mnist tensorflow可视化

mnist_inference.py

import tensorflow as tf

#定义神经网络结构相关的参数
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

#定义神经网络的前向传播
def inference(input_tensor, regularizer):
    #申明第一层神经网络的变量并完成前向传播过程
    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)

    #类似的申明第二层神经网络的变量并完成前向传播过程
    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

mnist_train_tensorboard.py

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

BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARAZTION_RATE = 0.0001
TRAINING_STEPS = 3000
MOVING_AVERAGE_DEACY = 0.99

MODEL_SAVE_PATH = r"E:\test\to-model"
MODEL_NAME = "model.ckpt"

def train(mnist):
    with tf.name_scope('input'):
        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-cinput')

    regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
    #直接使用mnist_inference.py中定义的前向传播过程
    y = mnist_inference.inference(x, regularizer)
    global_step = tf.Variable(0, trainable=False)

    with tf.name_scope("moving_average"):
        variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DEACY, global_step)
        variable_averages_op = variable_averages.apply(tf.trainable_variables())

    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'))

    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, variable_averages_op]):
            train_op = tf.no_op(name='train')

        saver = tf.train.Saver()
        with tf.Session() as sess:
        # tf.initialize_all_variables().run()
            init = tf.global_variables_initializer()
            sess.run(init)
            for i in range(TRAINING_STEPS):
                xs, ys = mnist.train.next_batch(BATCH_SIZE)
                _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})

                #每1000轮保存一次模型
                if  i % 1000 == 0:
                #输出当前训练的情况，输出模型在当前训练batch上的损失函数大小
                    print("After %d training step(s), loss on training "
                    "batch is %g." % (step, loss_value))
                    #保存当前模型
                    saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)

    writer = tf.summary.FileWriter(r"E:\test", tf.get_default_graph())
    writer.close()

def main(argv=None):
    mnist = input_data.read_data_sets(r"E:\data\data_mnist", one_hot=True)
    train(mnist)

if __name__ == '__main__':
    tf.app.run()

在cmd中运行后，输入tensorboard –logdir=E:\test
这里写图片描述
打开浏览器，网址输入 http://DESKTOP-V5346HK:6006
便可得到计算图可视化效果图

mnist tensorflow可视化

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