tensorflow实现自编码器

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
 @date 2018/08/09 20:08:45
"""

import sys
import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

def xavier_init(fan_in, fan_out, constant=1):
    """
    Briefs:
        xavier_init
    """
    low = - constant * np.sqrt(6.0 / (fan_in + fan_out))
    high = constant * np.sqrt(6.0 / (fan_in + fan_out))
    return tf.random_uniform((fan_in, fan_out), minval = low, maxval = high, dtype = tf.float32)

class AdditiveGaussianNoiseAutoencoder(object):
    """
    Briefs:自编码器
    """
    def __init__(self, n_input, n_hidden, transfer_function=tf.nn.softplus,optimizer=tf.train.AdamOptimizer(), scale=0.1):
        """
        Briefs:
            init
        """
        self.n_input = n_input
        self.n_hidden = n_hidden
        self.transfer = transfer_function
        self.scale = tf.placeholder(tf.float32)
        self.training_scale = scale
        network_weights = self._initialize_weights()
        self.weights = network_weights

        self.x = tf.placeholder(tf.float32, [None, self.n_input])
        self.hidden = self.transfer(tf.add(tf.matmul(self.x + scale * tf.random_normal((n_input,)), 
            self.weights['w1']), self.weights['b1']))
        self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']), self.weights['b2'])

        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0))
        self.optimizer = optimizer.minimize(self.cost)
        init = tf.global_variables_initializer()
        self.sess = tf.Session()
        self.sess.run(init)

    def _initialize_weights(self):
        """
        Briefs:
            _initialize weights
        """
        all_weights = dict()
        all_weights['w1'] = tf.Variable(xavier_init(self.n_input, self.n_hidden))
        all_weights['b1'] = tf.Variable(tf.zeros([self.n_hidden], dtype=tf.float32))
        all_weights['w2'] = tf.Variable(tf.zeros([self.n_hidden, self.n_input], dtype=tf.float32))
        all_weights['b2'] = tf.Variable(tf.zeros([self.n_input], dtype=tf.float32))
        return all_weights

    def partial_fit(self, X):
        """
        Briefs:
            partial fit
        """
        cost, opt = self.sess.run((self.cost, self.optimizer), feed_dict = {self.x: X, self.scale: 
            self.training_scale})
        return cost

    def calc_total_cost(self, X):
        """
        Briefs:
            calc total cost
        """
        return self.sess.run(self.cost, feed_dict = {self.x: X, self.scale: self.training_scale})

    def transform(self, X):
        """
        Briefs:
            transform
        """
        return self.sess.run(self.hidden, feed_dict = {self.x: X, self.scale: self.training_scale})

    def generate(self, hidden=None):
        """
        Briefs:
            generate
        """
        if hidden is None:
            hidden = np.random.normal(size = self.weights['b1'])
        return self.sess.run(self.reconstruction, feed_dict = {self.hidden: hidden})

    def reconstruct(self, X):
        """
        Briefs:
            reconstruction
        """
        return self.sess.run(self.reconstruction, feed_dict = {self.x: X, self.scale: 
                self.training_scale})

    def getWeights(self):
        """
        Briefs:
            get weigths
        """
        return self.sess.run(self.weights['w1'])

    def getBiases(self):
        """
        Briefs:
            get biases
        """
        return self.sess.run(self.weights['b1'])

mnist = input_data.read_data_sets('MNIST_data', one_hot = True)

def standard_scale(X_train, X_test):
    """
    Briefs:
        standard scale
        标准化处理：先减去均值再除以标准差
    """
    preprocessor = prep.StandardScaler().fit(X_train)
    X_train = preprocessor.transform(X_train)
    X_test = preprocessor.transform(X_test)
    return X_train, X_test

def get_random_block_from_data(data, batch_size):
    """
    Briefs:
        get random block from data
    """
    start_index = np.random.randint(0, len(data) - batch_size)
    return data[start_index:(start_index + batch_size)]

X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
n_samples = int(mnist.train.num_examples)
training_epochs = 20
batch_size = 128
display_step = 1

autoencoder = AdditiveGaussianNoiseAutoencoder(n_input = 784, n_hidden = 200, transfer_function = 
        tf.nn.softplus, optimizer = tf.train.AdamOptimizer(learning_rate = 0.001), scale = 0.01)

for epoch in range(training_epochs):
    avg_cost = 0
    total_batch = int(n_samples / batch_size)
    for i in range(total_batch):
        batch_xs = get_random_block_from_data(X_train, batch_size)

        cost = autoencoder.partial_fit(batch_xs)
        avg_cost += cost / n_samples * batch_size

    if epoch % display_step== 0:
        print("Epoch:", "%04d" % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))

print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))

if __name__ == '__main__':
    pass