时间：2022-08-13 12:17:21 | 栏目：Python代码 | 点击：次
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。
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 i%100 == 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)
这是一个RNN神经网络的例子，用于识别手写体。
import numpy as np
from keras.models import Sequential
from keras.layers import SimpleRNN,Activation,Dense ## 全连接层
from keras.datasets import mnist
from keras.utils import np_utils
from keras.optimizers import Adam
TIME_STEPS = 28
INPUT_SIZE = 28
BATCH_SIZE = 50
index_start = 0
OUTPUT_SIZE = 10
CELL_SIZE = 75
LR = 1e-3
(X_train,Y_train),(X_test,Y_test) = mnist.load_data()
X_train = X_train.reshape(-1,28,28)/255
X_test = X_test.reshape(-1,28,28)/255
Y_train = np_utils.to_categorical(Y_train,num_classes= 10)
Y_test = np_utils.to_categorical(Y_test,num_classes= 10)
model = Sequential()
# conv1
model.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)
## compile
model.compile(loss = 'categorical_crossentropy',optimizer = adam,metrics = ['accuracy'])
## tarin
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 i%100 == 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/step accuracy: 0.09329999938607215 ………………………… 10000/10000 [==============================] - 1s 112us/step accuracy: 0.9395000022649765 10000/10000 [==============================] - 1s 109us/step accuracy: 0.9422999995946885 10000/10000 [==============================] - 1s 114us/step accuracy: 0.9534000000357628 10000/10000 [==============================] - 1s 112us/step accuracy: 0.9566000008583069 10000/10000 [==============================] - 1s 113us/step accuracy: 0.950799999833107 10000/10000 [==============================] - 1s 116us/step 10000/10000 [==============================] - 1s 112us/step accuracy: 0.9474999988079071 10000/10000 [==============================] - 1s 111us/step accuracy: 0.9515000003576278 10000/10000 [==============================] - 1s 114us/step accuracy: 0.9288999977707862 10000/10000 [==============================] - 1s 115us/step accuracy: 0.9487999993562698