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.
87 lines
2.9 KiB
87 lines
2.9 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
#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.
|
|
import numpy as np
|
|
import paddle.v2 as paddle
|
|
import paddle.v2.fluid as fluid
|
|
|
|
PASS_NUM = 100
|
|
EMBED_SIZE = 32
|
|
HIDDEN_SIZE = 256
|
|
N = 5
|
|
BATCH_SIZE = 32
|
|
IS_SPARSE = True
|
|
|
|
word_dict = paddle.dataset.imikolov.build_dict()
|
|
dict_size = len(word_dict)
|
|
|
|
first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
|
|
second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
|
|
third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
|
|
forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
|
|
next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
|
|
|
|
embed_first = fluid.layers.embedding(
|
|
input=first_word,
|
|
size=[dict_size, EMBED_SIZE],
|
|
dtype='float32',
|
|
is_sparse=IS_SPARSE,
|
|
param_attr='shared_w')
|
|
embed_second = fluid.layers.embedding(
|
|
input=second_word,
|
|
size=[dict_size, EMBED_SIZE],
|
|
dtype='float32',
|
|
is_sparse=IS_SPARSE,
|
|
param_attr='shared_w')
|
|
embed_third = fluid.layers.embedding(
|
|
input=third_word,
|
|
size=[dict_size, EMBED_SIZE],
|
|
dtype='float32',
|
|
is_sparse=IS_SPARSE,
|
|
param_attr='shared_w')
|
|
embed_forth = fluid.layers.embedding(
|
|
input=forth_word,
|
|
size=[dict_size, EMBED_SIZE],
|
|
dtype='float32',
|
|
is_sparse=IS_SPARSE,
|
|
param_attr='shared_w')
|
|
|
|
concat_embed = fluid.layers.concat(
|
|
input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
|
|
hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
|
|
predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
|
|
cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
|
|
avg_cost = fluid.layers.mean(x=cost)
|
|
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
|
|
sgd_optimizer.minimize(avg_cost)
|
|
|
|
train_reader = paddle.batch(
|
|
paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
|
|
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
feeder = fluid.DataFeeder(
|
|
feed_list=[first_word, second_word, third_word, forth_word, next_word],
|
|
place=place)
|
|
|
|
exe.run(fluid.default_startup_program())
|
|
|
|
for pass_id in range(PASS_NUM):
|
|
for data in train_reader():
|
|
avg_cost_np = exe.run(fluid.default_main_program(),
|
|
feed=feeder.feed(data),
|
|
fetch_list=[avg_cost])
|
|
if avg_cost_np[0] < 5.0:
|
|
exit(0) # if avg cost less than 10.0, we think our code is good.
|
|
exit(1)
|