基于LSTM的脑电信号分类体系结构
基于LSTM的脑电信号分类体系结构
提问于 2021-01-20 21:54:09
我有一个多类分类问题，我在python3.6中使用了keras & tensorflow。我基于本文中提到的“叠层 LSTM层(a)”实现了高精度的分类：深入学习人类思维实现视觉自动分类。
有些事情是这样的：
model.add(LSTM(256,input_shape=(32, 15360), return_sequences=True))
model.add(LSTM(128), return_sequences=True)
model.add(LSTM(64), return_sequences=False)
model.add(Dense(6, activation='softmax'))设32为脑电通道#，15360为信号长度为160 Hz的96秒记录。
我想实现上面提到的“通道LSTM和通用LSTM (b)”策略，但我不知道该如何通过这个新策略来建立我的模型。
请帮帮我。Thx
回答 1
Stack Overflow用户
回答已采纳
发布于 2021-01-21 04:09:18
首先，在使用Common 实现编码器时遇到了问题，默认情况下，角膜LSTM层采用形状为(batch, timesteps, channel)的输入，因此如果设置input_shape=(32, 15360)，则模型将读入为timesteps=32和channel=15360，这与您的意图相反。
因为使用的第一层编码器通用LSTM描述为：
在每个时间步骤t中，第一层接受输入s(·，t)(从这个意义上说，“公共”意味着所有脑电通道最初都是fed8进入同一层)。
因此，使用通用LSTM实现编码器的正确实现应该是：
import tensorflow as tf
from tensorflow.keras import layers, models
timesteps = 15360
channels_num = 32
model = models.Sequential()
model.add(layers.LSTM(256,input_shape=(timesteps, channels_num), return_sequences=True))
model.add(layers.LSTM(128, return_sequences=True))
model.add(layers.LSTM(64, return_sequences=False))
model.add(layers.Dense(6, activation='softmax'))
model.summary()哪个输出(PS:您可以总结您最初的实现，然后您将看到Total params要大得多)：
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (None, 15360, 256) 295936
_________________________________________________________________
lstm_1 (LSTM) (None, 15360, 128) 197120
_________________________________________________________________
lstm_2 (LSTM) (None, 64) 49408
_________________________________________________________________
dense (Dense) (None, 6) 390
=================================================================
Total params: 542,854
Trainable params: 542,854
Non-trainable params: 0
_________________________________________________________________第二，因为使用信道LSTM和通用LSTM的编码器被描述为：
第一编码层由几个LSTM组成，每个LSTM仅连接到一个输入信道:例如，第一LSTM处理输入数据(1，·)，第二LSTM进程(2，·)等等。这样，每个“通道LSTM”的输出就是单个通道数据的汇总。然后，第二编码层通过接收所有信道LSTM的级联输出向量作为输入来执行信道间分析。如上所述，在最后一步使用最深的LSTM输出作为编码器的输出矢量。
由于第一层中的每个LSTM只处理一个信道，所以我们需要在第一层中使用等于信道数的LSTM数，下面的代码将演示如何使用信道LSTM和通用LSTM构建一个编码器。
import tensorflow as tf
from tensorflow.keras import layers, models
timesteps = 15360
channels_num = 32
first_layer_inputs = []
second_layer_inputs = []
for i in range(channels_num):
l_input = layers.Input(shape=(timesteps, 1))
first_layer_inputs.append(l_input)
l_output = layers.LSTM(1, return_sequences=True)(l_input)
second_layer_inputs.append(l_output)
x = layers.Concatenate()(second_layer_inputs)
x = layers.LSTM(128, return_sequences=True)(x)
x = layers.LSTM(64, return_sequences=False)(x)
outputs = layers.Dense(6, activation='softmax')(x)
model = models.Model(inputs=first_layer_inputs, outputs=outputs)
model.summary()产出：
Model: "functional_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_3 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_4 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_5 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_6 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_7 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_8 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_9 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_10 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_11 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_12 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_13 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_14 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_15 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_16 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_17 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_18 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_19 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_20 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_21 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_22 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_23 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_24 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_25 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_26 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_27 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_28 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_29 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_30 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_31 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
input_32 (InputLayer) [(None, 15360, 1)] 0
__________________________________________________________________________________________________
lstm (LSTM) (None, 15360, 1) 12 input_1[0][0]
__________________________________________________________________________________________________
lstm_1 (LSTM) (None, 15360, 1) 12 input_2[0][0]
__________________________________________________________________________________________________
lstm_2 (LSTM) (None, 15360, 1) 12 input_3[0][0]
__________________________________________________________________________________________________
lstm_3 (LSTM) (None, 15360, 1) 12 input_4[0][0]
__________________________________________________________________________________________________
lstm_4 (LSTM) (None, 15360, 1) 12 input_5[0][0]
__________________________________________________________________________________________________
lstm_5 (LSTM) (None, 15360, 1) 12 input_6[0][0]
__________________________________________________________________________________________________
lstm_6 (LSTM) (None, 15360, 1) 12 input_7[0][0]
__________________________________________________________________________________________________
lstm_7 (LSTM) (None, 15360, 1) 12 input_8[0][0]
__________________________________________________________________________________________________
lstm_8 (LSTM) (None, 15360, 1) 12 input_9[0][0]
__________________________________________________________________________________________________
lstm_9 (LSTM) (None, 15360, 1) 12 input_10[0][0]
__________________________________________________________________________________________________
lstm_10 (LSTM) (None, 15360, 1) 12 input_11[0][0]
__________________________________________________________________________________________________
lstm_11 (LSTM) (None, 15360, 1) 12 input_12[0][0]
__________________________________________________________________________________________________
lstm_12 (LSTM) (None, 15360, 1) 12 input_13[0][0]
__________________________________________________________________________________________________
lstm_13 (LSTM) (None, 15360, 1) 12 input_14[0][0]
__________________________________________________________________________________________________
lstm_14 (LSTM) (None, 15360, 1) 12 input_15[0][0]
__________________________________________________________________________________________________
lstm_15 (LSTM) (None, 15360, 1) 12 input_16[0][0]
__________________________________________________________________________________________________
lstm_16 (LSTM) (None, 15360, 1) 12 input_17[0][0]
__________________________________________________________________________________________________
lstm_17 (LSTM) (None, 15360, 1) 12 input_18[0][0]
__________________________________________________________________________________________________
lstm_18 (LSTM) (None, 15360, 1) 12 input_19[0][0]
__________________________________________________________________________________________________
lstm_19 (LSTM) (None, 15360, 1) 12 input_20[0][0]
__________________________________________________________________________________________________
lstm_20 (LSTM) (None, 15360, 1) 12 input_21[0][0]
__________________________________________________________________________________________________
lstm_21 (LSTM) (None, 15360, 1) 12 input_22[0][0]
__________________________________________________________________________________________________
lstm_22 (LSTM) (None, 15360, 1) 12 input_23[0][0]
__________________________________________________________________________________________________
lstm_23 (LSTM) (None, 15360, 1) 12 input_24[0][0]
__________________________________________________________________________________________________
lstm_24 (LSTM) (None, 15360, 1) 12 input_25[0][0]
__________________________________________________________________________________________________
lstm_25 (LSTM) (None, 15360, 1) 12 input_26[0][0]
__________________________________________________________________________________________________
lstm_26 (LSTM) (None, 15360, 1) 12 input_27[0][0]
__________________________________________________________________________________________________
lstm_27 (LSTM) (None, 15360, 1) 12 input_28[0][0]
__________________________________________________________________________________________________
lstm_28 (LSTM) (None, 15360, 1) 12 input_29[0][0]
__________________________________________________________________________________________________
lstm_29 (LSTM) (None, 15360, 1) 12 input_30[0][0]
__________________________________________________________________________________________________
lstm_30 (LSTM) (None, 15360, 1) 12 input_31[0][0]
__________________________________________________________________________________________________
lstm_31 (LSTM) (None, 15360, 1) 12 input_32[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 15360, 32) 0 lstm[0][0]
lstm_1[0][0]
lstm_2[0][0]
lstm_3[0][0]
lstm_4[0][0]
lstm_5[0][0]
lstm_6[0][0]
lstm_7[0][0]
lstm_8[0][0]
lstm_9[0][0]
lstm_10[0][0]
lstm_11[0][0]
lstm_12[0][0]
lstm_13[0][0]
lstm_14[0][0]
lstm_15[0][0]
lstm_16[0][0]
lstm_17[0][0]
lstm_18[0][0]
lstm_19[0][0]
lstm_20[0][0]
lstm_21[0][0]
lstm_22[0][0]
lstm_23[0][0]
lstm_24[0][0]
lstm_25[0][0]
lstm_26[0][0]
lstm_27[0][0]
lstm_28[0][0]
lstm_29[0][0]
lstm_30[0][0]
lstm_31[0][0]
__________________________________________________________________________________________________
lstm_32 (LSTM) (None, 15360, 128) 82432 concatenate[0][0]
__________________________________________________________________________________________________
lstm_33 (LSTM) (None, 64) 49408 lstm_32[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 6) 390 lstm_33[0][0]
==================================================================================================
Total params: 132,614
Trainable params: 132,614
Non-trainable params: 0
__________________________________________________________________________________________________现在，由于模型期望输入形状为(channel, batch, timesteps, 1)，所以在输入到模型之前，我们必须重新排序数据集的轴，下面的示例代码将向您展示如何重新排序从(batch, timesteps, channel)到(channel, batch, timesteps, 1)的轴。
import numpy as np
batch_size = 64
timesteps = 15360
channels_num = 32
x = np.random.rand(batch_size, timesteps, channels_num)
print(x.shape)
x = np.moveaxis(x, -1, 0)[..., np.newaxis]
print(x.shape)
x = [i for i in x]
print(x[0].shape)产出：
(64, 15360, 32)
(32, 64, 15360, 1)
(64, 15360, 1)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/65818241
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