.. e083b4dc8e350428ddf680e97b47cc8e

:orphan:

.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/hpo_quickstart_pytorch/model.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_tutorials_hpo_quickstart_pytorch_model.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_tutorials_hpo_quickstart_pytorch_model.py:

将 PyTorch 官方教程移植到NNI
============================
本文件是 `PyTorch 官方教程 <https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.htlm>`__ 的修改版。

您可以直接运行本文件，其结果和原版完全一致。同时，您也可以在 NNI 实验中使用本文件，进行超参调优。

我们建议您先直接运行一次本文件，在熟悉代码的同时检查运行环境。

和原版相比，我们做了两处修改：

1. 在 `获取调优后的参数`_ 部分，我们使用调优算法生成的参数替换默认参数；
2. 在 `训练模型并上传结果`_ 部分，我们将准确率数据报告给 NNI。

.. GENERATED FROM PYTHON SOURCE LINES 21-28

.. code-block:: default

    import nni
    import torch
    from torch import nn
    from torch.utils.data import DataLoader
    from torchvision import datasets
    from torchvision.transforms import ToTensor








.. GENERATED FROM PYTHON SOURCE LINES 29-32

准备调优的超参
--------------
以下超参将被调优：

.. GENERATED FROM PYTHON SOURCE LINES 32-38

.. code-block:: default

    params = {
        'features': 512,
        'lr': 0.001,
        'momentum': 0,
    }








.. GENERATED FROM PYTHON SOURCE LINES 39-43

获取调优后的参数
----------------
直接运行时 :func:`nni.get_next_parameter` 会返回空 dict，
而在 NNI 实验中使用时，它会返回调优算法生成的超参组合。

.. GENERATED FROM PYTHON SOURCE LINES 43-47

.. code-block:: default

    optimized_params = nni.get_next_parameter()
    params.update(optimized_params)
    print(params)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    {'features': 512, 'lr': 0.001, 'momentum': 0}




.. GENERATED FROM PYTHON SOURCE LINES 48-50

加载数据集
----------

.. GENERATED FROM PYTHON SOURCE LINES 50-58

.. code-block:: default

    training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
    test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())

    batch_size = 64

    train_dataloader = DataLoader(training_data, batch_size=batch_size)
    test_dataloader = DataLoader(test_data, batch_size=batch_size)








.. GENERATED FROM PYTHON SOURCE LINES 59-61

使用超参构建模型
----------------

.. GENERATED FROM PYTHON SOURCE LINES 61-86

.. code-block:: default

    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"Using {device} device")

    class NeuralNetwork(nn.Module):
        def __init__(self):
            super(NeuralNetwork, self).__init__()
            self.flatten = nn.Flatten()
            self.linear_relu_stack = nn.Sequential(
                nn.Linear(28*28, params['features']),
                nn.ReLU(),
                nn.Linear(params['features'], params['features']),
                nn.ReLU(),
                nn.Linear(params['features'], 10)
            )

        def forward(self, x):
            x = self.flatten(x)
            logits = self.linear_relu_stack(x)
            return logits

    model = NeuralNetwork().to(device)

    loss_fn = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Using cpu device




.. GENERATED FROM PYTHON SOURCE LINES 87-89

定义训练和测试函数
------------------

.. GENERATED FROM PYTHON SOURCE LINES 89-115

.. code-block:: default

    def train(dataloader, model, loss_fn, optimizer):
        size = len(dataloader.dataset)
        model.train()
        for batch, (X, y) in enumerate(dataloader):
            X, y = X.to(device), y.to(device)
            pred = model(X)
            loss = loss_fn(pred, y)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

    def test(dataloader, model, loss_fn):
        size = len(dataloader.dataset)
        num_batches = len(dataloader)
        model.eval()
        test_loss, correct = 0, 0
        with torch.no_grad():
            for X, y in dataloader:
                X, y = X.to(device), y.to(device)
                pred = model(X)
                test_loss += loss_fn(pred, y).item()
                correct += (pred.argmax(1) == y).type(torch.float).sum().item()
        test_loss /= num_batches
        correct /= size
        return correct








.. GENERATED FROM PYTHON SOURCE LINES 116-119

训练模型并上传结果
------------------
将准确率数据报告给 NNI 的调参算法，以使其能够预测更优的超参组合。

.. GENERATED FROM PYTHON SOURCE LINES 119-126

.. code-block:: default

    epochs = 5
    for t in range(epochs):
        print(f"Epoch {t+1}\n-------------------------------")
        train(train_dataloader, model, loss_fn, optimizer)
        accuracy = test(test_dataloader, model, loss_fn)
        nni.report_intermediate_result(accuracy)
    nni.report_final_result(accuracy)




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Epoch 1
    -------------------------------
    [2022-03-21 01:09:37] INFO (nni/MainThread) Intermediate result: 0.461  (Index 0)
    Epoch 2
    -------------------------------
    [2022-03-21 01:09:42] INFO (nni/MainThread) Intermediate result: 0.5529  (Index 1)
    Epoch 3
    -------------------------------
    [2022-03-21 01:09:47] INFO (nni/MainThread) Intermediate result: 0.6155  (Index 2)
    Epoch 4
    -------------------------------
    [2022-03-21 01:09:52] INFO (nni/MainThread) Intermediate result: 0.6345  (Index 3)
    Epoch 5
    -------------------------------
    [2022-03-21 01:09:56] INFO (nni/MainThread) Intermediate result: 0.6505  (Index 4)
    [2022-03-21 01:09:56] INFO (nni/MainThread) Final result: 0.6505





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  24.441 seconds)


.. _sphx_glr_download_tutorials_hpo_quickstart_pytorch_model.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: model.py <model.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: model.ipynb <model.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
