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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15"""LeNet."""
16import mindspore.nn as nn
17from mindspore.common.initializer import TruncatedNormal
18
19
20def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
21    """weight initial for conv layer"""
22    weight = weight_variable()
23    return nn.Conv2d(in_channels, out_channels,
24                     kernel_size=kernel_size, stride=stride, padding=padding,
25                     weight_init=weight, has_bias=False, pad_mode="valid")
26
27
28def fc_with_initialize(input_channels, out_channels):
29    """weight initial for fc layer"""
30    weight = weight_variable()
31    bias = weight_variable()
32    return nn.Dense(input_channels, out_channels, weight, bias)
33
34
35def weight_variable():
36    """weight initial"""
37    return TruncatedNormal(0.02)
38
39
40class LeNet5(nn.Cell):
41    """
42    Lenet network
43
44    Args:
45        num_class (int): Num classes. Default: 10.
46
47    Returns:
48        Tensor, output tensor
49    Examples:
50        >>> LeNet(num_class=10)
51
52    """
53    def __init__(self, num_class=10, channel=1):
54        super(LeNet5, self).__init__()
55        self.num_class = num_class
56        self.conv1 = conv(channel, 6, 5)
57        self.conv2 = conv(6, 16, 5)
58        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
59        self.fc2 = fc_with_initialize(120, 84)
60        self.fc3 = fc_with_initialize(84, self.num_class)
61        self.relu = nn.ReLU()
62        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
63        self.flatten = nn.Flatten()
64
65    def construct(self, x):
66        x = self.conv1(x)
67        x = self.relu(x)
68        x = self.max_pool2d(x)
69        x = self.conv2(x)
70        x = self.relu(x)
71        x = self.max_pool2d(x)
72        x = self.flatten(x)
73        x = self.fc1(x)
74        x = self.relu(x)
75        x = self.fc2(x)
76        x = self.relu(x)
77        x = self.fc3(x)
78        return x
79