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intermediate_source/tensorboard_tutorial.rst

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@@ -11,8 +11,7 @@ TensorBoard, a tool designed for visualizing the results of neural
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network training runs. This tutorial illustrates some of its
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functionality, using the
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`Fashion-MNIST dataset <https://github.com/zalandoresearch/fashion-mnist>`__
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which can be read into PyTorch using `torchvision.dataset` just as
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the CIFAR-10 data can.
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which can be read into PyTorch using `torchvision.datasets`.
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In this tutorial, we'll learn how to:
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* How to track our model's performance as it trains
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* How to assess our model's performance once it is trained.
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We'll begin with similar boilerplate code as in the prior tutorial:
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We'll begin with similar boilerplate code as in the `CIFAR-10 tutorial <https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`__:
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.. code:: python
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@@ -83,7 +82,7 @@ We'll begin with similar boilerplate code as in the prior tutorial:
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else:
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plt.imshow(np.transpose(npimg, (1, 2, 0)))
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We'll define a similar model architecture from before, making only
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We'll define a similar model architecture from that tutorial, making only
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minor modifications to account for the fact that the images are now
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one channel instead of three and 28x28 instead of 32x32:
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