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add exercise solutions
This commit is contained in:
rasbt
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176
appendix-A/03_main-chapter-code/exercise-solutions.ipynb
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176
appendix-A/03_main-chapter-code/exercise-solutions.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exercise A.3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"class NeuralNetwork(torch.nn.Module):\n",
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" def __init__(self, num_inputs, num_outputs):\n",
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" super().__init__()\n",
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"\n",
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" self.layers = torch.nn.Sequential(\n",
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" \n",
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" # 1st hidden layer\n",
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" torch.nn.Linear(num_inputs, 30),\n",
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" torch.nn.ReLU(),\n",
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"\n",
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" # 2nd hidden layer\n",
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" torch.nn.Linear(30, 20),\n",
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" torch.nn.ReLU(),\n",
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"\n",
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" # output layer\n",
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" torch.nn.Linear(20, num_outputs),\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" logits = self.layers(x)\n",
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" return logits"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total number of trainable model parameters: 752\n"
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]
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}
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],
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"source": [
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"model = NeuralNetwork(2, 2)\n",
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"\n",
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"num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
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"print(\"Total number of trainable model parameters:\", num_params)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exercise A.4"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "qGgnamiyLJxp"
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"a = torch.rand(100, 200)\n",
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"b = torch.rand(200, 300)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "CvGvIeVkLzXE",
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"outputId": "44d027be-0787-4348-9c06-4e559d94d0e1"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"63.8 µs ± 8.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
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]
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}
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],
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"source": [
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"%timeit a @ b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "OmRtZLa9L2ZG"
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},
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"outputs": [],
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"source": [
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"a, b = a.to(\"cuda\"), b.to(\"cuda\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "duLEhXDPL6k0",
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"outputId": "3486471d-fd62-446f-9855-2d01f41fd101"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"13.8 µs ± 425 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
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]
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}
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],
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"source": [
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"%timeit a @ b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "Zqqa-To2L749"
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"gpuType": "V100",
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"machine_shape": "hm",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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@ -142,7 +142,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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328
ch02/01_main-chapter-code/exercise-solutions.ipynb
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328
ch02/01_main-chapter-code/exercise-solutions.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab88d307-61ba-45ef-89bc-e3569443dfca",
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"metadata": {},
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"source": [
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"# Chapter 2 Exercise solutions"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6f678e62-7bcb-4405-86ae-dce94f494303",
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"metadata": {},
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"source": [
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"# Exercise 2.1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "7614337f-f639-42c9-a99b-d33f74fa8a03",
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"metadata": {},
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"outputs": [],
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"source": [
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"import tiktoken\n",
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"\n",
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"tokenizer = tiktoken.get_encoding(\"gpt2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "664397bc-6daa-4b88-90aa-e8fc1fbd5846",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[33901]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenizer.encode(\"Ak\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "d3664332-e6bb-447e-8b96-203aafde8b24",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[86]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenizer.encode(\"w\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2773c09d-c136-4372-a2be-04b58d292842",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[343]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenizer.encode(\"ir\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "8a6abd32-1e0a-4038-9dd2-673f47bcdeb5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[86]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenizer.encode(\"w\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "26ae940a-9841-4e27-a1df-b83fc8a488b3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[220]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenizer.encode(\" \")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "a606c39a-6747-4cd8-bb38-e3183f80908d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[959]"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenizer.encode(\"ier\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "47c7268d-8fdc-4957-bc68-5be6113f45a7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Akwirw ier'"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenizer.decode([33901, 86, 343, 86, 220, 959])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "29e5034a-95ed-46d8-9972-589354dc9fd4",
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"metadata": {},
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"source": [
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"# Exercise 2.2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "4d50af16-937b-49e0-8ffd-42d30cbb41c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"import tiktoken\n",
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"import torch\n",
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"from torch.utils.data import Dataset, DataLoader\n",
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"\n",
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"\n",
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"class GPTDatasetV1(Dataset):\n",
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" def __init__(self, txt, tokenizer, max_length, stride):\n",
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" self.tokenizer = tokenizer\n",
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" self.input_ids = []\n",
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" self.target_ids = []\n",
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"\n",
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" # Tokenize the entire text\n",
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" token_ids = tokenizer.encode(txt)\n",
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"\n",
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" # Use a sliding window to chunk the book into overlapping sequences of max_length\n",
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" for i in range(0, len(token_ids) - max_length, stride):\n",
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" input_chunk = token_ids[i:i + max_length]\n",
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" target_chunk = token_ids[i + 1: i + max_length + 1]\n",
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" self.input_ids.append(torch.tensor(input_chunk))\n",
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" self.target_ids.append(torch.tensor(target_chunk))\n",
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"\n",
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" def __len__(self):\n",
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" return len(self.input_ids)\n",
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"\n",
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" def __getitem__(self, idx):\n",
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" return self.input_ids[idx], self.target_ids[idx]\n",
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"\n",
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"\n",
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"def create_dataloader(txt, batch_size=4, max_length=256, stride=128):\n",
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" # Initialize the tokenizer\n",
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" tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
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"\n",
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" # Create dataset\n",
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" dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)\n",
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"\n",
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" # Create dataloader\n",
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" dataloader = DataLoader(dataset, batch_size=batch_size)\n",
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"\n",
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" return dataloader\n",
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"\n",
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"\n",
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"with open(\"the-verdict.txt\", \"r\", encoding=\"utf-8\") as f:\n",
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" raw_text = f.read()\n",
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"\n",
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"tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
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"encoded_text = tokenizer.encode(raw_text)\n",
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"\n",
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"vocab_size = 50257\n",
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"output_dim = 256\n",
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"token_embedding_layer = torch.nn.Embedding(vocab_size, output_dim)\n",
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"pos_embedding_layer = torch.nn.Embedding(vocab_size, output_dim)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "0128eefa-d7c8-4f76-9851-566dfa7c3745",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[ 40, 367],\n",
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" [2885, 1464],\n",
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" [1807, 3619],\n",
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" [ 402, 271]])"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dataloader = create_dataloader(raw_text, batch_size=4, max_length=2, stride=2)\n",
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"\n",
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"for batch in dataloader:\n",
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" x, y = batch\n",
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" break\n",
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"\n",
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"x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "ff5c1e90-c6de-4a87-adf6-7e19f603291c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[ 40, 367, 2885, 1464, 1807, 3619, 402, 271],\n",
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" [ 2885, 1464, 1807, 3619, 402, 271, 10899, 2138],\n",
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" [ 1807, 3619, 402, 271, 10899, 2138, 257, 7026],\n",
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" [ 402, 271, 10899, 2138, 257, 7026, 15632, 438]])"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dataloader = create_dataloader(raw_text, batch_size=4, max_length=8, stride=2)\n",
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"\n",
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"for batch in dataloader:\n",
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" x, y = batch\n",
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" break\n",
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"\n",
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"x"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
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Reference in New Issue
Block a user