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config.py
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config.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Deep Attention Matching Network
"""
import argparse
import six
def parse_args():
"""
Deep Attention Matching Network Config
"""
parser = argparse.ArgumentParser("DAM Config")
parser.add_argument(
'--do_train',
type=bool,
default=False,
help='Whether to perform training.')
parser.add_argument(
'--do_test',
type=bool,
default=False,
help='Whether to perform training.')
parser.add_argument(
'--batch_size',
type=int,
default=256,
help='Batch size for training. (default: %(default)d)')
parser.add_argument(
'--num_scan_data',
type=int,
default=2,
help='Number of pass for training. (default: %(default)d)')
parser.add_argument(
'--learning_rate',
type=float,
default=1e-3,
help='Learning rate used to train. (default: %(default)f)')
parser.add_argument(
'--data_path',
type=str,
default="data/data_small.pkl",
help='Path to training data. (default: %(default)s)')
parser.add_argument(
'--save_path',
type=str,
default="saved_models",
help='Path to save trained models. (default: %(default)s)')
parser.add_argument(
'--model_path',
type=str,
default=None,
help='Path to load well-trained models. (default: %(default)s)')
parser.add_argument(
'--use_cuda',
action='store_true',
help='If set, use cuda for training.')
parser.add_argument(
'--use_pyreader',
action='store_true',
help='If set, use pyreader for reading data.')
parser.add_argument(
'--ext_eval',
action='store_true',
help='If set, use MAP, MRR ect for evaluation.')
parser.add_argument(
'--max_turn_num',
type=int,
default=9,
help='Maximum number of utterances in context.')
parser.add_argument(
'--max_turn_len',
type=int,
default=50,
help='Maximum length of setences in turns.')
parser.add_argument(
'--word_emb_init',
type=str,
default=None,
help='Path to the initial word embedding.')
parser.add_argument(
'--vocab_size',
type=int,
default=434512,
help='The size of vocabulary.')
parser.add_argument(
'--emb_size',
type=int,
default=200,
help='The dimension of word embedding.')
parser.add_argument(
'--_EOS_',
type=int,
default=28270,
help='The id for the end of sentence in vocabulary.')
parser.add_argument(
'--stack_num',
type=int,
default=5,
help='The number of stacked attentive modules in network.')
parser.add_argument(
'--channel1_num',
type=int,
default=32,
help="The channels' number of the 1st conv3d layer's output.")
parser.add_argument(
'--channel2_num',
type=int,
default=16,
help="The channels' number of the 2nd conv3d layer's output.")
args = parser.parse_args()
return args
def print_arguments(args):
"""
Print Config
"""
print('----------- Configuration Arguments -----------')
for arg, value in sorted(six.iteritems(vars(args))):
print('%s: %s' % (arg, value))
print('------------------------------------------------')