tensorflow搭建RNN模型

向右看齐 2022-03-02 13:46 260阅读 0赞

### 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：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTkzMTU0MA_size_16_color_FFFFFF_t_70][]

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!")

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTkzMTU0MA_size_16_color_FFFFFF_t_70]: /images/20220302/54ceec15e2cb4effb3d7c13bc700280e.png