TensoFow官方教材代码（4）：TensorBoard例程

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

max_steps = 300
learning_rate = 0.001
dropout = 0.9
log_dir = \'MNIST_data\'

 # load data
mnist = input_data.read_data_sets(\"MNIST_data/\", 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(name=\'input\', tensor=image_shaped_input, max_outputs=10)

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)
        with tf.name_scope(\'stddev\'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar(\'mean\', 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_ (input_tensor, input_dim, output_dim,  _name, act=tf.nn.relu):
    with tf.name_scope( _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_ (x, 784, 500, \' 1\')

with tf.name_scope(\'dropout\'):
    keep_prob = tf.placeholder(tf.float32)
    tf.summary.scalar(\'keep_prob\', keep_prob)
    dropped = tf.nn.dropout(hidden1, keep_prob)

y = nn_ (dropped, 500, 10, \' 2\', 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_ data = tf.Run data()
            summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True), options=run_options,
                                  run_ data=run_ data)
            train_writer.add_run_ data(run_ data, \'step%03d\' % i)
            train_writer.add_summary(summary, i)
            saver.save(sess, log_dir + \'/model.ckpt\', i)
            print(\'Adding run  data for\', i)
        else:
            summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
            train_writer.add_summary(summary, i)
train_writer.close()
test_writer.close()

# 然后在命令行输入：tensorboard --logdir=logdir
# 复制出现的信息中的网址，在浏览器中打开