lstm模型java实现_如何在Keras中构建LSTM分类器模型

你想做的是：

from keras.models import Sequential

from keras.layers import LSTM, Dense

from keras.optimizers import SGD

import numpy as np

data_dim = 1 # EACH TIMESTAMP IS SCALAR SO SHAPE=1

timesteps = 6 # EACH EXAMPLE CONTAINS 6 TIMESTAMPS

num_classes = 1 # EACH LABEL IS ONE NUMBER SO SHAPE=1

batch_size = 1 # TAKE SIZE THAT CAN DIVIDE THE NUMBER OF EXAMPLES IN THE TRAIN DATA. THE HIGHER THE BATCH SIZE THE BETTER!

sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)

model = Sequential()

model.add(LSTM(32, return_sequences=True, stateful=True,batch_input_shape=

(batch_size, timesteps, data_dim)))

model.add(LSTM(32, return_sequences=True, stateful=True))

model.add(LSTM(32, stateful=True))

model.add(Dense(1, activation='softmax')) # AT THE END YOU WANT ONE VALUE (LIKE THE LABELS) -> SO DENSE SHOULD OUTPUT 1 NODE

model.compile(l