tensorboard使用入门

tensorboard使用入门

tensorboard在远程linux服务器上的使用

如果本机使用请跳过

参考问题 https://stackoverflow.com/questions/37987839/how-can-i-run-tensorboard-on-a-remote-server

解决办法

1 . 连接ssh时，将服务器的6006端口重定向到自己机器上来：

ssh -L 16006:127.0.0.1:6006 username@remote_server_ip

其中：16006:127.0.0.1代表自己机器上的16006号端口，6006是服务器上tensorboard使用的端口。

2 . 在服务器上使用6006端口正常启动tensorboard：

tensorboard --logdir=xxx --port=6006

3 . 在本地浏览器中输入地址：

127.0.0.1:16006

tensorflow运行demo

具体程序的解释请参考这里：http://blog.csdn.net/sinat_33761963/article/details/62433234

#coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

import tensorflow as tf
import pdb

from tensorflow.examples.tutorials.mnist import input_data

pdb.set_trace()

max_steps = 1000  # 最大迭代次数
learning_rate = 0.001   # 学习率
dropout = 0.9   # dropout时随机保留神经元的比例

data_dir = './'   # 样本数据存储的路径
log_dir = './'    # 输出日志保存的路径

mnist = input_data.read_data_sets(data_dir,one_hot=True)

sess = tf.InteractiveSession()

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)

def weight_variable(shape):
    """Create a weight variable with appropriate initialization."""
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    """Create a bias variable with appropriate initialization."""
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def variable_summaries(var):
    """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
    with tf.name_scope('summaries'):
      # 计算参数的均值，并使用tf.summary.scaler记录
      mean = tf.reduce_mean(var)
      tf.summary.scalar('mean', mean)

      # 计算参数的标准差
      with tf.name_scope('stddev'):
        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
      # 使用tf.summary.scaler记录记录下标准差，最大值，最小值
      tf.summary.scalar('stddev', stddev)
      tf.summary.scalar('max', tf.reduce_max(var))
      tf.summary.scalar('min', tf.reduce_min(var))
      # 用直方图记录参数的分布
      tf.summary.histogram('histogram', var)

def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
    """Reusable code for making a simple neural net layer.
    It does a matrix multiply, bias add, and then uses relu to nonlinearize.
    It also sets up name scoping so that the resultant graph is easy to read,
    and adds a number of summary ops.
    """
    # 设置命名空间
    with tf.name_scope(layer_name):
      # 调用之前的方法初始化权重w，并且调用参数信息的记录方法，记录w的信息
      with tf.name_scope('weights'):
        weights = weight_variable([input_dim, output_dim])
        variable_summaries(weights)
      # 调用之前的方法初始化权重b，并且调用参数信息的记录方法，记录b的信息
      with tf.name_scope('biases'):
        biases = bias_variable([output_dim])
        variable_summaries(biases)
      # 执行wx+b的线性计算，并且用直方图记录下来
      with tf.name_scope('linear_compute'):
        preactivate = tf.matmul(input_tensor, weights) + biases
        tf.summary.histogram('linear', preactivate)
      # 将线性输出经过激励函数，并将输出也用直方图记录下来
      activations = act(preactivate, name='activation')
      tf.summary.histogram('activations', activations)

      # 返回激励层的最终输出
      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('loss'):
    # 计算交叉熵损失（每个样本都会有一个损失）
    diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
    with tf.name_scope('total'):
      # 计算所有样本交叉熵损失的均值
      cross_entropy = tf.reduce_mean(diff)

tf.summary.scalar('loss', cross_entropy)

with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(
        cross_entropy)

with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
      # 分别将预测和真实的标签中取出最大值的索引，弱相同则返回1(true),不同则返回0(false)
      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    with tf.name_scope('accuracy'):
      # 求均值即为准确率
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)

# summaries合并
merged = tf.summary.merge_all()
# 写到指定的磁盘路径中
train_writer = tf.summary.FileWriter(log_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(log_dir + '/test')

# 运行初始化所有变量
tf.global_variables_initializer().run()

def feed_dict(train):
    """Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
    if train:
      xs, ys = mnist.train.next_batch(100)
      k = dropout
    else:
      xs, ys = mnist.test.images, mnist.test.labels
      k = 1.0
    return {x: xs, y_: ys, keep_prob: k}

for i in range(max_steps):
    if i % 10 == 0:  # 记录测试集的summary与accuracy
      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:  # 记录训练集的summary
      if i % 100 == 99:  # Record execution stats
        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),
                              options=run_options,
                              run_metadata=run_metadata)
        train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
        train_writer.add_summary(summary, i)
        print('Adding run metadata for', i)
      else:  # Record a summary
        summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
        train_writer.add_summary(summary, i)

train_writer.close()
test_writer.close()

需要在当前目录新建两个文件夹，train和test，然后按照第一步在浏览器中打开即可