TensorFlow的MNIST数据识别

1、读取数据

2、训练模型

3、完整样例

import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_dataimport syssys.path.append(r'G:\MNIST最佳实践')import mnist_inferencemnist = input_data.read_data_sets(r'G:\0tensorflow\MNIST_data', one_hot=True)BATCH_SIZE = 100LEARNING_RATE_BASE = 0.8LEARNING_RATE_DECAY = 0.99REGULARIZATION_RATE = 0.0001TRAINING_STEPS = 3000MOVING_AVERAGE_DECAY = 0.99def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):    # 不使用滑动平均类    if avg_class == None:        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)        return tf.matmul(layer1, weights2) + biases2    else:        # 使用滑动平均类        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)  def train(mnist):    #  输入数据的命名空间。    with tf.name_scope('input'):        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)    y = mnist_inference.inference(x, regularizer)    global_step = tf.Variable(0, trainable=False)    # 处理滑动平均的命名空间。    with tf.name_scope("moving_average"):        variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)        variables_averages_op = variable_averages.apply(tf.trainable_variables())    # 计算损失函数的命名空间。    with tf.name_scope("loss_function"):        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)        loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))    # 定义学习率、优化方法及每一轮执行训练的操作的命名空间。    with tf.name_scope("train_step"):        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')    writer = tf.summary.FileWriter("/log/modified_mnist_train.log", tf.get_default_graph())    # 训练模型。    with tf.Session() as sess:        tf.global_variables_initializer().run()        for i in range(TRAINING_STEPS):            xs, ys = mnist.train.next_batch(BATCH_SIZE)            if i % 1000 == 0:                # 配置运行时需要记录的信息。                run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)                # 运行时记录运行信息的proto。                run_metadata = tf.RunMetadata()                _, loss_value, step = sess.run(                    [train_op, loss, global_step], feed_dict={x: xs, y_: ys},                    options=run_options, run_metadata=run_metadata)                writer.add_run_metadata(run_metadata=run_metadata, tag=("tag%d" % i), global_step=i)                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))            else:                _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})    writer.close()def main(argv=None):     mnist = input_data.read_data_sets("/datasets/MNIST_data", one_hot=True)    train(mnist)if __name__ == '__main__':    main()

知识点：
1、添加路径

import syssys.path.append()

2.定义辅助函数来计算前向传播结果，使用ReLU做为激活函数

def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):    # 不使用滑动平均类    if avg_class == None:        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)        return tf.matmul(layer1, weights2) + biases2    else:        # 使用滑动平均类        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)  

3、获得前向传播算法 输出值

  #  输入数据的命名空间。    with tf.name_scope('input'):        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)#L2正则化项    y = mnist_inference.inference(x, regularizer)#获得输出值    global_step = tf.Variable(0, trainable=False)

4.处理滑动平均的命名空间

# 处理滑动平均的命名空间。    with tf.name_scope("moving_average"):        variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)#定义滑动平均的类        variables_averages_op = variable_averages.apply(tf.trainable_variables())#列表的变量都会被更新

4、变量管理

tf.get_variable():创建或者获取变量；
tf.variable_scope():用于上下文管理器，使tf.get_variable()不出现变量重复调用的错误。