{"cells":[{"cell_type":"markdown","source":["# MIT 6.7960 Fall 2026 PyTorch Tutorial:\n","\n","This colab was originally adapted from both https://github.com/davidbau/how-to-read-pytorch and https://pytorch.org/tutorials/beginner/basics/intro.html, which are great resources if you’re interested in learning more.\n","\n","Also see last year's PyTorch tutorial https://colab.research.google.com/drive/1nZg9_wYpVYWS9xZAiSft5_gyluuQpBWY?usp=sharing, which this notebook is an abridged version of.\n","\n"],"metadata":{"id":"xiYJKtcOgnm2"}},{"cell_type":"markdown","metadata":{"id":"pF2B17dkMZ2h"},"source":["Pytorch and its key features\n","===================\n","\n","Pytorch code is short, and a great deal happens per line. Consider:\n","\n","```\n","torch.nn.functional.cross_entropy(model(images.cuda()), labels.cuda()).backward()\n","optimizer.step()\n","```\n","\n","Those two lines move a batch onto the GPU, run the network, record how the loss depends on every parameter, compute a gradient for each one, and update all of them — without the CPU ever seeing a number. That is deliberate: a few python instructions load a lot of work onto the GPU, which is why a slow interpreter can still drive fast training.\n","\n","Knowing what the runtime is doing on your behalf is most of what it takes to read and write pytorch, so that is what the next five topics are about: **tensors**, **autograd**, **optimizers**, **modules**, and **data**."]},{"cell_type":"code","source":["import math\n","import numpy as np\n","import torch\n","from matplotlib import pyplot as plt"],"metadata":{"id":"_rAo2rkTjFeR"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Y9mohso4MZ2i"},"source":["Topic 1: Pytorch Tensors\n","===============\n","\n","A [`torch.Tensor`](https://pytorch.org/docs/stable/tensors.html) holds a d-dimensional array of numbers and is a deliberate lookalike for [`numpy.ndarray`](https://numpy.org/doc/stable/reference/arrays.ndarray.html): almost every numpy operation has a torch equivalent.\n","\n","**Two things tensors have that numpy arrays lack:**\n","\n"," 1. They can live on **GPU or CPU** (numpy is CPU-only).\n"," 2. They can **track their own computation history**, which is what makes automatic differentiation possible (Topic 2)."]},{"cell_type":"markdown","metadata":{"id":"QTGPPKm-MZ2i"},"source":["## 1.1 What is a tensor?\n","\n","Every torch tensor can be thought of as a (potentially nested) array of numbers. We initially consider 3 major properties: data type, shape, and device. Later, we'll see tensors' ability to track their gradients and computation graphs."]},{"cell_type":"markdown","metadata":{"id":"WpLUE2HYMZ2i"},"source":["### Creating a tensor from data\n","\n","`torch.tensor(...)` builds a tensor from Python lists and infers the dtype from what you gave it: whole numbers become `int64`, decimals become `float32`. Pass `dtype=` to override that. `torch.from_numpy(...)` wraps an existing numpy array instead."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9CrrGbP3MZ2i","executionInfo":{"status":"ok","timestamp":1789682869006,"user_tz":240,"elapsed":113,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"6204dfdd-8631-463b-dfd6-56ab390e32d2"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[1, 2],\n","        [3, 4]]) torch.int64\n","tensor([[1., 2.],\n","        [3., 4.]]) torch.float32\n","tensor([[1., 2.],\n","        [3., 4.]], dtype=torch.float64)\n"]}],"source":["t = torch.tensor([[1, 2], [3, 4]])\n","print(t, t.dtype)\n","\n","t = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32)\n","print(t, t.dtype)\n","\n","print(torch.from_numpy(np.array([[1., 2.], [3., 4.]])))"]},{"cell_type":"markdown","metadata":{"id":"kNFCbpYBMZ2i"},"source":["### Creating a tensor from a shape\n","\n","Suppose you do not have the data yet and just want a tensor of a given size: `zeros`, `ones`, and `randn` (samples from a standard normal) all take a shape.\n","The PyTorch `shape` class subclasses tuple! The `_like` variants take a tensor instead and copy its shape and dtype. `arange` and `linspace` are the two range constructors you will reach for constantly."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wNFmNZUCMZ2j","executionInfo":{"status":"ok","timestamp":1789682913285,"user_tz":240,"elapsed":37,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"9c534721-879a-4ecc-f124-a82a2b046aca"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[0., 0., 0.],\n","        [0., 0., 0.]])\n","tensor([[1., 1., 1.],\n","        [1., 1., 1.]])\n","tensor([[-1.0225,  0.3101,  2.4418],\n","        [-0.5027, -0.1924,  0.2084]])\n","tensor([0, 1, 2, 3, 4])\n","tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])\n"]}],"source":["print(torch.zeros(2, 3))\n","print(torch.ones(2, 3))\n","print(torch.randn(2, 3))\n","\n","print(torch.arange(5))\n","print(torch.linspace(0, 1, 5))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1cA5GNuqMZ2j","executionInfo":{"status":"ok","timestamp":1789683193422,"user_tz":240,"elapsed":5,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"db85c25e-9823-451b-ac16-1674ff3c34ad"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([4, 5])\n","torch.Size([4, 5])\n","torch.Size([4, 5])\n"]}],"source":["x = torch.randn(4, 5)\n","\n","print(torch.zeros_like(x).shape)\n","print(torch.ones_like(x).shape)\n","print(torch.randn_like(x).shape)"]},{"cell_type":"markdown","metadata":{"id":"hi1TSDHrMZ2j"},"source":["### Copying a tensor\n","\n","`y = x` aliases the tensor x. `x.clone()` makes an independent copy, and it stays part of the autograd graph (see later), so cloning a parameter does not sever it from its gradients."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xu1idpeTMZ2j","executionInfo":{"status":"ok","timestamp":1789683189925,"user_tz":240,"elapsed":7,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"92ad7904-cf7e-47e7-a4f2-aec8e9245f08"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([99.,  1.,  1.])\n","tensor([1., 1., 1.])\n"]}],"source":["x = torch.ones(3)\n","y = x\n","z = x.clone()\n","\n","x[0] = 99\n","print(y)\n","print(z)"]},{"cell_type":"markdown","metadata":{"id":"r6aI-RTDMZ2j"},"source":["### The three attributes every tensor has\n","\n","**`shape`** is the size along each dimension, **`dtype`** is the number format, and **`device`** is where the memory lives. These three are what you print when something goes wrong, which is most of the time."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KLtZr2XrMZ2j","executionInfo":{"status":"ok","timestamp":1789683223896,"user_tz":240,"elapsed":54,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"f9568ac5-fc8d-449d-cd92-a0ab23784d9b"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([2, 3]) torch.float32 cpu\n","torch.Size([3]) torch.int64 cpu\n"]}],"source":["x = torch.tensor([[1., 2., 3.],\n","                  [4., 5., 6.]])\n","print(x.shape, x.dtype, x.device)\n","\n","y = torch.tensor([1, 2, 3])\n","print(y.shape, y.dtype, y.device)"]},{"cell_type":"markdown","metadata":{"id":"MqRKxdL0MZ2j"},"source":["### Changing the device\n","\n","`x.to(device)` returns a **copy** of the tensor on that device, so remember to assign the result. Two tensors must be on the same device to be combined with each other, and a tensor must be back on the CPU before numpy will touch it."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y83QRBLUMZ2j","executionInfo":{"status":"ok","timestamp":1789683265414,"user_tz":240,"elapsed":264,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"ddb3ad3e-9ba4-4656-de21-4e7de416aa82"},"outputs":[{"output_type":"stream","name":"stdout","text":["cuda\n","cuda:0\n","[[ 0.37361437  1.1608425  -0.3499447 ]\n"," [-0.49153587  1.7153903  -1.0208201 ]]\n"]}],"source":["device = 'cuda' if torch.cuda.is_available() else 'cpu'\n","print(device)\n","\n","x = torch.randn(2, 3).to(device)\n","print(x.device)\n","\n","print(x.cpu().numpy())"]},{"cell_type":"markdown","metadata":{"id":"Z47Q999-MZ2j"},"source":["*We can now make a tensor of any shape and dtype, and put it on any device.*"]},{"cell_type":"markdown","metadata":{"id":"gvKObwh8MZ2j"},"source":["## 1.2 Math on tensors\n","\n","Math on tensors is elementwise."]},{"cell_type":"markdown","metadata":{"id":"a_L6bolvMZ2j"},"source":["### Math is elementwise by default\n","\n","Arithmetic and most methods run on every element independently and hand back a tensor of the same shape."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XO6KnZQfMZ2j","executionInfo":{"status":"ok","timestamp":1789683428160,"user_tz":240,"elapsed":63,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"f64dacb6-224a-4392-bbde-6d0ad6fe2f83"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([11., 22., 33.])\n","tensor([10., 40., 90.])\n","tensor([1., 4., 9.])\n","tensor([0.8415, 0.9093, 0.1411])\n","tensor([11., 22., 33.])\n"]}],"source":["x = torch.tensor([1., 2., 3.])\n","y = torch.tensor([10., 20., 30.])\n","\n","print(x + y)\n","print(x * y)\n","print(x.pow(2))\n","print(x.sin())\n","\n","x += y\n","print(x)"]},{"cell_type":"markdown","metadata":{"id":"lH_IqyQdMZ2j"},"source":["### `*` is elementwise, `@` is matrix multiplication\n","\n","`A @ B` (equivalently `A.matmul(B)`) contracts the last dimension of `A` against the first of `B`, so `(2, 3) @ (3, 4)` gives `(2, 4)`. `A * A` multiplies corresponding entries and keeps the shape it started with.\n","\n","With more than two dimensions, `@` uses the **last two** as the matrix and treats every leading dimension as a batch to loop over. 'Elementwise' if you think of the last two dims as the one datapoint/element. So `(2, 3, 4) @ (2, 4, 5)` is two independent `(3, 4) @ (4, 5)` products, stacked back into `(2, 3, 5)`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O5GLWOcMMZ2j","executionInfo":{"status":"ok","timestamp":1789683457070,"user_tz":240,"elapsed":43,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"9689b201-a9cd-4eae-cc0f-c17890d95fdd"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([3, 5])\n","torch.Size([3, 4])\n","torch.Size([2, 3, 5])\n"]}],"source":["A = torch.randn(3, 4)\n","B = torch.randn(4, 5)\n","\n","print((A @ B).shape)\n","print((A * A).shape)\n","\n","A = torch.randn(2, 3, 4)\n","B = torch.randn(2, 4, 5)\n","\n","print((A @ B).shape)"]},{"cell_type":"markdown","metadata":{"id":"-X-PIP7NMZ2k"},"source":["### Operations copy, unless they end in an underscore\n","\n","`x.mul(2)` leaves `x` alone and returns a new tensor. `x.mul_(2)` overwrites `x` in place. Every in-place variant ends in `_`, and you will see this again with `zero_()` when we start clearing gradients."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OqMTkWJgMZ2k","executionInfo":{"status":"ok","timestamp":1789683590349,"user_tz":240,"elapsed":17,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"78c6da01-d0c0-4cf1-a7dd-83a85890e872"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([1., 1., 1.]) tensor([2., 2., 2.])\n","tensor([2., 2., 2.])\n"]}],"source":["x = torch.ones(3)\n","\n","y = x.mul(2)\n","print(x, y)\n","\n","x.mul_(2)\n","print(x)"]},{"cell_type":"markdown","metadata":{"id":"GB-hCx7KMZ2k"},"source":["### `.item()` gets a plain Python number out\n","\n","A one-element tensor is still a tensor, carrying a dtype, a device, and (later) a computation history. `.item()` gives you an ordinary Python float, which is what you want for printing a loss or appending it to a list."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uJJRd537MZ2k","executionInfo":{"status":"ok","timestamp":1789683695990,"user_tz":240,"elapsed":39,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"cc34bdce-da16-47b4-aa0f-9c0c23e90e3b"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor(-0.0307)\n","-0.030675183981657028\n"]}],"source":["loss = torch.randn(4, 4).mean()\n","\n","print(loss)\n","print(loss.item())"]},{"cell_type":"markdown","metadata":{"id":"Xu_8vDKrMZ2k"},"source":["*We can now do elementwise math and matrix products, in place or out of place.*"]},{"cell_type":"markdown","metadata":{"id":"sLw47COXMZ2k"},"source":["## 1.3 Playing with dimensions\n","\n","We can select, combine, and summarize data along whichever dimension we want."]},{"cell_type":"markdown","metadata":{"id":"G1pzhv-_MZ2k"},"source":["### Indexing and slicing\n","\n","Exactly like numpy, including assigning into a slice."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9E59kYteMZ2k","executionInfo":{"status":"ok","timestamp":1789683824037,"user_tz":240,"elapsed":27,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"74915a23-1863-46d8-c99f-39dba1b497ef"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([0., 1., 2., 3.])\n","tensor([0., 4., 8.])\n","tensor(6.)\n","tensor([[ 0.,  0.,  2.,  3.],\n","        [ 4.,  0.,  6.,  7.],\n","        [ 8.,  0., 10., 11.]])\n"]}],"source":["x = torch.tensor([[ 0.,  1.,  2.,  3.],\n","                  [ 4.,  5.,  6.,  7.],\n","                  [ 8.,  9., 10., 11.]])\n","\n","print(x[0])\n","print(x[:, 0])\n","print(x[1, 2])\n","\n","x[:, 1] = 0\n","print(x)"]},{"cell_type":"markdown","source":["### Boolean Indexing\n","\n","Also like numpy/pandas! Remember masks are tensors, not lists.\n","torch.where(mask, x, y) takes the value of x when mask is True and y where mask\n","is False."],"metadata":{"id":"XI_l4Nn_a3te"}},{"cell_type":"code","source":["x = torch.tensor([[ 0.,  1.,  2.,  3.],\n","                  [ 4.,  5.,  6.,  7.],\n","                  [ 8.,  9., 10., 11.]])\n","\n","mask = (x % 2) == 0\n","print(mask)\n","print(x[mask])\n","\n","print(torch.where(mask, x*x, x))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6ZsBG3_QZTWE","executionInfo":{"status":"ok","timestamp":1789683844070,"user_tz":240,"elapsed":14,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"87e92640-6a42-43c5-aa73-b6cacf1b487e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[ True, False,  True, False],\n","        [ True, False,  True, False],\n","        [ True, False,  True, False]])\n","tensor([ 0.,  2.,  4.,  6.,  8., 10.])\n","tensor([[  0.,   1.,   4.,   3.],\n","        [ 16.,   5.,  36.,   7.],\n","        [ 64.,   9., 100.,  11.]])\n"]}]},{"cell_type":"markdown","metadata":{"id":"fIHUYPFgMZ2k"},"source":["### Joining tensors: `cat` versus `stack`\n","\n","`torch.cat` glues tensors together along a dimension that already exists, so two `(2, 3)` tensors become `(4, 3)` along `dim=0` or `(2, 6)` along `dim=1`. `torch.stack` instead creates a new dimension to hold them, giving `(2, 2, 3)` — this is how you assemble separate examples into a batch."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rSxyVLJ2MZ2k","executionInfo":{"status":"ok","timestamp":1789684020987,"user_tz":240,"elapsed":7,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"fe9d5fdb-c55f-4526-d662-107b8b95b52f"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([4, 3])\n","torch.Size([2, 6])\n","torch.Size([2, 2, 3])\n"]}],"source":["a = torch.zeros(2, 3)\n","b = torch.ones(2, 3)\n","\n","print(torch.cat([a, b], dim=0).shape)\n","print(torch.cat([a, b], dim=1).shape)\n","print(torch.stack([a, b], dim=0).shape)"]},{"cell_type":"markdown","metadata":{"id":"DnFpAeWkMZ2k"},"source":["### Reductions collapse a dimension\n","\n","`sum`, `mean`, `std`, `min`, `max`, and `norm` with no arguments reduce the whole tensor to a single number. With `dim=n` they reduce only along that dimension, which then disappears from the shape: summing a `(2, 3)` tensor along `dim=1` leaves `(2,)`. Passing a tuple, `dim=(0, 1)`, reduces over several dimensions at once.\n","\n","Passing `keepdim=True` leaves the reduced dimension in place with size 1, giving `(2, 1)` instead. That is what lets the result line back up against the tensor it came from, which we use in the walkthrough."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WeEYtqupMZ2k","executionInfo":{"status":"ok","timestamp":1789684059835,"user_tz":240,"elapsed":44,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"7c5c7fbc-7394-4309-bb68-6994cff95da2"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor(21.)\n","tensor([5., 7., 9.])\n","tensor([ 6., 15.])\n","tensor(21.)\n","torch.Size([2, 1])\n"]}],"source":["x = torch.tensor([[1., 2., 3.],\n","                  [4., 5., 6.]])\n","\n","print(x.sum())\n","print(x.sum(dim=0))\n","print(x.sum(dim=1))\n","print(x.sum(dim=(0, 1)))\n","print(x.sum(dim=1, keepdim=True).shape)"]},{"cell_type":"markdown","metadata":{"id":"DtJWydgzMZ2k"},"source":["### `max` and `min` also tell you *where*\n","\n","Given a `dim`, they return two tensors rather than one: the values and the indices those values came from. No need for seperate argmax calls!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CPnKpMmNMZ2k","executionInfo":{"status":"ok","timestamp":1789684086901,"user_tz":240,"elapsed":18,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"ec3acbe0-90ff-4f6d-ddc4-60be8d572b21"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([9., 6.])\n","tensor([1, 2])\n"]}],"source":["x = torch.tensor([[1., 9., 3.],\n","                  [4., 5., 6.]])\n","\n","values, indices = x.max(dim=1)\n","print(values)\n","print(indices)"]},{"cell_type":"markdown","metadata":{"id":"XEsobqZBMZ2k"},"source":["*We can now pull out any slice of a tensor, combine tensors, and summarize along any dimension.*"]},{"cell_type":"markdown","metadata":{"id":"x7Jijs3YMZ2k"},"source":["## 1.4 Conventions in dimensions\n","\n","We use conventions to line dimensions up, often implicitly."]},{"cell_type":"markdown","metadata":{"id":"3NCCqBFcMZ2k"},"source":["### The big 4D objects\n","\n","**Data** is ordered `[batch, channel, y, x]`. Data without grid geometry drops the trailing dimensions, and 3d grid data adds a `z` before `y`.\n","\n","**Weights** are ordered `[out_features, in_features, kernel_y, kernel_x]`, output first — so a `Linear(10, 5)` layer stores a `(5, 10)` weight matrix.\n","\n","Data puts the *batch* in dimension 0 while weights put *output features* in dimension 0. That mismatch is why you will see a transpose in front of so many matrix multiplies: `W.T` swaps the last two dimensions, turning the `(5, 10)` weight into the `(10, 5)` matrix that a `(64, 10)` batch can actually be multiplied by."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ocHBbY9hMZ2l","executionInfo":{"status":"ok","timestamp":1789684156501,"user_tz":240,"elapsed":18,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"7e28aefd-a87c-4261-f047-5885fa54ad4e"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([64, 3, 32, 32])\n","torch.Size([5, 10])\n","torch.Size([64, 5])\n"]}],"source":["images = torch.randn(64, 3, 32, 32)\n","print(images.shape)\n","\n","layer = torch.nn.Linear(10, 5)\n","print(layer.weight.shape)\n","\n","x = torch.randn(64, 10)\n","print((x @ layer.weight.T).shape)"]},{"cell_type":"markdown","metadata":{"id":"F87v4_gXMZ2l"},"source":["### Rearranging: `view`, `reshape`, and `permute`\n","\n","`view` regroups the same numbers into a new shape and `permute` reorders the dimensions. Neither one moves any data — they only change how the underlying block of memory is read — so both are cheap. A `-1` in a shape means \"infer this one from the number of elements.\"\n","\n","Some permute-then-flatten combinations cannot be expressed as a view of the original memory. `reshape` handles those by copying when it has to, which makes it the safer default. Generally, use `reshape`!\n","\n","`unsqueeze(n)` and `squeeze(n)` are the special case of adding and removing a dimension of size 1. Since pytorch models always expect a batch dimension, this is how you hand one a single example, and how you strip the leftover dimension off afterwards."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IzMYEt3kMZ2l","executionInfo":{"status":"ok","timestamp":1789684178997,"user_tz":240,"elapsed":43,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"8ae58fdf-5c22-4fba-8382-27326433e5b1"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([2, 12])\n","torch.Size([2, 12])\n","torch.Size([2, 4, 3])\n","torch.Size([2, 12])\n","torch.Size([1, 2, 3, 4])\n","torch.Size([2, 3, 4])\n"]}],"source":["x = torch.randn(2, 3, 4)\n","\n","print(x.view(2, 12).shape)\n","print(x.view(2, -1).shape)\n","print(x.permute(0, 2, 1).shape)\n","print(x.reshape(2, -1).shape)\n","\n","print(x.unsqueeze(0).shape)\n","print(x.unsqueeze(0).squeeze(0).shape)"]},{"cell_type":"markdown","metadata":{"id":"UiiSvF5dMZ2l"},"source":["### Broadcasting\n","\n","Shapes that do not match can still be combined. Line the two shapes up **from the right** and read down each column: equal is fine, a `1` stretches to match the other side, and a dimension that is not there counts as `1`. Anything else is an error.\n","\n","Nothing is copied — a stretched dimension just reads the same memory over and over. [Full rules here](https://pytorch.org/docs/stable/notes/broadcasting.html)."]},{"cell_type":"code","source":["batch = torch.randn(100, 4)\n","mean = batch.mean(dim=0)\n","\n","# batch   (100, 4)\n","# mean         (4,)   missing dimension counts as 1, so it stretches to 100\n","print((batch - mean).shape)          # (100, 4)\n","\n","\n","images = torch.randn(16, 3, 8, 8)\n","W = torch.randn(8, 800)\n","\n","# images  (16, 3,  8,  8)   last two dims are the matrix\n","# W       (    2   8, 12)   the rest broadcasts: 3 vs nothing -> 3, 16 vs nothing -> 16\n","print((images @ W).shape)            # (16, 3, 8, 12)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rcQaU739l9Rf","executionInfo":{"status":"ok","timestamp":1789684423468,"user_tz":240,"elapsed":15,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"43d48051-e8ca-4c6c-b90a-98af025c5584"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([100, 4])\n","torch.Size([16, 3, 8, 800])\n"]}]},{"cell_type":"markdown","metadata":{"id":"R22TkXDbMZ2l"},"source":["*We can now get any tensor into the shape the next operation expects.*"]},{"cell_type":"markdown","metadata":{"id":"SzAFd6SoMZ2l"},"source":["## Walkthrough: a forward pass with nothing but tensors\n","\n","Here is a batch of images running through one linear layer, using only the operations above and nothing else. `torch.nn` will do all of this for us later, but it is worth seeing once without the abstraction in the way. Watch the shapes at each step."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rRESuZp2MZ2l","executionInfo":{"status":"ok","timestamp":1789684528548,"user_tz":240,"elapsed":44,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"d8a08a21-25b1-4678-b474-dbd40688ec9b"},"outputs":[{"output_type":"stream","name":"stdout","text":["images:  (16, 3, 8, 8)\n","mean:    (1, 3, 1, 1) broadcast against (16, 3, 8, 8)\n","flat:    (16, 192)\n","logits:  (16, 10)\n","predicted classes: tensor([4, 6, 2, 5, 8, 9, 9, 8, 3, 4, 9, 9, 9, 3, 8, 1], device='cuda:0')\n","each row sums to 1: tensor([1., 1., 1.], device='cuda:0')\n"]}],"source":["torch.manual_seed(0)\n","device = 'cuda' if torch.cuda.is_available() else 'cpu'\n","\n","# A batch of 16 RGB 8x8 \"images\", in the standard [batch, channel, y, x] order\n","images = torch.randn(16, 3, 8, 8).to(device)\n","print(f'images:  {tuple(images.shape)}')\n","\n","# 1. Normalize each channel: average over batch and space, but not over channels\n","mean = images.mean(dim=(0, 2, 3), keepdim=True)\n","std = images.std(dim=(0, 2, 3), keepdim=True)\n","images = (images - mean) / std\n","print(f'mean:    {tuple(mean.shape)} broadcast against {tuple(images.shape)}')\n","\n","# 2. Flatten each image into one long vector\n","flat = images.reshape(images.shape[0], -1)\n","print(f'flat:    {tuple(flat.shape)}')\n","\n","# 3. One linear layer by hand, with the weight stored as [out_features, in_features]\n","W = torch.randn(10, 192).to(device) * 0.01\n","b = torch.zeros(10).to(device)\n","logits = flat @ W.T + b\n","print(f'logits:  {tuple(logits.shape)}')\n","\n","# 4. Scores into predictions\n","probs = logits.softmax(dim=1)\n","confidence, predicted = probs.max(dim=1)\n","print(f'predicted classes: {predicted}')\n","print(f'each row sums to 1: {probs.sum(dim=1)[:3]}')"]},{"cell_type":"markdown","metadata":{"id":"vomimfRlMZ2l"},"source":["Topic 2: Autograd\n","================\n","\n","Deep learning is just repeated gradient descent, so the whole library rests on being able to differentiate anything you can write. Pytorch does this by **recording every operation as you run it** and then replaying that record backwards.\n","\n","<img src=\"https://raw.githubusercontent.com/davidbau/how-to-read-pytorch/6ce891301e79aa8e2164a703c08257a52b2d1ad3/notebooks/autograd-graph.png\" style=\"max-width:100%\">"]},{"cell_type":"markdown","metadata":{"id":"PcTNgE-pMZ2l"},"source":["## 2.1 Tracking a computation\n","\n","A tensor can remember where it came from."]},{"cell_type":"markdown","metadata":{"id":"zP35ZamJMZ2l"},"source":["### `requires_grad` turns tracking on\n","\n","Flag a tensor with `requires_grad=True` and pytorch starts recording every operation that touches it. An existing tensor can be flagged after the fact with `x.requires_grad_(True)` — the underscore rule again. Anything computed from a tracked tensor comes back with a `grad_fn`, the record of how it was made and the node that knows how to reverse this step."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aW-gXSfAMZ2l","executionInfo":{"status":"ok","timestamp":1789672584980,"user_tz":240,"elapsed":44,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"c64877f4-3bdb-40dd-caa9-4b8e0f84d4a9"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor(4., grad_fn=<SumBackward0>)\n","<SumBackward0 object at 0x7e6da2ab2440>\n"]}],"source":["x = torch.tensor([2.0], requires_grad=True)\n","y = (x ** 2).sum()\n","\n","print(y)\n","print(y.grad_fn)"]},{"cell_type":"markdown","metadata":{"id":"CBiOaGIEMZ2l"},"source":["### `torch.autograd.grad` computes the derivative\n","\n","`torch.autograd.grad(output, [inputs])` walks that record backwards and returns d(output)/d(input) for each input you asked about. The output has to be a **single number**, which is why you will see `.sum()` everywhere: for a function applied elementwise, the derivative of the sum with respect to each entry is just that entry's own derivative."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PRWwspqIMZ2l","executionInfo":{"status":"ok","timestamp":1789684740849,"user_tz":240,"elapsed":5,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"1d520ba2-bd8d-4a27-a8cf-16c273b5dc6b"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([100])\n","tensor([-0.0000, -0.0003, -0.0021, -0.0070])\n"]}],"source":["x = torch.linspace(0, 5, 100, requires_grad=True)\n","y = (x ** 2).cos()\n","\n","[dydx] = torch.autograd.grad(y.sum(), [x])\n","print(dydx.shape)\n","print(dydx[:4])"]},{"cell_type":"markdown","metadata":{"id":"4n18xddeMZ2m"},"source":["### Multiple Derivatives on the Same Graph\n","\n","Ask for `df/dx` and `df/dy` separately and the second call fails: the recorded graph is freed as soon as it has been walked once, since holding it would waste memory in the common case. `retain_graph=True` keeps it alive for another pass.\n","\n","Note the best approach here is to just do `torch.autograd.grad(f, [x, y])`."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QMS9khSvMZ2m","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1789684947881,"user_tz":240,"elapsed":10,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"cb4f2eb4-7f43-4845-f917-d51e7e51208a"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([12.]) tensor([4.])\n"]}],"source":["x = torch.tensor([2.0], requires_grad=True)\n","y = torch.tensor([3.0], requires_grad=True)\n","f = ((x ** 2) * y).sum()\n","\n","[dfdx, dfdy] = torch.autograd.grad(f, [x, y])\n","\n","print(dfdx, dfdy)"]},{"cell_type":"markdown","metadata":{"id":"AB115xyMMZ2m"},"source":["### `detach` steps back out of the graph\n","\n","A tensor that requires grad refuses to convert to numpy, because that conversion would leave the graph without pytorch noticing. `x.detach()` gives you the same numbers with the history stripped off, which is what you want before plotting or logging."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PH_Mcy6TMZ2m","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1789684955056,"user_tz":240,"elapsed":6,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"f6574525-4128-4c54-ea08-0638e46c1ec8"},"outputs":[{"output_type":"stream","name":"stdout","text":["True\n","False\n"]}],"source":["x = torch.linspace(0, 1, 5, requires_grad=True)\n","y = x * 2\n","\n","print(y.requires_grad)\n","print(y.detach().requires_grad)"]},{"cell_type":"markdown","metadata":{"id":"2wYZWnk6MZ2m"},"source":["*We can now ask for the derivative of any scalar with respect to any tensor it was built from.*"]},{"cell_type":"markdown","metadata":{"id":"lgUCTQICMZ2m"},"source":["## 2.2 Backpropagation\n","\n","Real networks have tons of parameters, so keeping track of everything would be a massive pain. It's best for gradients to be delivered to the parameters themselves."]},{"cell_type":"markdown","metadata":{"id":"m6DvvTMcMZ2m"},"source":["### `.backward()` puts gradients in `.grad`\n","\n","Catching return values works for two inputs, not for a network with three hundred parameter tensors. Since a gradient has exactly the same shape as the thing it differentiates, pytorch hangs it on that tensor instead: `loss.backward()` runs backprop over the whole graph and fills in `.grad` on every tensor marked `requires_grad=True`.\n","\n","This is the call you will actually use. `torch.autograd.grad` is for when you want a gradient as a value — higher-order derivatives, or a gradient penalty — rather than accumulated onto parameters."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wZmKRgX0MZ2m","executionInfo":{"status":"ok","timestamp":1789684980539,"user_tz":240,"elapsed":13,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"8e0c36a0-e1aa-44e9-8a0f-d95b46b7d73e"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([6.])\n"]}],"source":["x = torch.tensor([3.0], requires_grad=True)\n","y = (x ** 2).sum()\n","y.backward()\n","\n","print(x.grad)"]},{"cell_type":"markdown","metadata":{"id":"e4OfzM_cMZ2m"},"source":["### Gradients accumulate, so clear them\n","\n","Calling `.backward()` again **adds** to whatever is already in `.grad` rather than replacing it. That is deliberate — it lets you split a batch too large for memory into pieces and sum their gradients — but a forgotten reset silently corrupts training.\n","\n","`x.grad.zero_()` resets in place (there is that underscore again). In practice you will call `optimizer.zero_grad()`, which does this for every parameter at once, and we will see it in the next topic."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FNDP8Hm0MZ2m","executionInfo":{"status":"ok","timestamp":1789685012189,"user_tz":240,"elapsed":14,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"c0933d8c-b831-4946-ec60-25aae5ab411e"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([6.])\n","tensor([12.])\n","tensor([6.])\n"]}],"source":["x = torch.tensor([3.0], requires_grad=True)\n","\n","(x ** 2).sum().backward()\n","print(x.grad)\n","\n","(x ** 2).sum().backward()\n","print(x.grad)\n","\n","x.grad.zero_()\n","(x ** 2).sum().backward()\n","print(x.grad)"]},{"cell_type":"markdown","metadata":{"id":"HPlQbDFkMZ2m"},"source":["### `torch.no_grad()` when you are not training\n","\n","Every tracked operation holds onto intermediate tensors so they can be reused on the backward pass, which costs real GPU memory. If you are only running the network to look at its predictions, wrap the call in `with torch.no_grad():` and none of that is kept.\n","\n","This is about *memory and speed*, not about network behavior. The related `net.eval()` — which changes how dropout and batchnorm act — is a separate switch, covered in Topic 4."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K6JJljChMZ2m","executionInfo":{"status":"ok","timestamp":1789685072266,"user_tz":240,"elapsed":42,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"8b5d4c70-2407-418e-f07a-0f0afdd387df"},"outputs":[{"output_type":"stream","name":"stdout","text":["False\n"]}],"source":["x = torch.tensor([3.0], requires_grad=True)\n","\n","with torch.no_grad():\n","    y = (x ** 2).sum()\n","\n","print(y.requires_grad)"]},{"cell_type":"markdown","metadata":{"id":"2Az3Ds5KMZ2m"},"source":["*We can now get gradients for every input at once, and control when they accumulate.*"]},{"cell_type":"markdown","metadata":{"id":"JFVGy3EcMZ2m"},"source":["## Walkthrough: derivatives we never had to write down\n","\n","First the one-dimensional case, where we can check the answer by eye: plot `cos(x²)` next to the gradient autograd computed for it."]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"3FFpukT8MZ2m"},"outputs":[],"source":["#@title Run this cell to set up the plotting helper for the 2-D example\n","def plot_2d(Z, dZdx, dZdy, extent=(-3, 3, -3, 3)):\n","    fig, axes = plt.subplots(1, 3, figsize=(13, 3.5))\n","    panels = [(Z, 'f(x, y)'), (dZdx, 'df/dx'), (dZdy, 'df/dy')]\n","    for ax, (data, title) in zip(axes, panels):\n","        im = ax.imshow(data.detach().cpu(), extent=extent, origin='lower', cmap='RdBu')\n","        ax.set_title(title)\n","        ax.set_xlabel('x')\n","        ax.set_ylabel('y')\n","        fig.colorbar(im, ax=ax)\n","    plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"prBNcVldMZ2m","executionInfo":{"status":"ok","timestamp":1789685141022,"user_tz":240,"elapsed":294,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"565e058c-a8c7-4ccd-90c9-dc6fb6a54483"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["x = torch.linspace(0, 5, 200, requires_grad=True)\n","y = (x ** 2).cos()\n","\n","y.sum().backward()\n","\n","plt.plot(x.detach(), y.detach(), label='f(x) = cos(x^2)')\n","plt.plot(x.detach(), x.grad, label='df/dx')\n","plt.legend()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"1doAze46MZ2m"},"source":["Now the same thing on a function of **two** inputs, `f(x, y) = sin(x)·cos(y)`, evaluated on a grid. One `backward()` call fills in `X.grad` and `Y.grad` — two different partial derivatives, each landing on the tensor it belongs to.\n","\n","Nothing here is named `df_dx` or `df_dy`, and there is no `retain_graph` — one `backward()` call filled in both. With a hundred parameters instead of two, the bookkeeping does not change at all. (`torch.meshgrid` just builds the two coordinate grids to evaluate on.)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":356},"id":"RD1Vi1IQMZ2m","executionInfo":{"status":"ok","timestamp":1789685178641,"user_tz":240,"elapsed":581,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"f715120b-1b79-4dfd-9891-ed934ae86c61"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([60, 60]) torch.Size([60, 60])\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1300x350 with 6 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["grid = torch.linspace(-3, 3, 60)\n","X, Y = torch.meshgrid(grid, grid, indexing='xy')\n","X = X.clone().requires_grad_(True)\n","Y = Y.clone().requires_grad_(True)\n","\n","Z = X.sin() * Y.cos()\n","Z.sum().backward()\n","\n","print(X.grad.shape, Y.grad.shape)\n","plot_2d(Z, X.grad, Y.grad)"]},{"cell_type":"markdown","metadata":{"id":"vff_0PKgMZ2n"},"source":["Topic 3: Optimizers\n","================\n","\n","You already know what SGD and Adam do. This topic is only about what they look like in pytorch, and what they are doing on your behalf."]},{"cell_type":"markdown","metadata":{"id":"MN-61QgxMZ2n"},"source":["## 3.1 Optimizers\n","\n","An optimizer is a rule for turning `.grad` into a parameter update."]},{"cell_type":"markdown","metadata":{"id":"HJbXYVSGMZ2n"},"source":["### Creating one, and the three lines you will write forever\n","\n","An optimizer is constructed with the list of tensors it is allowed to change, plus its hyperparameters. It holds references to those exact tensors, so updating them is something it does in place.\n","\n","Every training loop then contains the same three calls, in this order: clear the old gradients, run backward to fill in new ones, let the optimizer apply them. Getting the order wrong, or dropping `zero_grad`, is the most common bug in a first training loop."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pu1q9GjjMZ2n","executionInfo":{"status":"ok","timestamp":1789685283749,"user_tz":240,"elapsed":5,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"5fad5f89-04cf-4724-ef67-42fac10267cc"},"outputs":[{"output_type":"stream","name":"stdout","text":["SGD (\n","Parameter Group 0\n","    dampening: 0\n","    differentiable: False\n","    foreach: None\n","    fused: None\n","    lr: 0.1\n","    maximize: False\n","    momentum: 0.9\n","    nesterov: False\n","    weight_decay: 0\n",")\n","tensor([10., -6.])\n","tensor([ 4.0000, -2.4000], requires_grad=True)\n"]}],"source":["x = torch.tensor([5.0, -3.0], requires_grad=True)\n","optimizer = torch.optim.SGD([x], lr=0.1, momentum=0.9)\n","print(optimizer)\n","\n","loss = (x ** 2).sum()\n","\n","optimizer.zero_grad()\n","loss.backward()\n","print(x.grad)\n","optimizer.step()\n","\n","print(x)"]},{"cell_type":"markdown","metadata":{"id":"6pGQU-TFMZ2n"},"source":["### Adam is a drop-in replacement\n","\n","Every optimizer in `torch.optim` exposes the same `zero_grad` / `step` interface, so switching algorithms is a one-line change and the loop never moves. Adam adapts a per-parameter step size as it goes and is the usual default; `lr=3e-4` is a reasonable starting point for it."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1FFUMVvNMZ2n","executionInfo":{"status":"ok","timestamp":1789685323657,"user_tz":240,"elapsed":186,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"8aea278c-f52d-4975-f5d1-927f9da46948"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([0.9011, 0.1689], requires_grad=True)\n"]}],"source":["x = torch.tensor([5.0, -3.0], requires_grad=True)\n","optimizer = torch.optim.Adam([x], lr=0.1)\n","\n","for step in range(50):\n","    loss = (x ** 2).sum()\n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","\n","print(x)"]},{"cell_type":"markdown","metadata":{"id":"igRk7c_GMZ2n"},"source":["### Learning rate schedules\n","\n","A scheduler wraps an optimizer and rewrites its learning rate as training goes on. You call `scheduler.step()` once per epoch, after `optimizer.step()`, and read the current rate with `get_last_lr()`.\n","\n","`CosineAnnealingLR` is the cosine decay you have seen in lecture: `T_max` is the number of steps to go from the optimizer's starting `lr` down to `eta_min`. `StepLR`, `LinearLR`, `OneCycleLR` and [the rest](https://pytorch.org/docs/stable/optim.html) all swap in the same way."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"hg3eXNmIMZ2n","executionInfo":{"status":"ok","timestamp":1789685350831,"user_tz":240,"elapsed":133,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"ad13c1f3-c7dc-45b8-ae7e-31e34333b96f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["optimizer = torch.optim.SGD([x], lr=0.1)\n","scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)\n","\n","lrs = []\n","for epoch in range(50):\n","    optimizer.step()\n","    scheduler.step()\n","    lrs.append(scheduler.get_last_lr()[0])\n","\n","plt.plot(lrs)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"nt2JzVU_MZ2n"},"source":["*We can now update parameters with any algorithm in `torch.optim`, and decay the learning rate as training goes on.*"]},{"cell_type":"markdown","metadata":{"id":"CVrGKmowMZ2n"},"source":["## Walkthrough: the same descent, with and without the optimizer\n","\n","`torch.optim.SGD` is convenient, not magic. Here is the same problem solved twice — once by subtracting the gradient by hand, once through the optimizer API — from the same starting point, so you can see they land in the same place.\n","\n","First, by hand. Note the `torch.no_grad()` block: we are changing `x` as data, and we do not want that edit recorded as part of the next graph. The blue ellipses are level sets of the loss, and the marked path is where `x` went."]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"ENKgBK0YMZ2n"},"outputs":[],"source":["#@title Run this cell to set up plot_progress(), which draws the descent path\n","def plot_progress(bowl, track, losses):\n","    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n","    for size in torch.linspace(0.1, 1.0, 10):\n","        angle = torch.linspace(0, 6.3, 100)\n","        circle = torch.stack([angle.sin(), angle.cos()])\n","        ellipse = torch.inverse(bowl) @ circle * size\n","        ax1.plot(ellipse[0, :], ellipse[1, :], color='skyblue')\n","    path = torch.stack(track).t()\n","    ax1.plot(path[0, :], path[1, :], marker='o')\n","    ax1.set_title('path of x')\n","    ax1.set_xlabel('x[0]')\n","    ax1.set_ylabel('x[1]')\n","    ax1.set_xlim(-1.6, 1.6)\n","    ax1.set_ylim(-1, 1)\n","    ax2.plot(range(len(losses)), losses, marker='o')\n","    ax2.set_title('loss')\n","    ax2.set_xlabel('iteration')\n","    plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"id":"zjE26_TtMZ2n","executionInfo":{"status":"ok","timestamp":1789685404009,"user_tz":240,"elapsed":452,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"e04d2a03-5c1d-4095-9783-54252645b44a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1100x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["torch.manual_seed(1)\n","bowl = torch.randn(2, 2) #transforms axes\n","x_init = torch.randn(2)\n","\n","x = x_init.clone().requires_grad_(True)\n","track, losses = [], []\n","\n","for step in range(20):\n","    loss = (bowl @ x).norm()\n","    loss.backward()\n","    with torch.no_grad():\n","        x -= 0.1 * x.grad\n","        x.grad.zero_()\n","    track.append(x.detach().clone())\n","    losses.append(loss.item())\n","\n","plot_progress(bowl, track, losses)"]},{"cell_type":"markdown","metadata":{"id":"a2gFOn5uMZ2n"},"source":["And now the same twenty steps through `torch.optim.SGD`. The loop body is the three lines from earlier, `optimizer.step()` is doing exactly the subtraction we just wrote out, and the path below is identical."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"id":"7YOrcRogMZ2n","executionInfo":{"status":"ok","timestamp":1789685408489,"user_tz":240,"elapsed":571,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"530f1640-03dc-4b46-9a25-3da432f491da"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1100x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["x = x_init.clone().requires_grad_(True)\n","optimizer = torch.optim.SGD([x], lr=0.1)\n","track, losses = [], []\n","\n","for step in range(20):\n","    loss = (bowl @ x).norm()\n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","    track.append(x.detach().clone())\n","    losses.append(loss.item())\n","\n","plot_progress(bowl, track, losses)"]},{"cell_type":"markdown","metadata":{"id":"3QUOL8O4MZ2n"},"source":["Topic 4: Neural Network Modules\n","================\n","\n","A `torch.nn.Module` is a callable object that owns its parameters. That is the whole idea: it bundles some trainable tensors together with the math that uses them, and knows how to list, move, and save them as a unit."]},{"cell_type":"markdown","metadata":{"id":"QbqdSAJ5MZ2n"},"source":["## 4.1 Writing a module\n","\n","Parameters go in `__init__`, math goes in `forward`."]},{"cell_type":"markdown","metadata":{"id":"m9nEvqYpMZ2n"},"source":["### Subclassing `nn.Module`\n","\n","Two rules. Call `super().__init__()` first, or nothing else will work. Wrap trainable tensors in `torch.nn.Parameter`, which marks them `requires_grad=True` and registers them with the module so it can find them later.\n","\n","Printing a module shows its structure, which is handy for checking you built what you meant to."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UJTLAnTHMZ2n","executionInfo":{"status":"ok","timestamp":1789685492986,"user_tz":240,"elapsed":7,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"dffc7fe3-4fed-42e8-fc82-bd645c9e84dd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Scale()\n"]}],"source":["class Scale(torch.nn.Module):\n","    def __init__(self):\n","        super().__init__()\n","        self.weight_cool = torch.nn.Parameter(torch.tensor([2.0]))\n","\n","    def forward(self, x):\n","        return x * self.weight_cool\n","\n","net = Scale()\n","print(net)"]},{"cell_type":"markdown","metadata":{"id":"tbjCAjTnMZ2n"},"source":["### Calling the module runs `forward`\n","\n","You never call `net.forward(x)` yourself. Calling the object runs `forward` for you, with some bookkeeping wrapped around it, and that is the convention every pytorch module follows.\n","\n","Because `forward` is ordinary Python, anything you can write goes in there — a loop, an `if`, or a skip connection like `x = x + self.block(x)`, which is all a residual network is."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zQ8ez23gMZ2o","executionInfo":{"status":"ok","timestamp":1789685494086,"user_tz":240,"elapsed":5,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"0c333c19-ea87-4335-86a1-76eefcaa5d76"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([2., 4., 6.], grad_fn=<MulBackward0>)\n"]}],"source":["x = torch.tensor([1.0, 2.0, 3.0])\n","\n","print(net(x))"]},{"cell_type":"markdown","metadata":{"id":"K1bWYhxuMZ2o"},"source":["### A module can list its own parameters\n","\n","`named_parameters()` walks everything registered on the module, and `parameters()` gives the same tensors without the names — that second one is what you hand to an optimizer. It is also the first place to look when a network will not train: if a tensor is not in this list, nothing will ever update it.\n","\n","On a module built out of other modules, this recurses: the outermost one can see every parameter underneath it, under a fully qualified name like `layer1.weight`. We will see that a few cells down."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QDAx_t1bMZ2o","executionInfo":{"status":"ok","timestamp":1789685496686,"user_tz":240,"elapsed":5,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"01602709-b832-4ae2-bff8-1ae31f46e6b4"},"outputs":[{"output_type":"stream","name":"stdout","text":["weight_cool torch.Size([1]) True\n"]}],"source":["for name, param in net.named_parameters():\n","    print(name, param.shape, param.requires_grad)"]},{"cell_type":"markdown","metadata":{"id":"ME5kZ9ikMZ2o"},"source":["*We can now write a module: parameters in `__init__`, math in `forward`, and call it like a function.*"]},{"cell_type":"markdown","metadata":{"id":"cuVIP9PhMZ2o"},"source":["## 4.2 Modules we do not have to write\n","\n","Almost everything you need already exists in `torch.nn`."]},{"cell_type":"markdown","metadata":{"id":"oyIDthnIMZ2o"},"source":["### Linear layers\n","\n","`torch.nn.Linear(in_features, out_features)` computes `x @ W.T + b`, creating and initializing `W` and `b` for you. It expects a **batch**: dimension 0 is the batch, the last dimension is the features, and anything in between is carried along untouched. The stored weight is `[out, in]`, which is where that transpose comes from."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LocdbffiMZ2o","executionInfo":{"status":"ok","timestamp":1789685548026,"user_tz":240,"elapsed":16,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"d34d5eff-e749-4816-8529-f5fbc0b1962a"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([4, 2])\n","torch.Size([2, 3]) torch.Size([2])\n"]}],"source":["layer = torch.nn.Linear(3, 2)\n","x = torch.randn(4, 3)\n","\n","print(layer(x).shape)\n","print(layer.weight.shape, layer.bias.shape)"]},{"cell_type":"markdown","metadata":{"id":"6K39RTFOMZ2o"},"source":["### Activation functions\n","\n","Activations come in two forms, and you will meet both. `torch.nn.ReLU` is a **module**: you instantiate it and call it, which is what lets it sit inside a `Sequential`. `torch.nn.functional.relu` is the same computation as a plain function, which is what you call inside your own `forward`. Same for `Sigmoid`, `Tanh`, `GELU`, and the rest."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MzT0fZ5IMZ2o","executionInfo":{"status":"ok","timestamp":1789685592731,"user_tz":240,"elapsed":11,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"c0f7f2c4-7176-4140-a35d-6d9f56d90c6a"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([0., 0., 1.])\n","tensor([0., 0., 1.])\n"]}],"source":["import torch.nn.functional as F\n","\n","x = torch.tensor([-2.0, -0.5, 1.0])\n","\n","\n","print(torch.nn.ReLU()(x))\n","print(F.relu(x))"]},{"cell_type":"markdown","metadata":{"id":"BD6aAdkWMZ2o"},"source":["### `Sequential` chains modules together\n","\n","`torch.nn.Sequential` takes a list of modules and runs them in order, feeding each output into the next input. It is itself a module, so it can be nested, called, and trained like any other.\n","\n","Listing its parameters shows the recursion: six tensors, named by their path through the structure. Those names are how you pick out a subset later, to freeze it or to load weights into part of a network."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3W9zMGkyMZ2o","executionInfo":{"status":"ok","timestamp":1789685623723,"user_tz":240,"elapsed":19,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"86746abc-b065-4e27-c9ca-e5816326dd20"},"outputs":[{"output_type":"stream","name":"stdout","text":["Sequential(\n","  (0): Linear(in_features=2, out_features=16, bias=True)\n","  (1): ReLU()\n","  (2): Linear(in_features=16, out_features=2, bias=True)\n",")\n","torch.Size([8, 2])\n","0.weight (16, 2)\n","0.bias (16,)\n","2.weight (2, 16)\n","2.bias (2,)\n"]}],"source":["mlp = torch.nn.Sequential(\n","    torch.nn.Linear(2, 16),\n","    torch.nn.ReLU(),\n","    torch.nn.Linear(16, 2),\n",")\n","\n","print(mlp)\n","print(mlp(torch.randn(8, 2)).shape)\n","\n","for name, param in mlp.named_parameters():\n","    print(name, tuple(param.shape))"]},{"cell_type":"markdown","metadata":{"id":"IJfNfuciMZ2o"},"source":["### Loss functions\n","\n","Losses follow the same module/function split: `torch.nn.CrossEntropyLoss` is the module, `torch.nn.functional.cross_entropy` is the function. Two pytorch-specific details worth knowing: it wants **raw logits**, not probabilities, because the softmax is fused inside for numerical stability; and it wants **integer class indices**, not one-hot vectors. It averages over the batch by default (`reduction='mean'`)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7AiBwf3ZMZ2o","executionInfo":{"status":"ok","timestamp":1789685701690,"user_tz":240,"elapsed":17,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"47a942d6-07f8-4fbf-9f43-6b8b832e88f9"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor(0.2444)\n"]}],"source":["logits = torch.tensor([[2.0, 0.5], [0.1, 1.2]])\n","labels = torch.tensor([0, 1])\n","\n","print(F.cross_entropy(logits, labels))"]},{"cell_type":"markdown","metadata":{"id":"eez1ZBcYMZ2o"},"source":["### Housekeeping: devices, checkpoints, and modes\n","\n","`net.to(device)` moves every parameter at once, and unlike `tensor.to(device)` it acts **in place** on the module. `state_dict()` returns a plain dictionary of the module's tensors — save and load that rather than the object, so a checkpoint does not depend on your class definition staying put. It also carries **buffers**, like batchnorm's running mean: tensors that are saved and moved with the module but learned by observation rather than by the optimizer.\n","\n","Modules also carry a `training` flag that `net.train()` and `net.eval()` flip recursively, since dropout and batchnorm act differently at inference. Forgetting `eval()` before measuring accuracy gives you quietly wrong numbers. This is separate from `torch.no_grad()`: one changes what the network computes, the other whether the computation is recorded. A real evaluation loop uses both."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ymh4lXuKMZ2o","executionInfo":{"status":"ok","timestamp":1789685718573,"user_tz":240,"elapsed":6,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"73d5df20-46ea-4967-ef3f-0ec2d8683f82"},"outputs":[{"output_type":"stream","name":"stdout","text":["cuda:0\n","False\n"]},{"output_type":"execute_result","data":{"text/plain":["Sequential(\n","  (0): Linear(in_features=2, out_features=16, bias=True)\n","  (1): ReLU()\n","  (2): Linear(in_features=16, out_features=2, bias=True)\n",")"]},"metadata":{},"execution_count":59}],"source":["device = 'cuda' if torch.cuda.is_available() else 'cpu'\n","mlp.to(device)\n","print(next(mlp.parameters()).device)\n","\n","torch.save(mlp.state_dict(), 'mlp.pth')\n","mlp.load_state_dict(torch.load('mlp.pth'))\n","\n","mlp.eval()\n","print(mlp.training)\n","mlp.train()"]},{"cell_type":"markdown","metadata":{"id":"jAC1phPrMZ2o"},"source":["*We can now build networks out of ready-made layers, list their parameters, move them, and save them.*"]},{"cell_type":"markdown","metadata":{"id":"yuARc4pPMZ2o"},"source":["## Walkthrough: the Topic 1 forward pass, rebuilt as a module\n","\n","This is the same computation as the tensor walkthrough — a batch of 16 images in, 10 class scores out — except that `nn.Module` now owns the weights. Then we do the thing the earlier version could not: compute a loss, backpropagate, and take one optimizer step.\n","\n","Watch the loss printed before and after that single step."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dpwd_12WMZ2o","executionInfo":{"status":"ok","timestamp":1789685773974,"user_tz":240,"elapsed":351,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"197ea11c-3839-4341-f684-69f56a2e24b9"},"outputs":[{"output_type":"stream","name":"stdout","text":["logits: (16, 10), loss before: 2.2976\n","loss after one step: 1.3764\n"]}],"source":["torch.manual_seed(0)\n","device = 'cuda' if torch.cuda.is_available() else 'cpu'\n","\n","class ImageClassifier(torch.nn.Module):\n","    def __init__(self, in_features=192, hidden=64, classes=10):\n","        super().__init__()\n","        self.layers = torch.nn.Sequential(\n","            torch.nn.Linear(in_features, hidden),\n","            torch.nn.ReLU(),\n","            torch.nn.Linear(hidden, classes),\n","        )\n","\n","    def forward(self, images):\n","        flat = images.view(images.shape[0], -1)\n","        return self.layers(flat)\n","\n","net = ImageClassifier().to(device)\n","optimizer = torch.optim.Adam(net.parameters(), lr=0.01)\n","\n","images = torch.randn(16, 3, 8, 8).to(device)\n","labels = (torch.arange(16) % 10).to(device)\n","\n","logits = net(images)\n","loss = F.cross_entropy(logits, labels)\n","print(f'logits: {tuple(logits.shape)}, loss before: {loss.item():.4f}')\n","\n","optimizer.zero_grad()\n","loss.backward()\n","optimizer.step()\n","\n","print(f'loss after one step: {F.cross_entropy(net(images), labels).item():.4f}')"]},{"cell_type":"markdown","metadata":{"id":"1ULxPUAKMZ2o"},"source":["Topic 5: Datasets and DataLoaders\n","================\n","\n","The last piece is getting data in. Pytorch splits this into two objects: a `Dataset`, which knows how to produce one example, and a `DataLoader`, which turns that into shuffled batches, in parallel, without you writing any of it."]},{"cell_type":"markdown","metadata":{"id":"GOEXoAhrMZ2o"},"source":["## 5.1 Datasets\n","\n","A `Dataset` is anything that can hand back one example at a time."]},{"cell_type":"markdown","metadata":{"id":"Yh15FFA6MZ2o"},"source":["### Built-in datasets\n","\n","`torchvision.datasets` ships with the standard benchmarks, downloading on first use. The `transform` argument says what to do to each example on its way out: `ToTensor()` converts a PIL image into a `(channel, y, x)` float tensor scaled to `[0, 1]`. Bigger pipelines chain several transforms together with `transforms.Compose`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_C1emV2VMZ2p","executionInfo":{"status":"ok","timestamp":1789685898391,"user_tz":240,"elapsed":8259,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"1b309354-eb81-45c3-fb50-6861be43ad89"},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 26.4M/26.4M [00:02<00:00, 11.1MB/s]\n","100%|██████████| 29.5k/29.5k [00:00<00:00, 172kB/s]\n","100%|██████████| 4.42M/4.42M [00:01<00:00, 3.14MB/s]\n","100%|██████████| 5.15k/5.15k [00:00<00:00, 23.1MB/s]"]},{"output_type":"stream","name":"stdout","text":["60000 10000\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}],"source":["from torchvision import datasets\n","from torchvision.transforms import ToTensor\n","\n","training_data = datasets.FashionMNIST(root='data', train=True, download=True, transform=ToTensor())\n","test_data = datasets.FashionMNIST(root='data', train=False, download=True, transform=ToTensor())\n","\n","print(len(training_data), len(test_data))"]},{"cell_type":"markdown","metadata":{"id":"WNeqtrb3MZ2p"},"source":["### A dataset indexes like a list\n","\n","`dataset[i]` returns one `(features, label)` pair. Note the shape: a single FashionMNIST image is `(1, 28, 28)` — one channel, 28 by 28 — with no batch dimension, because batching is the DataLoader's job."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"iQZZ4SobMZ2p","executionInfo":{"status":"ok","timestamp":1789685898433,"user_tz":240,"elapsed":40,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"a361848b-0714-4fbc-f5d9-d2db9908d6db"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([1, 28, 28]) 9\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["image, label = training_data[0]\n","print(image.shape, label)\n","\n","plt.imshow(image.squeeze(), cmap='gray')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"znlduoagMZ2p"},"source":["### Writing your own Dataset\n","\n","Your own data needs exactly three methods: `__init__` to set things up, `__len__` to say how many examples there are, and `__getitem__` to return the one at index `idx`.\n","\n","The version below holds everything in memory, which is fine for small data. A realistic one stores paths in `__init__` and reads the file inside `__getitem__`, so only the examples actually being used are ever loaded."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xwpTUixJMZ2p","executionInfo":{"status":"ok","timestamp":1789685930804,"user_tz":240,"elapsed":5,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"bc9cd9f5-0047-431c-d221-c5346b7ee4db"},"outputs":[{"output_type":"stream","name":"stdout","text":["100\n","(tensor([-0.1180,  0.2289,  0.0322]), tensor(0))\n"]}],"source":["class MyDataset(torch.utils.data.Dataset):\n","    def __init__(self, n):\n","        self.features = torch.randn(n, 3)\n","        self.labels = torch.randint(0, 2, (n,))\n","\n","    def __len__(self):\n","        return len(self.features)\n","\n","    def __getitem__(self, idx):\n","        return self.features[idx], self.labels[idx]\n","\n","dataset = MyDataset(100)\n","print(len(dataset))\n","print(dataset[0])"]},{"cell_type":"markdown","metadata":{"id":"HvlF0mWAMZ2p"},"source":["*We can now load a standard dataset, or wrap our own data so pytorch can read it.*"]},{"cell_type":"markdown","metadata":{"id":"uzEEWF0PMZ2p"},"source":["## 5.2 DataLoaders\n","\n","A `DataLoader` turns a dataset into shuffled batches."]},{"cell_type":"markdown","metadata":{"id":"yBJXGlHuMZ2p"},"source":["### Wrapping a dataset\n","\n","`DataLoader` handles batching and shuffling. Shuffle the training data so the model does not see the same order every epoch; leave the test set unshuffled, since the order there does not matter and unshuffled is easier to debug. `len(loader)` is the number of batches, not the number of examples.\n","\n","Three arguments worth knowing about for later: `num_workers=n` prepares batches on background processes, `drop_last=True` discards the short final batch, and `transforms.Compose` on the dataset side is where augmentation goes."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wvLNVJLxMZ2p","executionInfo":{"status":"ok","timestamp":1789685989406,"user_tz":240,"elapsed":17,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"4d4df116-d561-4c9e-e21c-84d5989f5454"},"outputs":[{"output_type":"stream","name":"stdout","text":["1875\n"]}],"source":["from torch.utils.data import DataLoader\n","\n","train_dataloader = DataLoader(training_data, batch_size=32, shuffle=True)\n","test_dataloader = DataLoader(test_data, batch_size=64)\n","\n","print(len(train_dataloader))"]},{"cell_type":"markdown","metadata":{"id":"4LrsMmAgMZ2p"},"source":["### Iterating gives you batches\n","\n","Looping over the loader yields `(features, labels)` with a batch dimension stacked on the front — the `torch.stack` from Topic 1, done for you. One full pass is one epoch.\n","\n","That stacking step is `collate_fn`, and you can replace it. Anything whose examples are not all the same shape — variable-length sequences, most obviously — needs a custom one to pad the batch."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QPoYVXh4MZ2p","executionInfo":{"status":"ok","timestamp":1789685991039,"user_tz":240,"elapsed":17,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"9209cb41-3a66-47fc-dcaf-d3b05b01db87"},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([32, 1, 28, 28])\n","torch.Size([32])\n"]}],"source":["images, labels = next(iter(train_dataloader))\n","\n","print(images.shape)\n","print(labels.shape)"]},{"cell_type":"markdown","metadata":{"id":"FkNtHjL2MZ2p"},"source":["*We can now turn any dataset into shuffled batches with one line.*"]},{"cell_type":"markdown","metadata":{"id":"BmTm-2mXMZ2p"},"source":["## Walkthrough: the whole tutorial in one training loop\n","\n","Everything from all five topics, doing the job it exists for. Reading down the cell: a **module** holding parameters, an **optimizer** pointed at those parameters, a **DataLoader** producing batches, **tensors** moved to the device, **autograd** filling in gradients, and the optimizer stepping.\n","\n","The evaluation half is the same loop with the training parts removed: `eval()` mode, `no_grad()` for memory, and `max(dim=1)` to turn scores into predicted classes."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4ZL5uwN9MZ2p","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1789686104514,"user_tz":240,"elapsed":11181,"user":{"displayName":"Aryan Bora","userId":"03098968441100854345"}},"outputId":"893e92cb-cdc9-4ec6-f197-d7c3fdfb747a"},"outputs":[{"output_type":"stream","name":"stdout","text":["epoch 0: train loss 0.3073, test accuracy 0.8507\n"]}],"source":["torch.manual_seed(0)\n","device = 'cuda' if torch.cuda.is_available() else 'cpu'\n","\n","class Classifier(torch.nn.Module):\n","    def __init__(self):\n","        super().__init__()\n","        self.layers = torch.nn.Sequential(\n","            torch.nn.Linear(28 * 28, 128),\n","            torch.nn.ReLU(),\n","            torch.nn.Linear(128, 10),\n","        )\n","\n","    def forward(self, images):\n","        return self.layers(images.view(images.shape[0], -1))\n","\n","model = Classifier().to(device)\n","optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n","\n","epochs = 1\n","\n","for epoch in range(epochs):\n","    model.train()\n","    for images, labels in train_dataloader:\n","        images, labels = images.to(device), labels.to(device)\n","\n","        loss = F.cross_entropy(model(images), labels)\n","\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","\n","    model.eval()\n","    correct = 0\n","    with torch.no_grad():\n","        for images, labels in test_dataloader:\n","            images, labels = images.to(device), labels.to(device)\n","            values, predicted = model(images).max(dim=1)\n","            correct += (predicted == labels).sum().item()\n","\n","    print(f'epoch {epoch}: train loss {loss.item():.4f}, test accuracy {correct / len(test_data):.4f}')"]}],"metadata":{"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.9"},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}