tensorflow搭建RNN模型

使用RNN处理MNIST手写数据集

一般来说CNN用来处理图像会更好一些，RNN一般用来处理语言或者情感方面的知识，这次就简单使用RNN来处理MNIST数据集

原理框图入下：

 

代码：

import tensorflow as tf
# 因为这个文件夹中有input_data.py文件，所以可以直接导入这个模块，否则需要输入完整的input_data
import input_data
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.contrib import rnn
print ("Packages imported")

mnist = input_data.read_data_sets("data/", one_hot=True)
trainimgs, trainlabels, testimgs, testlabels \
 = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels 
ntrain, ntest, dim, nclasses \
 = trainimgs.shape[0], testimgs.shape[0], trainimgs.shape[1], trainlabels.shape[1]
print ("MNIST loaded")

diminput  = 28
dimhidden = 128
dimoutput = 10
nsteps    = 28
weights = {
    'hidden': tf.Variable(tf.random_normal([diminput, dimhidden])), 
    'out': tf.Variable(tf.random_normal([dimhidden, dimoutput]))
}
biases = {
    'hidden': tf.Variable(tf.random_normal([dimhidden])),
    'out': tf.Variable(tf.random_normal([dimoutput]))
}

# 对于mnsit数据集，如果是通过RNN来实现，那么我们首先就是要将28*28的图片格式改为28个1*28个数据
def _RNN(_X, _W, _b, _nsteps, _name):
    # 1. Permute input from [batchsize, nsteps, diminput] 
    #   => [nsteps, batchsize, diminput]
    _X = tf.transpose(_X, [1, 0, 2])
    # 2. Reshape input to [nsteps*batchsize, diminput] 
    _X = tf.reshape(_X, [-1, diminput])
    # 3. Input layer => Hidden layer
    _H = tf.matmul(_X, _W['hidden']) + _b['hidden']
    print(_H)
    # 4. Splite data to 'nsteps' chunks. An i-th chunck indicates i-th batch data 
    _Hsplit = tf.split(_H, _nsteps, 0) 
    print(_Hsplit)
    # 5. Get LSTM's final output (_LSTM_O) and state (_LSTM_S)
    #    Both _LSTM_O and _LSTM_S consist of 'batchsize' elements
    #    Only _LSTM_O will be used to predict the output. 
    with tf.variable_scope(_name) as scope:
        
        # scope.reuse_variables()
        # tf封装好了完整的LSTM单元，forget_bias表示不遗忘信息
        lstm_cell = rnn.BasicLSTMCell(dimhidden, forget_bias=1.0)
        _LSTM_O, _LSTM_S = rnn.static_rnn(lstm_cell, _Hsplit,dtype=tf.float32)
    # 6. Output
    _O = tf.matmul(_LSTM_O[-1], _W['out']) + _b['out']    
    # Return! 
    return {
        'X': _X, 'H': _H, 'Hsplit': _Hsplit,
        'LSTM_O': _LSTM_O, 'LSTM_S': _LSTM_S, 'O': _O 
    }
print ("Network ready")

learning_rate = 0.001
x      = tf.placeholder("float", [None, nsteps, diminput])
y      = tf.placeholder("float", [None, dimoutput])
myrnn  = _RNN(x, weights, biases, nsteps, 'basic')
pred   = myrnn['O']
cost   = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) 
optm   = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Adam Optimizer
accr   = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(pred,1), tf.argmax(y,1)), tf.float32))
init   = tf.global_variables_initializer()
print ("Network Ready!")