tensorflow：搭建Rnn(一）

Rnn原理图：

Rnn的权重配置图：

注，上图中的n表示的是输入的维度dim

tensorflow搭建基本的Rnn

class tf.contrib.rnn.BasicRNNCell(num_units, activation=None, reuse=None, name=None) 
输入参数：

num_units： int, the number of units in the RNN cell.
activation： Nonlinearity to use. Default: tanh.
reuse： (optional) Python boolean describing whether to reuse variables in an existing scope. If not True, and the existing scope already has the given variables, an error is raised.
name： String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
输出：

一个隐层神经元数量为 num_units 的 RNN 基本单元（实例化的 cell）
常用属性：

state_size：size(s) of state(s) used by this cell，等于隐层神经元数量
output_size： size of outputs produced by this cell
注意： 在此函数中，state_size 永远等于 output_size

常用方法：
call(inputs, state)： 返回两个一模一样的隐层状态值
zero_state(batch_size, dtype)： 返回一个形状为 [batch_size, state_size] 的全零张量

代码示例

output_size = 10
#  RNN的输出模块的大小
batch_size = 32
# 输入的维度
cell = tf.nn.rnn_cell.BasicRNNCell(num_units=output_size)
# 一个隐层神经元数量为 num_units 的 RNN 基本单元（实例化的 cell）
input = tf.placeholder(dtype=tf.float32,shape=[batch_size,150])
# 输入的维度是 batch_size * 150 
h0 = cell.zero_state(batch_size=batch_size,dtype=tf.float32)
#  构建h0  维度是  batch_size *  output_size 
output,h1 = cell.call(input,h0) 
#   调用 call 函数, 在时间序列上推进一步

按照上面的推断各个参数的维度为：input: [32,150]; W: [150,10]; h0: [32,10]; U: [10,10] B: [10]；所以最终输出的维度就应该为[32,10]

import tensorflow as tf
import numpy as np
from tensorflow.python.ops import variable_scope as vs
output_size = 4
batch_size = 3
dim = 5
cell = tf.nn.rnn_cell.BasicRNNCell(num_units=output_size)
input = tf.placeholder(dtype=tf.float32, shape=[batch_size, dim])
h0 = cell.zero_state(batch_size=batch_size, dtype=tf.float32)
output, h1 = cell.call(input, h0)

x = np.array([[1, 2, 1, 1, 1], [2, 0, 0, 1, 1], [2, 1, 0, 1, 0]])

scope = vs.get_variable_scope()
with vs.variable_scope(scope,reuse=True) as outer_scope:
    weights = vs.get_variable(
        "kernel", [9, output_size],
        dtype=tf.float32,
        initializer= None)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    a,b,w= sess.run([output,h1,weights],feed_dict={input:x})
    print('output:')
    print(a)
    print('h1:')
    print(b)
    print("weights:")
    print(w)# shape = (9,4)

state = np.zeros(shape=(3,4))# shape = (3,4)
all_input = np.concatenate((x,state),axis=1)# shape = (3,9)
result = np.tanh(np.matmul(all_input,w))
print('result:')
print(result)