吴裕雄--天生自然深度学习TensorBoard可视化：监控指标可视化

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

# 1. 生成变量监控信息并定义生成监控信息日志的操作。

SUMMARY_DIR = "F:\\temp\\log"

BATCH_SIZE = 100

TRAIN_STEPS = 3000

def variable_summaries(var, name):

    with tf.name_scope('summaries'):

        tf.summary.histogram(name, var)

        mean = tf.reduce_mean(var)

        tf.summary.scalar('mean/' + name, mean)

        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))

        tf.summary.scalar('stddev/' + name, stddev)  

# 2. 生成一层全链接的神经网络。

def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):

    with tf.name_scope(layer_name):

        with tf.name_scope('weights'):

            weights = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1))

            variable_summaries(weights, layer_name + '/weights')

        with tf.name_scope('biases'):

            biases = tf.Variable(tf.constant(0.0, shape=[output_dim]))

            variable_summaries(biases, layer_name + '/biases')

        with tf.name_scope('Wx_plus_b'):

            preactivate = tf.matmul(input_tensor, weights) + biases

            tf.summary.histogram(layer_name + '/pre_activations', preactivate)

        activations = act(preactivate, name='activation')

        # 记录神经网络节点输出在经过激活函数之后的分布。

        tf.summary.histogram(layer_name + '/activations', activations)

        return activations

def main():

    mnist = input_data.read_data_sets("F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data", one_hot=True)

    with tf.name_scope('input'):

        x = tf.placeholder(tf.float32, [None, 784], name='x-input')

        y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')

    with tf.name_scope('input_reshape'):

        image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])

        tf.summary.image('input', image_shaped_input, 10)

    hidden1 = nn_layer(x, 784, 500, 'layer1')

    y = nn_layer(hidden1, 500, 10, 'layer2', act=tf.identity)

    with tf.name_scope('cross_entropy'):

        cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))

        tf.summary.scalar('cross_entropy', cross_entropy)

    with tf.name_scope('train'):

        train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

    with tf.name_scope('accuracy'):

        with tf.name_scope('correct_prediction'):

            correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))

        with tf.name_scope('accuracy'):

            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        tf.summary.scalar('accuracy', accuracy)

    merged = tf.summary.merge_all()

    with tf.Session() as sess:

        summary_writer = tf.summary.FileWriter(SUMMARY_DIR, sess.graph)

        tf.global_variables_initializer().run()

        for i in range(TRAIN_STEPS):

            xs, ys = mnist.train.next_batch(BATCH_SIZE)

            # 运行训练步骤以及所有的日志生成操作，得到这次运行的日志。

            summary, _ = sess.run([merged, train_step], feed_dict={x: xs, y_: ys})

            # 将得到的所有日志写入日志文件，这样TensorBoard程序就可以拿到这次运行所对应的

            # 运行信息。

            summary_writer.add_summary(summary, i)

    summary_writer.close()

if __name__ == '__main__':

    main()