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docs(tutorials): update tutorials (#120)
Co-authored-by: Xuehai Pan <XuehaiPan@pku.edu.cn> Co-authored-by: Benjamin-eecs <benjaminliu.eecs@gmail.com>
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| Original file line number | Diff line number | Diff line change |
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| Get Started with Jupyter Notebook | ||
| ================================= | ||
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| In this tutorial, we will use Google Colaboratory to show you the most basic usages of TorchOpt. | ||
| In this tutorial, we will use Google Colab notebooks to show you the most basic usages of TorchOpt. | ||
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| - 1: `Functional Optimizer <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/1_Functional_Optimizer.ipynb>`_ | ||
| - 2: `Visualization <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/2_Visualization.ipynb>`_ | ||
| - 3: `Meta Optimizer <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/3_Meta_Optimizer.ipynb>`_ | ||
| - 4: `Stop Gradient <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/4_Stop_Gradient.ipynb>`_ | ||
| - 5: `Implicit Differentiation <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/5_Implicit_Differentiation.ipynb>`_ | ||
| - 1: `Functional Optimizer <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/1_Functional_Optimizer.ipynb>`_ | ||
| - 2: `Visualization <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/2_Visualization.ipynb>`_ | ||
| - 3: `Meta-Optimizer <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/3_Meta_Optimizer.ipynb>`_ | ||
| - 4: `Stop Gradient <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/4_Stop_Gradient.ipynb>`_ | ||
| - 5: `Implicit Differentiation <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/5_Implicit_Differentiation.ipynb>`_ | ||
| - 6: `Zero-order Differentiation <https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/6_Zero_Order_Differentiation>`_ |
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| # Copyright 2022 MetaOPT Team. All Rights Reserved. | ||
| # | ||
| # 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. | ||
| # ============================================================================== | ||
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| import functorch | ||
| import torch | ||
| import torch.nn as nn | ||
| import torch.types | ||
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| import helpers | ||
| import torchopt | ||
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| BATCH_SIZE = 8 | ||
| NUM_UPDATES = 5 | ||
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| class FcNet(nn.Module): | ||
| def __init__(self, dim, out): | ||
| super().__init__() | ||
| self.fc = nn.Linear(in_features=dim, out_features=out, bias=True) | ||
| nn.init.ones_(self.fc.weight) | ||
| nn.init.zeros_(self.fc.bias) | ||
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| def forward(self, x): | ||
| return self.fc(x) | ||
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| @helpers.parametrize( | ||
| dtype=[torch.float64, torch.float32], | ||
| lr=[1e-2, 1e-3], | ||
| method=['naive', 'forward', 'antithetic'], | ||
| sigma=[0.01, 0.1, 1], | ||
| ) | ||
| def test_zero_order(dtype: torch.dtype, lr: float, method: str, sigma: float) -> None: | ||
| helpers.seed_everything(42) | ||
| input_size = 32 | ||
| output_size = 1 | ||
| batch_size = BATCH_SIZE | ||
| coef = 0.1 | ||
| num_iterations = NUM_UPDATES | ||
| num_samples = 500 | ||
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| model = FcNet(input_size, output_size) | ||
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| fmodel, params = functorch.make_functional(model) | ||
| x = torch.randn(batch_size, input_size) * coef | ||
| y = torch.randn(input_size) * coef | ||
| distribution = torch.distributions.Normal(loc=0, scale=1) | ||
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| @torchopt.diff.zero_order.zero_order( | ||
| distribution=distribution, method=method, argnums=0, sigma=sigma, num_samples=num_samples | ||
| ) | ||
| def forward_process(params, fn, x, y): | ||
| y_pred = fn(params, x) | ||
| loss = torch.mean((y - y_pred) ** 2) | ||
| return loss | ||
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| optimizer = torchopt.adam(lr=lr) | ||
| opt_state = optimizer.init(params) | ||
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| for i in range(num_iterations): | ||
| opt_state = optimizer.init(params) # init optimizer | ||
| loss = forward_process(params, fmodel, x, y) # compute loss | ||
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| grads = torch.autograd.grad(loss, params) # compute gradients | ||
| updates, opt_state = optimizer.update(grads, opt_state) # get updates | ||
| params = torchopt.apply_updates(params, updates) # update network parameters |
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