张量运算仿真神经网络的运行

 1 import tensorflow as tf
 2 import numpy as np
 3 ts_norm=tf.random_normal([1000])
 4 with tf.Session() as sess:
 5     norm_data=ts_norm.eval()
 6 print(norm_data[:5])
 7 import matplotlib.pyplot as plt
 8 plt.hist(norm_data)
 9 plt.show()
10 def layer_debug(output_dim,input_dim,inputs,activation=None):
11     W=tf.Variable(tf.random_normal([input_dim,output_dim]))
12     b=tf.Variable(tf.random_normal([1,output_dim]))
13     XWb=tf.matmul(inputs,W)+b
14     if activation is None:
15         outputs=XWb
16     else:
17         outputs=activation(XWb)
18     return outputs,W,b
19 X=tf.placeholder("float",[None,4])
20 h,W1,b1=layer_debug(output_dim=3,input_dim=4,inputs=X,
21        activation=tf.nn.relu)
22 y,W2,b2=layer_debug(output_dim=2,input_dim=3,inputs=h)
23 with tf.Session() as sess:
24     init=tf.global_variables_initializer()
25     sess.run(init)
26     X_array=np.array([[0.4,0.2,0.4,0.5]])
27     (layer_X,layer_h,layer_y,W1,W2,b1,b2)=sess.run((X,h,y,W1,W2,b1,b2),feed_dict={X:X_array})
28     print(‘input layer x:‘);print(layer_X)
29     print(‘w1:‘);print(W1)
30     print(‘b1:‘);print(b1)
31     print(‘input layer h:‘);print(layer_h)
32     print(‘w2:‘);print(W2)
33     print(‘b2:‘);print(b2)
34     print(‘input layer y:‘);print(layer_y)