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Table B.1.

VGGNET-like model configuration.

Layer Output shape Params #
Input layer (64, 64, NC) 0
Conv2D (64, 64, 64) 6976
Leaky ReLU (64, 64, 64) 0
Conv2D (64, 64, 64) 36928
Leaky ReLU (64, 64, 64) 0
Max Pool2D (32, 32, 64) 0
Conv2D (32, 32, 128) 73856
Leaky ReLU (32, 32, 128) 0
Conv2D (32, 32, 128) 147584
Leaky ReLU (32, 32, 128) 0
Max Pool2D (16, 16, 128) 0
Conv2D (16, 16, 256) 295168
Leaky ReLU (16, 32, 256) 0
Conv2D (16, 16, 256) 590080
Leaky ReLU (16, 16, 256) 0
Conv2D (16, 16, 256) 590080
Leaky ReLU (16, 32, 256) 0
Conv2D (16, 16, 256) 590080
Leaky ReLU (16, 16, 256) 0
Max Pool2D (8, 8, 256) 0
Conv2D (8, 8, 512) 1180160
Leaky ReLU (8, 8, 512) 0
Conv2D (8, 8, 512) 2359808
Leaky ReLU (8, 8, 512) 0
Conv2D (8, 8, 512) 2359808
Leaky ReLU (8, 8, 512) 0
Conv2D (8, 8, 512) 2359808
Leaky ReLU (8, 8, 512) 0
Max Pool2D (4, 4, 512) 0
Conv2D (4, 4, 512) 2359808
Leaky ReLU (4, 4, 512) 0
Conv2D (8, 8, 512) 2359808
Leaky ReLU (4, 4, 512) 0
Conv2D (4, 4, 512) 2359808
Leaky ReLU (4, 4, 512) 0
Conv2D (4, 4, 512) 2359808
Leaky ReLU (4, 4, 512) 0
Max Pool2D (2, 2, 512) 0
Flatten (2048) 0
Dense (4096) 8392704
Leaky ReLU (4096) 0
Dropout (4096) 0
Dense (4096) 16781312
Leaky ReLU (4096) 0
Dropout (4096) 0
Dense (2) 8194
Output Layer (2) 0

Notes. The columns specify the layer operation, the shape of the output and the number of parameters to fit. The output shape of a layer is a 4-D matrix, but, since the first dimension is the fixed size of the input data batch (with a size of 64 patterns), we do not mention this number to prevent confusion. The total amount of trainable parameters is larger than 45M. The last dimension of the input layer is the involved number of channels (i.e. the number of photometric bands used), a quantity depending on the specific experiment (see Sect. 2).

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