變數初始化及函式定義

batch_size = 100
original_dim = 784
latent_dim = 2
intermediate_dim = 256
nb_epoch = 50
epsilon_std = 1.0
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train/=255
x_test/=255
#np.prob返回陣列元素在給定軸上的乘積。reshape將3D轉化成2D
 

x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
def sampling(args):
    z_mean, z_log_var = args
    epsilon = K.random_normal(shape=(batch_size, latent_dim),mean=0.0
 
, stddev=1.0, dtype=None, seed=None)
    return z_mean + K.exp(z_log_var / 2) * epsilon
def vae_loss(x, x_decoded_mean):
    xent_loss = original_dim * objectives.binary_crossentropy(x, x_decoded_mean)
    kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
    return xent_loss + kl_loss
 

構造編碼器

x = Input(batch_shape=(batch_size, original_dim))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
encoder = Model(x, z_mean)

構造解碼器

z_log_var = Dense(latent_dim)(h)
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])#Lambda是匿名函式
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
#可以用Model直接建模型，也可以逐個model.add()
vae = Model(x, x_decoded_mean)#解碼器
vae.compile(optimizer='rmsprop', loss=vae_loss)
vae.fit(x_train, x_train,
        shuffle=True,
        epochs=nb_epoch,
        batch_size=batch_size,
        validation_data=(x_test, x_test))

顯示一個數字類的二維圖

x_test_encoded = encoder.predict(x_test, batch_size=batch_size)
#figsize是影象的長和寬
plt.figure(figsize=(6, 6))
# 散點圖，前兩個引數是資料，c引數是散點顏色
plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)
#顏色漸變條
plt.colorbar()
plt.show()

構造數字生成器

decoder_input = Input(shape=(latent_dim,))
_h_decoded = decoder_h(decoder_input)
_x_decoded_mean = decoder_mean(_h_decoded)
generator = Model(decoder_input, _x_decoded_mean)

顯示數字的二維流形

n = 15  # 用15x15數字圖
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
# 我們將在[-15, 15 ]標準偏差( 等距離)中取樣n個點。
grid_x = np.linspace(-15, 15, n)
grid_y = np.linspace(-15, 15, n)
#enumerate用於列舉
for i, yi in enumerate(grid_x):
    for j, xi in enumerate(grid_y):
        z_sample = np.array([[xi, yi]])
        x_decoded = generator.predict(z_sample)
        digit = x_decoded[0].reshape(digit_size, digit_size)
        figure[i * digit_size: (i + 1) * digit_size,
               j * digit_size: (j + 1) * digit_size] = digit

plt.figure(figsize=(10, 10))
plt.imshow(figure)
plt.show()

原始碼地址：