BetaGo consists of a just few components, all of which you have already seen. Go_model = KerasBot(model=model, processor=processor)Īlright, alright. # Create a bot from processor and model, then run it. Webbrowser.open('file://' + os.getcwd() + '/ui/demoBot.html', new=2) # Open web frontend, assuming you cd'ed into betago With processor and model we can initialize a so called KerasBot, which will serve the model for us. Model.fit(X, Y, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1) Model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) Model.add(Convolution2D(nb_filters, nb_conv, nb_conv)) Input_shape=(input_channels, go_board_rows, go_board_cols))) 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

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