def main():
parser = argparse.ArgumentParser()
parser.add_argument('--board-size', type=int, default=19)
parser.add_argument('--network', default='large')
parser.add_argument('--hidden-size', type=int, default=512)
parser.add_argument('output_file')
args = parser.parse_args()
encoder = encoders.get_encoder_by_name('simple', args.board_size)
board_input = Input(shape=encoder.shape(), name='board_input')
processed_board = board_input
network = getattr(dlgo.networks, args.network)
for layer in network.layers(encoder.shape()):
processed_board = layer(processed_board)
policy_hidden_layer = Dense(args.hidden_size,
activation='relu')(processed_board)
policy_output = Dense(encoder.num_points(),
activation='softmax')(policy_hidden_layer)
value_hidden_layer = Dense(args.hidden_size,
activation='relu')(processed_board)
value_output = Dense(1, activation='tanh')(value_hidden_layer)
model = Model(inputs=[board_input], outputs=[policy_output, value_output])
new_agent = rl.ACAgent(model, encoder)
with h5py.File(args.output_file, 'w') as outf:
new_agent.serialize(outf)
def main():
workdir = r'/media/nail/SSD_Disk/Models/'
board_size = 19
network = 'large'
hidden_size = 512
output_file = workdir + 'ac_agent.h5'
encoder = encoders.get_encoder_by_name('simple', board_size)
board_input = Input(shape=encoder.shape(), name='board_input')
processed_board = board_input
network = getattr(dlgo.networks, network)
for layer in network.layers(encoder.shape()):
processed_board = layer(processed_board)
policy_hidden_layer = Dense(hidden_size,
activation='relu')(processed_board)
policy_output = Dense(encoder.num_points(),
activation='softmax')(policy_hidden_layer)
value_hidden_layer = Dense(hidden_size, activation='relu')(processed_board)
value_output = Dense(1, activation='tanh')(value_hidden_layer)
model = Model(inputs=[board_input], outputs=[policy_output, value_output])
new_agent = rl.ACAgent(model, encoder)
with h5py.File(output_file, 'w') as outf:
new_agent.serialize(outf)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--board-size', type=int, default=19)
parser.add_argument('--output-file')
args = parser.parse_args()
board_size = args.board_size
output_file = args.output_file
encoder = encoders.get_encoder_by_name('simple', board_size)
board_input = Input(shape=encoder.shape(), name='board_input')
conv1 = Conv2D(64, (3, 3), padding='same', activation='relu')(board_input)
conv2 = Conv2D(64, (3, 3), padding='same', activation='relu')(conv1)
conv3 = Conv2D(64, (3, 3), padding='same', activation='relu')(conv2)
flat = Flatten()(conv3)
processed_board = Dense(512)(flat)
policy_hidden_layer = Dense(512, activation='relu')(processed_board)
policy_ouput = Dense(encoder.num_points(),
activation='softmax')(policy_hidden_layer)
value_hidden_layer = Dense(512, activation='relu')(processed_board)
value_output = Dense(1, activation='tanh')(value_hidden_layer)
model = Model(inputs=board_input, outputs=[policy_ouput, value_output])
new_agent = rl.ACAgent(model, encoder)
with h5py.File(output_file, 'w') as outf:
new_agent.serialize(outf)
def main():
"""
board_input = Input(shape=encoder.shape(), name='board_input')
# Add as many convolutional layers as you like
conv1 = Conv2D(64, (3, 3),
padding='same',
activation='relu')(board_input)
conv2 = Conv2D(64, (3, 3),
padding='same',
activation='relu')(conv1)
conv3 = Conv2D(64, (3, 3),
padding='same',
activation='relu')(conv2)
flat = Flatten()(conv3)
# This example uses hidden layers of size 512.
# Experiment to find the best size.
# The three hidden layers don't need to be the same size
processed_board = Dense(512)(flat)
# This output yields the policy function
policy_hidden_layer = Dense(512, activation='relu')(processed_board)
policy_output = Dense(encoder.num_points(), activation='softmax')(policy_hidden_layer)
# This output yields the value function
value_hidden_layer = Dense(512, activation='relu')(processed_board)
value_output = Dense(1, activation='tanh')(value_hidden_layer)
model = Model(inputs=board_input,
outputs=[policy_output, value_output])
"""
# added from gh repo
parser = argparse.ArgumentParser()
parser.add_argument('--board-size', type=int, default=19)
parser.add_argument('--network', default='large')
parser.add_argument('--hidden-size', type=int, default=512)
parser.add_argument('output_file')
args = parser.parse_args()
encoder = encoders.get_encoder_by_name('sevenplane', args.board_size)
board_input = Input(shape=encoder.shape(), name='board_input')
processed_board = board_input
network = getattr(dlgo.networks, args.network)
for layer in network.layers(encoder.shape()):
processed_board = layer(processed_board)
policy_hidden_layer = Dense(args.hidden_size,
activation='relu')(processed_board)
policy_output = Dense(encoder.num_points(),
activation='softmax')(policy_hidden_layer)
value_hidden_layer = Dense(args.hidden_size,
activation='relu')(processed_board)
value_output = Dense(1, activation='tanh')(value_hidden_layer)
model = Model(inputs=[board_input], outputs=[policy_output, value_output])
new_agent = rl.ACAgent(model, encoder)
with h5py.File(args.output_file, 'w') as outf:
new_agent.serialize(outf)