前言

這篇博文主要是依照官網，介紹各種型別的summary的使用，包括標量scalar型別的，影象型別的image，直方圖累心的histogram．

程式碼

該程式碼主要是使用普通的神經網路對MNIST手寫體數字進行分類識別．網路各層的神經元個數為：784-500-10．７８4代表輸入神經元個數為784，隱層神經元個數為500，輸出層為10，代表10個類別．

# coding=utf-8
"""　tensorboard 的使用"""
from __future__ import absolute_import
from __future__ import division
from __future__ import
 
 print_function

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

FLAGS = None


def train():
    # 讀取MNIST資料
    mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, fake_data=FLAGS.fake_data)
    sess = tf.InteractiveSession()
    # 給下面的 tensor　加上詞頭; 【注：】with　不能改變其下變數的作用域
 

    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_shape'):
        image_shaped_input = tf.reshape(x, shape=[-1, 28, 28, 1])
        # image型別的summary
        tf.summary.image('input'
 
, image_shaped_input, 10)

    # 網路引數初始化
    def weight_variable(shape):
        initial = tf.truncated_normal(shape=shape, stddev=0.1)
        return tf.Variable(initial)

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

    # 要寫到summary的資訊
    def variable_summaries(var):
        with tf.name_scope('summaries'):
            mean = tf.reduce_mean(var)
            # 標量型summary
            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))
            # 直方圖型summary，可以用來檢視tensor的值的分佈
            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_varibale([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(labels=y_, logits=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(FLAGS.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)

    # 合併所有型別的 summary
    merged = tf.summary.merge_all()
    train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
    test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')
    tf.global_variables_initializer().run()

    # 獲取sess.run中需要的feed_dict
    def feed_dict(train):
        if train or FLAGS.fake_data:
            xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
            k = FLAGS.dropout
        else:
            xs, ys = mnist.test.images, mnist.test.labels
            k = 1.0
        return {x: xs, y_: ys, keep_prob: k}

    # 網路的訓練和summary的寫入
    for i in range(FLAGS.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()
                summary, _ = sess.run([merged, train_step],
                                      feed_dict=feed_dict(True))
                # train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
                train_writer.add_summary(summary, i)
                print('Adding run metadata 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()


def main(_):
    # tensorflow 中的檔案操作操作類tf.gfile
    if tf.gfile.Exists(FLAGS.log_dir):
        tf.gfile.DeleteRecursively(FLAGS.log_dir)
    tf.gfile.MakeDirs(FLAGS.log_dir)
    train()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
                        default=False,
                        help='If true, uses fake data for unit testing.')
    parser.add_argument('--max_steps', type=int, default=1000,
                        help='Number of steps to run trainer.')
    parser.add_argument('--learning_rate', type=float, default=0.001,
                        help='Initial learning rate')
    parser.add_argument('--dropout', type=float, default=0.9,
                        help='Keep probability for training dropout.')
    parser.add_argument('--data_dir', type=str, default='MNIST_data',
                        help='Directory for storing input data')
    parser.add_argument('--log_dir', type=str, default='MNIST_Log',
                        help='Summaries log directory')
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

程式碼中產生各種型別的summary的視覺化結果如下：
標量型別：

圖片型別：

圖模型型別：

分佈型別：

直方圖型別：

注意事項

（１）對於tf.summary.image，其輸入的圖片必須是4-D的，[batch_size, height, width, channels]．

參考網址