tensorflow可以通过tensorboard来可视化结果，自带的可视化界面是其它框架没有的。 安装好tensorflow后指定日志文件夹，输入

tensorboard --logdir=C:/Users/Administrator/Desktop/test/log

命令即可使用可视化界面。

用浏览器打开http://localhost:6006/

此时没有运行任何程序。

如果使用改进的mnist数据集训练程序：

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

#定义神经网络的参数
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 3000
MOVING_AVERAGE_DECAY = 0.99
INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500

def get_weight_variable(shape, regularizer):
    weights = tf.get_variable("weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1))
    if regularizer != None: tf.add_to_collection('losses', regularizer(weights))
    return weights

def inference(input_tensor, regularizer):
    with tf.variable_scope('layer1'):
        weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)
        biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))
        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases)

    with tf.variable_scope('layer2'):
        weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)
        biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))
        layer2 = tf.matmul(layer1, weights) + biases

    return layer2

#定义训练的过程并保存TensorBoard的log文件
def train(mnist):
    #  输入数据的命名空间。
    with tf.name_scope('input'):
        x = tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')
        y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')
        regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
        y = inference(x, regularizer)
        global_step = tf.Variable(0, trainable=False)

    # 处理滑动平均的命名空间。
    with tf.name_scope("moving_average"):
        variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
        variables_averages_op = variable_averages.apply(tf.trainable_variables())

    # 计算损失函数的命名空间。
    with tf.name_scope("loss_function"):
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
        cross_entropy_mean = tf.reduce_mean(cross_entropy)
        loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))

    # 定义学习率、优化方法及每一轮执行训练的操作的命名空间。
    with tf.name_scope("train_step"):
        learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY,staircase=True)

        train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

    with tf.control_dependencies([train_step, variables_averages_op]):
        train_op = tf.no_op(name='train')

   # 训练模型。
    with tf.Session() as sess:
        tf.global_variables_initializer().run()
        writer = tf.summary.FileWriter("C:/Users/Administrator/Desktop/test/log/mnist_train.log", tf.get_default_graph())
        for i in range(TRAINING_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            if i % 1000 == 0:
            # 配置运行时需要记录的信息。
                run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
                    # 运行时记录运行信息的proto。
                run_metadata = tf.RunMetadata()
                _, loss_value, step = sess.run(
                [train_op, loss, global_step], feed_dict={x: xs, y_: ys},
                options=run_options, run_metadata=run_metadata)
                writer.add_run_metadata(run_metadata=run_metadata, tag=("tag%d"%i), global_step=i),
                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
            else:
                _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})

        writer.close()

#主函数
def main(argv=None): 
    mnist = input_data.read_data_sets("C:/Users/Administrator/Desktop/test/datasets/MNIST_data", one_hot=True)
    train(mnist)

if __name__ == '__main__':
    main()

程序结束后在GRAPHS栏目下可以可视化模型：

各个节点可以进行点击，移动操作。

除了可视化计算图，tensorflow还可以对运行日志等进行可视化

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

#生成变量监控信息并定义生成监控信息日志的操作
SUMMARY_DIR = "C:/Users/Administrator/Desktop/test/log/supervisor.log"
BATCH_SIZE = 100
TRAIN_STEPS = 3000

def variable_summaries(var, name):
    with tf.name_scope('summaries'):
        tf.summary.histogram(name, var)
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean/' + name, mean)
        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev/' + name, stddev)  

#生成一层全链接的神经网络
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 = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1))
            variable_summaries(weights, layer_name + '/weights')
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.constant(0.0, shape=[output_dim]))
            variable_summaries(biases, layer_name + '/biases')
        with tf.name_scope('Wx_plus_b'):
            preactivate = tf.matmul(input_tensor, weights) + biases
            tf.summary.histogram(layer_name + '/pre_activations', preactivate)
        activations = act(preactivate, name='activation')        

        # 记录神经网络节点输出在经过激活函数之后的分布。
        tf.summary.histogram(layer_name + '/activations', activations)
        return activations

def main():
    mnist = input_data.read_data_sets("C:/Users/Administrator/Desktop/test/datasets/MNIST_data", one_hot=True)

    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)

    hidden1 = nn_layer(x, 784, 500, 'layer1')
    y = nn_layer(hidden1, 500, 10, 'layer2', act=tf.identity)

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

    with tf.name_scope('train'):
        train_step = tf.train.AdamOptimizer(0.001).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()

    with tf.Session() as sess:

        summary_writer = tf.summary.FileWriter(SUMMARY_DIR, sess.graph)
        tf.global_variables_initializer().run()

        for i in range(TRAIN_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            # 运行训练步骤以及所有的日志生成操作，得到这次运行的日志。
            summary, _ = sess.run([merged, train_step], feed_dict={x: xs, y_: ys})
            # 将得到的所有日志写入日志文件，这样TensorBoard程序就可以拿到这次运行所对应的
            # 运行信息。
            summary_writer.add_summary(summary, i)

    summary_writer.close()

if __name__ == '__main__':
    main()

可视化运行信息：