79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
# 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 TruncatedNormal
|
|
|
|
|
|
def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
|
|
"""weight initial for conv layer"""
|
|
weight = weight_variable()
|
|
return nn.Conv2d(in_channels, out_channels,
|
|
kernel_size=kernel_size, stride=stride, padding=padding,
|
|
weight_init=weight, has_bias=False, pad_mode="valid")
|
|
|
|
|
|
def fc_with_initialize(input_channels, out_channels):
|
|
"""weight initial for fc layer"""
|
|
weight = weight_variable()
|
|
bias = weight_variable()
|
|
return nn.Dense(input_channels, out_channels, weight, bias)
|
|
|
|
|
|
def weight_variable():
|
|
"""weight initial"""
|
|
return TruncatedNormal(0.02)
|
|
|
|
|
|
class LeNet5(nn.Cell):
|
|
"""
|
|
Lenet network
|
|
|
|
Args:
|
|
num_class (int): Num classes. Default: 10.
|
|
|
|
Returns:
|
|
Tensor, output tensor
|
|
Examples:
|
|
>>> LeNet(num_class=10)
|
|
|
|
"""
|
|
def __init__(self, num_class=10, channel=1):
|
|
super(LeNet5, self).__init__()
|
|
self.num_class = num_class
|
|
self.conv1 = conv(channel, 6, 5)
|
|
self.conv2 = conv(6, 16, 5)
|
|
self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
|
|
self.fc2 = fc_with_initialize(120, 84)
|
|
self.fc3 = fc_with_initialize(84, self.num_class)
|
|
self.relu = nn.ReLU()
|
|
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
|
|
self.flatten = nn.Flatten()
|
|
|
|
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)
|
|
x = self.flatten(x)
|
|
x = self.fc1(x)
|
|
x = self.relu(x)
|
|
x = self.fc2(x)
|
|
x = self.relu(x)
|
|
x = self.fc3(x)
|
|
return x
|