TensorFlow MNIST LeNet 模型持久化

前向传播过程mnist_inference.py

import tensorflow as tf# 定义神经网络相关的参数INPUT_NODE = 784OUTPUT_NODE = 10def inference(inputs, dropout_keep_prob):    x_image = tf.reshape(inputs, [-1, 28, 28, 1])    # 第一层：卷积层    conv1_weights = tf.get_variable("conv1_weights", [5, 5, 1, 32], initializer=tf.truncated_normal_initializer(stddev=0.1))  # 过滤器大小为5*5, 当前层深度为1， 过滤器的深度为32    conv1 = tf.nn.conv2d(x_image, filter=conv1_weights, strides=[1, 1, 1, 1], padding='SAME')  # 移动步长为1, 使用全0填充    conv1_biases = tf.get_variable("conv1_biases", [32], initializer=tf.constant_initializer(0.0))    relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))  # 激活函数Relu去线性化    # 第二层：最大池化层    # 池化层过滤器的大小为2*2, 移动步长为2，使用全0填充    pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #输出14*14*32    # 第三层：卷积层    conv2_weights = tf.get_variable("conv2_weights", [5, 5, 32, 64], initializer=tf.truncated_normal_initializer(stddev=0.1))  # 过滤器大小为5*5, 当前层深度为32， 过滤器的深度为64    conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')  # 移动步长为1, 使用全0填充    conv2_biases = tf.get_variable("conv2_biases", [64], initializer=tf.constant_initializer(0.0))    relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))    # 第四层：最大池化层    # 池化层过滤器的大小为2*2, 移动步长为2，使用全0填充    pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #输出7*7*64    # 第五层：全连接层    pool2_vector = tf.reshape(pool2, [-1, 7 * 7 * 64])    fc1_weights = tf.get_variable("fc1_weights", [7 * 7 * 64, 1024], initializer=tf.truncated_normal_initializer(stddev=0.1))  # 7*7*64=3136把前一层的输出变成特征向量    fc1_baises = tf.get_variable("fc1_baises", [1024], initializer=tf.constant_initializer(0.1))    fc1 = tf.nn.relu(tf.matmul(pool2_vector, fc1_weights) + fc1_baises)    # 为了减少过拟合，加入Dropout层    fc1_dropout = tf.nn.dropout(fc1, dropout_keep_prob)    # 第六层：全连接层    fc2_weights = tf.get_variable("fc2_weights", [1024, 10], initializer=tf.truncated_normal_initializer(stddev=0.1))  # 神经元节点数1024, 分类节点10    fc2_biases = tf.get_variable("fc2_biases", [10], initializer=tf.constant_initializer(0.1))    fc2 = tf.matmul(fc1_dropout, fc2_weights) + fc2_biases    return fc2

训练mnist_train.py

import osimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport mnist_inference#BATCH_SIZE = 100#学习率LEARN_RATE = 0.001MODEL_SAVE_PATH = "model/"MODEL_NAME = "model.ckpt"EPOCH = 2def train(mnist):    inputs = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE])    labels = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE])    dropout_keep_prob = tf.placeholder(tf.float32)    logits = mnist_inference.inference(inputs, dropout_keep_prob)    global_step = tf.Variable(0, trainable=False)    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)    #tf.nn.sparse_softmax_cross_entropy_with_logits    cost = tf.reduce_mean(cross_entropy)    train_op = tf.train.AdamOptimizer(LEARN_RATE).minimize(cost, global_step=global_step)    saver = tf.train.Saver()    with tf.Session() as sess:        tf.global_variables_initializer().run()        print(mnist.train.images.shape)        for i in range(20000):            batch_inputs, batch_labels = mnist.train.next_batch(BATCH_SIZE)            _, cost_value, step = sess.run([train_op, cost, global_step], feed_dict={inputs: batch_inputs, labels: batch_labels, dropout_keep_prob:0.5})            if i % 1000 == 0:                print("After %d training step(s), loss on training batch is %f." % (step, cost_value))                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)def main(argv=None):    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)    train(mnist)if __name__ == '__main__':    tf.app.run()

评估mnis_eval.py

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport mnist_inferenceimport mnist_traindef evaluate(mnist):    inputs = tf.placeholder(tf.float32, [None, 784])    labels = tf.placeholder(tf.float32, [None, 10])    dropout_keep_prob = tf.placeholder(tf.float32)    logits = mnist_inference.inference(inputs, dropout_keep_prob)    print(logits)    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    saver = tf.train.Saver()    with tf.Session() as sess:        ckpt = tf.train.get_checkpoint_state(mnist_train.MODEL_SAVE_PATH)        if ckpt and ckpt.model_checkpoint_path:            saver.restore(sess, ckpt.model_checkpoint_path)            global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]            accuracy_score = sess.run(accuracy, feed_dict={inputs: mnist.test.images, labels: mnist.test.labels, dropout_keep_prob:1.0})            print("After %s training step(s), validation accuracy = %f" % (global_step, accuracy_score))        else:            print("No checkpoint file found")            returndef main(argv=None):    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)    evaluate(mnist)if __name__ == '__main__':    tf.app.run()