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lenet.py
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lenet.py
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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.
import paddle
from paddle import nn
__all__ = []
class LeNet(nn.Layer):
"""LeNet model from
`"Gradient-based learning applied to document recognition" <https://ieeexplore.ieee.org/document/726791>`_.
Args:
num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
will not be defined. Default: 10.
Returns:
:ref:`api_paddle_nn_Layer`. An instance of LeNet model.
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import LeNet
model = LeNet()
x = paddle.rand([1, 1, 28, 28])
out = model(x)
print(out.shape)
# [1, 10]
"""
def __init__(self, num_classes=10):
super().__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2D(1, 6, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2D(2, 2),
nn.Conv2D(6, 16, 5, stride=1, padding=0),
nn.ReLU(),
nn.MaxPool2D(2, 2),
)
if num_classes > 0:
self.fc = nn.Sequential(
nn.Linear(400, 120),
nn.Linear(120, 84),
nn.Linear(84, num_classes),
)
def forward(self, inputs):
x = self.features(inputs)
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x)
return x