在windows环境下，TensorBoard的使用

1. 代码附录

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
max_steps = 1000
learning_rate = 0.001
dropout = 0.9 #保留比

#C:\Users\eclipse-workspace\java\TensorFlow\src\data
log_dir = 'C:/Users/eclipse-workspace/java/TensorFlow/src/data'
data_dir = 'C:/Usershuangluxiang/eclipse-workspace/java/TensorFlow/src/MNIST_data'
mnist = input_data.read_data_sets(data_dir, one_hot = True)
sess = tf.InteractiveSession();
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)
    
#权重初始化
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

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)

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 = weight_variable([input_dim, output_dim])
            variable_summaries(weights)
        with tf.name_scope('biases'):
            biases = bias_variable([output_dim])
            variable_summaries(biases)
        with tf.name_scope('Wx_plus_b'):
            preactivate = tf.matmul(input_tensor, weights) + biases
            tf.summary.histogram('pre_activations', preactivate)
        activations= act(preactivate, name = 'activation')
        tf.summary.histogram('activations', activations)
        return activations
    
hidden1 = nn_layer(x, 784, 500, 'layer1')

with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    tf.summary.scalar('dropout_keep_probability', keep_prob)
    dropped = tf.nn.dropout(hidden1, keep_prob)
    
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)

with tf.name_scope('cross_entropy'):
    diff = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
    with tf.name_scope('total'):
        cross_entropy = tf.reduce_mean(diff)
tf.summary.scalar('cross_entropy', cross_entropy)

with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(learning_rate).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()
train_writer = tf.summary.FileWriter(log_dir + 'train', sess.graph)
test_writer = tf.summary.FileWriter(log_dir + '/test')
tf.global_variables_initializer().run()

def feed_dict(train):
    if train:
        xs, ys = mnist.train.next_batch(100)
        k = dropout
    else:
        xs, ys = mnist.test.images, mnist.test.labels
        k = 1.0
    return {x:xs, y_: ys, keep_prob: k}

saver = tf.train.Saver()
for i in range(max_steps):
    if i % 10 == 0:
        summary, acc = sess.run([merged, accuracy], feed_dict = feed_dict(False))
        test_writer.add_summary(summary, i)
        print('Accuracy at step %s: %s' % (i, acc))
    else:
        if i % 100 == 99:
            run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
            run_metadata = tf.RunMetadata()
            summay, _ = sess.run([merged, train_step], feed_dict = feed_dict(True),options = run_options, run_metadata = run_metadata)
            train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
            train_writer.add_summary(summary, i)
            saver.save(sess, log_dir + "/model.ckpt", i)
            print('Adding run metadata for', i)
        else:
            summay, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
            train_writer.add_summary(summary, i)
train_writer.close()
test_writer.close()

2. 设置日志相关的路径，注意路径格式

3. 启动TensorBoard

将网站输入到浏览器

4. 运行程序，然后刷新网页