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Running
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Zero
| #!/usr/bin/env python3 | |
| # BSD 3-Clause License | |
| # | |
| # Copyright (c) 2017, | |
| # | |
| # Redistribution and use in source and binary forms, with or without | |
| # modification, are permitted provided that the following conditions are met: | |
| # | |
| # 1. Redistributions of source code must retain the above copyright notice, this | |
| # list of conditions and the following disclaimer. | |
| # | |
| # 2. Redistributions in binary form must reproduce the above copyright notice, | |
| # this list of conditions and the following disclaimer in the documentation | |
| # and/or other materials provided with the distribution. | |
| # | |
| # 3. Neither the name of the copyright holder nor the names of its | |
| # contributors may be used to endorse or promote products derived from | |
| # this software without specific prior written permission. | |
| # | |
| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
| # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | |
| # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | |
| # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE | |
| # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | |
| # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | |
| # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | |
| # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | |
| # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | |
| # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | |
| # license-headers: type="bsd-3-clause" | |
| import torch # pytype: disable=import-error | |
| import torch.nn as nn # pytype: disable=import-error | |
| import torch.nn.functional as functional # pytype: disable=import-error | |
| # Network structure is based on original PyTorch MNIST example. | |
| class Net(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.conv1 = nn.Conv2d(1, 32, 3, 1) | |
| self.conv2 = nn.Conv2d(32, 64, 3, 1) | |
| self.dropout1 = nn.Dropout(0.25) | |
| self.dropout2 = nn.Dropout(0.5) | |
| self.fc1 = nn.Linear(9216, 128) | |
| self.fc2 = nn.Linear(128, 10) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = functional.relu(x) | |
| x = self.conv2(x) | |
| x = functional.relu(x) | |
| x = functional.max_pool2d(x, 2) | |
| x = self.dropout1(x) | |
| x = torch.flatten(x, 1) | |
| x = self.fc1(x) | |
| x = functional.relu(x) | |
| x = self.dropout2(x) | |
| x = self.fc2(x) | |
| output = functional.log_softmax(x, dim=1) | |
| return output | |