wehub-resource-sync
84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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)
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import paddle
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from paddle import nn
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if TYPE_CHECKING:
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from paddle import Tensor
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__all__ = []
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class LeNet(nn.Layer):
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"""LeNet model from
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`"Gradient-based learning applied to document recognition" <https://ieeexplore.ieee.org/document/726791>`_.
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Args:
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num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
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will not be defined. Default: 10.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of LeNet model.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.vision.models import LeNet
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>>> model = LeNet()
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>>> x = paddle.rand([1, 1, 28, 28])
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>>> out = model(x)
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>>> print(out.shape)
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paddle.Size([1, 10])
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"""
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num_classes: int
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def __init__(self, num_classes: int = 10) -> None:
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super().__init__()
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self.num_classes = num_classes
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self.features = nn.Sequential(
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nn.Conv2D(1, 6, 3, stride=1, padding=1),
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nn.ReLU(),
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nn.MaxPool2D(2, 2),
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nn.Conv2D(6, 16, 5, stride=1, padding=0),
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nn.ReLU(),
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nn.MaxPool2D(2, 2),
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)
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if num_classes > 0:
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self.fc = nn.Sequential(
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nn.Linear(400, 120),
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nn.Linear(120, 84),
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nn.Linear(84, num_classes),
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)
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def forward(self, inputs: Tensor) -> Tensor:
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x = self.features(inputs)
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if self.num_classes > 0:
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x = paddle.flatten(x, 1)
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x = self.fc(x)
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return x
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