[tensorflow deep learning] 1. The startup of tensorflow, the use of variables, and the use of fetch and feed

1. Basic concepts of tensorflow

    Use graphs to represent computational tasks

    Execute the graph in a context called a Session

    Use tensor to represent data

    Maintaining state through variables

    Use feed and fetch to assign values ​​to or fetch data from any operation

    Tensorflow is a programming system that uses graphs to represent computing tasks. The nodes in the graphs are called ops (operations). An op obtains 0 or more Tensors, performs calculations, and generates 0 or more Tensor. Tensor is seen as an n-dimensional array or list. The graph must be started in a session.

2. Start the graph with python

import tensorflow as tf

#create a constant op
m1=tf.constant([[3,3]])
#create a constant op
m2=tf.constant([[2],[3]])
#Create a matrix multiplication, passing in m1 and m2
product=tf.matmul(m1,m2)
print (product)

#Define a session to start the default graph
sex = tf.Session ()
#Call the run method of sess to perform matrix multiplication op
#run(product) triggers 3 ops in the figure
result=sess.run(product)
print(result)
#Close the session
sess.close()

#The following definition method does not require close:
#with tf.Session() as sess:
#    result=sess.run(product)
#    print(result)

3. Use of variables

import tensorflow as tf
x=tf.Variable([1,2])
a=tf.constant([3,3])
#Add a subtraction op
sub=tf.subtract(x,a)
#Add an addition op
add=tf.add(x,a)
#initialize all variables
init=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    print (sess.run (sub))
    print (sess.run (add))

result:

    

import tensorflow as tf
#Create a variable, initialized to 0
state=tf.Variable(0,name='counter')
#Create an addition op that adds 1 to the state
new_value=tf.add(state,1)
#assign op
update=tf.assign(state,new_value)
#initialize all variables
init=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    print (sess.run (state))
    for _ in range(5):
        sess.run(update)
        print (sess.run (state))

result:

    

4.fetch executes multiple ops in a session and gets the results

import tensorflow as tf
#Fetch
input1=tf.constant(3.0)
input2=tf.constant(2.0)
input3=tf.constant(5.0)
add=tf.add(input2,input3)
mul=tf.multiply(input1,add)

with tf.Session() as sess:
    result=sess.run([mul,add])
    print(result)

result:

    

A feed can temporarily replace a tensor in any operation in the graph, and a patch can be submitted for any operation in the graph, directly inserting a tensor.

import tensorflow as tf
#Feed
#create placeholder
input1=tf.placeholder(tf.float32)
input2=tf.placeholder(tf.float32)
output=tf.multiply(input1, input2)
with tf.Session() as sess:
    #feed data is passed in as a dictionary
    print(sess.run(output,feed_dict={input1:[7.0],input2:[2.0]}))

result:

    

5. Use Cases

import tensorflow as tf
import numpy as np

#Use numpy to generate 100 random points
x_data=np.random.rand(100)
y_data=x_data*0.1+0.2
#Construct a linear model
b=tf.Variable(0.)
k=tf.Variable(0.)
y=k*x_data+b

#Secondary cost function
loss=tf.reduce_mean(tf.square(y_data-y))
#Define a gradient descent method to train an optimizer with a 0.2 learning rate
optimizer=tf.train.GradientDescentOptimizer(0.2)
#define a minimization cost function
train=optimizer.minimize(loss)
#Initialize variables
init=tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for step in range(201):
        sess.run (train)
        if step%20==0:
            print(step,sess.run([k,b]))
    

result: