Discriminator: _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= d_0 (Dense) (None, 256) 200960 _________________________________________________________________ leaky_re_lu_4 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_1 (Dropout) (None, 256) 0 _________________________________________________________________ d_out (Dense) (None, 1) 257 ================================================================= Total params: 201,217 Trainable params: 201,217 Non-trainable params: 0 _________________________________________________________________ ``` 1. 接下来，定义 GAN 网络，并将判别器模型的可训练属性转换为`false`，因为 GAN 仅用于训练生成器： ```py # define GAN network d_model.trainable=False z_in = Input(shape=(n_z,),name='z_in') x_in = g_model(z_in) gan_out = d_model(x_in) gan_model = Model(inputs=z_in,outputs=gan_out,name='gan') print('GAN:') gan_model.summary() ``` ```py gan_model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(lr=g_learning_rate) ) ``` 这就是 GAN 模型的样子： ```py GAN: _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= z_in (InputLayer) (None, 256) 0 _________________________________________________________________ sequential_1 (Sequential) (None, 784) 1526288 _________________________________________________________________ sequential_2 (Sequential) (None, 1) 201217 ================================================================= Total params: 1,727,505 Trainable params: 1,526,288 Non-trainable params: 201,217 _________________________________________________________________ ``` 1. 太好了，现在我们已经定义了三个模型，我们必须训练模型。训练按照以下算法进行： ```py For each epoch: For each batch: get real images x_batch generate noise z_batch generate images g_batch using generator model combine g_batch and x_batch into x_in and create labels y_out set discriminator model as trainable train discriminator using x_in and y_out generate noise z_batch set x_in = z_batch and labels y_out = 1 set discriminator model as non-trainable train gan model using x_in and y_out, (effectively training generator model) ``` 为了设置标签，我们分别对真实和假图像应用标签 0.9 和 0.1。通常，建议您使用标签平滑，通过为假数据选择 0.0 到 0.3 的随机值，为实际数据选择 0.8 到 1.0。 以下是笔记本电脑训练的完整代码： ```py n_epochs = 400 batch_size = 100 n_batches = int(mnist.train.num_examples / batch_size) n_epochs_print = 50 for epoch in range(n_epochs+1): epoch_d_loss = 0.0 epoch_g_loss = 0.0 for batch in range(n_batches): x_batch, _ = mnist.train.next_batch(batch_size) x_batch = norm(x_batch) z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z]) g_batch = g_model.predict(z_batch) x_in = np.concatenate([x_batch,g_batch]) y_out = np.ones(batch_size*2) y_out[:batch_size]=0.9 y_out[batch_size:]=0.1 d_model.trainable=True batch_d_loss = d_model.train_on_batch(x_in,y_out) z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z]) x_in=z_batch y_out = np.ones(batch_size) d_model.trainable=False batch_g_loss = gan_model.train_on_batch(x_in,y_out) epoch_d_loss += batch_d_loss epoch_g_loss += batch_g_loss if epoch%n_epochs_print == 0: average_d_loss = epoch_d_loss / n_batches average_g_loss = epoch_g_loss / n_batches print('epoch: {0:04d} d_loss = {1:0.6f} g_loss = {2:0.6f}' .format(epoch,average_d_loss,average_g_loss)) # predict images using generator model trained x_pred = g_model.predict(z_test) display_images(x_pred.reshape(-1,pixel_size,pixel_size)) ``` 我们每 50 个周期印刷结果，最多 350 个周期：  该模型慢慢地学习从随机噪声中生成高质量的手写数字图像。 GAN 有如此多的变化，它将需要另一本书来涵盖所有不同类型的 GAN。但是，实现技术几乎与我们在此处所示的相似。