def create_network(args, text_reader, feature_tables, md=None):
"""Creates and returns the neural network according to the task at hand."""
logger = logging.getLogger("Logger")
if args.task.startswith('srl') and args.task != 'srl_predicates':
num_tags = len(text_reader.tag_dict)
distance_tables = utils.set_distance_features(md, args.max_dist, args.target_features,
args.pred_features)
nn = ConvolutionalNetwork.create_new(feature_tables, distance_tables[0],
distance_tables[1], args.window,
args.convolution, args.hidden, num_tags)
padding_left = text_reader.converter.get_padding_left(False)
padding_right = text_reader.converter.get_padding_right(False)
if args.identify:
logger.info("Loading initial transition scores table for argument identification")
transitions = srl.train_srl.init_transitions_simplified(text_reader.tag_dict)
nn.transitions = transitions
nn.learning_rate_trans = args.learning_rate_transitions
elif not args.classify:
logger.info("Loading initial IOB transition scores table")
transitions = srl.train_srl.init_transitions(text_reader.tag_dict, 'iob')
nn.transitions = transitions
nn.learning_rate_trans = args.learning_rate_transitions
elif args.task == 'lm':
nn = LanguageModel.create_new(feature_tables, args.window, args.hidden)
padding_left = text_reader.converter.get_padding_left(tokens_as_string=True)
padding_right = text_reader.converter.get_padding_right(tokens_as_string=True)
else:
num_tags = len(text_reader.tag_dict)
nn = Network.create_new(feature_tables, args.window, args.hidden, num_tags)
if args.learning_rate_transitions > 0:
transitions = np.zeros((num_tags + 1, num_tags), np.float)
nn.transitions = transitions
nn.learning_rate_trans = args.learning_rate_transitions
padding_left = text_reader.converter.get_padding_left(args.task == 'pos')
padding_right = text_reader.converter.get_padding_right(args.task == 'pos')
nn.padding_left = np.array(padding_left)
nn.padding_right = np.array(padding_right)
nn.learning_rate = args.learning_rate
nn.learning_rate_features = args.learning_rate_features
if args.task == 'lm':
layer_sizes = (nn.input_size, nn.hidden_size, 1)
elif args.convolution > 0 and args.hidden > 0:
layer_sizes = (nn.input_size, nn.hidden_size, nn.hidden2_size, nn.output_size)
else:
layer_sizes = (nn.input_size, nn.hidden_size, nn.output_size)
logger.info("Created new network with the following layer sizes: %s"
% ', '.join(str(x) for x in layer_sizes))
return nn
def create_network(args, text_reader, feature_tables, md):
"""Creates and returns the neural network according to the task at hand."""
logger = logging.getLogger("Logger")
is_dependency = 'dependency' in args.task
is_convolution_srl = args.task.startswith('srl') and args.task != 'srl_predicates'
is_convolution = is_convolution_srl or is_dependency
if is_convolution:
# get some data structures used both by dep parsing and SRL
distance_tables = utils.set_distance_features(args.max_dist, args.target_features,
args.pred_features)
padding_left = text_reader.converter.get_padding_left(False)
padding_right = text_reader.converter.get_padding_right(False)
if is_dependency:
output_size = 1 if not args.labeled else len(text_reader.tag_dict)
nn = ConvolutionalDependencyNetwork.create_new(feature_tables, distance_tables[0],
distance_tables[1], args.window,
args.convolution, args.hidden, output_size)
else:
# not dependency (SRL)
num_tags = len(text_reader.tag_dict)
nn = ConvolutionalNetwork.create_new(feature_tables, distance_tables[0],
distance_tables[1], args.window,
args.convolution, args.hidden, num_tags)
if args.identify:
logger.info("Loading initial transition scores table for argument identification")
transitions = srl.init_transitions_simplified(text_reader.tag_dict)
nn.transitions = transitions
elif not args.classify:
logger.info("Loading initial IOB transition scores table")
transitions = srl.init_transitions(text_reader.tag_dict, 'iob')
nn.transitions = transitions
else:
# not convolution
num_tags = len(text_reader.tag_dict)
nn = Network.create_new(feature_tables, args.window, args.hidden, num_tags)
transitions = np.zeros((num_tags + 1, num_tags), np.float)
nn.transitions = transitions
padding_left = text_reader.converter.get_padding_left(args.task == 'pos')
padding_right = text_reader.converter.get_padding_right(args.task == 'pos')
nn.padding_left = np.array(padding_left)
nn.padding_right = np.array(padding_right)
nn.learning_rate = args.learning_rate
nn.l2_factor = args.l2
nn.dropout = args.dropout
nn.max_norm = args.max_norm
if 'convolution' in args and args.convolution > 0 and args.hidden > 0:
layer_sizes = (nn.input_size, nn.hidden_size, nn.hidden2_size, nn.output_size)
else:
layer_sizes = (nn.input_size, nn.hidden_size, nn.output_size)
logger.info("Created new network with the following layer sizes: %s\n"
% ', '.join(str(x) for x in layer_sizes))
nn.network_filename = config.FILES[md.network]
return nn
def create_network(args, text_reader, feature_tables, md=None):
"""Creates and returns the neural network according to the task at hand."""
logger = logging.getLogger("Logger")
if args.task.startswith('srl') and args.task != 'srl_predicates':
num_tags = len(text_reader.tag_dict)
distance_tables = utils.set_distance_features(args.max_dist,
args.target_features,
args.pred_features)
nn = ConvolutionalNetwork.create_new(feature_tables,
distance_tables[0],
distance_tables[1], args.window,
args.convolution, args.hidden,
num_tags)
padding_left = text_reader.converter.get_padding_left(False)
padding_right = text_reader.converter.get_padding_right(False)
if args.identify:
logger.info(
"Loading initial transition scores table for argument identification"
)
transitions = srl.train_srl.init_transitions_simplified(
text_reader.tag_dict)
nn.transitions = transitions
nn.learning_rate_trans = args.learning_rate_transitions
elif not args.classify:
logger.info("Loading initial IOB transition scores table")
transitions = srl.train_srl.init_transitions(
text_reader.tag_dict, 'iob')
nn.transitions = transitions
nn.learning_rate_trans = args.learning_rate_transitions
else:
num_tags = len(text_reader.tag_dict)
nn = Network.create_new(feature_tables, args.window, args.hidden,
num_tags)
if args.learning_rate_transitions > 0:
transitions = np.zeros((num_tags + 1, num_tags), np.float)
nn.transitions = transitions
nn.learning_rate_trans = args.learning_rate_transitions
padding_left = text_reader.converter.get_padding_left(
args.task == 'pos')
padding_right = text_reader.converter.get_padding_right(
args.task == 'pos')
nn.padding_left = np.array(padding_left)
nn.padding_right = np.array(padding_right)
nn.learning_rate = args.learning_rate
nn.learning_rate_features = args.learning_rate_features
if 'convolution' in args and args.convolution > 0 and args.hidden > 0:
layer_sizes = (nn.input_size, nn.hidden_size, nn.hidden2_size,
nn.output_size)
else:
layer_sizes = (nn.input_size, nn.hidden_size, nn.output_size)
logger.info("Created new network with the following layer sizes: %s" %
', '.join(str(x) for x in layer_sizes))
return nn
def create_network(args, text_reader, feature_tables, md):
"""Creates and returns the neural network according to the task at hand."""
logger = logging.getLogger("Logger")
is_dependency = 'dependency' in args.task
is_convolution_srl = args.task.startswith(
'srl') and args.task != 'srl_predicates'
is_convolution = is_convolution_srl or is_dependency
if is_convolution:
# get some data structures used both by dep parsing and SRL
distance_tables = utils.set_distance_features(args.max_dist,
args.target_features,
args.pred_features)
padding_left = text_reader.converter.get_padding_left(False)
padding_right = text_reader.converter.get_padding_right(False)
if is_dependency:
output_size = 1 if not args.labeled else len(text_reader.tag_dict)
nn = ConvolutionalDependencyNetwork.create_new(
feature_tables, distance_tables[0], distance_tables[1],
args.window, args.convolution, args.hidden, output_size)
else:
# not dependency (SRL)
num_tags = len(text_reader.tag_dict)
nn = ConvolutionalNetwork.create_new(feature_tables,
distance_tables[0],
distance_tables[1],
args.window, args.convolution,
args.hidden, num_tags)
if args.identify:
logger.info(
"Loading initial transition scores table for argument identification"
)
transitions = srl.init_transitions_simplified(
text_reader.tag_dict)
nn.transitions = transitions
elif not args.classify:
logger.info("Loading initial IOB transition scores table")
transitions = srl.init_transitions(text_reader.tag_dict, 'iob')
nn.transitions = transitions
else:
# not convolution
num_tags = len(text_reader.tag_dict)
nn = Network.create_new(feature_tables, args.window, args.hidden,
num_tags)
transitions = np.zeros((num_tags + 1, num_tags), np.float)
nn.transitions = transitions
padding_left = text_reader.converter.get_padding_left(
args.task == 'pos')
padding_right = text_reader.converter.get_padding_right(
args.task == 'pos')
nn.padding_left = np.array(padding_left)
nn.padding_right = np.array(padding_right)
nn.learning_rate = args.learning_rate
nn.l2_factor = args.l2
nn.dropout = args.dropout
nn.max_norm = args.max_norm
if 'convolution' in args and args.convolution > 0 and args.hidden > 0:
layer_sizes = (nn.input_size, nn.hidden_size, nn.hidden2_size,
nn.output_size)
else:
layer_sizes = (nn.input_size, nn.hidden_size, nn.output_size)
logger.info("Created new network with the following layer sizes: %s\n" %
', '.join(str(x) for x in layer_sizes))
nn.network_filename = config.FILES[md.network]
return nn