load shape-trained CNN
load_shape_cnn()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
#> ________________________________________________________________________________