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([1000])
with tf.Session() as sess:
    norm_data=ts_norm.eval()
print(norm_data[:5])
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([1,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,4])
h,W1,b1=layer_debug(output_dim=3,input_dim=4,inputs=X,
       activation=tf.nn.relu)
y,W2,b2=layer_debug(output_dim=2,input_dim=3,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：