• Tensorflow 搭建自己的神经网络(四)


    • tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True):

     n_hidden表示神经元的个数,

    forget_bias就是LSTM们的忘记系数,如果等于1,就是不会忘记任何信息。如果等于0,就都忘记。

    state_is_tuple默认就是True,官方建议用True,就是表示返回的状态用一个元祖表示。

    这个里面存在一个状态初始化函数,就是zero_state(batch_size,dtype)两个参数。batch_size就是输入样本批次的数目,dtype就是数据类型。

    • tf.nn.dynamic_rnn(cell,inputs,sequence_length=None, initial_state=None,dtype=None, parallel_iterations=None,swap_memory=False, time_major=False, scope=None)

    tf.nn.dynamic_rnn的作用:

      对于单个 RNNCell ,使用call 函数进行运算时,只在序列时间上前进了一步 ,如使用 x1、 ho 得到此h1,通过 x2 、h1 得到 h2 等 。

      如果序列长度为n,要调用n次call函数,比较麻烦。对此提供了一个tf.nn.dynamic_mn函数,使用该函数相当于调用了n次call函数。通过{ho, x1 , x2,…,xn} 直接得到{h1 , h2,…,hn} 。

      具体来说,设输入数据inputs格式为(batch_size, time_steps, input_size),其中batch_size表示batch的大小。time_steps序列长度,input_size输入数据单个序列单个时间维度上固有的长度。

      得到的outputs是time_steps步里所有的输出。它的形状为(batch_size, time_steps, cell.output_size)。state 是最后一步的隐状态,形状为(batch_size, cell . state_size) 。

     

    RNN_classification

    #!/usr/bin/env python2
    # -*- coding: utf-8 -*-
    """
    Created on Tue Apr  9 20:36:38 2019
    
    @author: xiexj
    """
    
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
    
    tf.reset_default_graph() 
    #hyperparameters
    lr = 0.001
    training_iters = 100000
    batch_size = 128
    
    n_inputs = 28
    n_steps = 28
    n_hidden_units = 128
    n_classes = 10
    
    #tf Graph input
    x = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
    y = tf.placeholder(tf.float32, [None, n_classes])
    
    #Define weights
    weights = {
            'in':tf.Variable(tf.random_normal([n_inputs, n_hidden_units])),
            'out':tf.Variable(tf.random_normal([n_hidden_units, n_classes]))
    }
    biases = {
            'in':tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),
            'out':tf.Variable(tf.constant(0.1, shape=[n_classes, ]))        
    }
    
    def RNN(X, weights, biases):
        #hidden layer for input to cell
        X = tf.reshape(X, [-1,n_inputs])
        X_in = tf.matmul(X, weights['in'])+biases['in']
        X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])
        #cell
        lstm = tf.contrib.rnn.BasicLSTMCell(n_hidden_units, forget_bias=1.0, state_is_tuple=True)
        init_state = lstm.zero_state(batch_size, dtype=tf.float32)
        outputs, final_state = tf.nn.dynamic_rnn(lstm, X_in, initial_state=init_state, time_major=False)
        #hidden layer for outputs and final results
        results = tf.matmul(final_state[1],weights['out']) + biases['out']
    ###    outputs = tf.unstack(tf.transpose(outputs, [1,0,2]))
    ###   results = tf.matmul(outputs[-1], weights['out']) + biases['out']    # shape = (128, 10)
    #    t_o = tf.convert_to_tensor(outputs,tf.float32) #128 28 128
    #    t_f = tf.convert_to_tensor(final_state,tf.float32)  #2 128 128
    #    print(t_o[-1].get_shape().as_list(),t_f[0].get_shape().as_list(),t_f[1].get_shape().as_list())
        return results
    
    pred = RNN(x, weights, biases)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
    train_op = tf.train.AdamOptimizer(lr).minimize(cost)
    
    correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
    
    init = tf.global_variables_initializer()
    
    with tf.Session() as sess:
        sess.run(init)
        step = 0
        while step*batch_size<training_iters:
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs])
            sess.run(train_op, feed_dict = {x:batch_xs, y:batch_ys})
            
            if step%20 == 0:
                print(sess.run(accuracy, feed_dict={x:batch_xs,y:batch_ys}))
            step+=1

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  • 原文地址:https://www.cnblogs.com/exciting/p/10679631.html
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