TF - tensorboard

作者: leo567 | 来源:发表于2018-11-06 11:52 被阅读23次

1. rensorboard的网络结构

首先定义命名空间，运行代码，生成log文件

import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

mnist = read_data_sets("MNIST_data/", one_hot=True)
batch_size = 100
n_batch = mnist.train.num_examples // batch_size

# 添加命名空间
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('layer'):
    with tf.name_scope('wights'):
        W = tf.Variable(tf.zeros([784, 10]), name='W')
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([10]), name='b')
    with tf.name_scope('wx_plus_b'):
        wx_plus_b = tf.matmul(x, W) + b
    with tf.name_scope('softmax'):
        prediction = tf.nn.softmax(wx_plus_b)

with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.square(y - prediction))

with tf.name_scope('train_step'):
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

init = tf.global_variables_initializer()

with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.arg_max(prediction, 1))
    with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

config = tf.ConfigProto()
config.gpu_options.allow_growth = True

with tf.Session(config=config) as sess:
    sess.run(init)
    # 输出graph
    writer = tf.summary.FileWriter('logs/', sess.graph)
    for epoch in range(1):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})

        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))

打开 anaconda prompt，运行

tensorboard --logdir=D:\python\demo\logs

运行成功后在chrome浏览器打开

http://localhost:6006

查看结构

2. 查看网络运行时的数据

TensorBoard是TensorFlow自带的一个强大的可视化工具，也是一个Web应用程序套件。TensorBoard目前支持7种可视化，Scalars,Images,Audio,Graphs,Distributions,Histograms和Embeddings。其中可视化的主要功能如下。

（1）Scalars:展示训练过程中的准确率、损失值、权重/偏置的变化情况。

（2）Images:展示训练过程中记录的图像。

（3）Audio:展示训练过程中记录的音频。

（4）Graphs:展示模型的数据流图，以及训练在各个设备上消耗的内存和时间。

（5）Distributions:展示训练过程中记录的数据的分部图。

（6）Histograms:展示训练过程中记录的数据的柱状图。

（7）Embeddings:展示词向量后的投影分部。

TensorBoard通过运行一个本地服务器，来监听6006端口。在浏览器发出请求时，分析训练时记录的数据，绘制训练过程中的图像。

wights和biases打印参数概要函数（variable_summaries），loss和accuracy打印数据变化，合并所有的summary，每次运行train_step同时运行merged输出一次summary，每次epoch写入summary到文件

import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

mnist = read_data_sets("MNIST_data/", one_hot=True)
batch_size = 100
n_batch = mnist.train.num_examples // batch_size


# 参数概要
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)  # 平均值
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)  # 标准差
        tf.summary.scalar('max', tf.reduce_max(var))  # 最大值
        tf.summary.scalar('min', tf.reduce_min(var))  # 最小值
        tf.summary.histogram('histogram', var)  # 直方图


# 添加命名空间
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('layer'):
    with tf.name_scope('wights'):
        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('wx_plus_b'):
        wx_plus_b = tf.matmul(x, W) + b
    with tf.name_scope('softmax'):
        prediction = tf.nn.softmax(wx_plus_b)

with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.square(y - prediction))
    tf.summary.scalar('loss', loss)

with tf.name_scope('train_step'):
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

init = tf.global_variables_initializer()

with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.arg_max(prediction, 1))
    with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        tf.summary.scalar('accuracy', accuracy)

# 合并所有的summary
merged = tf.summary.merge_all()

config = tf.ConfigProto()
config.gpu_options.allow_growth = True

with tf.Session(config=config) as sess:
    sess.run(init)
    # 输出graph
    writer = tf.summary.FileWriter('logs/', sess.graph)
    for epoch in range(51):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            summary, _ = sess.run([merged, train_step], feed_dict={x: batch_xs, y: batch_ys})

        writer.add_summary(summary, epoch)

        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))

3. tensorboard的高维可视化

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# 运行次数
max_steps = 1001
# 图片数量
image_num = 3000
# 文件路径
DIR = "D:/python/demo/"

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# 定义会话
sess = tf.Session(config=config)

# 载入图片
# stack函数 打包图片
embedding = tf.Variable(tf.stack(mnist.test.images[:image_num]), trainable=False, name='embedding')


# 参数概要
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)  # 平均值
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)  # 标准差
        tf.summary.scalar('max', tf.reduce_max(var))  # 最大值
        tf.summary.scalar('min', tf.reduce_min(var))  # 最小值
        tf.summary.histogram('histogram', var)  # 直方图


# 命名空间
with tf.name_scope('input'):
    # 这里的none表示第一个维度可以是任意的长度
    x = tf.placeholder(tf.float32, [None, 784], name='x-input')
    # 正确的标签
    y = tf.placeholder(tf.float32, [None, 10], name='y-input')

# 显示图片
with tf.name_scope('input_reshape'):
    # -1代表个数不限，28，28代表把784转成28行28列，1代表黑白照片维度为1，3彩色照片维度为3
    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
    tf.summary.image('input', image_shaped_input, 10)

with tf.name_scope('layer'):
    # 创建一个简单神经网络
    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('wx_plus_b'):
        wx_plus_b = tf.matmul(x, W) + b
    with tf.name_scope('softmax'):
        prediction = tf.nn.softmax(wx_plus_b)

with tf.name_scope('loss'):
    # 交叉熵代价函数
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
    tf.summary.scalar('loss', loss)
with tf.name_scope('train'):
    # 使用梯度下降法
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

# 初始化变量
sess.run(tf.global_variables_initializer())

with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        # 结果存放在一个布尔型列表中
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))  # argmax返回一维张量中最大的值所在的位置
    with tf.name_scope('accuracy'):
        # 求准确率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  # 把correct_prediction变为float32类型
        tf.summary.scalar('accuracy', accuracy)

# 产生metadata文件
# 如果文件存在，创建文件，如果不存在，删除文件
if tf.gfile.Exists(DIR + 'projector/projector/metadata.tsv'):
    tf.gfile.DeleteRecursively(DIR + 'projector/projector/metadata.tsv')
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')

    # 合并所有的summary
merged = tf.summary.merge_all()

# 定义图的结构
projector_writer = tf.summary.FileWriter(DIR + 'projector/projector', sess.graph)
# 保存网络的模型
saver = tf.train.Saver()
# 定义配置文件
config = projector.ProjectorConfig()
embed = config.embeddings.add()
embed.tensor_name = embedding.name
embed.metadata_path = DIR + 'projector/projector/metadata.tsv'
embed.sprite.image_path = DIR + 'projector/data/mnist_10k_sprite.png'
# 把图片切分，每一个大小28*28
embed.sprite.single_image_dim.extend([28, 28])
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:
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print("Iter " + str(i) + ", Testing Accuracy= " + str(acc))

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