Minor fixes in tutorial on basics (file names, spaces, links) (pytorch#1638)

* tensorqs_tutorial -> tensors_tutorial

* autogradqs_tutorial -> autograds_tutorial

* spaces

* fix link to torch.Tensor.scatter_ documentation

* undo file renaming

* autogradqs_tutorial.py in readme

Co-authored-by: Holly Sweeney <[email protected]>

Author: fzalkow

beginner_source/basics/README.txt

@@ -9,7 +9,7 @@ Learn the Basics
     Quickstart
     https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
 
-3. tensors_tutorial.py
+3. tensorqs_tutorial.py
     Tensors
     https://pytorch.org/tutorials/beginner/basics/tensor_tutorial.html
 
@@ -25,7 +25,7 @@ Learn the Basics
     Building the Neural Network
     https://pytorch.org/tutorials/beginner/basics/buildmodel_tutorial.html
 
-7. autograd_tutorial.py
+7. autogradqs_tutorial.py
     Automatic Differentiation with torch.autograd_tutorial
     https://pytorch.org/tutorials/beginner/basics/autograd_tutorial.html
 
@@ -36,4 +36,3 @@ Learn the Basics
 9. saveloadrun_tutorial.py
     Save and Load the Model
     https://pytorch.org/tutorials/beginner/basics/saveloadrun_tutorial.html
-

beginner_source/basics/autogradqs_tutorial.py

@@ -1,7 +1,7 @@
 """
 `Learn the Basics <intro.html>`_ ||
-`Quickstart <quickstart_tutorial.html>`_ || 
-`Tensors <tensorqs_tutorial.html>`_ || 
+`Quickstart <quickstart_tutorial.html>`_ ||
+`Tensors <tensorqs_tutorial.html>`_ ||
 `Datasets & DataLoaders <data_tutorial.html>`_ ||
 `Transforms <transforms_tutorial.html>`_ ||
 `Build Model <buildmodel_tutorial.html>`_ ||
@@ -64,7 +64,7 @@
 # documentation <https://pytorch.org/docs/stable/autograd.html#function>`__.
 #
 
-print('Gradient function for z =',z.grad_fn)
+print('Gradient function for z =', z.grad_fn)
 print('Gradient function for loss =', loss.grad_fn)
 
 ######################################################################
@@ -240,4 +240,3 @@
 # Further Reading
 # ~~~~~~~~~~~~~~~~~
 # - `Autograd Mechanics <https://pytorch.org/docs/stable/notes/autograd.html>`_
-

beginner_source/basics/buildmodel_tutorial.py

@@ -1,6 +1,6 @@
 """
 `Learn the Basics <intro.html>`_ ||
-`Quickstart <quickstart_tutorial.html>`_ || 
+`Quickstart <quickstart_tutorial.html>`_ ||
 `Tensors <tensorqs_tutorial.html>`_ ||
 `Datasets & DataLoaders <data_tutorial.html>`_ ||
 `Transforms <transforms_tutorial.html>`_ ||
@@ -12,9 +12,9 @@
 Build the Neural Network
 ===================
 
-Neural networks comprise of layers/modules that perform operations on data. 
-The `torch.nn <https://pytorch.org/docs/stable/nn.html>`_ namespace provides all the building blocks you need to 
-build your own neural network. Every module in PyTorch subclasses the `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_. 
+Neural networks comprise of layers/modules that perform operations on data.
+The `torch.nn <https://pytorch.org/docs/stable/nn.html>`_ namespace provides all the building blocks you need to
+build your own neural network. Every module in PyTorch subclasses the `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_.
 A neural network is a module itself that consists of other modules (layers). This nested structure allows for
 building and managing complex architectures easily.
 
@@ -32,20 +32,20 @@
 #############################################
 # Get Device for Training
 # -----------------------
-# We want to be able to train our model on a hardware accelerator like the GPU, 
-# if it is available. Let's check to see if 
-# `torch.cuda <https://pytorch.org/docs/stable/notes/cuda.html>`_ is available, else we 
-# continue to use the CPU. 
+# We want to be able to train our model on a hardware accelerator like the GPU,
+# if it is available. Let's check to see if
+# `torch.cuda <https://pytorch.org/docs/stable/notes/cuda.html>`_ is available, else we
+# continue to use the CPU.
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 print('Using {} device'.format(device))
 
 ##############################################
 # Define the Class
 # -------------------------
-# We define our neural network by subclassing ``nn.Module``, and 
+# We define our neural network by subclassing ``nn.Module``, and
 # initialize the neural network layers in ``__init__``. Every ``nn.Module`` subclass implements
-# the operations on input data in the ``forward`` method. 
+# the operations on input data in the ``forward`` method.
 
 class NeuralNetwork(nn.Module):
     def __init__(self):
@@ -66,7 +66,7 @@ def forward(self, x):
         return logits
 
 ##############################################
-# We create an instance of ``NeuralNetwork``, and move it to the ``device``, and print 
+# We create an instance of ``NeuralNetwork``, and move it to the ``device``, and print
 # its structure.
 
 model = NeuralNetwork().to(device)
@@ -75,14 +75,14 @@ def forward(self, x):
 
 ##############################################
 # To use the model, we pass it the input data. This executes the model's ``forward``,
-# along with some `background operations <https://github.com/pytorch/pytorch/blob/270111b7b611d174967ed204776985cefca9c144/torch/nn/modules/module.py#L866>`_. 
+# along with some `background operations <https://github.com/pytorch/pytorch/blob/270111b7b611d174967ed204776985cefca9c144/torch/nn/modules/module.py#L866>`_.
 # Do not call ``model.forward()`` directly!
-# 
+#
 # Calling the model on the input returns a 10-dimensional tensor with raw predicted values for each class.
 # We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module.
 
 X = torch.rand(1, 28, 28, device=device)
-logits = model(X) 
+logits = model(X)
 pred_probab = nn.Softmax(dim=1)(logits)
 y_pred = pred_probab.argmax(1)
 print(f"Predicted class: {y_pred}")
@@ -97,26 +97,26 @@ def forward(self, x):
 # Model Layers
 # -------------------------
 #
-# Let's break down the layers in the FashionMNIST model. To illustrate it, we 
-# will take a sample minibatch of 3 images of size 28x28 and see what happens to it as 
-# we pass it through the network. 
+# Let's break down the layers in the FashionMNIST model. To illustrate it, we
+# will take a sample minibatch of 3 images of size 28x28 and see what happens to it as
+# we pass it through the network.
 
 input_image = torch.rand(3,28,28)
 print(input_image.size())
 
 ##################################################
 # nn.Flatten
 # ^^^^^^^^^^^^^^^^^^^^^^
-# We initialize the `nn.Flatten  <https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html>`_ 
+# We initialize the `nn.Flatten  <https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html>`_
 # layer to convert each 2D 28x28 image into a contiguous array of 784 pixel values (
 # the minibatch dimension (at dim=0) is maintained).
- 
+
 flatten = nn.Flatten()
 flat_image = flatten(input_image)
 print(flat_image.size())
 
 ##############################################
-# nn.Linear 
+# nn.Linear
 # ^^^^^^^^^^^^^^^^^^^^^^
 # The `linear layer <https://pytorch.org/docs/stable/generated/torch.nn.Linear.html>`_
 # is a module that applies a linear transformation on the input using its stored weights and biases.
@@ -145,7 +145,7 @@ def forward(self, x):
 #################################################
 # nn.Sequential
 # ^^^^^^^^^^^^^^^^^^^^^^
-# `nn.Sequential <https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html>`_ is an ordered 
+# `nn.Sequential <https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html>`_ is an ordered
 # container of modules. The data is passed through all the modules in the same order as defined. You can use
 # sequential containers to put together a quick network like ``seq_modules``.
 
@@ -162,9 +162,9 @@ def forward(self, x):
 # nn.Softmax
 # ^^^^^^^^^^^^^^^^^^^^^^
 # The last linear layer of the neural network returns `logits` - raw values in [-\infty, \infty] - which are passed to the
-# `nn.Softmax <https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html>`_ module. The logits are scaled to values 
-# [0, 1] representing the model's predicted probabilities for each class. ``dim`` parameter indicates the dimension along 
-# which the values must sum to 1. 
+# `nn.Softmax <https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html>`_ module. The logits are scaled to values
+# [0, 1] representing the model's predicted probabilities for each class. ``dim`` parameter indicates the dimension along
+# which the values must sum to 1.
 
 softmax = nn.Softmax(dim=1)
 pred_probab = softmax(logits)
@@ -173,12 +173,12 @@ def forward(self, x):
 #################################################
 # Model Parameters
 # -------------------------
-# Many layers inside a neural network are *parameterized*, i.e. have associated weights 
-# and biases that are optimized during training. Subclassing ``nn.Module`` automatically 
-# tracks all fields defined inside your model object, and makes all parameters 
-# accessible using your model's ``parameters()`` or ``named_parameters()`` methods. 
-# 
-# In this example, we iterate over each parameter, and print its size and a preview of its values. 
+# Many layers inside a neural network are *parameterized*, i.e. have associated weights
+# and biases that are optimized during training. Subclassing ``nn.Module`` automatically
+# tracks all fields defined inside your model object, and makes all parameters
+# accessible using your model's ``parameters()`` or ``named_parameters()`` methods.
+#
+# In this example, we iterate over each parameter, and print its size and a preview of its values.
 #
 
 
@@ -195,6 +195,3 @@ def forward(self, x):
 # Further Reading
 # --------------
 # - `torch.nn API <https://pytorch.org/docs/stable/nn.html>`_
-
-
-

beginner_source/basics/data_tutorial.py

@@ -1,6 +1,6 @@
 """
 `Learn the Basics <intro.html>`_ ||
-`Quickstart <quickstart_tutorial.html>`_ || 
+`Quickstart <quickstart_tutorial.html>`_ ||
 `Tensors <tensorqs_tutorial.html>`_ ||
 **Datasets & DataLoaders** ||
 `Transforms <transforms_tutorial.html>`_ ||
@@ -22,7 +22,7 @@
 # ``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around
 # the ``Dataset`` to enable easy access to the samples.
 #
-# PyTorch domain libraries provide a number of pre-loaded datasets (such as FashionMNIST) that 
+# PyTorch domain libraries provide a number of pre-loaded datasets (such as FashionMNIST) that
 # subclass ``torch.utils.data.Dataset`` and implement functions specific to the particular data.
 # They can be used to prototype and benchmark your model. You can find them
 # here: `Image Datasets <https://pytorch.org/vision/stable/datasets.html>`_,
@@ -71,7 +71,7 @@
 # Iterating and Visualizing the Dataset
 # -----------------
 #
-# We can index ``Datasets`` manually like a list: ``training_data[index]``. 
+# We can index ``Datasets`` manually like a list: ``training_data[index]``.
 # We use ``matplotlib`` to visualize some samples in our training data.
 
 labels_map = {
@@ -111,9 +111,9 @@
 # Creating a Custom Dataset for your files
 # ---------------------------------------------------
 #
-# A custom Dataset class must implement three functions: `__init__`, `__len__`, and `__getitem__`. 
-# Take a look at this implementation; the FashionMNIST images are stored 
-# in a directory ``img_dir``, and their labels are stored separately in a CSV file ``annotations_file``. 
+# A custom Dataset class must implement three functions: `__init__`, `__len__`, and `__getitem__`.
+# Take a look at this implementation; the FashionMNIST images are stored
+# in a directory ``img_dir``, and their labels are stored separately in a CSV file ``annotations_file``.
 #
 # In the next sections, we'll break down what's happening in each of these functions.
 
@@ -148,8 +148,8 @@ def __getitem__(self, idx):
 # ^^^^^^^^^^^^^^^^^^^^
 #
 # The __init__ function is run once when instantiating the Dataset object. We initialize
-# the directory containing the images, the annotations file, and both transforms (covered 
-# in more detail in the next section). 
+# the directory containing the images, the annotations file, and both transforms (covered
+# in more detail in the next section).
 #
 # The labels.csv file looks like: ::
 #
@@ -183,9 +183,9 @@ def __len__(self):
 # __getitem__
 # ^^^^^^^^^^^^^^^^^^^^
 #
-# The __getitem__ function loads and returns a sample from the dataset at the given index ``idx``. 
-# Based on the index, it identifies the image's location on disk, converts that to a tensor using ``read_image``, retrieves the 
-# corresponding label from the csv data in ``self.img_labels``, calls the transform functions on them (if applicable), and returns the 
+# The __getitem__ function loads and returns a sample from the dataset at the given index ``idx``.
+# Based on the index, it identifies the image's location on disk, converts that to a tensor using ``read_image``, retrieves the
+# corresponding label from the csv data in ``self.img_labels``, calls the transform functions on them (if applicable), and returns the
 # tensor image and corresponding label in a tuple.
 
 def __getitem__(self, idx):
@@ -207,7 +207,7 @@ def __getitem__(self, idx):
 #################################################################
 # Preparing your data for training with DataLoaders
 # -------------------------------------------------
-# The ``Dataset`` retrieves our dataset's features and labels one sample at a time. While training a model, we typically want to 
+# The ``Dataset`` retrieves our dataset's features and labels one sample at a time. While training a model, we typically want to
 # pass samples in "minibatches", reshuffle the data at every epoch to reduce model overfitting, and use Python's ``multiprocessing`` to
 # speed up data retrieval.
 #
@@ -224,7 +224,7 @@ def __getitem__(self, idx):
 #
 # We have loaded that dataset into the ``DataLoader`` and can iterate through the dataset as needed.
 # Each iteration below returns a batch of ``train_features`` and ``train_labels`` (containing ``batch_size=64`` features and labels respectively).
-# Because we specified ``shuffle=True``, after we iterate over all batches the data is shuffled (for finer-grained control over 
+# Because we specified ``shuffle=True``, after we iterate over all batches the data is shuffled (for finer-grained control over
 # the data loading order, take a look at `Samplers <https://pytorch.org/docs/stable/data.html#data-loading-order-and-sampler>`_).
 
 # Display image and label.
@@ -245,5 +245,3 @@ def __getitem__(self, idx):
 # Further Reading
 # --------------
 # - `torch.utils.data API <https://pytorch.org/docs/stable/data.html>`_
-
-

beginner_source/basics/intro.py

@@ -1,7 +1,7 @@
 """
 **Learn the Basics** ||
-`Quickstart <quickstart_tutorial.html>`_ || 
-`Tensors <tensorqs_tutorial.html>`_ || 
+`Quickstart <quickstart_tutorial.html>`_ ||
+`Tensors <tensorqs_tutorial.html>`_ ||
 `Datasets & DataLoaders <data_tutorial.html>`_ ||
 `Transforms <transforms_tutorial.html>`_ ||
 `Build Model <buildmodel_tutorial.html>`_ ||
@@ -12,20 +12,20 @@
 Learn the Basics
 ===================
 
-Authors: 
+Authors:
 `Suraj Subramanian <https://github.com/suraj813>`_,
-`Seth Juarez <https://github.com/sethjuarez/>`_, 
-`Cassie Breviu <https://github.com/cassieview/>`_, 
-`Dmitry Soshnikov <https://soshnikov.com/>`_, 
-`Ari Bornstein <https://github.com/aribornstein/>`_ 
+`Seth Juarez <https://github.com/sethjuarez/>`_,
+`Cassie Breviu <https://github.com/cassieview/>`_,
+`Dmitry Soshnikov <https://soshnikov.com/>`_,
+`Ari Bornstein <https://github.com/aribornstein/>`_
 
-Most machine learning workflows involve working with data, creating models, optimizing model 
-parameters, and saving the trained models. This tutorial introduces you to a complete ML workflow 
+Most machine learning workflows involve working with data, creating models, optimizing model
+parameters, and saving the trained models. This tutorial introduces you to a complete ML workflow
 implemented in PyTorch, with links to learn more about each of these concepts.
 
-We'll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs 
-to one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, 
-Bag, or Ankle boot. 
+We'll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs
+to one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker,
+Bag, or Ankle boot.
 
 `This tutorial assumes a basic familiarity with Python and Deep Learning concepts.`
 
@@ -34,12 +34,12 @@
 ------------------
 You can run this tutorial in a couple of ways:
 
-- **In the cloud**: This is the easiest way to get started! Each section has a "Run in Microsoft Learn" link at the top, which opens an integrated notebook in Microsoft Learn with the code in a fully-hosted environment. 
+- **In the cloud**: This is the easiest way to get started! Each section has a "Run in Microsoft Learn" link at the top, which opens an integrated notebook in Microsoft Learn with the code in a fully-hosted environment.
 - **Locally**: This option requires you to setup PyTorch and TorchVision first on your local machine (`installation instructions <https://pytorch.org/get-started/locally/>`_). Download the notebook or copy the code into your favorite IDE.
 
 
 How to Use this Guide
------------------ 
+-----------------
 If you're familiar with other deep learning frameworks, check out the `0. Quickstart <quickstart_tutorial.html>`_ first
 to quickly familiarize yourself with PyTorch's API.
 
@@ -51,4 +51,4 @@
 .. toctree::
    :hidden:
 
-"""
+"""