load shape-trained CNN

load_shape_cnn()

Examples

load_shape_cnn()
#> Model: "sequential"
#> ________________________________________________________________________________
#>  Layer (type)                       Output Shape                    Param #     
#> ================================================================================
#>  conv2d_3 (Conv2D)                  (None, 32, 32, 32)              896         
#>  max_pooling2d_3 (MaxPooling2D)     (None, 16, 16, 32)              0           
#>  conv2d_2 (Conv2D)                  (None, 16, 16, 64)              18496       
#>  max_pooling2d_2 (MaxPooling2D)     (None, 8, 8, 64)                0           
#>  conv2d_1 (Conv2D)                  (None, 8, 8, 128)               73856       
#>  max_pooling2d_1 (MaxPooling2D)     (None, 4, 4, 128)               0           
#>  conv2d (Conv2D)                    (None, 4, 4, 256)               295168      
#>  max_pooling2d (MaxPooling2D)       (None, 2, 2, 256)               0           
#>  flatten (Flatten)                  (None, 1024)                    0           
#>  dropout (Dropout)                  (None, 1024)                    0           
#>  dense_1 (Dense)                    (None, 512)                     524800      
#>  dense (Dense)                      (None, 3)                       1539        
#> ================================================================================
#> Total params: 914,755
#> Trainable params: 914,755
#> Non-trainable params: 0
#> ________________________________________________________________________________