前言

上一节尝试了使用tensorboard可视化神经网络，这一节学习使用tensorboard可视化网络的数据，例如隐藏层的权重偏置值等。

代码

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 载入数据
mnist = input_data.read_data_sets("../MNIST_DATA", one_hot=True)

# 每个批次的大小
batch_size = 100
# 计算一共多少批次
n_batch = mnist.train.num_examples // batch_size

def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)
        tf.summary.scalar('max', tf.reduce_max(var))
        tf.summary.scalar('min', tf.reduce_min(var))
        tf.summary.histogram('histogram', var)


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")

# 创建简单神经网络（可优化）
with tf.name_scope("layout"):
    with tf.name_scope("wights"):
        W = tf.Variable(tf.zeros([784, 10]),name="W")
        variable_summaries(W) # 分析权值
    with tf.name_scope("biases"):
        b = tf.Variable(tf.zeros([10]),name="b")
        variable_summaries(b) # 分析偏置值
    with tf.name_scope("wx_plus_b"):
        wx_plus_b=tf.matmul(x, W) + b
    with tf.name_scope("predict"):
        predict = tf.nn.softmax(wx_plus_b)  # softmax将输出信号转化为概率值（10个概率值）

# 可使用交叉熵代价函数来优化
with tf.name_scope("loss"):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,logits=predict))
    tf.summary.scalar('loss',loss)
# 使用梯度下降法训练，使得loss最小（#可优化）
with tf.name_scope("train"):
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
# 初始化变量
init = tf.global_variables_initializer()

with tf.name_scope("accuracy"):
    # 比较概率最大的标签是否相同，结果存放在一个布尔型列表中
    with tf.name_scope("correct_predict"):
        correct_predict = tf.equal(tf.argmax(y, 1), tf.argmax(predict, 1))  # argmax返回一维张量中最大值所在的位置
    # 求准确率
    with tf.name_scope("accu"):
        accuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))  # reduce_mean求平均值
        tf.summary.scalar('accuracy', accuracy)

#合并所有的summary
merged = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(init)
    write=tf.summary.FileWriter("logs/", sess.graph)
    for epoch in range(51):  # 可优化
        for batch in range(n_batch):  # 把所有图片训练一次
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            summary, _ = sess.run([merged,train_step], feed_dict={x: batch_xs, y: batch_ys})
            #write.add_summary(summary, epoch * n_batch + batch)

		write.add_summary(summary, epoch)
        # 用测试数据来检验训练好的模型
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print("Iter " + str(epoch) + "Test accuracy" + str(acc))

打开可视化网页

tensorboard --logdir=E:\py_code\tensorflow\logs