forked from mindspore-Ecosystem/mindspore
51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class LeNet(nn.Cell):
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def __init__(self):
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super(LeNet, self).__init__()
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self.relu = P.ReLU()
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self.batch_size = 1
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weight1 = Tensor(np.ones([6, 3, 5, 5]).astype(np.float32) * 0.01)
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weight2 = Tensor(np.ones([16, 6, 5, 5]).astype(np.float32) * 0.01)
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self.conv1 = nn.Conv2d(3, 6, (5, 5), weight_init=weight1, stride=1, padding=0, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, (5, 5), weight_init=weight2, pad_mode='valid', stride=1, padding=0)
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self.pool = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="valid")
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self.reshape = P.Reshape()
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self.reshape1 = P.Reshape()
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self.fc1 = nn.Dense(400, 120)
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self.fc2 = nn.Dense(120, 84)
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self.fc3 = nn.Dense(84, 10)
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def construct(self, input_x):
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output = self.conv1(input_x)
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output = self.relu(output)
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output = self.pool(output)
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output = self.conv2(output)
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output = self.relu(output)
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output = self.pool(output)
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output = self.reshape(output, (self.batch_size, -1))
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output = self.fc1(output)
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output = self.fc2(output)
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output = self.fc3(output)
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return output
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