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本博客是对 用深度神经网络对Iris数据集进行分类的程序–tensorflow 里面的代码进行修改,使其可以记录训练日志,监控训练指标,并在TensorBoard中进行可视化.from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimp

"""DNNRegressor with custom input_fn for Housing dataset."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport itertoolsimport pandas as

先确保你已经安装了tensorflow…# 引入必要的modulefrom __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport osimport urllibimport numpy as npimport tensorflow

#下载数据集from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data",one_hot = True)#引入tensorflowimport tensorflow as tf#建立session对象sess = tf.InteractiveS

ubuntu运行散点图,不出图像. python默认没有安装tkinter,在终端输入下面的代码安装即可.sudo apt-get install python-tk

# 下载mnist数据集：from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)# 引入tensorflow：import tensorflow as tfx = tf.placeholder(tf.float

tensorflow的contrib.learn的基本用法import tensorflow as tf# NumPy is often used to load, manipulate and preprocess data.import numpy as np# Declare list of features. We only have one real-valued feature. T

ubuntu version:16.04 LTS tensorflow version:1.1.0 python version:2.7.12import numpy as npimport tensorflow as tf# Model parametersW = tf.Variable([.3], tf.float32)b = tf.Variable([-.3], tf.float32

打开终端，输入：sudo apt-get install python-pip python-dev输入你自己的密码，回车，继续安装； 输入y，回车，继续安装，等待安装完成。以上语句可以简单理解微安装tensorflow所必需的环境设置。继续输入：sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/c

本程序基于tensorflow下对MNIST数据集进行识别的程序代码 修改得到. 主要就是为了实现TensorBoard的可视化,加入了summary data到event file中去,有summary.scalar和summary.histogram还有name_scope的应用,这些都是为了可视化程序运行完成后,在命令行执行tensorboard --logdir=/tmp/tensorfl