ubuntu16.04 installation tensorflow1.x

I have learning needs, it is to be installed tensorlow, the reason why the installation of version 1.x, because the current study is to find a video for the 1.x versions, and the difference between the version 1.x and 2.x is still there Some, I think first with version 1.x to learn, after the installation of the day, I found that this installation is not so easy, but after also try to find a better method of installation, write here, the record about
I use ubuntu is the most basic version of the virtual machine, did not add anything else, so what cuda those with little trouble, but I do not have this version installed, as a relatively simple and the

tensorflow There are two versions, one for the 2.7 version of the python, and another for a 3.x version of python, here take 3.5 version of python (ubuntu16.04 comes with the system)
installation steps are as follows

1. Install pip3

In a system with a python2 and python3 in, pip for installation in the python2, pip3 is targeted to python3 mounting
input terminal sudo apt-get install python3-pipintermediate input y, wait for the end of the installation
update to the latest version pip and pip3, has entered in a terminal:

sudo pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade pip

sudo pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade pip

If during error should be on it and try again

2. Direct installation tensorflow

Input Terminal:

sudo pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow-gpu==1.14.0

I downloaded version 1.14.0 here is because I found that other versions have a problem with it, 1.14.0 directly on the line! ! !
Then install several tensorflow feature packages, here only to wait, if the intermediate appear red error trying to execute the command again!

See these basic installation can be considered complete! !

3. The test is complete

In the terminal input python3.5, note that if the input python, then open the default python2.7
input

import tensorflow as tf

The words appear to represent the installation of almost all the same, this jump is not error, but a number of variable declarations like too much trouble to do these logs will always occur, resulting in the feeling of use is not very good! ! !

Next New test.py file on the desktop:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
 
plotdata = {"batchsize":[], "loss":[]}
 
def moving_average(a, w=10):
    if len(a)<w:
        return a[:]
    return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]
 
#  模拟数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2*train_X + np.random.randn(*train_X.shape)*0.3  # 加入了噪声
 
# 图形展示
plt.plot(train_X,train_Y,'ro',label="original data") # label数据标签
plt.legend()
plt.show()
 
tf.reset_default_graph()  # 重置会话
 
# 创建模型
# 占位符
X = tf.placeholder("float")
Y = tf.placeholder("float")
# 模型参数
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")
 
# 前向结构
z = tf.multiply(X, W) +b
 
# 反向优化
cost = tf.reduce_mean(tf.square(Y-z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
 
# 初始化变量
init = tf.global_variables_initializer()
# 参数设置
training_epochs = 20
display_step = 2
saver = tf.train.Saver() # 模型生成，并保存
savedir = "log/"
 
# 启动session
with tf.Session() as sess:
    sess.run(init)
 
    for epoch in range(training_epochs):
        for (x, y) in zip(train_X,train_Y):
            sess.run(optimizer, feed_dict={X:x, Y:y})
 
        # 显示训练中的详细信息
        if epoch % display_step == 0:
            loss = sess.run(cost, feed_dict={X:train_X, Y:train_Y})
            print("Epoch:",epoch+1,"cost=", loss,"W=",sess.run(W),"b=",sess.run(b))
            if not (loss=="NA"):
                plotdata["batchsize"].append(epoch)
                plotdata["loss"].append(loss)
 
        print("finished!")
        saver.save(sess, savedir+"linermodel.cpkt")
        print("cost=",sess.run(cost, feed_dict={X:train_X, Y:train_Y}),"W=", sess.run(W),"b=",sess.run(b))
 
        # 图形显示
        plt.plot(train_X, train_Y, 'ro', label='Original data')
        plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
        plt.legend()
        plt.show()
 
        plotdata["avgloss"] = moving_average(plotdata["loss"])
        plt.figure(1)
        plt.subplot(211)
        plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
        plt.xlabel('Minibatch number')
        plt.ylabel('Loss')
        plt.title('Minibatch run vs. Training loss')
        plt.show()

Before running this file, you need to download some libraries used in this program:

sudo apt-get install python3-tk

sudo pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple matplotlib

Of course, more than two, use different modules have some libraries used to get library, you can download on demand
finally run:

cd Desktop/
python3.5 test.py

The basic page is shown, here on the instructions to install the basic use

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Final note, I learn here is to facilitate the installation of 1.14.0, the current version has been obtained to 2.1.0; or you want to upgrade later! ! ! !

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