4用于cifar10的卷积神经网络-4.2/4.3cifar10卷积神经网络的计算图设计（上下）

代码如下：

#-*- coding:utf-8 -*-#实现简单卷积神经网络对MNIST数据集进行分类：conv2d + activation + pool + fcimport csvimport tensorflow as tfimport osfrom tensorflow.examples.tutorials.mnist import input_dataos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'# import cifar10_input# 设置算法超参数learning_rate_init = 0.001training_epochs = 1batch_size = 100display_step = 10# Network Parametersn_input = 784 # MNIST data input (img shape: 28*28)# n_classes = 10 # MNIST total classes (0-9 digits)#数据集中输入图像的参数image_size = 24image_channel = 3n_classes = 10 #CiFar10中类的数量#根据指定的维数返回初始化好的指定名称的权重 Variabledef WeightsVariable(shape, name_str, stddev=0.1):    # initial = tf.random_normal(shape=shape, stddev=stddev, dtype=tf.float32)    initial = tf.truncated_normal(shape=shape, stddev=stddev, dtype=tf.float32)    return tf.Variable(initial, dtype=tf.float32, name=name_str)#根据指定的维数返回初始化好的指定名称的偏置 Variabledef BiasesVariable(shape, name_str, init_value=0.00001):    initial = tf.constant(init_value, shape=shape)    return tf.Variable(initial, dtype=tf.float32, name=name_str)# 二维卷积层activation(conv2d+bias)的封装def Conv2d(x, W, b, stride=1, padding='SAME',activation=tf.nn.relu,act_name='relu'):    with tf.name_scope('conv2d_bias'):        y = tf.nn.conv2d(x, W, strides=[1, stride, stride, 1], padding=padding)        y = tf.nn.bias_add(y, b)    with tf.name_scope(act_name):        y = activation(y)    return y# 二维池化层pool的封装def Pool2d(x, pool= tf.nn.max_pool, k=2, stride=2,padding='SAME'):    return pool(x, ksize=[1, k, k, 1], strides=[1, stride, stride, 1], padding=padding)# 全连接层activate(wx+b)的封装def FullyConnected(x, W, b, activate=tf.nn.relu, act_name='relu'):    with tf.name_scope('Wx_b'):        y = tf.matmul(x, W)        y = tf.add(y, b)    with tf.name_scope(act_name):        y = activate(y)    return ydef Inference(image_holder):    # 第一个卷积层activate(conv2d + biase)    with tf.name_scope('Conv2d_1'):        conv1_kernels_num = 64        weights = WeightsVariable(shape=[5, 5, image_channel, conv1_kernels_num],                                  name_str='weights',stddev=5e-2)        biases = BiasesVariable(shape=[conv1_kernels_num], name_str='biases',init_value=0.0)        conv1_out = Conv2d(image_holder, weights, biases, stride=1, padding='SAME')    # 第一个池化层(pool 2d)    with tf.name_scope('Pool2d_1'):        pool1_out = Pool2d(conv1_out, pool=tf.nn.max_pool, k=3, stride=2,padding='SAME')    # 第二个卷积层activate(conv2d + biase)    with tf.name_scope('Conv2d_2'):        conv2_kernels_num = 64        weights = WeightsVariable(shape=[5, 5, conv1_kernels_num, conv2_kernels_num],                                  name_str='weights', stddev=5e-2)        biases = BiasesVariable(shape=[conv2_kernels_num], name_str='biases', init_value=0.0)        conv2_out = Conv2d(pool1_out, weights, biases, stride=1, padding='SAME')    # 第二个池化层(pool 2d)    with tf.name_scope('Pool2d_2'):        pool2_out = Pool2d(conv2_out, pool=tf.nn.max_pool, k=3, stride=2, padding='SAME')    #将二维特征图变换为一维特征向量    with tf.name_scope('FeatsReshape'):        features = tf.reshape(pool2_out, [batch_size,-1])        feats_dim = features.get_shape()[1].value    # 第一个全连接层(fully connected layer)    with tf.name_scope('FC1_nonlinear'):        fc1_units_num = 384        weights = WeightsVariable(shape=[feats_dim, fc1_units_num],                                  name_str='weights',stddev=4e-2)        biases = BiasesVariable(shape=[fc1_units_num], name_str='biases',init_value=0.1)        fc1_out = FullyConnected(features, weights, biases, activate=tf.nn.relu, act_name='relu')    # 第二个全连接层(fully connected layer)    with tf.name_scope('FC2_nonlinear'):        fc2_units_num = 192        weights = WeightsVariable(shape=[fc1_units_num, fc2_units_num],                                  name_str='weights',stddev=4e-2)        biases = BiasesVariable(shape=[fc2_units_num], name_str='biases',init_value=0.1)        fc2_out = FullyConnected(fc1_out, weights, biases,activate=tf.nn.relu, act_name='relu')    # 第三个全连接层(fully connected layer)    with tf.name_scope('FC3_linear'):        fc3_units_num = n_classes        weights = WeightsVariable(shape=[fc2_units_num, fc3_units_num],                                  name_str='weights',stddev=1.0/fc2_units_num)        biases = BiasesVariable(shape=[fc3_units_num], name_str='biases',init_value=0.0)        logits = FullyConnected(fc2_out, weights, biases,activate=tf.identity, act_name='linear')    return logits#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('Inputs'):        image_holder = tf.placeholder(tf.float32, [batch_size, image_size,image_size,image_channel], name='images')        labels_holder = tf.placeholder(tf.int32, [batch_size], name='labels')    # 计算图前向推断过程    with tf.name_scope('Inference'):         logits = Inference(image_holder)    # 定义损失层(loss layer)    with tf.name_scope('Loss'):        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels_holder,logits=logits)        cross_entropy_mean = tf.reduce_mean(cross_entropy)        total_loss = cross_entropy_mean    # 定义优化训练层(train layer)    with tf.name_scope('Train'):        learning_rate = tf.placeholder(tf.float32)        global_step = tf.Variable(0, name='global_step', trainable=False, dtype=tf.int64)        optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)        trainer_op = optimizer.minimize(total_loss,global_step=global_step)    # 定义模型评估层(evaluate layer)    with tf.name_scope('Evaluate'):        top_K_op = tf.nn.in_top_k(predictions=logits,targets=labels_holder,k=1)    # 添加所有变量的初始化节点    init_op = tf.global_variables_initializer()    print('把计算图写入事件文件，在TensorBoard里面查看')    graph_writer = tf.summary.FileWriter(logdir='logs', graph=tf.get_default_graph())    graph_writer.close()