# Copyright 2020 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """LeNet.""" import mindspore.nn as nn from mindspore.common.initializer import Normal class LeNet5(nn.Cell): """ Lenet network Args: num_class (int): Number of classes. Default: 10. num_channel (int): Number of channels. Default: 1. Returns: Tensor, output tensor Examples: >>> LeNet(num_class=10) """ def __init__(self, num_class=10, num_channel=1, include_top=True): super(LeNet5, self).__init__() self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.include_top = include_top if self.include_top: self.flatten = nn.Flatten() self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) def construct(self, x): x = self.conv1(x) x = self.relu(x) x = self.max_pool2d(x) x = self.conv2(x) x = self.relu(x) x = self.max_pool2d(x) if not self.include_top: return x x = self.flatten(x) x = self.relu(self.fc1(x)) x = self.relu(self.fc2(x)) x = self.fc3(x) return x