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[INFO] Download dataset: MNIST
[INFO] Padronizando imagens de acordo com a lib utilizada em backend pelo Keras
[INFO] Tensorflow
[INFO] inicializando e otimizando a CNN...
[INFO] treinando a CNN...
Train on 60000 samples, validate on 10000 samples
Epoch 1/20
- 20s - loss: 1.2984 - acc: 0.5849 - val_loss: 0.4175 - val_acc: 0.8706
Epoch 2/20
- 20s - loss: 0.3105 - acc: 0.9071 - val_loss: 0.2555 - val_acc: 0.9243
Epoch 3/20
- 19s - loss: 0.2165 - acc: 0.9349 - val_loss: 0.1735 - val_acc: 0.9480
Epoch 4/20
- 21s - loss: 0.1715 - acc: 0.9487 - val_loss: 0.1541 - val_acc: 0.9511
Epoch 5/20
- 21s - loss: 0.1456 - acc: 0.9560 - val_loss: 0.1260 - val_acc: 0.9608
Epoch 6/20
- 19s - loss: 0.1269 - acc: 0.9614 - val_loss: 0.1133 - val_acc: 0.9654
Epoch 7/20
- 20s - loss: 0.1123 - acc: 0.9658 - val_loss: 0.1030 - val_acc: 0.9681
Epoch 8/20
- 21s - loss: 0.1030 - acc: 0.9682 - val_loss: 0.0909 - val_acc: 0.9721
Epoch 9/20
- 20s - loss: 0.0941 - acc: 0.9713 - val_loss: 0.0918 - val_acc: 0.9706
Epoch 10/20
- 21s - loss: 0.0873 - acc: 0.9727 - val_loss: 0.0774 - val_acc: 0.9763
Epoch 11/20
- 21s - loss: 0.0820 - acc: 0.9749 - val_loss: 0.0788 - val_acc: 0.9757
Epoch 12/20
- 21s - loss: 0.0774 - acc: 0.9764 - val_loss: 0.0710 - val_acc: 0.9767
Epoch 13/20
- 20s - loss: 0.0724 - acc: 0.9775 - val_loss: 0.0716 - val_acc: 0.9784
Epoch 14/20
- 20s - loss: 0.0689 - acc: 0.9790 - val_loss: 0.0753 - val_acc: 0.9761
Epoch 15/20
- 20s - loss: 0.0652 - acc: 0.9800 - val_loss: 0.0610 - val_acc: 0.9796
Epoch 16/20
- 22s - loss: 0.0624 - acc: 0.9805 - val_loss: 0.0611 - val_acc: 0.9806
Epoch 17/20
- 21s - loss: 0.0602 - acc: 0.9817 - val_loss: 0.0590 - val_acc: 0.9825
Epoch 18/20
- 20s - loss: 0.0581 - acc: 0.9821 - val_loss: 0.0634 - val_acc: 0.9807
Epoch 19/20
- 21s - loss: 0.0553 - acc: 0.9831 - val_loss: 0.0555 - val_acc: 0.9820
Epoch 20/20
- 19s - loss: 0.0536 - acc: 0.9837 - val_loss: 0.0547 - val_acc: 0.9815
[INFO] Salvando modelo treinado ...
[INFO] tempo de execução da CNN: 406.54 s
[INFO] avaliando a CNN...
precision recall f1-score support
0 0.99 0.99 0.99 980
1 0.99 0.99 0.99 1135
2 0.99 0.97 0.98 1032
3 0.98 0.99 0.98 1010
4 0.98 0.98 0.98 982
5 0.98 0.99 0.98 892
6 0.99 0.98 0.99 958
7 0.97 0.99 0.98 1028
8 0.97 0.97 0.97 974
9 0.97 0.98 0.97 1009
micro avg 0.98 0.98 0.98 10000
macro avg 0.98 0.98 0.98 10000
weighted avg 0.98 0.98 0.98 10000
[INFO] Summary:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 28, 28, 6) 156
_________________________________________________________________
activation_1 (Activation) (None, 28, 28, 6) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 6) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 10, 10, 16) 2416
_________________________________________________________________
activation_2 (Activation) (None, 10, 10, 16) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 5, 5, 16) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 400) 0
_________________________________________________________________
dense_1 (Dense) (None, 120) 48120
_________________________________________________________________
activation_3 (Activation) (None, 120) 0
_________________________________________________________________
dense_2 (Dense) (None, 84) 10164
_________________________________________________________________
activation_4 (Activation) (None, 84) 0
_________________________________________________________________
dense_3 (Dense) (None, 10) 850
_________________________________________________________________
activation_5 (Activation) (None, 10) 0
=================================================================
Total params: 61,706
Trainable params: 61,706
Non-trainable params: 0
_________________________________________________________________
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10000/10000 [==============================] - 4s 432us/step
[INFO] Accuracy: 98.15% | Loss: 0.05468
[INFO] Plot loss e accuracy para os datasets 'train' e 'test'
[INFO] Gerando imagem do modelo de camadas da CNN
[INFO] Finalizando ...