| # |
| # 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 |
| from singa import autograd |
| |
| |
| class LSGAN_MLP(model.Model): |
| |
| def __init__(self, noise_size=100, feature_size=784, hidden_size=128): |
| super(LSGAN_MLP, self).__init__() |
| self.noise_size = noise_size |
| self.feature_size = feature_size |
| self.hidden_size = hidden_size |
| |
| # Generative Net |
| self.gen_net_fc_0 = layer.Linear(self.hidden_size) |
| self.gen_net_relu_0 = layer.ReLU() |
| self.gen_net_fc_1 = layer.Linear(self.feature_size) |
| self.gen_net_sigmoid_1 = layer.Sigmoid() |
| |
| # Discriminative Net |
| self.dis_net_fc_0 = layer.Linear(self.hidden_size) |
| self.dis_net_relu_0 = layer.ReLU() |
| self.dis_net_fc_1 = layer.Linear(1) |
| self.mse_loss = layer.MeanSquareError() |
| |
| def forward(self, x): |
| # Cascaded Net |
| y = self.forward_gen(x) |
| y = self.forward_dis(y) |
| return y |
| |
| def forward_dis(self, x): |
| # Discriminative Net |
| y = self.dis_net_fc_0(x) |
| y = self.dis_net_relu_0(y) |
| y = self.dis_net_fc_1(y) |
| return y |
| |
| def forward_gen(self, x): |
| # Generative Net |
| y = self.gen_net_fc_0(x) |
| y = self.gen_net_relu_0(y) |
| y = self.gen_net_fc_1(y) |
| y = self.gen_net_sigmoid_1(y) |
| return y |
| |
| def train_one_batch(self, x, y): |
| # Training the Generative Net |
| out = self.forward(x) |
| loss = self.mse_loss(out, y) |
| # Only update the Generative Net |
| for p, g in autograd.backward(loss): |
| if "gen_net" in p.name: |
| self.optimizer.apply(p.name, p, g) |
| return out, loss |
| |
| def train_one_batch_dis(self, x, y): |
| # Training the Discriminative Net |
| out = self.forward_dis(x) |
| loss = self.mse_loss(out, y) |
| # Only update the Discriminative Net |
| for p, g in autograd.backward(loss): |
| if "dis_net" in p.name: |
| self.optimizer.apply(p.name, p, g) |
| 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 model pre-trained |
| """ |
| model = LSGAN_MLP(**kwargs) |
| |
| return model |
| |
| |
| __all__ = ['LSGAN_MLP', 'create_model'] |