.. note:: :class: sphx-glr-download-link-note Click :ref:here  to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_beginner_nlp_pytorch_tutorial.py: Introduction to PyTorch *********************** Introduction to Torch's tensor library ====================================== All of deep learning is computations on tensors, which are generalizations of a matrix that can be indexed in more than 2 dimensions. We will see exactly what this means in-depth later. First, let's look what we can do with tensors. .. code-block:: default # Author: Robert Guthrie import torch torch.manual_seed(1) Creating Tensors ~~~~~~~~~~~~~~~~ Tensors can be created from Python lists with the torch.tensor() function. .. code-block:: default # torch.tensor(data) creates a torch.Tensor object with the given data. V_data = [1., 2., 3.] V = torch.tensor(V_data) print(V) # Creates a matrix M_data = [[1., 2., 3.], [4., 5., 6]] M = torch.tensor(M_data) print(M) # Create a 3D tensor of size 2x2x2. T_data = [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]] T = torch.tensor(T_data) print(T) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none tensor([1., 2., 3.]) tensor([[1., 2., 3.], [4., 5., 6.]]) tensor([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]]) What is a 3D tensor anyway? Think about it like this. If you have a vector, indexing into the vector gives you a scalar. If you have a matrix, indexing into the matrix gives you a vector. If you have a 3D tensor, then indexing into the tensor gives you a matrix! A note on terminology: when I say "tensor" in this tutorial, it refers to any torch.Tensor object. Matrices and vectors are special cases of torch.Tensors, where their dimension is 2 and 1 respectively. When I am talking about 3D tensors, I will explicitly use the term "3D tensor". .. code-block:: default # Index into V and get a scalar (0 dimensional tensor) print(V[0]) # Get a Python number from it print(V[0].item()) # Index into M and get a vector print(M[0]) # Index into T and get a matrix print(T[0]) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none tensor(1.) 1.0 tensor([1., 2., 3.]) tensor([[1., 2.], [3., 4.]]) You can also create tensors of other data types. To create a tensor of integer types, try torch.tensor([[1, 2], [3, 4]]) (where all elements in the list are integers). You can also specify a data type by passing in dtype=torch.data_type. Check the documentation for more data types, but Float and Long will be the most common. You can create a tensor with random data and the supplied dimensionality with torch.randn() .. code-block:: default x = torch.randn((3, 4, 5)) print(x) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none tensor([[[-1.5256, -0.7502, -0.6540, -1.6095, -0.1002], [-0.6092, -0.9798, -1.6091, -0.7121, 0.3037], [-0.7773, -0.2515, -0.2223, 1.6871, 0.2284], [ 0.4676, -0.6970, -1.1608, 0.6995, 0.1991]], [[ 0.8657, 0.2444, -0.6629, 0.8073, 1.1017], [-0.1759, -2.2456, -1.4465, 0.0612, -0.6177], [-0.7981, -0.1316, 1.8793, -0.0721, 0.1578], [-0.7735, 0.1991, 0.0457, 0.1530, -0.4757]], [[-0.1110, 0.2927, -0.1578, -0.0288, 0.4533], [ 1.1422, 0.2486, -1.7754, -0.0255, -1.0233], [-0.5962, -1.0055, 0.4285, 1.4761, -1.7869], [ 1.6103, -0.7040, -0.1853, -0.9962, -0.8313]]]) Operations with Tensors ~~~~~~~~~~~~~~~~~~~~~~~ You can operate on tensors in the ways you would expect. .. code-block:: default x = torch.tensor([1., 2., 3.]) y = torch.tensor([4., 5., 6.]) z = x + y print(z) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none tensor([5., 7., 9.]) See the documentation __ for a complete list of the massive number of operations available to you. They expand beyond just mathematical operations. One helpful operation that we will make use of later is concatenation. .. code-block:: default # By default, it concatenates along the first axis (concatenates rows) x_1 = torch.randn(2, 5) y_1 = torch.randn(3, 5) z_1 = torch.cat([x_1, y_1]) print(z_1) # Concatenate columns: x_2 = torch.randn(2, 3) y_2 = torch.randn(2, 5) # second arg specifies which axis to concat along z_2 = torch.cat([x_2, y_2], 1) print(z_2) # If your tensors are not compatible, torch will complain. Uncomment to see the error # torch.cat([x_1, x_2]) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none tensor([[-0.8029, 0.2366, 0.2857, 0.6898, -0.6331], [ 0.8795, -0.6842, 0.4533, 0.2912, -0.8317], [-0.5525, 0.6355, -0.3968, -0.6571, -1.6428], [ 0.9803, -0.0421, -0.8206, 0.3133, -1.1352], [ 0.3773, -0.2824, -2.5667, -1.4303, 0.5009]]) tensor([[ 0.5438, -0.4057, 1.1341, -0.1473, 0.6272, 1.0935, 0.0939, 1.2381], [-1.1115, 0.3501, -0.7703, -1.3459, 0.5119, -0.6933, -0.1668, -0.9999]]) Reshaping Tensors ~~~~~~~~~~~~~~~~~ Use the .view() method to reshape a tensor. This method receives heavy use, because many neural network components expect their inputs to have a certain shape. Often you will need to reshape before passing your data to the component. .. code-block:: default x = torch.randn(2, 3, 4) print(x) print(x.view(2, 12)) # Reshape to 2 rows, 12 columns # Same as above. If one of the dimensions is -1, its size can be inferred print(x.view(2, -1)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none tensor([[[ 0.4175, -0.2127, -0.8400, -0.4200], [-0.6240, -0.9773, 0.8748, 0.9873], [-0.0594, -2.4919, 0.2423, 0.2883]], [[-0.1095, 0.3126, 1.5038, 0.5038], [ 0.6223, -0.4481, -0.2856, 0.3880], [-1.1435, -0.6512, -0.1032, 0.6937]]]) tensor([[ 0.4175, -0.2127, -0.8400, -0.4200, -0.6240, -0.9773, 0.8748, 0.9873, -0.0594, -2.4919, 0.2423, 0.2883], [-0.1095, 0.3126, 1.5038, 0.5038, 0.6223, -0.4481, -0.2856, 0.3880, -1.1435, -0.6512, -0.1032, 0.6937]]) tensor([[ 0.4175, -0.2127, -0.8400, -0.4200, -0.6240, -0.9773, 0.8748, 0.9873, -0.0594, -2.4919, 0.2423, 0.2883], [-0.1095, 0.3126, 1.5038, 0.5038, 0.6223, -0.4481, -0.2856, 0.3880, -1.1435, -0.6512, -0.1032, 0.6937]]) Computation Graphs and Automatic Differentiation ================================================ The concept of a computation graph is essential to efficient deep learning programming, because it allows you to not have to write the back propagation gradients yourself. A computation graph is simply a specification of how your data is combined to give you the output. Since the graph totally specifies what parameters were involved with which operations, it contains enough information to compute derivatives. This probably sounds vague, so let's see what is going on using the fundamental flag requires_grad. First, think from a programmers perspective. What is stored in the torch.Tensor objects we were creating above? Obviously the data and the shape, and maybe a few other things. But when we added two tensors together, we got an output tensor. All this output tensor knows is its data and shape. It has no idea that it was the sum of two other tensors (it could have been read in from a file, it could be the result of some other operation, etc.) If requires_grad=True, the Tensor object keeps track of how it was created. Let's see it in action. .. code-block:: default # Tensor factory methods have a requires_grad flag x = torch.tensor([1., 2., 3], requires_grad=True) # With requires_grad=True, you can still do all the operations you previously # could y = torch.tensor([4., 5., 6], requires_grad=True) z = x + y print(z) # BUT z knows something extra. print(z.grad_fn) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none tensor([5., 7., 9.], grad_fn=) So Tensors know what created them. z knows that it wasn't read in from a file, it wasn't the result of a multiplication or exponential or whatever. And if you keep following z.grad_fn, you will find yourself at x and y. But how does that help us compute a gradient? .. code-block:: default # Let's sum up all the entries in z s = z.sum() print(s) print(s.grad_fn) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none tensor(21., grad_fn=) So now, what is the derivative of this sum with respect to the first component of x? In math, we want .. math:: \frac{\partial s}{\partial x_0} Well, s knows that it was created as a sum of the tensor z. z knows that it was the sum x + y. So .. math:: s = \overbrace{x_0 + y_0}^\text{$z_0$} + \overbrace{x_1 + y_1}^\text{$z_1$} + \overbrace{x_2 + y_2}^\text{$z_2$} And so s contains enough information to determine that the derivative we want is 1! Of course this glosses over the challenge of how to actually compute that derivative. The point here is that s is carrying along enough information that it is possible to compute it. In reality, the developers of Pytorch program the sum() and + operations to know how to compute their gradients, and run the back propagation algorithm. An in-depth discussion of that algorithm is beyond the scope of this tutorial. Let's have Pytorch compute the gradient, and see that we were right: (note if you run this block multiple times, the gradient will increment. That is because Pytorch *accumulates* the gradient into the .grad property, since for many models this is very convenient.) .. code-block:: default # calling .backward() on any variable will run backprop, starting from it. s.backward() print(x.grad) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none tensor([1., 1., 1.]) Understanding what is going on in the block below is crucial for being a successful programmer in deep learning. .. code-block:: default x = torch.randn(2, 2) y = torch.randn(2, 2) # By default, user created Tensors have requires_grad=False print(x.requires_grad, y.requires_grad) z = x + y # So you can't backprop through z print(z.grad_fn) # .requires_grad_( ... ) changes an existing Tensor's requires_grad # flag in-place. The input flag defaults to True if not given. x = x.requires_grad_() y = y.requires_grad_() # z contains enough information to compute gradients, as we saw above z = x + y print(z.grad_fn) # If any input to an operation has requires_grad=True, so will the output print(z.requires_grad) # Now z has the computation history that relates itself to x and y # Can we just take its values, and **detach** it from its history? new_z = z.detach() # ... does new_z have information to backprop to x and y? # NO! print(new_z.grad_fn) # And how could it? z.detach() returns a tensor that shares the same storage # as z, but with the computation history forgotten. It doesn't know anything # about how it was computed. # In essence, we have broken the Tensor away from its past history .. rst-class:: sphx-glr-script-out Out: .. code-block:: none False False None True None You can also stop autograd from tracking history on Tensors with .requires_grad=True by wrapping the code block in with torch.no_grad(): .. code-block:: default print(x.requires_grad) print((x ** 2).requires_grad) with torch.no_grad(): print((x ** 2).requires_grad) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none True True False .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 3.669 seconds) .. _sphx_glr_download_beginner_nlp_pytorch_tutorial.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:Download Python source code: pytorch_tutorial.py  .. container:: sphx-glr-download :download:Download Jupyter notebook: pytorch_tutorial.ipynb  .. only:: html .. rst-class:: sphx-glr-signature Gallery generated by Sphinx-Gallery _