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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed 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 tqdm import tqdm
- import numpy as np
- import mindspore as ms
- import mindspore.nn as nn
- from mindspore.dataset import NumpySlicesDataset
- from mindspore import context, Tensor
-
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
- class AutoEncoderTrainNetwork(nn.Cell):
- def __init__(self):
- super(AutoEncoderTrainNetwork, self).__init__()
- self.loss_fun = nn.MSELoss()
- self.net = nn.CellList([nn.Dense(2, 32), nn.Dense(32, 2)])
- self.relu = nn.ReLU()
-
- def reconstruct_sample(self, x: Tensor):
- for _, layer in enumerate(self.net):
- x = layer(x)
- x = self.relu(x)
- return x
-
- def construct(self, x: Tensor):
- recon_x = self.reconstruct_sample(x)
- return self.loss_fun(recon_x, x)
-
- def sample_2d_data(self, n_normals=2000, n_outliers=400):
- z = np.random.randn(n_normals, 2)
- outliers = np.random.uniform(low=-6, high=6, size=(n_outliers, 2))
- centers = np.array([(2., 0), (-2., 0)])
- sigma = 0.3
- normal_points = sigma * z + centers[np.random.randint(len(centers), size=(n_normals,))]
- return np.vstack((normal_points, outliers))
-
- def create_synthetic_dataset(self):
- transformed_dataset = self.sample_2d_data()
- for dim in range(transformed_dataset.shape[1]):
- min_val = transformed_dataset[:, dim].min()
- max_val = transformed_dataset[:, dim].max()
- if min_val != max_val:
- transformed_dataset[:, dim] = (transformed_dataset[:, dim] - min_val) / (max_val - min_val)
- elif min_val != 1:
- transformed_dataset[:, dim] = transformed_dataset[:, dim] / min_val
- transformed_dataset = transformed_dataset.astype(np.float32)
- return transformed_dataset
-
-
- def test_auto_monad_layer():
- ae_with_loss = AutoEncoderTrainNetwork()
- transformed_dataset = ae_with_loss.create_synthetic_dataset()
- dataloader = NumpySlicesDataset(data=(transformed_dataset,), shuffle=True)
- dataloader = dataloader.batch(batch_size=16)
- optim = nn.RMSProp(params=ae_with_loss.trainable_params(), learning_rate=0.002,)
- train_net = nn.TrainOneStepCell(ae_with_loss, optim)
- train_net.set_train()
- gen_samples = dict()
- num_epoch = 21
- for epoch in tqdm(range(num_epoch)):
- loss = []
- for _, (batch,) in enumerate(dataloader):
- batch = Tensor(batch, dtype=ms.float32)
- loss_ = train_net(batch)
- loss.append(loss_.asnumpy())
- avg_loss = np.array(loss).mean()
- if epoch % 10 == 0:
- gen_samples[epoch] = ae_with_loss.reconstruct_sample(Tensor(transformed_dataset)).asnumpy()
- print(f"epoch: {epoch}/{num_epoch}, avg loss: {avg_loss}")
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