適応符号化機及び多層感知機

# -*- coding: utf-8 -*-
# zhibianmaji he duochengganzhiji
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):
    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):
    def __init__(self,n_input,n_hidden,transfer_function=tf.nn.softplus,
                 optimizer = tf.train.AdadeltaOptimizer(),scale=0.1):
        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):
        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):
        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):
        return self.sess.run(self.cost,feed_dict={self.x:X,self.scale:self.training_scale})
    
    def transform(self,X):
        return self.sess.run(self.hidden,feed_dict={self.x:X,self.scale:self.training_scale})
    
    def generate(self,hidden=None):
        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):
        return self.sess.run(self.reconstruction,feed_dict={self.x:X,self.scale:self.training_scale})

    def getWeights(self):
        return self.sess.run(self.weights['w1'])

    def getBiases(self):
        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):
    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):
    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)))