{ "cells": [ { "cell_type": "markdown", "id": "f896245e-57c4-48fd-854f-9e43f22e10c9", "metadata": {}, "source": [ "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "Supplementary code for the Build a Large Language Model From Scratch book by Sebastian Raschka
\n", "
Code repository: https://github.com/rasbt/LLMs-from-scratch\n", "\n", "		\n", "\n", "
\n" ] }, { "cell_type": "markdown", "id": "ca7fc8a0-280c-4979-b0c7-fc3a99b3b785", "metadata": {}, "source": [ "# Appendix A: Introduction to PyTorch (Part 1)" ] }, { "cell_type": "markdown", "id": "f5bf13d2-8fc2-483e-88cc-6b4310221e68", "metadata": {}, "source": [ "## A.1 What is PyTorch" ] }, { "cell_type": "code", "execution_count": 1, "id": "96ee5660-5327-48e2-9104-a882b3b2afa4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.4.0\n" ] } ], "source": [ "import torch\n", "\n", "print(torch.__version__)" ] }, { "cell_type": "code", "execution_count": 2, "id": "f73ad4e4-7ec6-4467-a9e9-0cdf6d195264", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "print(torch.cuda.is_available())" ] }, { "cell_type": "markdown", "id": "397ba1ab-3306-4965-8618-1ed5f24fb939", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "1e3c0555-88f6-4515-8c99-aa56b0769d54", "metadata": {}, "source": [ "\n", "\n", "\n", "\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "id": "2100cf2e-7459-4ab3-92a8-43e86ab35a9b", "metadata": {}, "source": [ "## A.2 Understanding tensors" ] }, { "cell_type": "markdown", "id": "3c484e87-bfc9-4105-b0a7-1e23b2a72a30", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "26d7f785-e048-42bc-9182-a556af6bb7f4", "metadata": {}, "source": [ "### A.2.1 Scalars, vectors, matrices, and tensors" ] }, { "cell_type": "code", "execution_count": 3, "id": "a3a464d6-cec8-4363-87bd-ea4f900baced", "metadata": {}, "outputs": [], "source": [ "import torch\n", "import numpy as np\n", "\n", "# create a 0D tensor (scalar) from a Python integer\n", "tensor0d = torch.tensor(1)\n", "\n", "# create a 1D tensor (vector) from a Python list\n", "tensor1d = torch.tensor([1, 2, 3])\n", "\n", "# create a 2D tensor from a nested Python list\n", "tensor2d = torch.tensor([[1, 2], \n", " [3, 4]])\n", "\n", "# create a 3D tensor from a nested Python list\n", "tensor3d_1 = torch.tensor([[[1, 2], [3, 4]], \n", " [[5, 6], [7, 8]]])\n", "\n", "# create a 3D tensor from NumPy array\n", "ary3d = np.array([[[1, 2], [3, 4]], \n", " [[5, 6], [7, 8]]])\n", "tensor3d_2 = torch.tensor(ary3d) # Copies NumPy array\n", "tensor3d_3 = torch.from_numpy(ary3d) # Shares memory with NumPy array" ] }, { "cell_type": "code", "execution_count": 4, "id": "dbe14c47-499a-4d48-b354-a0e6fd957872", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[[1, 2],\n", " [3, 4]],\n", "\n", " [[5, 6],\n", " [7, 8]]])\n" ] } ], "source": [ "ary3d[0, 0, 0] = 999\n", "print(tensor3d_2) # remains unchanged" ] }, { "cell_type": "code", "execution_count": 5, "id": "e3e4c23a-cdba-46f5-a2dc-5fb32bf9117b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[[999, 2],\n", " [ 3, 4]],\n", "\n", " [[ 5, 6],\n", " [ 7, 8]]])\n" ] } ], "source": [ "print(tensor3d_3) # changes because of memory sharing" ] }, { "cell_type": "markdown", "id": "63dec48d-2b60-41a2-ac06-fef7e718605a", "metadata": {}, "source": [ "### A.2.2 Tensor data types" ] }, { "cell_type": "code", "execution_count": 6, "id": "3f48c014-e1a2-4a53-b5c5-125812d4034c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.int64\n" ] } ], "source": [ "tensor1d = torch.tensor([1, 2, 3])\n", "print(tensor1d.dtype)" ] }, { "cell_type": "code", "execution_count": 7, "id": "5429a086-9de2-4ac7-9f14-d087a7507394", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.float32\n" ] } ], "source": [ "floatvec = torch.tensor([1.0, 2.0, 3.0])\n", "print(floatvec.dtype)" ] }, { "cell_type": "code", "execution_count": 8, "id": "a9a438d1-49bb-481c-8442-7cc2bb3dd4af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.float32\n" ] } ], "source": [ "floatvec = tensor1d.to(torch.float32)\n", "print(floatvec.dtype)" ] }, { "cell_type": "markdown", "id": "2020deb5-aa02-4524-b311-c010f4ad27ff", "metadata": {}, "source": [ "### A.2.3 Common PyTorch tensor operations" ] }, { "cell_type": "code", "execution_count": 9, "id": "c02095f2-8a48-4953-b3c9-5313d4362ce7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[1, 2, 3],\n", " [4, 5, 6]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor2d = torch.tensor([[1, 2, 3], \n", " [4, 5, 6]])\n", "tensor2d" ] }, { "cell_type": "code", "execution_count": 10, "id": "f33e1d45-5b2c-4afe-b4b2-66ac4099fd1a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([2, 3])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor2d.shape" ] }, { "cell_type": "code", "execution_count": 11, "id": "f3a4129d-f870-4e03-9c32-cd8521cb83fe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[1, 2],\n", " [3, 4],\n", " [5, 6]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor2d.reshape(3, 2)" ] }, { "cell_type": "code", "execution_count": 12, "id": "589ac0a7-adc7-41f3-b721-155f580e9369", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[1, 2],\n", " [3, 4],\n", " [5, 6]])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor2d.view(3, 2)" ] }, { "cell_type": "code", "execution_count": 13, "id": "344e307f-ba5d-4f9a-a791-2c75a3d1417e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[1, 4],\n", " [2, 5],\n", " [3, 6]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor2d.T" ] }, { "cell_type": "code", "execution_count": 14, "id": "19a75030-6a41-4ca8-9aae-c507ae79225c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[14, 32],\n", " [32, 77]])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor2d.matmul(tensor2d.T)" ] }, { "cell_type": "code", "execution_count": 15, "id": "e7c950bc-d640-4203-b210-3ac8932fe4d4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[14, 32],\n", " [32, 77]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor2d @ tensor2d.T" ] }, { "cell_type": "markdown", "id": "4c15bdeb-78e2-4870-8a4f-a9f591666f38", "metadata": {}, "source": [ "## A.3 Seeing models as computation graphs" ] }, { "cell_type": "markdown", "id": "0f3e16c3-07df-44b6-9106-a42fb24452a9", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 16, "id": "22af61e9-0443-4705-94d7-24c21add09c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0852)\n" ] } ], "source": [ "import torch.nn.functional as F\n", "\n", "y = torch.tensor([1.0]) # true label\n", "x1 = torch.tensor([1.1]) # input feature\n", "w1 = torch.tensor([2.2]) # weight parameter\n", "b = torch.tensor([0.0]) # bias unit\n", "\n", "z = x1 * w1 + b # net input\n", "a = torch.sigmoid(z) # activation & output\n", "\n", "loss = F.binary_cross_entropy(a, y)\n", "print(loss)" ] }, { "cell_type": "markdown", "id": "f9424f26-2bac-47e7-b834-92ece802247c", "metadata": {}, "source": [ "## A.4 Automatic differentiation made easy" ] }, { "cell_type": "markdown", "id": "33aa2ee4-6f1d-448d-8707-67cd5278233c", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 17, "id": "ebf5cef7-48d6-4d2a-8ab0-0fb10bdd7d1a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(tensor([-0.0898]),)\n", "(tensor([-0.0817]),)\n" ] } ], "source": [ "import torch.nn.functional as F\n", "from torch.autograd import grad\n", "\n", "y = torch.tensor([1.0])\n", "x1 = torch.tensor([1.1])\n", "w1 = torch.tensor([2.2], requires_grad=True)\n", "b = torch.tensor([0.0], requires_grad=True)\n", "\n", "z = x1 * w1 + b \n", "a = torch.sigmoid(z)\n", "\n", "loss = F.binary_cross_entropy(a, y)\n", "\n", "grad_L_w1 = grad(loss, w1, retain_graph=True)\n", "grad_L_b = grad(loss, b, retain_graph=True)\n", "\n", "print(grad_L_w1)\n", "print(grad_L_b)" ] }, { "cell_type": "code", "execution_count": 18, "id": "93c5875d-f6b2-492c-b5ef-7e132f93a4e0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([-0.0898])\n", "tensor([-0.0817])\n" ] } ], "source": [ "loss.backward()\n", "\n", "print(w1.grad)\n", "print(b.grad)" ] }, { "cell_type": "markdown", "id": "f53bdd7d-44e6-40ab-8a5a-4eef74ef35dc", "metadata": {}, "source": [ "## A.5 Implementing multilayer neural networks" ] }, { "cell_type": "markdown", "id": "d6cb9787-2bc8-4379-9e8c-a3401ac63c51", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 19, "id": "84b749e1-7768-4cfe-94d6-a08c7feff4a1", "metadata": {}, "outputs": [], "source": [ "class NeuralNetwork(torch.nn.Module):\n", " def __init__(self, num_inputs, num_outputs):\n", " super().__init__()\n", "\n", " self.layers = torch.nn.Sequential(\n", " \n", " # 1st hidden layer\n", " torch.nn.Linear(num_inputs, 30),\n", " torch.nn.ReLU(),\n", "\n", " # 2nd hidden layer\n", " torch.nn.Linear(30, 20),\n", " torch.nn.ReLU(),\n", "\n", " # output layer\n", " torch.nn.Linear(20, num_outputs),\n", " )\n", "\n", " def forward(self, x):\n", " logits = self.layers(x)\n", " return logits" ] }, { "cell_type": "code", "execution_count": 20, "id": "c5b59e2e-1930-456d-93b9-f69263e3adbe", "metadata": {}, "outputs": [], "source": [ "model = NeuralNetwork(50, 3)" ] }, { "cell_type": "code", "execution_count": 21, "id": "39d02a21-33e7-4879-8fd2-d6309faf2f8d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NeuralNetwork(\n", " (layers): Sequential(\n", " (0): Linear(in_features=50, out_features=30, bias=True)\n", " (1): ReLU()\n", " (2): Linear(in_features=30, out_features=20, bias=True)\n", " (3): ReLU()\n", " (4): Linear(in_features=20, out_features=3, bias=True)\n", " )\n", ")\n" ] } ], "source": [ "print(model)" ] }, { "cell_type": "code", "execution_count": 22, "id": "94535738-de02-4c2a-9b44-1cd186fa990a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of trainable model parameters: 2213\n" ] } ], "source": [ "num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "print(\"Total number of trainable model parameters:\", num_params)" ] }, { "cell_type": "code", "execution_count": 23, "id": "2c394106-ad71-4ccb-a3c9-9b60af3fa748", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameter containing:\n", "tensor([[ 0.1182, 0.0606, -0.1292, ..., -0.1126, 0.0735, -0.0597],\n", " [-0.0249, 0.0154, -0.0476, ..., -0.1001, -0.1288, 0.1295],\n", " [ 0.0641, 0.0018, -0.0367, ..., -0.0990, -0.0424, -0.0043],\n", " ...,\n", " [ 0.0618, 0.0867, 0.1361, ..., -0.0254, 0.0399, 0.1006],\n", " [ 0.0842, -0.0512, -0.0960, ..., -0.1091, 0.1242, -0.0428],\n", " [ 0.0518, -0.1390, -0.0923, ..., -0.0954, -0.0668, -0.0037]],\n", " requires_grad=True)\n" ] } ], "source": [ "print(model.layers[0].weight)" ] }, { "cell_type": "code", "execution_count": 24, "id": "b201882b-9285-4db9-bb63-43afe6a2ff9e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameter containing:\n", "tensor([[-0.0577, 0.0047, -0.0702, ..., 0.0222, 0.1260, 0.0865],\n", " [ 0.0502, 0.0307, 0.0333, ..., 0.0951, 0.1134, -0.0297],\n", " [ 0.1077, -0.1108, 0.0122, ..., 0.0108, -0.1049, -0.1063],\n", " ...,\n", " [-0.0787, 0.1259, 0.0803, ..., 0.1218, 0.1303, -0.1351],\n", " [ 0.1359, 0.0175, -0.0673, ..., 0.0674, 0.0676, 0.1058],\n", " [ 0.0790, 0.1343, -0.0293, ..., 0.0344, -0.0971, -0.0509]],\n", " requires_grad=True)\n" ] } ], "source": [ "torch.manual_seed(123)\n", "\n", "model = NeuralNetwork(50, 3)\n", "print(model.layers[0].weight)" ] }, { "cell_type": "code", "execution_count": 25, "id": "1da9a35e-44f3-460c-90fe-304519736fd6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([30, 50])\n" ] } ], "source": [ "print(model.layers[0].weight.shape)" ] }, { "cell_type": "code", "execution_count": 26, "id": "57eadbae-90fe-43a3-a33f-c23a095ba42a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[-0.1262, 0.1080, -0.1792]], grad_fn=)\n" ] } ], "source": [ "torch.manual_seed(123)\n", "\n", "X = torch.rand((1, 50))\n", "out = model(X)\n", "print(out)" ] }, { "cell_type": "code", "execution_count": 27, "id": "48d720cb-ef73-4b7b-92e0-8198a072defd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[-0.1262, 0.1080, -0.1792]])\n" ] } ], "source": [ "with torch.no_grad():\n", " out = model(X)\n", "print(out)" ] }, { "cell_type": "code", "execution_count": 28, "id": "10df3640-83c3-4061-a74d-08f07a5cc6ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[0.3113, 0.3934, 0.2952]])\n" ] } ], "source": [ "with torch.no_grad():\n", " out = torch.softmax(model(X), dim=1)\n", "print(out)" ] }, { "cell_type": "markdown", "id": "19858180-0f26-43a8-b2c3-7ed40abf9f85", "metadata": {}, "source": [ "## A.6 Setting up efficient data loaders" ] }, { "cell_type": "markdown", "id": "0f98d8fc-5618-47a2-bc72-153818972a24", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 29, "id": "b9dc2745-8be8-4344-80ef-325f02cda7b7", "metadata": {}, "outputs": [], "source": [ "X_train = torch.tensor([\n", " [-1.2, 3.1],\n", " [-0.9, 2.9],\n", " [-0.5, 2.6],\n", " [2.3, -1.1],\n", " [2.7, -1.5]\n", "])\n", "\n", "y_train = torch.tensor([0, 0, 0, 1, 1])" ] }, { "cell_type": "code", "execution_count": 30, "id": "88283948-5fca-461a-98a1-788b6be191d5", "metadata": {}, "outputs": [], "source": [ "X_test = torch.tensor([\n", " [-0.8, 2.8],\n", " [2.6, -1.6],\n", "])\n", "\n", "y_test = torch.tensor([0, 1])" ] }, { "cell_type": "code", "execution_count": 31, "id": "edf323e2-1789-41a0-8e44-f3cab16e5f5d", "metadata": {}, "outputs": [], "source": [ "from torch.utils.data import Dataset\n", "\n", "\n", "class ToyDataset(Dataset):\n", " def __init__(self, X, y):\n", " self.features = X\n", " self.labels = y\n", "\n", " def __getitem__(self, index):\n", " one_x = self.features[index]\n", " one_y = self.labels[index] \n", " return one_x, one_y\n", "\n", " def __len__(self):\n", " return self.labels.shape[0]\n", "\n", "train_ds = ToyDataset(X_train, y_train)\n", "test_ds = ToyDataset(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 32, "id": "b7014705-1fdc-4f72-b892-d8db8bebc331", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(train_ds)" ] }, { "cell_type": "code", "execution_count": 33, "id": "3ec6627a-4c3f-481a-b794-d2131be95eaf", "metadata": {}, "outputs": [], "source": [ "from torch.utils.data import DataLoader\n", "\n", "torch.manual_seed(123)\n", "\n", "train_loader = DataLoader(\n", " dataset=train_ds,\n", " batch_size=2,\n", " shuffle=True,\n", " num_workers=0\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "id": "8c9446de-5e4b-44fa-bf9a-a63e2661027e", "metadata": {}, "outputs": [], "source": [ "test_ds = ToyDataset(X_test, y_test)\n", "\n", "test_loader = DataLoader(\n", " dataset=test_ds,\n", " batch_size=2,\n", " shuffle=False,\n", " num_workers=0\n", ")" ] }, { "cell_type": "code", "execution_count": 35, "id": "99d4404c-9884-419f-979c-f659742d86ef", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch 1: tensor([[ 2.3000, -1.1000],\n", " [-0.9000, 2.9000]]) tensor([1, 0])\n", "Batch 2: tensor([[-1.2000, 3.1000],\n", " [-0.5000, 2.6000]]) tensor([0, 0])\n", "Batch 3: tensor([[ 2.7000, -1.5000]]) tensor([1])\n" ] } ], "source": [ "for idx, (x, y) in enumerate(train_loader):\n", " print(f\"Batch {idx+1}:\", x, y)" ] }, { "cell_type": "code", "execution_count": 36, "id": "9d003f7e-7a80-40bf-a7fb-7a0d7dbba9db", "metadata": {}, "outputs": [], "source": [ "train_loader = DataLoader(\n", " dataset=train_ds,\n", " batch_size=2,\n", " shuffle=True,\n", " num_workers=0,\n", " drop_last=True\n", ")" ] }, { "cell_type": "code", "execution_count": 37, "id": "4db4d7f4-82da-44a4-b94e-ee04665d9c3c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch 1: tensor([[-1.2000, 3.1000],\n", " [-0.5000, 2.6000]]) tensor([0, 0])\n", "Batch 2: tensor([[ 2.3000, -1.1000],\n", " [-0.9000, 2.9000]]) tensor([1, 0])\n" ] } ], "source": [ "for idx, (x, y) in enumerate(train_loader):\n", " print(f\"Batch {idx+1}:\", x, y)" ] }, { "cell_type": "markdown", "id": "eb03ed57-df38-4ee0-a553-0863450df39b", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "d904ca82-e50f-4f3d-a3ac-fc6ca53dd00e", "metadata": {}, "source": [ "## A.7 A typical training loop" ] }, { "cell_type": "code", "execution_count": 38, "id": "93f1791a-d887-4fc5-a307-5e5bde9e06f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 001/003 | Batch 000/002 | Train/Val Loss: 0.75\n", "Epoch: 001/003 | Batch 001/002 | Train/Val Loss: 0.65\n", "Epoch: 002/003 | Batch 000/002 | Train/Val Loss: 0.44\n", "Epoch: 002/003 | Batch 001/002 | Train/Val Loss: 0.13\n", "Epoch: 003/003 | Batch 000/002 | Train/Val Loss: 0.03\n", "Epoch: 003/003 | Batch 001/002 | Train/Val Loss: 0.00\n" ] } ], "source": [ "import torch.nn.functional as F\n", "\n", "\n", "torch.manual_seed(123)\n", "model = NeuralNetwork(num_inputs=2, num_outputs=2)\n", "optimizer = torch.optim.SGD(model.parameters(), lr=0.5)\n", "\n", "num_epochs = 3\n", "\n", "for epoch in range(num_epochs):\n", " \n", " model.train()\n", " for batch_idx, (features, labels) in enumerate(train_loader):\n", "\n", " logits = model(features)\n", " \n", " loss = F.cross_entropy(logits, labels) # Loss function\n", " \n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", " \n", " ### LOGGING\n", " print(f\"Epoch: {epoch+1:03d}/{num_epochs:03d}\"\n", " f\" | Batch {batch_idx:03d}/{len(train_loader):03d}\"\n", " f\" | Train/Val Loss: {loss:.2f}\")\n", "\n", " model.eval()\n", " # Optional model evaluation" ] }, { "cell_type": "code", "execution_count": 39, "id": "00dcf57f-6a7e-4af7-aa5a-df2cb0866fa5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[ 2.8569, -4.1618],\n", " [ 2.5382, -3.7548],\n", " [ 2.0944, -3.1820],\n", " [-1.4814, 1.4816],\n", " [-1.7176, 1.7342]])\n" ] } ], "source": [ "model.eval()\n", "\n", "with torch.no_grad():\n", " outputs = model(X_train)\n", "\n", "print(outputs)" ] }, { "cell_type": "code", "execution_count": 40, "id": "19be7390-18b8-43f9-9841-d7fb1919f6fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[ 0.9991, 0.0009],\n", " [ 0.9982, 0.0018],\n", " [ 0.9949, 0.0051],\n", " [ 0.0491, 0.9509],\n", " [ 0.0307, 0.9693]])\n", "tensor([0, 0, 0, 1, 1])\n" ] } ], "source": [ "torch.set_printoptions(sci_mode=False)\n", "probas = torch.softmax(outputs, dim=1)\n", "print(probas)\n", "\n", "predictions = torch.argmax(probas, dim=1)\n", "print(predictions)" ] }, { "cell_type": "code", "execution_count": 41, "id": "07e7e530-f8d3-429c-9f5e-cf8078078c0e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([0, 0, 0, 1, 1])\n" ] } ], "source": [ "predictions = torch.argmax(outputs, dim=1)\n", "print(predictions)" ] }, { "cell_type": "code", "execution_count": 42, "id": "5f756f0d-63c8-41b5-a5d8-01baa847e026", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([True, True, True, True, True])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions == y_train" ] }, { "cell_type": "code", "execution_count": 43, "id": "da274bb0-f11c-4c81-a880-7a031fbf2943", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(5)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.sum(predictions == y_train)" ] }, { "cell_type": "code", "execution_count": 44, "id": "16d62314-8dee-45b0-8f55-9e5aae2b24f4", "metadata": {}, "outputs": [], "source": [ "def compute_accuracy(model, dataloader):\n", "\n", " model = model.eval()\n", " correct = 0.0\n", " total_examples = 0\n", " \n", " for idx, (features, labels) in enumerate(dataloader):\n", " \n", " with torch.no_grad():\n", " logits = model(features)\n", " \n", " predictions = torch.argmax(logits, dim=1)\n", " compare = labels == predictions\n", " correct += torch.sum(compare)\n", " total_examples += len(compare)\n", "\n", " return (correct / total_examples).item()" ] }, { "cell_type": "code", "execution_count": 45, "id": "4f6c9c17-2a5f-46c0-804b-873f169b729a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "compute_accuracy(model, train_loader)" ] }, { "cell_type": "code", "execution_count": 46, "id": "311ed864-e21e-4aac-97c7-c6086caef27a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "compute_accuracy(model, test_loader)" ] }, { "cell_type": "markdown", "id": "4d5cd469-3a45-4394-944b-3ce543f41dac", "metadata": {}, "source": [ "## A.8 Saving and loading models" ] }, { "cell_type": "code", "execution_count": 47, "id": "b013127d-a2c3-4b04-9fb3-a6a7c88d83c5", "metadata": {}, "outputs": [], "source": [ "torch.save(model.state_dict(), \"model.pth\")" ] }, { "cell_type": "code", "execution_count": 48, "id": "b2b428c2-3a44-4d91-97c4-8298cf2b51eb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = NeuralNetwork(2, 2) # needs to match the original model exactly\n", "model.load_state_dict(torch.load(\"model.pth\", weights_only=True))" ] }, { "cell_type": "markdown", "id": "f891c013-43da-4a05-973d-997be313d2d8", "metadata": {}, "source": [ "## A.9 Optimizing training performance with GPUs" ] }, { "cell_type": "markdown", "id": "e68ae888-cabf-49c9-bad6-ecdce774db57", "metadata": {}, "source": [ "### A.9.1 PyTorch computations on GPU devices" ] }, { "cell_type": "markdown", "id": "141c845f-efe3-4614-b376-b8b7a9a2c887", "metadata": {}, "source": [ "See [code-part2.ipynb](code-part2.ipynb)" ] }, { "cell_type": "markdown", "id": "99811829-b817-42ea-b03e-d35374debcc0", "metadata": {}, "source": [ "### A.9.2 Single-GPU training" ] }, { "cell_type": "markdown", "id": "0b21456c-4af7-440f-9e78-37770277b5bc", "metadata": {}, "source": [ "See [code-part2.ipynb](code-part2.ipynb)" ] }, { "cell_type": "markdown", "id": "db6eb2d1-a341-4489-b04b-635c26945333", "metadata": {}, "source": [ "### A.9.3 Training with multiple GPUs" ] }, { "cell_type": "markdown", "id": "9d049a81-5fb0-49b5-9d6a-17a9976d8520", "metadata": {}, "source": [ "See [DDP-script.py](DDP-script.py)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "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": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 5 }