基础的RNN

import tensorflow as tf
import numpy as np
n_input=3
n_neurons=5
x0=tf.placeholder(tf.float32,[None,n_input])
x1=tf.placeholder(tf.float32,[None,n_input])

wx=tf.Variable(tf.random_normal(shape=[n_input,n_neurons],dtype=tf.float32))
wy=tf.Variable(tf.random_normal(shape=[n_neurons,n_neurons],dtype=tf.float32))
b=tf.Variable(tf.zeros([1,n_neurons],dtype=tf.float32))

y0=tf.tanh(tf.matmul(x0,wx)+b)
y1=tf.tanh(tf.matmul(y0,wy)+tf.matmul(x1,wx)+b)

init=tf.global_variables_initializer()
x0_batch=np.array([[0,1,2],[3,4,5],[6,7,8],[9,0,1]])
x1_batch=np.array([[9,8,7],[0,0,0],[6,5,4],[3,2,1]])
with tf.Session() as sess:
   init.run()
   yo_val,y2_val=sess.run([y0,y1],feed_dict={x0:x0_batch,x1:x1_batch})
   print(yo_val)
   print('---------')
   print(y2_val)

标签：RNN,基础,neurons,tf,input,x0,x1,float32
来源： https://blog.51cto.com/u_14540820/2759446