Delete appendix-A/03_main-chapter-code/exercise-solutions.ipynb

appendix-A/03_main-chapter-code/exercise-solutions.ipynb

@@ -1,176 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise A.3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "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": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of trainable model parameters: 752\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = NeuralNetwork(2, 2)\n",
-    "\n",
-    "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": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise A.4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "id": "qGgnamiyLJxp"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "a = torch.rand(100, 200)\n",
-    "b = torch.rand(200, 300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "CvGvIeVkLzXE",
-    "outputId": "44d027be-0787-4348-9c06-4e559d94d0e1"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "63.8 µs ± 8.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit a @ b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "OmRtZLa9L2ZG"
-   },
-   "outputs": [],
-   "source": [
-    "a, b = a.to(\"cuda\"), b.to(\"cuda\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "duLEhXDPL6k0",
-    "outputId": "3486471d-fd62-446f-9855-2d01f41fd101"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.8 µs ± 425 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit a @ b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Zqqa-To2L749"
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "V100",
-   "machine_shape": "hm",
-   "provenance": []
-  },
-  "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": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}