TensorFlow—张量运算仿真神经网络的运行

 import tensorflow as tf
 import numpy as np
 ts_norm=tf.random_normal([])
 with tf.Session() as sess:
     norm_data=ts_norm.eval()
 print(norm_data[:])
 import matplotlib.pyplot as plt
 plt.hist(norm_data)
 plt.show()
 def layer_debug(output_dim,input_dim,inputs,activation=None):
     W=tf.Variable(tf.random_normal([input_dim,output_dim]))
     b=tf.Variable(tf.random_normal([,output_dim]))
     XWb=tf.matmul(inputs,W)+b
     if activation is None:
         outputs=XWb
     else:
         outputs=activation(XWb)
     return outputs,W,b
 X=tf.placeholder("float",[None,])
 h,W1,b1=layer_debug(output_dim=,input_dim=,inputs=X,
        activation=tf.nn.relu)
 y,W2,b2=layer_debug(output_dim=,input_dim=,inputs=h)
 with tf.Session() as sess:
     init=tf.global_variables_initializer()
     sess.run(init)
     X_array=np.array([[0.4,0.2,0.4,0.5]])
     (layer_X,layer_h,layer_y,W1,W2,b1,b2)=sess.run((X,h,y,W1,W2,b1,b2),feed_dict={X:X_array})
     print('input layer x:');print(layer_X)
     print('w1:');print(W1)
     print('b1:');print(b1)
     print('input layer h:');print(layer_h)
     print('w2:');print(W2)
     print('b2:');print(b2)
     print('input layer y:');print(layer_y)

运行结果：