1.需要在會話中完成op

2.初始化操作

init = tf.global_variables_initializer()

3.fetch run多個op

print(sess.run([mul,add]))

4.placeholder + feed_dict 

input1 = tf.placeholder(tf.float32,shape=[1,2])
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1,input2)
with tf.Session() as sess:
    print
 
(sess.run(output,feed_dict={input1:[[1,2]],input2:3}))

小練習

下面貼一個非線性迴歸練習：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

x_data = np.linspace(-0.5,0.5,200).reshape(-1,1)
noise = np.random.normal(0,0.02,x_data.shape)
y_data= np.square(x_data )+noise

#定義兩個placeholder
x = tf.placeholder(tf.float32,[None
 
,1])
y = tf.placeholder(tf.float32, [None,1])

#定義神經網路
Weights_L1 = tf.Variable(tf.random_normal([1,10]))
bias_L1 = tf.Variable(tf.zeros([1,10]))
Wx_plus_b_L1 = tf.matmul(x,Weights_L1) + bias_L1
L1 = tf.nn.tanh(Wx_plus_b_L1)
#定義輸出層
Weight_L2 = tf.Variable(tf.random_normal([10,1]))
bias_L2 = tf.Variable(tf.zeros([1
 
,1]))
Wx_plus_b_L2 = tf.matmul(L1,Weight_L2) + bias_L2
presiction = tf.nn.tanh(Wx_plus_b_L2)

#loss
loss = tf.reduce_mean(tf.square(presiction-y_data))
optimizer = tf.train.GradientDescentOptimizer(0.2)
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for step in range(2000):
        sess.run(train, feed_dict={x:x_data,y:y_data})
        if step % 20 ==0:
            print(step, sess.run(Weight_L2))
    prediction_value = sess.run(presiction,feed_dict={x:x_data,y:y_data})
    plt.figure()
    plt.scatter(x_data,y_data)
    plt.plot(x_data,prediction_value,'r-',lw=5)
    plt.show()