tensorflow学习笔记（七）：tensorboard可视化

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport osimport timeos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'start = time.time()mnist = input_data.read_data_sets('MNIST_data',one_hot=True)# print mnist.train.images.shape,mnist.train.labels.shape# (55000, 784) (55000, 10)# 784 = 28*28# print mnist.test.images.shape,mnist.test.labels.shape\# (10000, 784) (10000, 10)# print mnist.validation.images.shape,mnist.validation.labels.shape# (5000, 784) (5000, 10)with tf.name_scope('input'):    xs = tf.placeholder(tf.float32, [None, 784], name='x_input')    x_image = tf.reshape(xs, [-1, 28, 28, 1])    ys = tf.placeholder(tf.float32, [None, 10], name='y_input')    keep_prob = tf.placeholder(tf.float32, name='drop')with tf.name_scope('hidden'):    with tf.name_scope('conv32'):        with tf.name_scope('weight1'):            W_1 = tf.Variable(tf.random_normal([5, 5, 1, 32], stddev=0.1), name='conv32_W')            tf.summary.histogram('conv32/W_1', W_1)        with tf.name_scope('bias1'):            b_1 = tf.Variable(tf.constant(0.1, tf.float32, [32]), name='conv32_b')            tf.summary.histogram('conv32/b_1', b_1)        with tf.name_scope('active1'):            h_conv32 = tf.nn.relu(tf.nn.conv2d(x_image, W_1, strides=[1, 1, 1, 1], padding='SAME') + b_1, name='relu')    with tf.name_scope("pool1"):        h_pool1 = tf.nn.max_pool(h_conv32, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME", name='pool')    # -1 14 14 32    with tf.name_scope('conv64'):        with tf.name_scope('weight2'):            W_2 = tf.Variable(tf.random_normal([5, 5, 32, 64], stddev=0.1), name='conv64_W')            tf.summary.histogram('conv64/W_2', W_2)        with tf.name_scope('bias2'):            b_2 = tf.Variable(tf.constant(0.1, tf.float32, [64]), name='conv64_b')            tf.summary.histogram('conv64/b_2', b_2)        with tf.name_scope('active2'):            h_conv64 = tf.nn.relu(tf.nn.conv2d(h_pool1, W_2, strides=[1, 1, 1, 1], padding='SAME') + b_2, name='relu')    with tf.name_scope("pool2"):        h_pool2 = tf.nn.max_pool(h_conv64, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME", name='pool')# -1 7 7 64    with tf.name_scope("fc1"):        h_pool2_flatten = tf.reshape(h_pool2, [-1, 7*7*64])        with tf.name_scope('weight3'):            W_3 = tf.Variable(tf.random_normal([7*7*64, 1024], stddev=0.1), name='fc1_W')            tf.summary.histogram('fc1/W_3', W_3)        with tf.name_scope('bias3'):            b_3 = tf.Variable(tf.constant(0.1, tf.float32, [1024]), name='fc1_b')            tf.summary.histogram('fc1/b_3', b_3)        with tf.name_scope('active3'):            h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flatten, W_3) + b_3, name='relu')        with tf.name_scope('dropout'):            h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob=keep_prob)with tf.name_scope("output"):    with tf.name_scope('weight4'):        W_4 = tf.Variable(tf.random_normal([1024, 10], stddev=0.1), name='fc2_W')        tf.summary.histogram('fc2/W_4', W_4)    with tf.name_scope('bias4'):        b_4 = tf.Variable(tf.constant(0.1, tf.float32, [10]), name='fc2_b')        tf.summary.histogram('fc2/b_4', b_4)    with tf.name_scope('active4'):        y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_4) + b_4, name='softmax')with tf.name_scope('loss'):    loss = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(y_conv), reduction_indices=[1]))    tf.summary.scalar('loss', loss)with tf.name_scope('train'):    train = tf.train.AdamOptimizer(1e-4).minimize(loss)# accuracycorrect_prediction = tf.equal(tf.argmax(ys, 1), tf.argmax(y_conv, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))init = tf.global_variables_initializer()config = tf.ConfigProto()config.gpu_options.allow_growth = Truewith tf.Session(config=config) as sess:    sess.run(init)    merged = tf.summary.merge_all()    writer = tf.summary.FileWriter('logs', sess.graph)    for i in range(1001):        x_batch, y_batch = mnist.train.next_batch(50)        sess.run(train, feed_dict={xs:x_batch, ys:y_batch, keep_prob:0.5})        if i%100 == 0:            result = sess.run(merged, feed_dict={xs: x_batch, ys: y_batch, keep_prob: 1})            writer.add_summary(result, i)            print(i, ' step train ', sess.run(accuracy, feed_dict={xs: x_batch, ys: y_batch, keep_prob: 1}))    x_test, y_test = mnist.test.next_batch(10)    print(sess.run(tf.argmax(sess.run(y_conv, feed_dict={xs: img, keep_prob: 1}),1)))end = time.time()print("function time is : ", end-start)

使用tensorboard：首先找到生成的event文件位置,

tensorboard --logdir=logs

比较奇葩的是，回车后无信息返回，刚开始还以为是有问题的，后来直接输入网址，发现可以正常运行…………无语

浏览器输入：http://localhost:6006/
在其他浏览器打开会出现一些 问题。
最好用谷歌浏览器打开：