| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| # |
| |
| from singa import layer |
| from singa import model |
| |
| |
| class CNN(model.Model): |
| |
| def __init__(self, num_classes=10, num_channels=1): |
| super(CNN, self).__init__() |
| self.num_classes = num_classes |
| self.input_size = 28 |
| self.dimension = 4 |
| self.conv1 = layer.Conv2d(num_channels, 20, 5, padding=0, activation="RELU") |
| self.conv2 = layer.Conv2d(20, 50, 5, padding=0, activation="RELU") |
| self.linear1 = layer.Linear(500) |
| self.linear2 = layer.Linear(num_classes) |
| self.pooling1 = layer.MaxPool2d(2, 2, padding=0) |
| self.pooling2 = layer.MaxPool2d(2, 2, padding=0) |
| self.relu = layer.ReLU() |
| self.flatten = layer.Flatten() |
| self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() |
| |
| def forward(self, x): |
| y = self.conv1(x) |
| y = self.pooling1(y) |
| y = self.conv2(y) |
| y = self.pooling2(y) |
| y = self.flatten(y) |
| y = self.linear1(y) |
| y = self.relu(y) |
| y = self.linear2(y) |
| return y |
| |
| def train_one_batch(self, x, y, dist_option, spars): |
| out = self.forward(x) |
| loss = self.softmax_cross_entropy(out, y) |
| |
| if dist_option == 'plain': |
| self.optimizer(loss) |
| elif dist_option == 'half': |
| self.optimizer.backward_and_update_half(loss) |
| elif dist_option == 'partialUpdate': |
| self.optimizer.backward_and_partial_update(loss) |
| elif dist_option == 'sparseTopK': |
| self.optimizer.backward_and_sparse_update(loss, |
| topK=True, |
| spars=spars) |
| elif dist_option == 'sparseThreshold': |
| self.optimizer.backward_and_sparse_update(loss, |
| topK=False, |
| spars=spars) |
| return out, loss |
| |
| def set_optimizer(self, optimizer): |
| self.optimizer = optimizer |
| |
| |
| def create_model(pretrained=False, **kwargs): |
| """Constructs a CNN model. |
| |
| Args: |
| pretrained (bool): If True, returns a pre-trained model. |
| |
| Returns: |
| The created CNN model. |
| """ |
| model = CNN(**kwargs) |
| |
| return model |
| |
| |
| __all__ = ['CNN', 'create_model'] |
