Model.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='valid',
# connecting the (num_samples, 7, 19, 19) input to the 19*19 output vector. # Specify a keras model with two convolutional layers and two dense layers, Nb_pool = 2 # size of pooling area for max pooling
Nb_filters = 32 # number of convolutional filters to use Go_board_rows, go_board_cols = 19, 19 # input dimensions of go board
Nb_classes = 19 * 19 # One class for each position on the board
