Keras：CNN 模型没有学习

Question

Keras：CNN 模型没有学习

Oza*_*ver 4 python neural-network deep-learning keras tensorflow

我想训练一个模型来从物理信号中预测一个人的情绪。我有一个物理信号并将其用作输入功能；

心电图（心电图）

在我的数据集中，共有312条记录属于参与者，每条记录有18000行数据。因此，当我将它们组合成一个数据框时，总共有5616000行。

这是我的train_x数据框；

            ecg  
0        0.1912 
1        0.3597 
2        0.3597 
3        0.3597 
4        0.3597 
5        0.3597 
6        0.2739 
7        0.1641 
8        0.0776 
9        0.0005 
10      -0.0375 
11      -0.0676 
12      -0.1071 
13      -0.1197 
..      ....... 
..      ....... 
..      ....... 
5616000 0.0226

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我有6个类对应于情绪。我用数字对这些标签进行了编码；

愤怒 = 0，冷静 = 1，厌恶 = 2，恐惧 = 3，快乐 = 4，悲伤 = 5

这是我的 train_y；

         emotion
0              0
1              0
2              0
3              0
4              0
.              .
.              .
.              .
18001          1
18002          1
18003          1
.              .
.              .
.              .
360001         2
360002         2
360003         2
.              .
.              .
.              .
.              .
5616000        5

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为了馈送我的 CNN，我正在重塑 train_x 和一个热编码 train_y 数据。

train_x = train_x.values.reshape(312,18000,1) 
train_y = train_y.values.reshape(312,18000)
train_y = train_y[:,:1]  # truncated train_y to have single corresponding value to a complete signal.
train_y = pd.DataFrame(train_y)
train_y = pd.get_dummies(train_y[0]) #one hot encoded labels

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在这些过程之后，这是它们的样子； 重塑后的train_x；

[[[0.60399908]
  [0.79763273]
  [0.79763273]
  ...
  [0.09779361]
  [0.09779361]
  [0.14732245]]

 [[0.70386905]
  [0.95101687]
  [0.95101687]
  ...
  [0.41530258]
  [0.41728671]
  [0.42261905]]

 [[0.75008021]
  [1.        ]
  [1.        ]
  ...
  [0.46412148]
  [0.46412148]
  [0.46412148]]

 ...

 [[0.60977509]
  [0.7756791 ]
  [0.7756791 ]
  ...
  [0.12725148]
  [0.02755331]
  [0.02755331]]

 [[0.59939494]
  [0.75514785]
  [0.75514785]
  ...
  [0.0391334 ]
  [0.0391334 ]
  [0.0578706 ]]

 [[0.5786066 ]
  [0.71539303]
  [0.71539303]
  ...
  [0.41355098]
  [0.41355098]
  [0.4112712 ]]]

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一次热编码后的train_y；

    0  1  2  3  4  5
0    1  0  0  0  0  0
1    1  0  0  0  0  0
2    0  1  0  0  0  0
3    0  1  0  0  0  0
4    0  0  0  0  0  1
5    0  0  0  0  0  1
6    0  0  1  0  0  0
7    0  0  1  0  0  0
8    0  0  0  1  0  0
9    0  0  0  1  0  0
10   0  0  0  0  1  0
11   0  0  0  0  1  0
12   0  0  0  1  0  0
13   0  0  0  1  0  0
14   0  1  0  0  0  0
15   0  1  0  0  0  0
16   1  0  0  0  0  0
17   1  0  0  0  0  0
18   0  0  1  0  0  0
19   0  0  1  0  0  0
20   0  0  0  0  1  0
21   0  0  0  0  1  0
22   0  0  0  0  0  1
23   0  0  0  0  0  1
24   0  0  0  0  0  1
25   0  0  0  0  0  1
26   0  0  1  0  0  0
27   0  0  1  0  0  0
28   0  1  0  0  0  0
29   0  1  0  0  0  0
..  .. .. .. .. .. ..
282  0  0  0  1  0  0
283  0  0  0  1  0  0
284  1  0  0  0  0  0
285  1  0  0  0  0  0
286  0  0  0  0  1  0
287  0  0  0  0  1  0
288  1  0  0  0  0  0
289  1  0  0  0  0  0
290  0  1  0  0  0  0
291  0  1  0  0  0  0
292  0  0  0  1  0  0
293  0  0  0  1  0  0
294  0  0  1  0  0  0
295  0  0  1  0  0  0
296  0  0  0  0  0  1
297  0  0  0  0  0  1
298  0  0  0  0  1  0
299  0  0  0  0  1  0
300  0  0  0  1  0  0
301  0  0  0  1  0  0
302  0  0  1  0  0  0
303  0  0  1  0  0  0
304  0  0  0  0  0  1
305  0  0  0  0  0  1
306  0  1  0  0  0  0
307  0  1  0  0  0  0
308  0  0  0  0  1  0
309  0  0  0  0  1  0
310  1  0  0  0  0  0
311  1  0  0  0  0  0

[312 rows x 6 columns]

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重塑后，我已经创建了我的CNN模型；

model = Sequential()
model.add(Conv1D(100,700,activation='relu',input_shape=(18000,1))) #kernel_size is 700 because 18000 rows = 60 seconds so 700 rows = ~2.33 seconds and there is two heart beat peak in every 2 second for ecg signal.
model.add(Conv1D(50,700))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling1D(4))
model.add(Flatten())
model.add(Dense(6,activation='softmax'))

adam = keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

model.compile(optimizer = adam, loss = 'categorical_crossentropy', metrics = ['acc'])
model.fit(train_x,train_y,epochs = 50, batch_size = 32, validation_split=0.33, shuffle=False)

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问题是，准确度不会超过 0.2，而且会上下波动。看起来模型没有学到任何东西。我试图增加层数，调整学习率，改变损失函数，改变优化器，缩放数据，规范化数据，但没有任何帮助我解决这个问题。我还尝试了更简单的 Dense 模型或 LSTM 模型，但找不到有效的方法。

我怎么解决这个问题？提前致谢。

添加：

我想添加 50 个 epoch 后的训练结果；

Epoch 1/80
249/249 [==============================] - 24s 96ms/step - loss: 2.3118 - acc: 0.1406 - val_loss: 1.7989 - val_acc: 0.1587
Epoch 2/80
249/249 [==============================] - 19s 76ms/step - loss: 2.0468 - acc: 0.1647 - val_loss: 1.8605 - val_acc: 0.2222
Epoch 3/80
249/249 [==============================] - 19s 76ms/step - loss: 1.9562 - acc: 0.1767 - val_loss: 1.8203 - val_acc: 0.2063
Epoch 4/80
249/249 [==============================] - 19s 75ms/step - loss: 1.9361 - acc: 0.2169 - val_loss: 1.8033 - val_acc: 0.1905
Epoch 5/80
249/249 [==============================] - 19s 74ms/step - loss: 1.8834 - acc: 0.1847 - val_loss: 1.8198 - val_acc: 0.2222
Epoch 6/80
249/249 [==============================] - 19s 75ms/step - loss: 1.8278 - acc: 0.2410 - val_loss: 1.7961 - val_acc: 0.1905
Epoch 7/80
249/249 [==============================] - 19s 75ms/step - loss: 1.8022 - acc: 0.2450 - val_loss: 1.8092 - val_acc: 0.2063
Epoch 8/80
249/249 [==============================] - 19s 75ms/step - loss: 1.7959 - acc: 0.2369 - val_loss: 1.8005 - val_acc: 0.2222
Epoch 9/80
249/249 [==============================] - 19s 75ms/step - loss: 1.7234 - acc: 0.2610 - val_loss: 1.7871 - val_acc: 0.2381
Epoch 10/80
249/249 [==============================] - 19s 75ms/step - loss: 1.6861 - acc: 0.2972 - val_loss: 1.8017 - val_acc: 0.1905
Epoch 11/80
249/249 [==============================] - 19s 75ms/step - loss: 1.6696 - acc: 0.3173 - val_loss: 1.7878 - val_acc: 0.1905
Epoch 12/80
249/249 [==============================] - 19s 75ms/step - loss: 1.5868 - acc: 0.3655 - val_loss: 1.7771 - val_acc: 0.1270
Epoch 13/80
249/249 [==============================] - 19s 75ms/step - loss: 1.5751 - acc: 0.3936 - val_loss: 1.7818 - val_acc: 0.1270
Epoch 14/80
249/249 [==============================] - 19s 75ms/step - loss: 1.5647 - acc: 0.3735 - val_loss: 1.7733 - val_acc: 0.1429
Epoch 15/80
249/249 [==============================] - 19s 75ms/step - loss: 1.4621 - acc: 0.4177 - val_loss: 1.7759 - val_acc: 0.1270
Epoch 16/80
249/249 [==============================] - 19s 75ms/step - loss: 1.4519 - acc: 0.4498 - val_loss: 1.8005 - val_acc: 0.1746
Epoch 17/80
249/249 [==============================] - 19s 75ms/step - loss: 1.4489 - acc: 0.4378 - val_loss: 1.8020 - val_acc: 0.1270
Epoch 18/80
249/249 [==============================] - 19s 75ms/step - loss: 1.4449 - acc: 0.4297 - val_loss: 1.7852 - val_acc: 0.1587
Epoch 19/80
249/249 [==============================] - 19s 75ms/step - loss: 1.3600 - acc: 0.5301 - val_loss: 1.7922 - val_acc: 0.1429
Epoch 20/80
249/249 [==============================] - 19s 75ms/step - loss: 1.3349 - acc: 0.5422 - val_loss: 1.8061 - val_acc: 0.2222
Epoch 21/80
249/249 [==============================] - 19s 75ms/step - loss: 1.2885 - acc: 0.5622 - val_loss: 1.8235 - val_acc: 0.1746
Epoch 22/80
249/249 [==============================] - 19s 75ms/step - loss: 1.2291 - acc: 0.5823 - val_loss: 1.8173 - val_acc: 0.1905
Epoch 23/80
249/249 [==============================] - 19s 75ms/step - loss: 1.1890 - acc: 0.6506 - val_loss: 1.8293 - val_acc: 0.1905
Epoch 24/80
249/249 [==============================] - 19s 75ms/step - loss: 1.1473 - acc: 0.6627 - val_loss: 1.8274 - val_acc: 0.1746
Epoch 25/80
249/249 [==============================] - 19s 75ms/step - loss: 1.1060 - acc: 0.6747 - val_loss: 1.8142 - val_acc: 0.1587
Epoch 26/80
249/249 [==============================] - 19s 75ms/step - loss: 1.0210 - acc: 0.7510 - val_loss: 1.8126 - val_acc: 0.1905
Epoch 27/80
249/249 [==============================] - 19s 75ms/step - loss: 0.9699 - acc: 0.7631 - val_loss: 1.8094 - val_acc: 0.1746
Epoch 28/80
249/249 [==============================] - 19s 75ms/step - loss: 0.9127 - acc: 0.8193 - val_loss: 1.8012 - val_acc: 0.1746
Epoch 29/80
249/249 [==============================] - 19s 75ms/step - loss: 0.9176 - acc: 0.7871 - val_loss: 1.8371 - val_acc: 0.1746
Epoch 30/80
249/249 [==============================] - 19s 75ms/step - loss: 0.8725 - acc: 0.8233 - val_loss: 1.8215 - val_acc: 0.1587
Epoch 31/80
249/249 [==============================] - 19s 75ms/step - loss: 0.8316 - acc: 0.8514 - val_loss: 1.8010 - val_acc: 0.1429
Epoch 32/80
249/249 [==============================] - 19s 75ms/step - loss: 0.7958 - acc: 0.8474 - val_loss: 1.8594 - val_acc: 0.1270
Epoch 33/80
249/249 [==============================] - 19s 75ms/step - loss: 0.7452 - acc: 0.8795 - val_loss: 1.8260 - val_acc: 0.1587
Epoch 34/80
249/249 [==============================] - 19s 75ms/step - loss: 0.7395 - acc: 0.8916 - val_loss: 1.8191 - val_acc: 0.1587
Epoch 35/80
249/249 [==============================] - 19s 75ms/step - loss: 0.6794 - acc: 0.9357 - val_loss: 1.8344 - val_acc: 0.1429
Epoch 36/80
249/249 [==============================] - 19s 75ms/step - loss: 0.6106 - acc: 0.9357 - val_loss: 1.7903 - val_acc: 0.1111
Epoch 37/80
249/249 [==============================] - 19s 75ms/step - loss: 0.5609 - acc: 0.9598 - val_loss: 1.7882 - val_acc: 0.1429
Epoch 38/80
249/249 [==============================] - 19s 75ms/step - loss: 0.5788 - acc: 0.9478 - val_loss: 1.8036 - val_acc: 0.1905
Epoch 39/80
249/249 [==============================] - 19s 75ms/step - loss: 0.5693 - acc: 0.9398 - val_loss: 1.7712 - val_acc: 0.1746
Epoch 40/80
249/249 [==============================] - 19s 75ms/step - loss: 0.4911 - acc: 0.9598 - val_loss: 1.8497 - val_acc: 0.1429
Epoch 41/80
249/249 [==============================] - 19s 75ms/step - loss: 0.4824 - acc: 0.9518 - val_loss: 1.8105 - val_acc: 0.1429
Epoch 42/80
249/249 [==============================] - 19s 75ms/step - loss: 0.4198 - acc: 0.9759 - val_loss: 1.8332 - val_acc: 0.1111
Epoch 43/80
249/249 [==============================] - 19s 75ms/step - loss: 0.3890 - acc: 0.9880 - val_loss: 1.9316 - val_acc: 0.1111
Epoch 44/80
249/249 [==============================] - 19s 75ms/step - loss: 0.3762 - acc: 0.9920 - val_loss: 1.8333 - val_acc: 0.1746
Epoch 45/80
249/249 [==============================] - 19s 75ms/step - loss: 0.3510 - acc: 0.9880 - val_loss: 1.8090 - val_acc: 0.1587
Epoch 46/80
249/249 [==============================] - 19s 75ms/step - loss: 0.3306 - acc: 0.9880 - val_loss: 1.8230 - val_acc: 0.1587
Epoch 47/80
249/249 [==============================] - 19s 75ms/step - loss: 0.2814 - acc: 1.0000 - val_loss: 1.7843 - val_acc: 0.2222
Epoch 48/80
249/249 [==============================] - 19s 75ms/step - loss: 0.2794 - acc: 1.0000 - val_loss: 1.8147 - val_acc: 0.2063
Epoch 49/80
249/249 [==============================] - 19s 75ms/step - loss: 0.2430 - acc: 1.0000 - val_loss: 1.8488 - val_acc: 0.1587
Epoch 50/80
249/249 [==============================] - 19s 75ms/step - loss: 0.2216 - acc: 1.0000 - val_loss: 1.8215 - val_acc: 0.1587

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Answer 1

Van*_*tam 6

我建议退后几步，考虑一个更简单的方法。
基于以下...

我尝试增加层数，调整学习率，改变损失函数，改变优化器，缩放数据，规范化数据，但没有任何帮助我解决这个问题。我还尝试了更简单的 Dense 模型或 LSTM 模型，但找不到有效的方法。

听起来您对自己的数据和工具的理解不够深入……这很好，因为这是一个学习的机会。

几个问题

你有基线模型吗？您是否尝试过仅运行多项逻辑回归？如果没有，我强烈建议从那里开始。随着您增加模型的复杂性，完成制作此类模型所需的特征工程将是无价的。
你检查过阶级不平衡吗？
你为什么使用CNN？你想用卷积层完成什么？对我来说，当我构建一个视觉模型来对我衣柜里的鞋子进行分类时，我使用了几个卷积层来提取空间特征，例如边缘和曲线。
与第三个问题有关......你从哪里得到这个架构？是出自出版物吗？这是当前最先进的心电图模型吗？或者这是最容易获得的模型？有时两者并不相同。我会深入研究文献并在网络上搜索更多，以找到有关神经网络和分析心电图轨迹的更多信息。

我想如果你能回答这些问题，你就能自己解决你的问题。

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