使用L2正则化和平均滑动模型的LeNet-5MNIST手写数字识别模型
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参考文献
实验平台: Tensorflow1.4.0 python3.5.0将四个文件下载后放到当前目录下的MNIST_data文件夹下
定义模型框架与前向传播
import tensorflow as tf# 配置神经网络的参数INPUT_NODE = 784OUTPUT_NODE = 10IMAGE_SIZE = 28NUM_CHANNELS = 1NUM_LABELS = 10# 第一层卷积层的尺寸和深度CONV1_DEEP = 32CONV1_SIZE = 5# 第二层卷积层的尺寸和深度CONV2_DEEP = 64CONV2_SIZE = 5# 全连接层的节点个数FC_SIZE = 512# 定义卷积神经网络的前向传播过程，这里添加了一个参数train,用于区分训练过程和测试过程。# 这里使用dropout方法，dropout方法可以进一步提升模型可靠性并防止过拟合，dropout只在训练过程中使用。def inference(input_tensor, train, regularizer): # 通过使用不同的命名空间来隔离变量，可以使每一层的变量命名只需要考虑在当前层的作用，而不需要考虑重名的问题 with tf.variable_scope('layer1-conv1'): conv1_weights = tf.get_variable( "weight", [CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_DEEP], initializer=tf.truncated_normal_initializer(stddev=0.1)) conv1_biases = tf.get_variable("bias", [CONV1_DEEP], initializer=tf.constant_initializer(0.0)) conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME') relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases)) with tf.name_scope("layer2-pool1"): pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") with tf.variable_scope("layer3-conv2"): conv2_weights = tf.get_variable( "weight", [CONV2_SIZE, CONV2_SIZE, CONV1_DEEP, CONV2_DEEP], initializer=tf.truncated_normal_initializer(stddev=0.1)) conv2_biases = tf.get_variable("bias", [CONV2_DEEP], initializer=tf.constant_initializer(0.0)) conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME') relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases)) with tf.name_scope("layer4-pool2"): pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # pool2.getshape函数可以得到第四层输出矩阵的维度而不需要手工计算。 # 注意因为每一层神经网络的输入输出都为一个batch矩阵，所以这里得到的维度也包含了一个batch中数据的个数。 pool_shape = pool2.get_shape().as_list() # 计算将矩阵拉直成向量后的长度，这个长度就是矩阵的长宽及深度的乘积，注意这里的pool_shape[0]为一个batch中数据的个数 nodes = pool_shape[1]*pool_shape[2]*pool_shape[3] # 通过tf.shape函数将第四层的输出变成一个batch的向量 reshaped = tf.reshape(pool2, [pool_shape[0], nodes]) # dropout一般只在全连接层而不是卷积层或者池化层使用 with tf.variable_scope('layer5-fc1'): fc1_weights = tf.get_variable("weight", [nodes, FC_SIZE], initializer=tf.truncated_normal_initializer(stddev=0.1)) # 只有全连接层的权重需要加入正则化 if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights)) fc1_biases = tf.get_variable("bias", [FC_SIZE], initializer=tf.constant_initializer(0.1)) fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases) # 如果train标签为真，则引入dropout函数使输出层一半的神经元失活 if train: fc1 = tf.nn.dropout(fc1, 0.5) with tf.variable_scope('layer6-fc2'): fc2_weights = tf.get_variable("weight", [FC_SIZE, NUM_LABELS], initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights)) fc2_biases = tf.get_variable("bias", [NUM_LABELS], initializer=tf.constant_initializer(0.1)) logit = tf.matmul(fc1, fc2_weights) + fc2_biases return logit 训练基于LeNet的MNIST模型
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport LeNet5_inferneceimport osimport numpy as np# #### 1. 定义神经网络相关的参数BATCH_SIZE = 100 # 批处理数量大小LEARNING_RATE_BASE = 0.01 # 基础学习率LEARNING_RATE_DECAY = 0.99 # 学习率衰减速率REGULARIZATION_RATE = 0.0001 # 正则化参数TRAINING_STEPS = 6000 # 训练周期数MOVING_AVERAGE_DECAY = 0.99 # 平均滑动步长# #### 2. 定义训练过程def train(mnist): # 定义输出为4维矩阵的placeholder x = tf.placeholder(tf.float32, [ BATCH_SIZE, LeNet5_infernece.IMAGE_SIZE, LeNet5_infernece.IMAGE_SIZE, LeNet5_infernece.NUM_CHANNELS], name='x-input') # y_表示正确的标签 y_ = tf.placeholder(tf.float32, [None, LeNet5_infernece.OUTPUT_NODE], name='y-input') # 定义L2正则化 regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE) y = LeNet5_infernece.inference(x, False, regularizer) # 表示不使用dropout,但是使用正则化 global_step = tf.Variable(0, trainable=False) # 定义损失函数、学习率、滑动平均操作以及训练过程。 variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) # 使用平均滑动模型 variables_averages_op = variable_averages.apply(tf.trainable_variables()) # 定以交叉熵函数 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) # 将权重的L2正则化部分加到损失函数中 loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses')) # 定义递减的学习率 learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE, global_step, mnist.train.num_examples/BATCH_SIZE, LEARNING_RATE_DECAY, staircase=True) train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) # with tf.control_dependencies([train_step, variables_averages_op]): # train_op = tf.no_op(name='train') # 在反向传播梯度下降的过程中更新变量的滑动平均值 train_op = tf.group(train_step, variables_averages_op) # 初始化TensorFlow持久化类。 saver = tf.train.Saver() with tf.Session() as sess: tf.global_variables_initializer().run() for i in range(TRAINING_STEPS): xs, ys = mnist.train.next_batch(BATCH_SIZE) reshaped_xs = np.reshape(xs, ( BATCH_SIZE, LeNet5_infernece.IMAGE_SIZE, LeNet5_infernece.IMAGE_SIZE, LeNet5_infernece.NUM_CHANNELS)) _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: reshaped_xs, y_: ys}) if i%1000 == 0: print("After %d training step(s), loss on training batch is %g."%(step, loss_value))# #### 3. 主程序入口def main(argv=None): mnist = input_data.read_data_sets("./MNIST_data", one_hot=True) train(mnist)if __name__ == '__main__': main()