自適應編碼機及多層感知機
阿新 • • 發佈:2018-12-12
4_1code 自編碼機
# -*- 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)))