wehub-resource-sync
61 lines
2.5 KiB
Python
61 lines
2.5 KiB
Python
# 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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from paddlenlp.seq2vec import CNNEncoder
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class TextCNNModel(nn.Layer):
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"""
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This class implements the Text Convolution Neural Network model.
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At a high level, the model starts by embedding the tokens and running them through
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a word embedding. Then, we encode these representations with a `CNNEncoder`.
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The CNN has one convolution layer for each ngram filter size. Each convolution operation gives
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out a vector of size num_filter. The number of times a convolution layer will be used
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is `num_tokens - ngram_size + 1`. The corresponding maxpooling layer aggregates all these
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outputs from the convolution layer and outputs the max.
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Lastly, we take the output of the encoder to create a final representation,
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which is passed through some feed-forward layers to output a logits (`output_layer`).
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"""
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def __init__(
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self,
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vocab_size,
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num_classes,
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emb_dim=128,
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padding_idx=0,
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num_filter=128,
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ngram_filter_sizes=(1, 2, 3),
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fc_hidden_size=96,
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):
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super().__init__()
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self.embedder = nn.Embedding(vocab_size, emb_dim, padding_idx=padding_idx)
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self.encoder = CNNEncoder(emb_dim=emb_dim, num_filter=num_filter, ngram_filter_sizes=ngram_filter_sizes)
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self.fc = nn.Linear(self.encoder.get_output_dim(), fc_hidden_size)
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self.output_layer = nn.Linear(fc_hidden_size, num_classes)
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def forward(self, text):
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# Shape: (batch_size, num_tokens, embedding_dim)
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embedded_text = self.embedder(text)
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# Shape: (batch_size, len(ngram_filter_sizes) * num_filter)
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encoder_out = paddle.tanh(self.encoder(embedded_text))
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# Shape: (batch_size, fc_hidden_size)
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fc_out = paddle.tanh(self.fc(encoder_out))
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# Shape: (batch_size, num_classes)
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logits = self.output_layer(fc_out)
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return logits
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