You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
65 lines
2.1 KiB
65 lines
2.1 KiB
# 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.fluid as fluid
|
|
from paddle.nn import Conv2d, Pool2D, Linear, ReLU, Sequential
|
|
|
|
__all__ = ['LeNet']
|
|
|
|
|
|
class LeNet(fluid.dygraph.Layer):
|
|
"""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.
|
|
classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
|
|
|
|
Examples:
|
|
.. code-block:: python
|
|
|
|
from paddle.incubate.hapi.vision.models import LeNet
|
|
|
|
model = LeNet()
|
|
"""
|
|
|
|
def __init__(self, num_classes=10, classifier_activation='softmax'):
|
|
super(LeNet, self).__init__()
|
|
self.num_classes = num_classes
|
|
self.features = Sequential(
|
|
Conv2d(
|
|
1, 6, 3, stride=1, padding=1),
|
|
ReLU(),
|
|
Pool2D(2, 'max', 2),
|
|
Conv2d(
|
|
6, 16, 5, stride=1, padding=0),
|
|
ReLU(),
|
|
Pool2D(2, 'max', 2))
|
|
|
|
if num_classes > 0:
|
|
self.fc = Sequential(
|
|
Linear(400, 120),
|
|
Linear(120, 84),
|
|
Linear(
|
|
84, 10, act=classifier_activation))
|
|
|
|
def forward(self, inputs):
|
|
x = self.features(inputs)
|
|
|
|
if self.num_classes > 0:
|
|
x = fluid.layers.flatten(x, 1)
|
|
x = self.fc(x)
|
|
return x
|