| # |
| # 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 tensor |
| |
| |
| class MLP(model.Model): |
| |
| def __init__(self, data_size=10, perceptron_size=100, num_classes=10): |
| super(MLP, self).__init__() |
| self.num_classes = num_classes |
| self.dimension = 2 |
| |
| self.relu = layer.ReLU() |
| self.linear1 = layer.Linear(perceptron_size) |
| self.linear2 = layer.Linear(num_classes) |
| self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() |
| |
| def forward(self, inputs): |
| y = self.linear1(inputs) |
| 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 == 'fp32': |
| self.optimizer(loss) |
| elif dist_option == 'fp16': |
| 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 model pre-trained |
| """ |
| model = MLP(**kwargs) |
| |
| return model |
| |
| |
| __all__ = ['MLP', 'create_model'] |
| |
| if __name__ == "__main__": |
| |
| import numpy as np |
| from singa import opt |
| from singa import device |
| |
| # generate the boundary |
| f = lambda x: (5 * x + 1) |
| bd_x = np.linspace(-1.0, 1, 200) |
| bd_y = f(bd_x) |
| # generate the training data |
| x = np.random.uniform(-1, 1, 400) |
| y = f(x) + 2 * np.random.randn(len(x)) |
| # convert training data to 2d space |
| label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)]).astype(np.int32) |
| data = np.array([[a, b] for (a, b) in zip(x, y)], dtype=np.float32) |
| |
| dev = device.create_cuda_gpu_on(0) |
| sgd = opt.SGD(0.05) |
| tx = tensor.Tensor((400, 2), dev, tensor.float32) |
| ty = tensor.Tensor((400,), dev, tensor.int32) |
| model = MLP(data_size=2, perceptron_size=3, num_classes=2) |
| |
| # attached model to graph |
| model.set_optimizer(sgd) |
| model.compile([tx], is_train=True, use_graph=True, sequential=False) |
| model.train() |
| |
| for i in range(1001): |
| tx.copy_from_numpy(data) |
| ty.copy_from_numpy(label) |
| out, loss = model(tx, ty, 'fp32', spars=None) |
| |
| if i % 100 == 0: |
| print("training loss = ", tensor.to_numpy(loss)[0]) |