黄文坚的tensorflow实战一书中的第四章，讲述了tensorflow实现多层感知机。Hiton早年提出过自编码器的非监督学习算法，书中的代码给出了一个隐藏层的神经网络，本人扩展到了多层，改进了代码。实现多层神经网络时，把每层封装成一个NetLayer对象(本质是单向链表)，然后计算隐藏层输出值的时候，运用递归算法，最后定义外层管理类。main函数里面，寻找出一个最优的模型出来。代码如下：
# encoding:utf-8
# selfEncodingWithTF.py
import numpy as np
import tensorflow as tf
import sklearn.preprocessing as prep
from tensorflow.examples.tutorials.mnist import input_data
'''
tensorflow实现自编码器，非监督学习
@author XueQiang Tong
'''
'''
xavier初始化器，把权重初始化在low和high范围内(满足N(0,2/Nin+Nout))
'''
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)
'''数据零均值，特征方差归一化处理'''
def standard_scale(X_train,X_validation,X_test):
preprocessor = prep.StandardScaler().fit(X_train)
X_train = preprocessor.transform(X_train)
X_validation = preprocessor.transform(X_validation)
X_test = preprocessor.transform(X_test)
return X_train,X_validation,X_test
'''获取批量文本的策略'''
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)]
'''定义的hidden层，数据结构本质是链表，其中n_node:本层节点数，n_input为输入节点数目'''
class NetLayer:
def __init__(self,n_node,n_input):
self.n_node = n_node
self.n_input = n_input
self.next_layer = None
'''初始化权重'''
def _initialize_weights(self):
weights = dict()
if self.next_layer == None:#如果是最后一层，由于只聚合不激活，全部初始化为0
weights['w'] = tf.Variable(tf.zeros([self.n_input, self.n_node], dtype=tf.float32))
weights['b'] = tf.Variable(tf.zeros([self.n_node], dtype=tf.float32))
else:
weights['w'] = tf.Variable(xavier_init(self.n_input, self.n_node))
weights['b'] = tf.Variable(tf.zeros([self.n_node], dtype=tf.float32))
self.weights = weights
return self.weights
'''递归计算各层的输出值，返回最后一层的输出值'''
def cal_output(self,transfer,index,X,scale):
if index == 0:
self.output = transfer(tf.add(tf.matmul(X + scale * tf.random_normal([self.n_input]),self.weights['w']),self.weights['b']))
else:
if self.next_layer is not None:
self.output = transfer(tf.add(tf.matmul(X,self.weights['w']),self.weights['b']))
else:self.output = tf.add(tf.matmul(X,self.weights['w']),self.weights['b'])
if self.next_layer is not None:
return self.next_layer.cal_output(transfer,++index,self.output,scale)
return self.output
def get_weights(self):
return self.weights['w']
def get_bias(self):
return self.weights['b']
'''定义的外层管理类'''
class AdditiveGaussianNoiseAutoencoder(object):
def __init__(self,layers=[],transfer_function=tf.nn.softplus,optimizer=tf.train.AdamOptimizer(),scale=0.1):
self.layers = []
self.training_scale = scale
self.scale = tf.placeholder(tf.float32)
self.construct_network(layers)
self._initialize_weights(self.layers)
self.x = tf.placeholder(tf.float32,[None,layers[0]])
self.reconstruction = self.layers[0].cal_output(transfer_function,0,self.x,scale)
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)
'''初始化各层并构建各层的关联'''
def construct_network(self,layers):
last_layer = None
for i,layer in enumerate(layers):
if i == 0:
continue
cur_layer = NetLayer(layer,layers[i-1])
self.layers.append(cur_layer)
if last_layer is not None:
last_layer.next_layer = cur_layer
last_layer = cur_layer
'''外层调用初始化权重'''
def _initialize_weights(self,layers):
for i,layer in enumerate(layers):
layer._initialize_weights()
'''训练参数，并且返回损失函数节点的值'''
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
'''运行cost节点'''
def calc_total_cost(self,X):
return self.sess.run(self.cost,feed_dict={self.x:X,self.scale:self.training_scale})
'''运行reconstruction节点'''
def reconstruct(self,X):
return self.sess.run(self.reconstruction,feed_dict={self.x:X,self.scale:self.training_scale})
def fit(self,X_train,training_epochs,n_samples,batch_size):
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 = self.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))
if __name__ == '__main__':
mnist = input_data.read_data_sets("E:\Python35\Lib\site-packages\tensorflow\examples\tutorials\mnist\MNIST_data",one_hot=True)
X_train,X_validation,X_test = standard_scale(mnist.train.images,mnist.validation.images,mnist.test.images) #得到训练样本和测试样本
n_samples = int(mnist.train.num_examples) #获取样本总数
training_epochs = [20,40,60] #迭代次数
list_layers = [[784,500,200,784],[784,200,200,784],[784,300,200,784]]
batch_size = 128 #批次
display_step = 1 #每隔一步显示损失函数
mincost = (1 << 31) - 1.
bestIter = 0
best_layers = []
bestModel = None
'''训练出最优模型'''
for epoch in training_epochs:
for layers in list_layers:
autoencoder = AdditiveGaussianNoiseAutoencoder(layers,transfer_function=tf.nn.softplus, optimizer=
tf.train.AdamOptimizer(learning_rate=0.001), scale=0.01)
autoencoder.fit(X_train,training_epochs,n_samples,batch_size)
cost = autoencoder.calc_total_cost(X_validation)
if cost < mincost:
mincost = cost
bestModel = autoencoder
bestIter = epoch
best_layers = layers
'''训练完毕后，用测试样本验证一下cost'''
print("Total cost: " + str(bestModel.calc_total_cost(X_test)))