forked from mindspore-Ecosystem/mindspore
82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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from tqdm import tqdm
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.dataset import NumpySlicesDataset
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from mindspore import context, Tensor
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class AutoEncoderTrainNetwork(nn.Cell):
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def __init__(self):
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super(AutoEncoderTrainNetwork, self).__init__()
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self.loss_fun = nn.MSELoss()
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self.net = nn.CellList([nn.Dense(2, 32), nn.Dense(32, 2)])
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self.relu = nn.ReLU()
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def reconstruct_sample(self, x: Tensor):
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for _, layer in enumerate(self.net):
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x = layer(x)
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x = self.relu(x)
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return x
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def construct(self, x: Tensor):
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recon_x = self.reconstruct_sample(x)
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return self.loss_fun(recon_x, x)
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def sample_2d_data(self, n_normals=2000, n_outliers=400):
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z = np.random.randn(n_normals, 2)
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outliers = np.random.uniform(low=-6, high=6, size=(n_outliers, 2))
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centers = np.array([(2., 0), (-2., 0)])
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sigma = 0.3
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normal_points = sigma * z + centers[np.random.randint(len(centers), size=(n_normals,))]
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return np.vstack((normal_points, outliers))
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def create_synthetic_dataset(self):
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transformed_dataset = self.sample_2d_data()
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for dim in range(transformed_dataset.shape[1]):
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min_val = transformed_dataset[:, dim].min()
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max_val = transformed_dataset[:, dim].max()
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if min_val != max_val:
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transformed_dataset[:, dim] = (transformed_dataset[:, dim] - min_val) / (max_val - min_val)
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elif min_val != 1:
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transformed_dataset[:, dim] = transformed_dataset[:, dim] / min_val
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transformed_dataset = transformed_dataset.astype(np.float32)
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return transformed_dataset
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def test_auto_monad_layer():
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ae_with_loss = AutoEncoderTrainNetwork()
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transformed_dataset = ae_with_loss.create_synthetic_dataset()
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dataloader = NumpySlicesDataset(data=(transformed_dataset,), shuffle=True)
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dataloader = dataloader.batch(batch_size=16)
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optim = nn.RMSProp(params=ae_with_loss.trainable_params(), learning_rate=0.002,)
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train_net = nn.TrainOneStepCell(ae_with_loss, optim)
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train_net.set_train()
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gen_samples = dict()
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num_epoch = 21
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for epoch in tqdm(range(num_epoch)):
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loss = []
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for _, (batch,) in enumerate(dataloader):
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batch = Tensor(batch, dtype=ms.float32)
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loss_ = train_net(batch)
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loss.append(loss_.asnumpy())
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avg_loss = np.array(loss).mean()
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if epoch % 10 == 0:
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gen_samples[epoch] = ae_with_loss.reconstruct_sample(Tensor(transformed_dataset)).asnumpy()
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print(f"epoch: {epoch}/{num_epoch}, avg loss: {avg_loss}")
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