def __init__(self, model):
if model is None: #create new model
# init
garbage = GameState()
shape = garbage.board.shape
output_size = garbage.action_size()
del garbage
# Neural net
input_board = Input(shape=shape)
input_board_reshaped = Reshape(
(shape[0], shape[1], 1))(input_board)
conv1 = Activation('relu')(BatchNormalization(axis=3)(
Conv2D(nnet_args.filters, (4, 4),
padding='same')(input_board_reshaped)))
conv2 = Activation('relu')(BatchNormalization(axis=3)(Conv2D(
nnet_args.filters, (3, 3), padding='same')(conv1)))
conv3 = Activation('relu')(BatchNormalization(axis=3)(Conv2D(
nnet_args.filters, (3, 3), padding='same')(conv2)))
conv4 = Activation('relu')(BatchNormalization(axis=3)(Conv2D(
nnet_args.filters, (3, 3), padding='same')(conv3)))
#conv4 -> pi
conv_pi = Activation('relu')(BatchNormalization(axis=3)(Conv2D(
nnet_args.pi_filters, (1, 1))(conv4)))
conv_pi_flat = Flatten()(conv_pi)
pi = Dense(output_size, activation='softmax',
name='pi')(conv_pi_flat)
#conv4 -> v
conv_v = Activation('relu')(BatchNormalization(axis=3)(Conv2D(
nnet_args.v_filters, (1, 1))(conv4)))
conv_v_flat = Flatten()(conv_v)
hidden_v = Dense(nnet_args.hidden_dense,
activation='relu')(conv_v_flat)
v = Dense(1, activation='sigmoid', name='v')(hidden_v)
self.model = Model(inputs=input_board, outputs=[pi, v])
self.model.compile(
loss=['categorical_crossentropy', 'mean_squared_error'],
optimizer=Adam(nnet_args.lr)) #maybe todo loss function
else:
self.model = load_model(global_args.save_dir + str(model) + '.h5')
