TensorFlow搭建神经网络最佳实践

一、TensorFLow完整样例

在MNIST数据集上，搭建一个简单神经网络结构，一个包含ReLU单元的非线性化处理的两层神经网络。在训练神经网络的时候，使用带指数衰减的学习率设置、使用正则化来避免过拟合、使用滑动平均模型来使得最终的模型更加健壮。

程序将计算神经网络前向传播的部分单独定义一个函数inference，训练部分定义一个train函数，再定义一个主函数main。

完整程序：

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu May 25 08:56:30 2017  @author: marsjhao """  import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data  INPUT_NODE = 784 # 输入节点数 OUTPUT_NODE = 10 # 输出节点数 LAYER1_NODE = 500 # 隐含层节点数 BATCH_SIZE = 100 LEARNING_RETE_BASE = 0.8 # 基学习率 LEARNING_RETE_DECAY = 0.99 # 学习率的衰减率 REGULARIZATION_RATE = 0.0001 # 正则化项的权重系数 TRAINING_STEPS = 10000 # 迭代训练次数 MOVING_AVERAGE_DECAY = 0.99 # 滑动平均的衰减系数  # 传入神经网络的权重和偏置，计算神经网络前向传播的结果 def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):   # 判断是否传入ExponentialMovingAverage类对象   if avg_class == None:     layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)     return tf.matmul(layer1, weights2) + biases2   else:     layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1))                    + avg_class.average(biases1))     return tf.matmul(layer1, avg_class.average(weights2))/              + avg_class.average(biases2)  # 神经网络模型的训练过程 def train(mnist):   x = tf.placeholder(tf.float32, [None,INPUT_NODE], name='x-input')   y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')    # 定义神经网络结构的参数   weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER1_NODE],                         stddev=0.1))   biases1 = tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))   weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE],                         stddev=0.1))   biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))    # 计算非滑动平均模型下的参数的前向传播的结果   y = inference(x, None, weights1, biases1, weights2, biases2)      global_step = tf.Variable(0, trainable=False) # 定义存储当前迭代训练轮数的变量    # 定义ExponentialMovingAverage类对象   variable_averages = tf.train.ExponentialMovingAverage(             MOVING_AVERAGE_DECAY, global_step) # 传入当前迭代轮数参数   # 定义对所有可训练变量trainable_variables进行更新滑动平均值的操作op   variables_averages_op = variable_averages.apply(tf.trainable_variables())    # 计算滑动模型下的参数的前向传播的结果   average_y = inference(x, variable_averages, weights1, biases1, weights2, biases2)    # 定义交叉熵损失值   cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(           logits=y, labels=tf.argmax(y_, 1))   cross_entropy_mean = tf.reduce_mean(cross_entropy)   # 定义L2正则化器并对weights1和weights2正则化   regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)   regularization = regularizer(weights1) + regularizer(weights2)   loss = cross_entropy_mean + regularization # 总损失值    # 定义指数衰减学习率   learning_rate = tf.train.exponential_decay(LEARNING_RETE_BASE, global_step,           mnist.train.num_examples / BATCH_SIZE, LEARNING_RETE_DECAY)   # 定义梯度下降操作op，global_step参数可实现自加1运算   train_step = tf.train.GradientDescentOptimizer(learning_rate)/              .minimize(loss, global_step=global_step)   # 组合两个操作op   train_op = tf.group(train_step, variables_averages_op)   '''''   # 与tf.group()等价的语句   with tf.control_dependencies([train_step, variables_averages_op]):     train_op = tf.no_op(name='train')   '''   # 定义准确率   # 在最终预测的时候，神经网络的输出采用的是经过滑动平均的前向传播计算结果   correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))   accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    # 初始化回话sess并开始迭代训练   with tf.Session() as sess:     sess.run(tf.global_variables_initializer())     # 验证集待喂入数据     validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}     # 测试集待喂入数据     test_feed = {x: mnist.test.images, y_: mnist.test.labels}     for i in range(TRAINING_STEPS):       if i % 1000 == 0:         validate_acc = sess.run(accuracy, feed_dict=validate_feed)         print('After %d training steps, validation accuracy'            ' using average model is %f' % (i, validate_acc))       xs, ys = mnist.train.next_batch(BATCH_SIZE)       sess.run(train_op, feed_dict={x: xs, y_:ys})      test_acc = sess.run(accuracy, feed_dict=test_feed)     print('After %d training steps, test accuracy'        ' using average model is %f' % (TRAINING_STEPS, test_acc))  # 主函数 def main(argv=None):   mnist = input_data.read_data_sets("MNIST_data", one_hot=True)   train(mnist)  # 当前的python文件是shell文件执行的入口文件，而非当做import的python module。 if __name__ == '__main__': # 在模块内部执行   tf.app.run() # 调用main函数并传入所需的参数list