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I want to train a model to predict one's emotion from the physical signals. I have a physical signal and using it as input feature;
ecg(Electrocardiography)
In my dataset, there are 312 total records belonging to the participants and there are 18000 rows of data in each record. So when I combine them into a single data frame, there are 5616000 rows in total.
Here is my train_x dataframe;
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
And I have 6 classes which are corresponding to emotions. I have encoded these labels with numbers;
anger = 0, calmness = 1, disgust = 2, fear = 3, happiness = 4, sadness = 5
Here is my 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
To feed my CNN, I am reshaping the train_x and one hot encoding the train_y data.
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
After these processes, here is how they look like; train_x after reshape;
[[[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 ]]]
train_y after one hot encoding;
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]
After reshaping, I have created my CNN model;
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)
The problem is, accuracy is not going more than 0.2 and it is fluctuating up and down. Looks like the model does not learn anything. I have tried to increase layers, play with the learning rate, changing the loss function, changing the optimizer, scaling the data, normalizing the data, but nothing helped me to solve this problem. I also tried more simple Dense models or LSTM models but I can't find a way which works.
How Can I solve this problem? Thanks in advance.
Addition:
I wanted to add the training results after 50 epochs;
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
397
I would recommend taking several steps back and consider a much simpler approach.
Based on the following...
I have tried to increase layers, play with the learning rate, changing the loss function, changing the optimizer, scaling the data, normalizing the data, but nothing helped me to solve this problem. I also tried more simple Dense models or LSTM models but I can't find a way which works.
It doesn't sound like you have as strong of an understanding of your data and your tooling... which is fine cause it's an opportunity to learn.
A few questions
Do you have a base line model? Have you tried just running a multinomial logistic regression? If not I would strongly suggest starting there. Going through the feature engineering needed to make such a model will be invaluable as you increase the complexity of your model.
Did you check for class imbalances?
Why are you using a CNN? What do you want to accomplish with the convolutional layers? For me when I'm constructing a vision model for let say classifying the shoes in my closet I use several convolutional layers to extract spatial features such as edges and curves.
Related to the third question... Where did you get this architecture from? Is it from a publication? Is this the current state of the art model for ECG traces? Or is this the most accessible model? Sometimes the two are not the same. I would dig into the literature and search the web a bit more to find some more information about neural networks and analyzing ECG traces.
I think if you can answer these questions you will be able to solve your problem yourself.
190
Current problem in your implementation is, as you have used data with shape of (312,18000,1) for your model, you only have 312 samples and you have used 0.33 validation split so, you are using only 209 samples for training purpose.
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d_1 (Conv1D) (None, 17301, 100) 70100
_________________________________________________________________
conv1d_2 (Conv1D) (None, 16602, 50) 3500050
_________________________________________________________________
dropout_1 (Dropout) (None, 16602, 50) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 16602, 50) 200
_________________________________________________________________
activation_1 (Activation) (None, 16602, 50) 0
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 4150, 50) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 207500) 0
_________________________________________________________________
dense_1 (Dense) (None, 6) 1245006
=================================================================
Total params: 4,815,356
Trainable params: 4,815,256
Non-trainable params: 100
_________________________________________________________________
As I seen model.summary(), your model has 4,815,256 total trainable parameters. So, your model is easily overfitting the training data. Issue is, you have so many parameters to learn without enough samples. You can try to reduce your model size as shown below:
model = Sequential()
model.add(Conv1D(100,2,activation='relu',input_shape=(18000,1)))
model.add(Conv1D(10,2))
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'))
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d_1 (Conv1D) (None, 17999, 100) 300
_________________________________________________________________
conv1d_2 (Conv1D) (None, 17998, 10) 2010
_________________________________________________________________
dropout_1 (Dropout) (None, 17998, 10) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 17998, 10) 40
_________________________________________________________________
activation_1 (Activation) (None, 17998, 10) 0
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 4499, 10) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 44990) 0
_________________________________________________________________
dense_1 (Dense) (None, 6) 269946
=================================================================
Total params: 272,296
Trainable params: 272,276
Non-trainable params: 20
_________________________________________________________________
As I know you have 3 types of data ecg, gsr, temp. So, you can use train_x as (312,18000,3). and your train_y will be (312,6).
If above solution is not working than,
22
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