tensorflow示例代码注释5

06_autoencoder.py

import tensorflow as tf

import numpy as np
import input_data


mnist_width = 28
n_visible = mnist_width * mnist_width
n_hidden = 500
corruption_level = 0.3


# create node for input data
X = tf.placeholder("float", [None, n_visible], name='X')


# create node for corruption mask
mask = tf.placeholder("float", [None, n_visible], name='mask')

// tf.random_uniform(shape,minval=0,maxval=None,dtype=tf.float32,seed=None,name=None) // 返回一个形状为shape的tensor，其中的元素服从minval和maxval之间的均匀分布。
# create nodes for hidden variables
W_init_max = 4 * np.sqrt(6. / (n_visible + n_hidden))
W_init = tf.random_uniform(shape=[n_visible, n_hidden],
                           minval=-W_init_max,
                           maxval=W_init_max)


W = tf.Variable(W_init, name='W')
b = tf.Variable(tf.zeros([n_hidden]), name='b')

//transpose 用于交换维度
W_prime = tf.transpose(W)  # tied weights between encoder and decoder
b_prime = tf.Variable(tf.zeros([n_visible]), name='b_prime')




def model(X, mask, W, b, W_prime, b_prime):
    tilde_X = mask * X  # corrupted X


    Y = tf.nn.sigmoid(tf.matmul(tilde_X, W) + b)  # hidden state
    Z = tf.nn.sigmoid(tf.matmul(Y, W_prime) + b_prime)  # reconstructed input
    return Z


# build model graph
Z = model(X, mask, W, b, W_prime, b_prime)


# create cost function
cost = tf.reduce_sum(tf.pow(X - Z, 2))  # minimize squared error
train_op = tf.train.GradientDescentOptimizer(0.02).minimize(cost)  # construct an optimizer


# load MNIST data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels


# Launch the graph in a session
with tf.Session() as sess:
    # you need to initialize all variables
    tf.initialize_all_variables().run()

//随机地从 n 个数中以概率 p 对其进行选择，我们可以先生成一个掩膜（mask）

//mask = np.random.binomial(1, p, n)
# 也即对这个n个数，分别以p进行确定其值为1（选中该值），
# 以(1-p)确定其值为0（也即是未选中该值）

    for i in range(100):
        for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)):
            input_ = trX[start:end]
            mask_np = np.random.binomial(1, 1 - corruption_level, input_.shape)
            sess.run(train_op, feed_dict={X: input_, mask: mask_np})


        mask_np = np.random.binomial(1, 1 - corruption_level, teX.shape)
        print(i, sess.run(cost, feed_dict={X: teX, mask: mask_np}))