| # 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 autograd |
| from singa import layer |
| from singa import model |
| |
| |
| class IMDBModel(model.Model): |
| |
| def __init__(self, |
| hidden_size, |
| mode='lstm', |
| return_sequences=False, |
| bidirectional="False", |
| num_layers=1): |
| super().__init__() |
| batch_first = True |
| self.lstm = layer.CudnnRNN(hidden_size=hidden_size, |
| batch_first=batch_first, |
| rnn_mode=mode, |
| return_sequences=return_sequences, |
| num_layers=1, |
| dropout=0.9, |
| bidirectional=bidirectional) |
| self.l1 = layer.Linear(64) |
| self.l2 = layer.Linear(2) |
| |
| def forward(self, x): |
| y = self.lstm(x) |
| y = autograd.reshape(y, (y.shape[0], -1)) |
| y = self.l1(y) |
| y = autograd.relu(y) |
| y = self.l2(y) |
| return y |
| |
| def train_one_batch(self, x, y): |
| out = self.forward(x) |
| loss = autograd.softmax_cross_entropy(out, y) |
| self.optimizer(loss) |
| return out, loss |
| |
| def set_opt(self, optimizer): |
| self.optimizer = optimizer |