吴裕雄--天生自然深度学习TensorBoard可视化：监控指标可视化 - 吴裕雄

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()