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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn as nn
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import paddle.nn.initializer as I
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class BiLSTM(nn.Layer):
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"""LSTM for Punctuation Restoration
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"""
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def __init__(self,
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vocab_size,
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embedding_size,
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hidden_size,
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num_layers,
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num_class,
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init_scale=0.1):
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super(BiLSTM, self).__init__()
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# hyper parameters
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self.vocab_size = vocab_size
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self.embedding_size = embedding_size
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.num_class = num_class
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# 网络中的层
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self.embedding = nn.Embedding(
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vocab_size,
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embedding_size,
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weight_attr=paddle.ParamAttr(initializer=I.Uniform(
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low=-init_scale, high=init_scale)))
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# print(hidden_size)
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# print(embedding_size)
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self.lstm = nn.LSTM(
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input_size=embedding_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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direction="bidirect",
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weight_ih_attr=paddle.ParamAttr(initializer=I.Uniform(
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low=-init_scale, high=init_scale)),
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weight_hh_attr=paddle.ParamAttr(initializer=I.Uniform(
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low=-init_scale, high=init_scale)))
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# Here is a one direction LSTM. If bidirection LSTM, (hidden_size*2(,))
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self.fc = nn.Linear(
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in_features=hidden_size * 2,
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out_features=num_class,
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weight_attr=paddle.ParamAttr(initializer=I.Uniform(
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low=-init_scale, high=init_scale)),
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bias_attr=paddle.ParamAttr(initializer=I.Uniform(
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low=-init_scale, high=init_scale)))
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# self.fc = nn.Linear(hidden_size, num_class)
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self.softmax = nn.Softmax()
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def forward(self, input):
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"""The forward process of Net
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Parameters
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----------
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inputs : tensor
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Training data, batch first
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"""
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# Inherit the knowledge of context
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# hidden = self.init_hidden(inputs.size(0))
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# print('input_size',inputs.size())
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embedding = self.embedding(input)
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# print('embedding_size', embedding.size())
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# packed = pack_sequence(embedding, inputs_lengths, batch_first=True)
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# embedding本身是同样长度的，用这个函数主要是为了用pack
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# *****************************************************************************
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y, (_, _) = self.lstm(embedding)
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# print(y.size())
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y = self.fc(y)
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y = paddle.reshape(y, shape=[-1, self.num_class])
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logit = self.softmax(y)
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return y, logit
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