cvae_keras.py

#! -*- coding: utf-8 -*-


'''用Keras实现的CVAE
   目前只保证支持Tensorflow后端

 #来自
  https://github.com/keras-team/keras/blob/master/examples/variational_autoencoder.py
'''

from __future__ import print_function

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm

from keras.layers import Input, Dense, Lambda
from keras.models import Model
from keras import backend as K
from keras import metrics
from keras.datasets import mnist
from keras.utils import to_categorical

batch_size = 100
original_dim = 784
latent_dim = 2 # 隐变量取2维只是为了方便后面画图
intermediate_dim = 256
epochs = 100
epsilon_std = 1.0
num_classes = 10

# 加载MNIST数据集
(x_train, y_train_), (x_test, y_test_) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
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:])))
y_train = to_categorical(y_train_, num_classes)
y_test = to_categorical(y_test_, num_classes)


x = Input(shape=(original_dim,))
h = Dense(intermediate_dim, activation='relu')(x)

# 算p(Z|X)的均值和方差
z_mean = Dense(latent_dim)(h)
z_log_var = Dense(latent_dim)(h)

y = Input(shape=(num_classes,)) # 输入类别
yh = Dense(latent_dim)(y) # 这里就是直接构建每个类别的均值

# 重参数技巧
def sampling(args):
    z_mean, z_log_var = args
    epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0.,
                              stddev=epsilon_std)
    return z_mean + K.exp(z_log_var / 2) * epsilon

# 重参数层，相当于给输入加入噪声
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])

# 解码层，也就是生成器部分
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)

# 建立模型
vae = Model([x, y], [x_decoded_mean, yh])

# xent_loss是重构loss，kl_loss是KL loss
xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)

# 只需要修改K.square(z_mean)为K.square(z_mean - yh)，也就是让隐变量向类内均值看齐
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean - yh) - K.exp(z_log_var), axis=-1)
vae_loss = K.mean(xent_loss + kl_loss)

# add_loss是新增的方法，用于更灵活地添加各种loss
vae.add_loss(vae_loss)
vae.compile(optimizer='rmsprop')
vae.summary()

vae.fit([x_train, y_train], 
        shuffle=True,
        epochs=epochs,
        batch_size=batch_size,
        validation_data=([x_test, y_test], None))


# 构建encoder，然后观察各个数字在隐空间的分布
encoder = Model(x, z_mean)

x_test_encoded = encoder.predict(x_test, batch_size=batch_size)
plt.figure(figsize=(6, 6))
plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test_)
plt.colorbar()
plt.show()

# 构建生成器
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)

# 输出每个类的均值向量
mu = Model(y, yh)
mu = mu.predict(np.eye(num_classes))

# 观察能否通过控制隐变量的均值来输出特定类别的数字
n = 15  # figure with 15x15 digits
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))

output_digit = 9 # 指定输出数字

# 用正态分布的分位数来构建隐变量对
grid_x = norm.ppf(np.linspace(0.05, 0.95, n)) + mu[output_digit][1]
grid_y = norm.ppf(np.linspace(0.05, 0.95, n)) + mu[output_digit][0]

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, cmap='Greys_r')
plt.show()