一个简单的TensorFlow可视化MNIST数据集识别程序

下面是TensorFlow可视化MNIST数据集识别程序，可视化内容是，TensorFlow计算图，表（loss，直方图，标准差（stddev））

# -*- coding： utf-8 -*-

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

from tensorflow.contrib.tensorboard.plugins import projector

old_v = tf.logging.get_verbosity()

tf.logging.set_verbosity(tf.logging.ERROR)

# 载入数据集

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# 运行次数

max_steps = 3001

# 图片数量

image_num = 5000

# 文件路径

DIR = "D:/AIdata/tf_data/tf_test1/"

sess = tf.Session()

# 载入图片，

# tf.stack矩阵拼接函数，

embedding = tf.Variable(tf.stack(mnist.test.images[:image_num]),

                        trainable=False, name="embedding")

def variable_summaries(var):

    with tf.name_scope("summaries"):

        mean = tf.reduce_mean(var)

        with tf.name_scope("stddev"):

            # 计算标准差

            stddev = tf.sqrt(tf.reduce_mean(tf.square(var-mean)))

        # 绘制标准差信息

        tf.summary.scalar("stddev", stddev)

        # 绘制最大值

        tf.summary.scalar("max", tf.reduce_max(var))

        tf.summary.scalar("min", tf.reduce_min(var))

        # 绘制直方图信息

        tf.summary.histogram("histogram", var)

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")

    LR = tf.Variable(0.001, dtype=tf.float32)

# 显示图片

with tf.name_scope("input_reshape"):

    # 改变x的形状（28x28x1）

    image_shape_input = tf.reshape(x, [-1, 28, 28, 1])

    # 将图像写入summary，输出带图像的probuf

    tf.summary.image("Input", image_shape_input, 10)

with tf.name_scope('layer'):

    with tf.name_scope('weights'):

        W = tf.Variable(tf.zeros([784, 10]), name='W')

        variable_summaries(W)

    with tf.name_scope('biases'):

        b = tf.Variable(tf.zeros([10]), name='b')

        variable_summaries(b)

    with tf.name_scope('wxb'):

        # tf.matmul实现矩阵乘法功能

        wxb = tf.matmul(x, W) + b

    with tf.name_scope('softmax'):

        prediction = tf.nn.softmax(wxb)

with tf.name_scope("loss"):

    # 交叉熵函数

    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,

                                                                     logits=prediction))

    # 绘制loss值

    tf.summary.scalar("loss", loss)

with tf.name_scope("Train"):

    # AdamOptimizer优化器

    train_step = tf.train.AdamOptimizer(LR).minimize(loss)

init_op = tf.global_variables_initializer()

sess.run(init_op)   # 变量初始化

with tf.name_scope("Result"):

    with tf.name_scope("correct_prediction"):

        # 记录预测值和标签值对比结果

        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))

    with tf.name_scope("Accuracy"):

        # 求准确率

        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        # 绘制准确率

        tf.summary.scalar("accuracy", accuracy)

# 判断是否已存在metadata.tsv文件，若存在则删除

if tf.gfile.Exists(DIR+"projector/projector/metadata.tsv"):

    tf.gfile.Remove(DIR+"projector/projector/metadata.tsv")

# 创建并写入metadata.tsv文件

with open(DIR+"projector/projector/metadata.tsv", 'w') as f:

    labels = sess.run(tf.argmax(mnist.test.labels[:], 1))

    for i in range(image_num):

        f.write(str(labels[i]) + '\n')

# 合并默认图表管理summary

merged = tf.summary.merge_all()

projector_writer = tf.summary.FileWriter(DIR+"/projector/projector", sess.graph)

# 定义saver对象，以保存和恢复模型变量

saver = tf.train.Saver()

# 定义配置

config = projector.ProjectorConfig()

embed = config.embeddings.add()

embed.tensor_name = embedding.name

# metadata_path文件路径

embed.metadata_path = DIR+"projector/projector/metadata.tsv"

# sprite image文件路径

embed.sprite.image_path = DIR+'projector/data/mnist_10k_sprite.png'

# sprite image中每一单个图像的大小

embed.sprite.single_image_dim.extend([28, 28])

# 写入可视化配置

projector.visualize_embeddings(projector_writer, config)

for i in range(max_steps):

    # 每个批次100个样本

    batch_xs, batch_ys = mnist.train.next_batch(100)

    run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)

    run_metadata = tf.RunMetadata()

    summary, _ = sess.run([merged, train_step], feed_dict={x: batch_xs, y: batch_ys},

                          options=run_options, run_metadata=run_metadata)

    projector_writer.add_run_metadata(run_metadata, 'step%03d' % i)

    projector_writer.add_summary(summary, i)

    if i % 100 == 0:

        sess.run(tf.assign(LR, 0.001))

        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})

        print("Iter " + str(i) + ", Testing Accuracy= " + str(acc))

# 保存模型

saver.save(sess, DIR+'projector/projector/mnist_model.ckpt', global_step=max_steps)

projector_writer.close()

sess.close()