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86 lines
2.8 KiB
86 lines
2.8 KiB
3 years ago
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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 RnnLm(nn.Layer):
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def __init__(self,
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vocab_size,
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punc_size,
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hidden_size,
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num_layers=1,
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init_scale=0.1,
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dropout=0.0):
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super(RnnLm, self).__init__()
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.init_scale = init_scale
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self.punc_size = punc_size
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self.embedder = nn.Embedding(
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vocab_size,
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hidden_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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self.lstm = nn.LSTM(
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input_size=hidden_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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dropout=dropout,
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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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self.fc = nn.Linear(
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hidden_size,
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punc_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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bias_attr=paddle.ParamAttr(initializer=I.Uniform(
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low=-init_scale, high=init_scale)))
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self.dropout = nn.Dropout(p=dropout)
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self.softmax = nn.Softmax()
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def forward(self, inputs):
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x = inputs
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x_emb = self.embedder(x)
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x_emb = self.dropout(x_emb)
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y, (_, _) = self.lstm(x_emb)
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y = self.dropout(y)
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y = self.fc(y)
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y = paddle.reshape(y, shape=[-1, self.punc_size])
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logit = self.softmax(y)
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return y, logit
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class CrossEntropyLossForLm(nn.Layer):
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def __init__(self):
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super(CrossEntropyLossForLm, self).__init__()
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def forward(self, y, label):
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label = paddle.unsqueeze(label, axis=2)
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loss = paddle.nn.functional.cross_entropy(
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input=y, label=label, reduction='none')
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loss = paddle.squeeze(loss, axis=[2])
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loss = paddle.mean(loss, axis=[0])
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loss = paddle.sum(loss)
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return loss
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