# 使用 Keras 的簡單的 GAN 您可以按照 Jupyter 筆記本中的代碼`ch-14a_SimpleGAN`。 現在讓我們在 Keras 實現相同的模型： 1. 超參數定義與上一節保持一致： ```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. 接下來，定義生成器網絡： ```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) ) ``` 這就是生成器模型的樣子： ```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，我們沒有定義兩個判別器網絡，就像我們在 TensorFlow 示例中定義的那樣。相反，我們定義一個判別器網絡，然后將生成器和判別器網絡縫合到 GAN 網絡中。然后，GAN 網絡僅用于訓練生成器參數，判別器網絡用于訓練判別器參數： ```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) ) ``` 這是判別器模型的外觀： ```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. 接下來，定義 GAN 網絡，并將判別器模型的可訓練屬性轉換為`false`，因為 GAN 僅用于訓練生成器： ```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) ) ``` 這就是 GAN 模型的樣子： ```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，現在我們已經定義了三個模型，我們必須訓練模型。訓練按照以下算法進行： ```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) ``` 為了設置標簽，我們分別對真實和假圖像應用標簽 0.9 和 0.1。通常，建議您使用標簽平滑，通過為假數據選擇 0.0 到 0.3 的隨機值，為實際數據選擇 0.8 到 1.0。 以下是筆記本電腦訓練的完整代碼： ```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)) ``` 我們每 50 個周期印刷結果，最多 350 個周期：  該模型慢慢地學習從隨機噪聲中生成高質量的手寫數字圖像。 GAN 有如此多的變化，它將需要另一本書來涵蓋所有不同類型的 GAN。但是，實現技術幾乎與我們在此處所示的相似。