Python get_dict Examples

Example #1

File: test_parallel_executor_crf.py Project: absorbguo/Paddle

#
#     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 paddle.dataset.conll05 as conll05
import paddle.fluid as fluid
import unittest
import paddle
import numpy as np

word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_dict_len = len(verb_dict)
mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 512
depth = 8
mix_hidden_lr = 1e-3
embedding_name = 'emb'


def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
            is_sparse, **ignored):
    # 8 features

Example #2

File: test_parallel_executor_crf.py Project: siju-samuel/paddle

# 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 __future__ import print_function

import paddle.dataset.conll05 as conll05
import paddle.fluid as fluid
import paddle.fluid.core as core
import unittest
import paddle
import numpy as np
import os

word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_dict_len = len(verb_dict)
mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 512
depth = 8
mix_hidden_lr = 1e-3
embedding_name = 'emb'


def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
            is_sparse, **ignored):
    # 8 features

Example #3

File: BiGRU_CRF.py Project: Jeff654/my_machine_learning

# -*- coding: utf-8 -*-

import paddle
from paddle import fluid
from paddle.dataset import conll05

word_dict, _, label_dict = conll05.get_dict()

word_dim = 32
batch_size = 10
epoch_num = 20
hidden_size = 512
learning_rate = 0.1

word = fluid.layers.data(name="word_data", shape=[1], dtype="int64", lod_level=1)
target = fluid.layers.data(name="target", shape=[1], dtype="int64", lod_level=1)

embedding = fluid.layers.embedding(size=[len(word_dict), word_dim],
                                   input=word,
                                   param_attr=fluid.ParamAttr(name="emb", trainable=False))
hidden_0 = fluid.layers.fc(input=embedding, size=hidden_size, act="tanh")

hidden_1 = fluid.layers.dynamic_lstm(input=hidden_0,
                                     size=hidden_size,
                                     gate_activation="sigmoid",
                                     candidate_activation="relu",
                                     cell_activation="sigmoid")
feature_out = fluid.layers.fc(input=hidden_1, size=len(label_dict), act="tanh")

# 调用内置 CRF 函数，并针对状态转换进行解码
crf_cost = fluid.layers.linear_chain_crf(input=feature_out,