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如何在Keras中构建LSTM分类器模型

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我是Keras和RNN的新手我需要在Keras中使用LSTM RNN为数据集构建一个分类器模型，该数据集包含一组形状（1795575,6）和标签形状阵列（1795575,1） . 标签为11类（从0到10）形状测试集（575643,6）和标签形状阵列（575643,1.Again，标签是11（从0到10）

如何塑造以下Keras模型以满足我的数据集 . 我应该选择哪些值？

from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.optimizers import SGD
import numpy as np
data_dim = ?
timesteps = ?
num_classes = ?
batch_size = ?
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(?, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd', 
metrics=['accuracy'])
model.fit(train_X_arr, train_y_arr,batch_size=batch_size, epochs=epochs, 
 shuffle=False,validation_data=(test_X_arr, test_y_arr))

我感谢您的帮助，并提前致谢

keras rnn

1 回答

  • 0

    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(loss='sparse_categorical_crossentropy',optimizer='sgd', 
metrics=['accuracy'])
model.fit(train_X_arr, train_y_arr,batch_size=batch_size, epochs=epochs, 
 shuffle=False,validation_data=(test_X_arr, test_y_arr))

    就是这样 .

    编辑：此外，请确保您重塑您的列车数据：（1795575,6,1） - > 1795575示例，每个有6个时间戳，每个时间戳是标量 .
    您可以使用np.expand_dims（train_data，-1）轻松实现 .

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