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- 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
- 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
- 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
- [[[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 ]]]
- 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]
- 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)
- 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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