python神经网络怎么使用Keras构建RNN

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Keras中构建RNN的重要函数

1、SimpleRNN

SimpleRNN用于在Keras中构建普通的简单RNN层，在使用前需要import。

from keras.layers import SimpleRNN

在实际使用时，需要用到几个参数。

model.add(    SimpleRNN(        batch_input_shape = (BATCH_SIZE,TIME_STEPS,INPUT_SIZE),        output_dim = CELL_SIZE,    ))

其中，batch_input_shape代表RNN输入数据的shape，shape的内容分别是每一次训练使用的BATCH，TIME_STEPS表示这个RNN按顺序输入的时间点的数量，INPUT_SIZE表示每一个时间点的输入数据大小。
CELL_SIZE代表训练每一个时间点的神经元数量。

2、model.train_on_batch

与之前的训练CNN网络和普通分类网络不同，RNN网络在建立时就规定了batch_input_shape，所以训练的时候也需要一定量一定量的传入训练数据。
model.train_on_batch在使用前需要对数据进行处理。获取指定BATCH大小的训练集。

X_batch = X_train[index_start:index_start + BATCH_SIZE,:,:]Y_batch = Y_train[index_start:index_start + BATCH_SIZE,:]index_start += BATCH_SIZE

具体训练过程如下：

for i in range(500):    X_batch = X_train[index_start:index_start + BATCH_SIZE,:,:]    Y_batch = Y_train[index_start:index_start + BATCH_SIZE,:]    index_start += BATCH_SIZE    cost = model.train_on_batch(X_batch,Y_batch)    if index_start >= X_train.shape[0]:        index_start = 0    if i0 == 0:        ## acc        cost,accuracy = model.evaluate(X_test,Y_test,batch_size=50)        ## W,b = model.layers[0].get_weights()        print("accuracy:",accuracy)        x = X_test[1].reshape(1,28,28)

全部代码

这是一个RNN神经网络的例子，用于识别手写体。

import numpy as npfrom keras.models import Sequentialfrom keras.layers import SimpleRNN,Activation,Dense ## 全连接层from keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.optimizers import AdamTIME_STEPS = 28INPUT_SIZE = 28BATCH_SIZE = 50index_start = 0OUTPUT_SIZE = 10CELL_SIZE = 75LR = 1e-3(X_train,Y_train),(X_test,Y_test) = mnist.load_data() X_train = X_train.reshape(-1,28,28)/255X_test = X_test.reshape(-1,28,28)/255Y_train = np_utils.to_categorical(Y_train,num_classes= 10)Y_test = np_utils.to_categorical(Y_test,num_classes= 10)model = Sequential()# conv1model.add(    SimpleRNN(        batch_input_shape = (BATCH_SIZE,TIME_STEPS,INPUT_SIZE),        output_dim = CELL_SIZE,    ))model.add(Dense(OUTPUT_SIZE))model.add(Activation("softmax"))adam = Adam(LR)## compilemodel.compile(loss = 'categorical_crossentropy',optimizer = adam,metrics = ['accuracy'])## tarinfor i in range(500):    X_batch = X_train[index_start:index_start + BATCH_SIZE,:,:]    Y_batch = Y_train[index_start:index_start + BATCH_SIZE,:]    index_start += BATCH_SIZE    cost = model.train_on_batch(X_batch,Y_batch)    if index_start >= X_train.shape[0]:        index_start = 0    if i0 == 0:        ## acc        cost,accuracy = model.evaluate(X_test,Y_test,batch_size=50)        ## W,b = model.layers[0].get_weights()        print("accuracy:",accuracy)

实验结果为：

10000/10000 [==============================] - 1s 147us/stepaccuracy: 0.09329999938607215…………………………10000/10000 [==============================] - 1s 112us/stepaccuracy: 0.939500002264976510000/10000 [==============================] - 1s 109us/stepaccuracy: 0.942299999594688510000/10000 [==============================] - 1s 114us/stepaccuracy: 0.953400000035762810000/10000 [==============================] - 1s 112us/stepaccuracy: 0.956600000858306910000/10000 [==============================] - 1s 113us/stepaccuracy: 0.95079999983310710000/10000 [==============================] - 1s 116us/step10000/10000 [==============================] - 1s 112us/stepaccuracy: 0.947499998807907110000/10000 [==============================] - 1s 111us/stepaccuracy: 0.951500000357627810000/10000 [==============================] - 1s 114us/stepaccuracy: 0.928899997770786210000/10000 [==============================] - 1s 115us/stepaccuracy: 0.9487999993562698

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