Question

我正在尝试为二进制分类0或1

构建RNN / LSTM模型

我的数据集样本（患者编号，磨时/秒的时间，X Y和Z的归一化，峰度，偏度，俯仰，滚动和偏航，标签）。

1,15,-0.248010047716,0.00378335508419,-0.0152548459993,-86.3738760481,0.872322164158,-3.51314800063,0

1,31,-0.248010047716,0.00378335508419,-0.0152548459993,-86.3738760481,0.872322164158,-3.51314800063,0

1,46,-0.267422664673,0.0051143782875,-0.0191247001961,-85.7662354031,1.0928406847,-4.08015176908,0

1,62,-0.267422664673,0.0051143782875,-0.0191247001961,-85.7662354031,1.0928406847,-4.08015176908,0

我的尝试。

import numpy as np
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Bidirectional
from keras.preprocessing import sequence
# fix random seed for reproducibility
np.random.seed(7)

train = np.loadtxt("featwithsignalsTRAIN.txt", delimiter=",")
test = np.loadtxt("featwithsignalsTEST.txt", delimiter=",")

x_train = train[:,[2,3,4,5,6,7]]
x_test = test[:,[2,3,4,5,6,7]]
y_train = train[:,8]
y_test = test[:,8]

# create the model
model = Sequential()
model.add(LSTM(20, dropout=0.2, input_dim=6))
model.add(Dense(4, activation = 'sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train, epochs = 2)

但是它给了我以下错误

检查输入时出错：预期lstm_1_input有3个维度，但是有阵列形状（1415684,6）

Answer 1

LSTM图层采用3维输入，对应于（batch_size，timesteps，features）。在您的情况下，您只有一个二维输入，即（batch_size，features）。

LSTM层适用于序列格式（句子，股票价格......）。您需要重新整形数据，以便以这种方式使用它。更具体地说，您需要重塑您的数据以使每位患者拥有一条线（或者您可以选择每位患者拥有多个序列，但我们现在要求每位患者使用一条线），并且每条线都需要包含多个阵列，每个阵列对应于对患者的观察。

RNN / LSTM深度学习模型？

1 个答案: