コードは以下の通りであります：
mnist_lenet5_forward.py
＃1 コーディング：UTF 8 インポートAS tensorflowのTF ＃28 * 28の画像の解像度ごと image_sizeでは= 28 ＃MNIST階調画像データセットは、入力画像値がNUM_CHANNELSチャネルになるように1。 NUM_CHANNELS 1 =。 ＃第一層ボリュームコンボリューションカーネルサイズ5 CONV1_SIZE。5 = ＃1 コアの畳み込み数は32である CONV1_KERNEL_NUM = 32 ＃コンボリューションカーネルの第二層サイズの5 CONV2_SIZE。5 = ＃1 コアの畳み込み数は64である CONV2_KERNEL_NUM = 64 ＃の全体接続層第一層512個のニューロン FC_SIZE = 512 ＃完全に接続された第2層10のニューロン OUTPUT_NODE = 10の＃の重みWが算出されるDEF ：（形状、正則）をget_weight wは tf.Variable（tf.truncated_normal（形状、STDDEV = 0.1 = )) if regularizer != None: tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w)) return w #偏置b计算 def get_bias(shape): b = tf.Variable(tf.zeros(shape)) return b #卷积层计算 def conv2d(x,w): return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME') #最大池化层计算 def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def forward(x, train, regularizer): #实现第一层卷积 conv1_w = get_weight([CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_KERNEL_NUM], regularizer) conv1_b = get_bias([CONV1_KERNEL_NUM]) conv1 = conv2d(x, conv1_w) #非线性激活 relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_b)) #最大池化 pool1 = max_pool_2x2(relu1) #实现第二层卷积 conv2_w = get_weight([CONV2_SIZE, CONV2_SIZE, CONV1_KERNEL_NUM, CONV2_KERNEL_NUM],regularizer) conv2_b = get_bias([CONV2_KERNEL_NUM]) conv2 = conv2d(pool1, conv2_w) relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_b)) pool2 = max_pool_2x2(relu2) #获取一个张量的维度 pool_shape = pool2.get_shape().as_list() #pool_shape[1] 为长 pool_shape[2] 为宽 pool_shape[3]为高 nodes = pool_shape[1] * pool_shape[2] * pool_shape[3] #得到矩阵被拉长后的长度，pool_shape[0]为batch值 reshaped = tf.reshape(pool2, [pool_shape[0], nodes]) #实现第三层全连接层 fc1_w = get_weight([nodes, FC_SIZE], regularizer) fc1_b = get_bias([FC_SIZE]) fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b) #如果是训练阶段，则对该层输出使用dropout if train: fc1 = tf.nn.dropout(fc1, 0.5) #实现第四层全连接层 fc2_w = get_weight([FC_SIZE, OUTPUT_NODE], regularizer) fc2_b = get_bias([OUTPUT_NODE]) y = tf.matmul(fc1, fc2_w) + fc2_b return y
mnist_lenet5_backward.py
#coding:utf-8 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import mnist_lenet5_forward import os import numpy as np #batch的数量 BATCH_SIZE = 100 #初始学习率 LEARNING_RATE_BASE = 0.005 #学习率衰减率 LEARNING_RATE_DECAY = 0.99 #正则化 REGULARIZER = 0.0001 #最大迭代次数 STEPS = 50000 #滑动平均衰减率 MOVING_AVERAGE_DECAY = 0.99 #模型保存路径 MODEL_SAVE_PATH="./model/" #模型名称 MODEL_NAME="mnist_model" def backward(mnist): #卷积层输入为四阶张量 #第一阶表示每轮喂入的图片数量，第二阶和第三阶分别表示图片的行分辨率和列分辨率，第四阶表示通道数 x = tf.placeholder(tf.float32,[ BATCH_SIZE, mnist_lenet5_forward.IMAGE_SIZE, mnist_lenet5_forward.IMAGE_SIZE, mnist_lenet5_forward.NUM_CHANNELS]) y_ = tf.placeholder(tf.float32, [None, mnist_lenet5_forward.OUTPUT_NODE]) #前向传播过程 y = mnist_lenet5_forward.forward(x,True, REGULARIZER) #声明一个全局计数器 global_step = tf.Variable(0, trainable=False) #对网络最后一层的输出y做softmax，求取输出属于某一类的概率 ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1)) #向量求均值 cem = tf.reduce_mean(ce) #正则化的损失值 loss = cem + tf.add_n(tf.get_collection('losses')) #指数衰减学习率 learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY, staircase=True) #梯度下降算法的优化器 #train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) train_step = tf.train.MomentumOptimizer(learning_rate,0.9).minimize(loss, global_step=global_step) #采用滑动平均的方法更新参数 ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) ema_op = ema.apply(tf.trainable_variables()) #将train_step和ema_op两个训练操作绑定到train_op上 with tf.control_dependencies([train_step, ema_op]): train_op = tf.no_op(name='train') #实例化一个保存和恢复变量的saver saver = tf.train.Saver() #创建一个会话 with tf.Session() as sess: init_op = tf.global_variables_initializer() sess.run(init_op) #通过 checkpoint 文件定位到最新保存的模型，若文件存在，则加载最新的模型 ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) for i in range(STEPS): #读取一个batch数据，将输入数据xs转成与网络输入相同形状的矩阵 xs, ys = mnist.train.next_batch(BATCH_SIZE) reshaped_xs = np.reshape(xs,( BATCH_SIZE, mnist_lenet5_forward.IMAGE_SIZE, mnist_lenet5_forward.IMAGE_SIZE, mnist_lenet5_forward.NUM_CHANNELS)) #读取一个batch数据，将输入数据xs转成与网络输入相同形状的矩阵 _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: reshaped_xs, y_: ys}) if i % 100 == 0: print("After %d training step(s), loss on training batch is %g." % (step, loss_value)) saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step) def main(): mnist = input_data.read_data_sets("./data/", one_hot=True) backward(mnist) if __name__ == '__main__': main()
python mnist_lenet5_test.py
#coding:utf-8 import time import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import mnist_lenet5_forward import mnist_lenet5_backward import numpy as np TEST_INTERVAL_SECS = 5 #创建一个默认图，在该图中执行以下操作 def test(mnist): with tf.Graph().as_default() as g: x = tf.placeholder(tf.float32,[ mnist.test.num_examples, mnist_lenet5_forward.IMAGE_SIZE, mnist_lenet5_forward.IMAGE_SIZE, mnist_lenet5_forward.NUM_CHANNELS]) y_ = tf.placeholder(tf.float32, [None, mnist_lenet5_forward.OUTPUT_NODE]) #训练好的网络，故不使用 dropout y = mnist_lenet5_forward.forward(x,False,None) ema = tf.train.ExponentialMovingAverage(mnist_lenet5_backward.MOVING_AVERAGE_DECAY) ema_restore = ema.variables_to_restore() saver = tf.train.Saver(ema_restore) #判断预测值和实际值是否相同 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) ## 求平均得到准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) while True: with tf.Session() as sess: ckpt = tf.train.get_checkpoint_state(mnist_lenet5_backward.MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) # 根据读入的模型名字切分出该模型是属于迭代了多少次保存的 global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] reshaped_x = np.reshape(mnist.test.images,( mnist.test.num_examples, mnist_lenet5_forward.IMAGE_SIZE, mnist_lenet5_forward.IMAGE_SIZE, mnist_lenet5_forward.NUM_CHANNELS)) #利用多线程提高图片和标签的批获取效率 coord = tf.train.Coordinator()#3 threads = tf.train.start_queue_runners(sess=sess, coord=coord)#4 accuracy_score = sess.run(accuracy, feed_dict={x:reshaped_x,y_:mnist.test.labels}) print("After %s training step(s), test accuracy = %g" % (global_step, accuracy_score)) #关闭线程协调器 coord.request_stop()#6 coord.join(threads)#7 else: print('No checkpoint file found') return time.sleep(TEST_INTERVAL_SECS) def main(): mnist = input_data.read_data_sets("./data/", one_hot=True) test(mnist) if __name__ == '__main__': main()