Table 2 Modified parameters in the convolution network to classify the 4 categories
Model: “sequential” | ||
|---|---|---|
Layer (type) | Output shape | Param # |
Conv2d (Conv2D) | (None, 80, 80, 64) | 832 |
Batch_normalization (BatchNormalization) | (None, 80, 80, 64) | 256 |
Conv2d_1 (Conv2D) | (None, 80, 80, 64) | 16,448 |
Batch_normalization_1 (BatchNormalization) | (None, 80, 80, 64) | 256 |
Max_pooling2d (MaxPooling2D) | (None, 40, 40, 64) | 0 |
dropout (Dropout) | (None, 40, 40, 64) | 0 |
Conv2d_2 (Conv2D) | (None, 40, 40, 32) | 8224 |
Batch_normalization_2 (BatchNormalization) | (None, 40, 40, 32) | 128 |
Conv2d_3 (Conv2D) | (None, 40, 40, 32) | 4128 |
Batch_normalization_3 (BatchNormalization) | (None, 40, 40, 32) | 128 |
Max_pooling2d_1 (MaxPooling2D) | (None, 20, 20, 32) | 0 |
Dropout_1 (Dropout) | (None, 20, 20, 32) | 0 |
Conv2d_4 (Conv2D) | (None, 20, 20, 16) | 2064 |
Batch_normalization_4 (BatchNormalization) | (None, 20, 20, 16) | 64 |
Conv2d_5 (Conv2D) | (None, 20, 20, 16) | 1040 |
Batch_normalization_5 (BatchNormalization) | (None, 20, 20, 16) | 64 |
Max_pooling2d_2 (MaxPooling2D) | (None, 10, 10, 16) | 0 |
Dropout_2 (Dropout) | (None, 10, 10, 16) | 0 |
Conv2d_6 (Conv2D) | (None, 10, 10, 8) | 520 |
Batch_normalization_6 (BatchNormalization) | (None, 10, 10, 8) | 32 |
Conv2d_7 (Conv2D) | (None, 10, 10, 8) | 264 |
Batch_normalization_7 (BatchNormalization) | (None, 10, 10, 8) | 32 |
Max_pooling2d_3 (MaxPooling2D) | (None, 5, 5, 8) | 0 |
Dropout_3 (Dropout) | (None, 5, 5, 8) | 0 |
Flatten (Flatten) | (None, 200) | 0 |
Dense (Dense) | (None, 1024) | 205,824 |
Dropout_4 (Dropout) | (None, 1024) | 0 |
Dense_1 (Dense) | (None, 4) | 4100 |