week one and two assignment

Part_1_Deep_Learning_with_Pytorch/exercise_submissions/week1.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNmgmQPfwDlOMc4/UDTVN3a",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/maryamalka85/ArewaDS-Deep-Learning/blob/main/Part_1_Deep_Learning_with_Pytorch/exercise_submissions/week1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "V5ABxNIOnyAr"
+      },
+      "outputs": [],
+      "source": [
+        "import torch"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# create random tensor\n",
+        "X = torch.rand(size=(7,7))\n",
+        "X,X.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4sPUYe-AR22P",
+        "outputId": "ff2f59aa-db29-467f-a2ab-a196be08ee1a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor([[0.3142, 0.4865, 0.7705, 0.3263, 0.7264, 0.6868, 0.0522],\n",
+              "         [0.5438, 0.9234, 0.6737, 0.3396, 0.4684, 0.9051, 0.9022],\n",
+              "         [0.5038, 0.2225, 0.9657, 0.4975, 0.0989, 0.6913, 0.5937],\n",
+              "         [0.7634, 0.8749, 0.1176, 0.0565, 0.5362, 0.1045, 0.6571],\n",
+              "         [0.2290, 0.1231, 0.1060, 0.2285, 0.8602, 0.9666, 0.6981],\n",
+              "         [0.5703, 0.9126, 0.0165, 0.9157, 0.9846, 0.1595, 0.7734],\n",
+              "         [0.0339, 0.1776, 0.6518, 0.5989, 0.2043, 0.2849, 0.2325]]),\n",
+              " torch.Size([7, 7]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create another random tensor\n",
+        "Y = torch.rand(size=(1, 7))\n",
+        "# Perform matrix multiplication\n",
+        "Z = torch.matmul(X, Y.T)\n",
+        "Z, Z.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RduF2fcySFm8",
+        "outputId": "c85f229b-27d6-4215-8292-adff242d0708"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor([[1.4617],\n",
+              "         [1.7799],\n",
+              "         [1.3014],\n",
+              "         [0.9893],\n",
+              "         [1.7503],\n",
+              "         [1.7336],\n",
+              "         [0.8774]]),\n",
+              " torch.Size([7, 1]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set manual seed\n",
+        "torch.manual_seed(0)\n",
+        "\n",
+        "# Create two random tensors\n",
+        "X = torch.rand(size=(7, 7))\n",
+        "Y = torch.rand(size=(1, 7))\n",
+        "\n",
+        "# Matrix multiply tensors\n",
+        "Z = torch.matmul(X, Y.T)\n",
+        "Z, Z.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XJT1P2NBSfMu",
+        "outputId": "cf85f2aa-6bf2-4cf9-855d-0e451701d738"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor([[1.8542],\n",
+              "         [1.9611],\n",
+              "         [2.2884],\n",
+              "         [3.0481],\n",
+              "         [1.7067],\n",
+              "         [2.5290],\n",
+              "         [1.7989]]),\n",
+              " torch.Size([7, 1]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set random seed on the GPU\n",
+        "torch.cuda.manual_seed(1234)"
+      ],
+      "metadata": {
+        "id": "rcgfaPeMWPg2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set random seed\n",
+        "torch.manual_seed(1234)\n",
+        "\n",
+        "# Check for access to GPU\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "print(f\"Device: {device}\")\n",
+        "\n",
+        "# Create two random tensors on GPU\n",
+        "tensor_A = torch.rand(size=(2,3)).to(device)\n",
+        "tensor_B = torch.rand(size=(2,3)).to(device)\n",
+        "tensor_A, tensor_B"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kXGUX9tLWtQR",
+        "outputId": "e715efb1-6115-4218-9c82-df5f494945be"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Device: cpu\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor([[0.0290, 0.4019, 0.2598],\n",
+              "         [0.3666, 0.0583, 0.7006]]),\n",
+              " tensor([[0.0518, 0.4681, 0.6738],\n",
+              "         [0.3315, 0.7837, 0.5631]]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Perform matmul on tensor_A and tensor_B\n",
+        "tensor_C = torch.matmul(tensor_A, tensor_B.T)\n",
+        "tensor_C, tensor_C.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EzvxTswjW2GP",
+        "outputId": "1cf4adf2-6618-4b23-b23c-f451f482741d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor([[0.3647, 0.4709],\n",
+              "         [0.5184, 0.5617]]),\n",
+              " torch.Size([2, 2]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Find max\n",
+        "max = torch.max(tensor_C)\n",
+        "\n",
+        "# Find min\n",
+        "min = torch.min(tensor_C)\n",
+        "max, min"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lHPg2D23XDo6",
+        "outputId": "140287af-d71f-4e8d-b7c1-03ddb285f902"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor(0.5617), tensor(0.3647))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Find arg max\n",
+        "arg_max = torch.argmax(tensor_C)\n",
+        "\n",
+        "# Find arg min\n",
+        "arg_min = torch.argmin(tensor_C)\n",
+        "arg_max, arg_min"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6Fcr_F6QXK_r",
+        "outputId": "b6045ba0-92a8-472f-de2f-104187b1b546"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor(3), tensor(0))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed\n",
+        "torch.manual_seed(7)\n",
+        "\n",
+        "# Create random tensor\n",
+        "tensor_D = torch.rand(size=(1, 1, 1, 10))\n",
+        "\n",
+        "# Remove single dimensions\n",
+        "tensor_E = tensor_D.squeeze()\n",
+        "\n",
+        "# Print out tensors\n",
+        "print(tensor_D, tensor_D.shape)\n",
+        "print(tensor_E, tensor_E.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f9OBG49yXRVN",
+        "outputId": "e96e91a7-a407-4660-bba7-9867b3f6bb7f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([[[[0.5349, 0.1988, 0.6592, 0.6569, 0.2328, 0.4251, 0.2071, 0.6297,\n",
+            "           0.3653, 0.8513]]]]) torch.Size([1, 1, 1, 10])\n",
+            "tensor([0.5349, 0.1988, 0.6592, 0.6569, 0.2328, 0.4251, 0.2071, 0.6297, 0.3653,\n",
+            "        0.8513]) torch.Size([10])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "tz9_OxvrXXyr"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}