使用Keras编写GAN的入门
Time: 2017-5-31
前言
主要参考了网页[1]的教程，同时主要算法来自Ian J. Goodfellow 的论文，算法如下：
gan
代码
%matplotlib inlineimport numpy as npimport pandas as pdfrom keras.models import Modelfrom keras.layers import Dense, Activation, Input, Reshapefrom keras.layers import Conv1D, Flatten, Dropoutfrom keras.optimizers import SGD, Adamfrom tqdm import tqdm_notebook as tqdm # 进度条# 生成随机正弦曲线的数据def sample_data(n_samples=10000, x_vals=np.arange(0, 5, .1), max_offset=1000, mul_range=[1, 2]): vectors = [] for i in range(n_samples): offset = np.random.random() * max_offset mul = mul_range[0] + np.random.random() * (mul_range[1] - mul_range[0]) vectors.append(np.sin(offset + x_vals * mul) / 2 + .5) return np.array(vectors) # 创建生成模型def get_generative(G_in, dense_dim=200, out_dim=50, lr=1e-3): x = Dense(dense_dim)(G_in) x = Activation('tanh')(x) G_out = Dense(out_dim, activation='tanh')(x) G = Model(G_in, G_out) opt = SGD(lr=lr) G.compile(loss='binary_crossentropy', optimizer=opt) return G, G_out # 创建判别模型def get_discriminative(D_in, lr=1e-3, drate = .25, n_channels=50, conv_sz=5, leak=.2): x = Reshape((-1, 1))(D_in) x = Conv1D(n_channels, conv_sz, activation='relu')(x) x = Dropout(drate)(x) x = Flatten()(x) x = Dense(n_channels)(x) D_out = Dense(2, activation='sigmoid')(x) D = Model(D_in, D_out) dopt = Adam(lr=lr) D.compile(loss='binary_crossentropy', optimizer=dopt) return D, D_out def set_trainability(model, trainable=False): model.trainable = trainable for layer in model.layers: layer.trainable = trainable def make_gan(GAN_in, G, D): set_trainability(D, False) x = G(GAN_in) GAN_out = D(x) GAN = Model(GAN_in, GAN_out) GAN.compile(loss='binary_crossentropy', optimizer=G.optimizer) return GAN, GAN_out# 通过生成数据 预训练判别模型def sample_data_and_gen(G, noise_dim=10, n_samples=10000): XT = sample_data(n_samples=n_samples) XN_noise = np.random.uniform(0, 1, size=[n_samples, noise_dim]) XN = G.predict(XN_noise) X = np.concatenate((XT, XN)) y = np.zeros((2*n_samples, 2)) y[:n_samples, 1] = 1 y[n_samples:, 0] = 1 return X, y def pretrain(G, D, noise_dim=10, n_samples=10000, batch_size=32): X, y = sample_data_and_gen(G, noise_dim=noise_dim, n_samples=n_samples) set_trainability(D, True) D.fit(X, y, epochs=1, batch_size=batch_size) # 开始交叉训练步骤def sample_noise(G, noise_dim=10, n_samples=10000): X = np.random.uniform(0, 1, size=[n_samples, noise_dim]) y = np.zeros((n_samples, 2)) y[:, 1] = 1 return X, y def train(GAN, G, D, epochs=500, n_samples=10000, noise_dim=10, batch_size=32, verbose=False, v_freq=50): d_loss = [] g_loss = [] e_range = range(epochs) if verbose: e_range = tqdm(e_range) for epoch in e_range: X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim) # 对D进行训练 set_trainability(D, True) d_loss.append(D.train_on_batch(X, y)) X, y = sample_noise(G, n_samples=n_samples, noise_dim=noise_dim) # 对G训练 set_trainability(D, False) g_loss.append(GAN.train_on_batch(X, y)) if verbose and (epoch + 1) % v_freq == 0: print("Epoch #{}: Generative Loss: {}, Discriminative Loss: {}".format(epoch + 1, g_loss[-1], d_loss[-1])) return d_loss, g_loss ax = pd.DataFrame(np.transpose(sample_data(5))).plot()G_in = Input(shape=[10])G, G_out = get_generative(G_in)G.summary()D_in = Input(shape=[50])D, D_out = get_discriminative(D_in)D.summary()
_________________________________________________________________Layer (type) Output Shape Param # =================================================================input_9 (InputLayer) (None, 10) 0 _________________________________________________________________dense_13 (Dense) (None, 200) 2200 _________________________________________________________________activation_4 (Activation) (None, 200) 0 _________________________________________________________________dense_14 (Dense) (None, 50) 10050 =================================================================Total params: 12,250Trainable params: 12,250Non-trainable params: 0__________________________________________________________________________________________________________________________________Layer (type) Output Shape Param # =================================================================input_10 (InputLayer) (None, 50) 0 _________________________________________________________________reshape_4 (Reshape) (None, 50, 1) 0 _________________________________________________________________conv1d_4 (Conv1D) (None, 46, 50) 300 _________________________________________________________________dropout_4 (Dropout) (None, 46, 50) 0 _________________________________________________________________flatten_4 (Flatten) (None, 2300) 0 _________________________________________________________________dense_15 (Dense) (None, 50) 115050 _________________________________________________________________dense_16 (Dense) (None, 2) 102 =================================================================Total params: 115,452Trainable params: 115,452Non-trainable params: 0_________________________________________________________________
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GAN_in = Input([10])GAN, GAN_out = make_gan(GAN_in, G, D)GAN.summary()
_________________________________________________________________Layer (type) Output Shape Param # =================================================================input_11 (InputLayer) (None, 10) 0 _________________________________________________________________model_9 (Model) (None, 50) 12250 _________________________________________________________________model_10 (Model) (None, 2) 115452 =================================================================Total params: 127,702Trainable params: 12,250Non-trainable params: 115,452_________________________________________________________________
pretrain(G, D)
Epoch 1/120000/20000 [==============================] - 3s - loss: 0.0072
d_loss, g_loss = train(GAN, G, D, verbose=True)
Epoch #50: Generative Loss: 4.41527795791626, Discriminative Loss: 0.6733301877975464Epoch #100: Generative Loss: 3.8898046016693115, Discriminative Loss: 0.09901376813650131Epoch #150: Generative Loss: 6.2410054206848145, Discriminative Loss: 0.034074194729328156Epoch #200: Generative Loss: 5.206066608428955, Discriminative Loss: 0.13078376650810242Epoch #250: Generative Loss: 3.5144925117492676, Discriminative Loss: 0.07160962373018265Epoch #300: Generative Loss: 3.705162525177002, Discriminative Loss: 0.05893774330615997Epoch #350: Generative Loss: 3.511479616165161, Discriminative Loss: 0.09775738418102264Epoch #400: Generative Loss: 4.141300678253174, Discriminative Loss: 0.03169865906238556Epoch #450: Generative Loss: 3.500260829925537, Discriminative Loss: 0.05957922339439392Epoch #500: Generative Loss: 2.9797921180725098, Discriminative Loss: 0.10566817969083786
ax = pd.DataFrame( { 'Generative Loss': g_loss, 'Discriminative Loss': d_loss, }).plot(title='Training loss', logy=True)ax.set_xlabel("Epochs")ax.set_ylabel("Loss") 
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N_VIEWED_SAMPLES = 2data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES)pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).plot()
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N_VIEWED_SAMPLES = 2data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES)pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).rolling(5).mean()[5:].plot()
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reference
[1]