Classificação de dígitos manuscritos MNIST usando sklearn e Keras.
Dataset MNIST de dígitos manuscritos.
- linux system (Ubuntu 20.04)
- python 3.8.10
- matplotlib==3.4.3
- numpy==1.21.4
- pandas==1.3.4
- scikit-learn==1.0.1
- seaborn==0.11.2
- tensorflow==2.7.0
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= flatten (Flatten) (None, 784) 0 _________________________________________________________________ dense (Dense) (None, 128) 100480 _________________________________________________________________ dense_1 (Dense) (None, 10) 1290 ================================================================= Total params: 101,770 Trainable params: 101,770 Non-trainable params: 0 _________________________________________________________________ None
97.71%
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 26, 26, 32) 320 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0 _________________________________________________________________ flatten (Flatten) (None, 5408) 0 _________________________________________________________________ dense (Dense) (None, 100) 540900 _________________________________________________________________ dense_1 (Dense) (None, 10) 1010 ================================================================= Total params: 542,230 Trainable params: 542,230 Non-trainable params: 0 _________________________________________________________________ None
98.62%
96.88%
