学习进度笔记17

观看Tensorflow案例实战视频课程17 RNN网络模型

import tensorflow as tf
import input_data
import numpy as np
import matplotlib.pyplot as plt
print("Packages imported")

mnist=input_data.read_data_sets("data/",one_hot=True)
trainimgs,trainlables,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],trainlables.shape[1]
print("MNIST loaded")

diminput=28
dimhidden=128
dimoutput=nclasses
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([dimhidden]))
}

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']
    #4.Splite data to 'nsteps' chunks. An i_th chunck indicates i_th batch data
    _Hsplit=tf.split(0,_nsteps,_H)
    #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()
        lstm_cell=tf.nn.run_cell.BasicLSTMCell(dimhidden,forget_bias=1.0)
        _LSTM_O,_LSTM_S=tf.nn.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")