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Update README.rst
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‎README.rst‎

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@@ -1327,11 +1327,110 @@ run RNN and see our result:
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print(metrics.classification_report(y_test, predicted))
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Model summary:
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::
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_________________________________________________________________
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Layer (type) Output Shape Param #
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=================================================================
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embedding_1 (Embedding) (None, 500, 50) 8960500
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_________________________________________________________________
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gru_1 (GRU) (None, 500, 256) 235776
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_________________________________________________________________
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dropout_1 (Dropout) (None, 500, 256) 0
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_________________________________________________________________
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gru_2 (GRU) (None, 500, 256) 393984
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_________________________________________________________________
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dropout_2 (Dropout) (None, 500, 256) 0
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_________________________________________________________________
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gru_3 (GRU) (None, 500, 256) 393984
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_________________________________________________________________
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dropout_3 (Dropout) (None, 500, 256) 0
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_________________________________________________________________
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gru_4 (GRU) (None, 256) 393984
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_________________________________________________________________
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dense_1 (Dense) (None, 20) 5140
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=================================================================
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Total params: 10,383,368
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Trainable params: 10,383,368
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Non-trainable params: 0
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_________________________________________________________________
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Output:
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::
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Train on 11314 samples, validate on 7532 samples
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Epoch 1/20
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- 268s - loss: 2.5347 - acc: 0.1792 - val_loss: 2.2857 - val_acc: 0.2460
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Epoch 2/20
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- 271s - loss: 1.6751 - acc: 0.3999 - val_loss: 1.4972 - val_acc: 0.4660
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Epoch 3/20
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- 270s - loss: 1.0945 - acc: 0.6072 - val_loss: 1.3232 - val_acc: 0.5483
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Epoch 4/20
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- 269s - loss: 0.7761 - acc: 0.7312 - val_loss: 1.1009 - val_acc: 0.6452
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Epoch 5/20
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- 269s - loss: 0.5513 - acc: 0.8112 - val_loss: 1.0395 - val_acc: 0.6832
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Epoch 6/20
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- 269s - loss: 0.3765 - acc: 0.8754 - val_loss: 0.9977 - val_acc: 0.7086
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Epoch 7/20
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- 270s - loss: 0.2481 - acc: 0.9202 - val_loss: 1.0485 - val_acc: 0.7270
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Epoch 8/20
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- 269s - loss: 0.1717 - acc: 0.9463 - val_loss: 1.0269 - val_acc: 0.7394
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Epoch 9/20
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- 269s - loss: 0.1130 - acc: 0.9644 - val_loss: 1.1498 - val_acc: 0.7369
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Epoch 10/20
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- 269s - loss: 0.0640 - acc: 0.9808 - val_loss: 1.1442 - val_acc: 0.7508
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Epoch 11/20
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- 269s - loss: 0.0567 - acc: 0.9828 - val_loss: 1.2318 - val_acc: 0.7414
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Epoch 12/20
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- 268s - loss: 0.0472 - acc: 0.9858 - val_loss: 1.2204 - val_acc: 0.7496
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Epoch 13/20
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- 269s - loss: 0.0319 - acc: 0.9910 - val_loss: 1.1895 - val_acc: 0.7657
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Epoch 14/20
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- 268s - loss: 0.0466 - acc: 0.9853 - val_loss: 1.2821 - val_acc: 0.7517
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Epoch 15/20
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- 271s - loss: 0.0269 - acc: 0.9917 - val_loss: 1.2869 - val_acc: 0.7557
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Epoch 16/20
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- 271s - loss: 0.0187 - acc: 0.9950 - val_loss: 1.3037 - val_acc: 0.7598
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Epoch 17/20
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- 268s - loss: 0.0157 - acc: 0.9959 - val_loss: 1.2974 - val_acc: 0.7638
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Epoch 18/20
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- 270s - loss: 0.0121 - acc: 0.9966 - val_loss: 1.3526 - val_acc: 0.7602
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Epoch 19/20
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- 269s - loss: 0.0262 - acc: 0.9926 - val_loss: 1.4182 - val_acc: 0.7517
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Epoch 20/20
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- 269s - loss: 0.0249 - acc: 0.9918 - val_loss: 1.3453 - val_acc: 0.7638
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precision recall f1-score support
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0 0.71 0.71 0.71 319
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1 0.72 0.68 0.70 389
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2 0.76 0.62 0.69 394
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3 0.67 0.58 0.62 392
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4 0.68 0.67 0.68 385
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5 0.75 0.73 0.74 395
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6 0.82 0.74 0.78 390
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7 0.83 0.83 0.83 396
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8 0.81 0.90 0.86 398
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9 0.92 0.90 0.91 397
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10 0.91 0.94 0.93 399
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11 0.87 0.76 0.81 396
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12 0.57 0.70 0.63 393
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13 0.81 0.85 0.83 396
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14 0.74 0.93 0.82 394
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15 0.82 0.83 0.83 398
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16 0.74 0.78 0.76 364
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17 0.96 0.83 0.89 376
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18 0.64 0.60 0.62 310
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19 0.48 0.56 0.52 251
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avg / total 0.77 0.76 0.76 7532
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-----------------------------------------
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Convolutional Neural Networks (CNN)

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