1TensorFlow实现自编码器-1.5 TensorFlow实现单隐层自编码器--计算图设计

#1.5 TensorFlow实现单隐层自编码器--计算图设计import numpy as npimport sklearn.preprocessing as prepimport tensorflow as tfimport matplotlib.pyplot as pltfrom tensorflow.examples.tutorials.mnist import input_dataimport osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'#控制训练过程的参数learning_rate = 0.01training_epochs = 20batch_size = 256display_step = 1examples_to_show = 10#网络模型参数n_hidden_units = 256 #隐藏层神经元数量（让编码器和解码器都有同样规模的隐藏层）n_input_units = 784 #输入层神经元数量MNIST data input(img shape :28*28)n_output_units = n_input_units #解码器输出晨神经元数量必须等于输入数据的units数量#根据输入输出节点数量返回初始化好的指定名称的权重Variabledef WeightsVariable(n_in,n_out,name_str):    return tf.Variable(tf.random_normal([n_in,n_out]),dtype=tf.float32,name=name_str)#根据输出节点数量返回初始化好的指定名称的偏置Variabledef BiasesVariable(n_out,name_str):    return tf.Variable(tf.random_normal([n_out]),dtype=tf.float32,name=name_str)#构建编码器# def Encoder(x_origin,activate_func=tf.nn.sigmoid):def Encoder(x_origin,activate_func=tf.nn.sigmoid):    #编码器第一隐藏层    with tf.name_scope('Layer'):        weights = WeightsVariable(n_input_units,n_hidden_units,'weights')        biases = BiasesVariable(n_hidden_units,'biases')        x_code = activate_func(tf.add(tf.matmul(x_origin,weights),biases))        # x_code = tf.nn.sigmoid(tf.add(tf.matmul(x_origin,weights),biases))    return x_code#构建解码器def Decoder(x_code,activate_func):    #解码器第一隐藏层    with tf.name_scope('Layer'):        weights = WeightsVariable(n_hidden_units, n_output_units, 'weights')        biases = BiasesVariable(n_output_units, 'biases')        x_decode = activate_func(tf.add(tf.matmul(x_code, weights), biases))    return x_decode#调用上面写的函数构造计算图with tf.Graph().as_default():    #计算图输入    with tf.name_scope('X_origin'):        X_Origin = tf.placeholder(tf.float32,[None,n_input_units])    #构建编码器模型    with tf.name_scope('Encoder'):        X_code = Encoder(X_Origin,activate_func=tf.nn.sigmoid)    # 构建解码器模型    with tf.name_scope('Decoder'):        X_decode = Decoder(X_code,activate_func=tf.nn.sigmoid)    # 定义损失节点：重构数据与原始数据的误差平方和损失    with tf.name_scope('Loss'):        Loss = tf.reduce_mean(tf.pow(X_Origin - X_decode , 2))    # 定义优化器，训练节点    with tf.name_scope('Train'):        Optimizer = tf.train.RMSPropOptimizer(learning_rate)        Train = Optimizer.minimize(Loss)    # 为所有变量添加初始化节点    Init = tf.global_variables_initializer()    print('把计算图写入事件文件，在Tesorboard里面查看')    summary_write = tf.summary.FileWriter(logdir='logs',graph=tf.get_default_graph())    summary_write.flush()