You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
74 lines
2.6 KiB
74 lines
2.6 KiB
# edit-mode: -*- python -*-
|
|
|
|
# Copyright (c) 2016 Baidu, Inc. 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.
|
|
|
|
from paddle.trainer_config_helpers import *
|
|
|
|
dict_file = "./data/dict.txt"
|
|
word_dict = dict()
|
|
with open(dict_file, 'r') as f:
|
|
for i, line in enumerate(f):
|
|
w = line.strip().split()[0]
|
|
word_dict[w] = i
|
|
|
|
is_predict = get_config_arg('is_predict', bool, False)
|
|
trn = 'data/train.list' if not is_predict else None
|
|
tst = 'data/test.list' if not is_predict else 'data/pred.list'
|
|
process = 'process' if not is_predict else 'process_predict'
|
|
|
|
# define the data sources for the model.
|
|
# We need to use different process for training and prediction.
|
|
# For training, the input data includes both word IDs and labels.
|
|
# For prediction, the input data only includs word Ids.
|
|
define_py_data_sources2(train_list=trn,
|
|
test_list=tst,
|
|
module="dataprovider_bow",
|
|
obj=process,
|
|
args={"dictionary": word_dict})
|
|
|
|
batch_size = 128 if not is_predict else 1
|
|
settings(
|
|
batch_size=batch_size,
|
|
learning_rate=2e-3,
|
|
learning_method=AdamOptimizer(),
|
|
regularization=L2Regularization(8e-4),
|
|
gradient_clipping_threshold=25
|
|
)
|
|
|
|
# Define the data for text features. The size of the data layer is the number
|
|
# of words in the dictionary.
|
|
data = data_layer(name="word", size=len(word_dict))
|
|
|
|
# Define a fully connected layer with logistic activation.
|
|
# (also called softmax activation).
|
|
output = fc_layer(input=data, size=2, act=SoftmaxActivation())
|
|
|
|
if not is_predict:
|
|
# For training, we need label and cost
|
|
|
|
# define the category id for each example.
|
|
# The size of the data layer is the number of labels.
|
|
label = data_layer(name="label", size=2)
|
|
|
|
# Define cross-entropy classification loss and error.
|
|
classification_cost(input=output, label=label)
|
|
cls = classification_cost(input=output, label=label)
|
|
outputs(cls)
|
|
else:
|
|
# For prediction, no label is needed. We need to output
|
|
# We need to output classification result, and class probabilities.
|
|
maxid = maxid_layer(output)
|
|
outputs([maxid, output])
|