lenet.py 1.9 KB
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#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
#    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.

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import paddle
import paddle.nn as nn
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__all__ = ['LeNet']


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class LeNet(nn.Layer):
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    """LeNet model from
    `"LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.`_

    Args:
        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
                            will not be defined. Default: 10.

    Examples:
        .. code-block:: python

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            from paddle.vision.models import LeNet
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            model = LeNet()
    """

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    def __init__(self, num_classes=10):
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        super(LeNet, self).__init__()
        self.num_classes = num_classes
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        self.features = nn.Sequential(
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            nn.Conv2D(
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                1, 6, 3, stride=1, padding=1),
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            nn.ReLU(),
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            nn.MaxPool2D(2, 2),
            nn.Conv2D(
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                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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        if num_classes > 0:
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            self.fc = nn.Sequential(
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                nn.Linear(400, 120),
                nn.Linear(120, 84), nn.Linear(84, num_classes))
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    def forward(self, inputs):
        x = self.features(inputs)

        if self.num_classes > 0:
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            x = paddle.flatten(x, 1)
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            x = self.fc(x)
        return x