Modifica going modular

My images

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extras/exercises/00_pytorch_fundamentals_exercises.ipynb

@@ -5,7 +5,7 @@
     "colab": {
       "name": "00_pytorch_fundamentals_exercises.ipynb",
       "provenance": [],
-      "collapsed_sections": []
+      "gpuType": "T4"
     },
     "kernelspec": {
       "name": "python3",
@@ -22,7 +22,7 @@
       "source": [
         "# 00. PyTorch Fundamentals Exercises\n",
         "\n",
-        "### 1. Documentation reading \n",
+        "### 1. Documentation reading\n",
         "\n",
         "A big part of deep learning (and learning to code in general) is getting familiar with the documentation of a certain framework you're using. We'll be using the PyTorch documentation a lot throughout the rest of this course. So I'd recommend spending 10-minutes reading the following (it's okay if you don't get some things for now, the focus is not yet full understanding, it's awareness):\n",
         "  * The documentation on [`torch.Tensor`](https://pytorch.org/docs/stable/tensors.html#torch-tensor).\n",
@@ -41,7 +41,7 @@
       "metadata": {
         "id": "bGD0oD8Kizak"
       },
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -57,15 +57,38 @@
       "cell_type": "code",
       "source": [
         "# Import torch\n",
+        "import torch\n",
         "\n",
-        "\n",
-        "# Create random tensor\n"
+        "# Create random tensor\n",
+        "tensor = torch.rand(7,7)\n",
+        "tensor"
       ],
       "metadata": {
-        "id": "6pUq9Dc8i2L7"
+        "id": "6pUq9Dc8i2L7",
+        "outputId": "a6081379-f7dd-4b2f-9809-eebec0ea5d42",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": 2,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "tensor([[0.9473, 0.4796, 0.8238, 0.2162, 0.4292, 0.2930, 0.4013],\n",
+              "        [0.0909, 0.9036, 0.1976, 0.7709, 0.3027, 0.3923, 0.5768],\n",
+              "        [0.2172, 0.4720, 0.0324, 0.1137, 0.2709, 0.5308, 0.0708],\n",
+              "        [0.4380, 0.2407, 0.6845, 0.4044, 0.4465, 0.0734, 0.6406],\n",
+              "        [0.6649, 0.3130, 0.4927, 0.1371, 0.5819, 0.9410, 0.6062],\n",
+              "        [0.3131, 0.9110, 0.0606, 0.9862, 0.9770, 0.1302, 0.8300],\n",
+              "        [0.3633, 0.5432, 0.0048, 0.7753, 0.5374, 0.2534, 0.7470]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -80,14 +103,30 @@
       "cell_type": "code",
       "source": [
         "# Create another random tensor\n",
-        "\n",
-        "# Perform matrix multiplication \n"
+        "tensor2= torch.rand(1,7)\n",
+        "# Perform matrix multiplication\n",
+        "torch.mm(tensor2, tensor)"
       ],
       "metadata": {
-        "id": "NcLqR0Sbi_vT"
+        "id": "NcLqR0Sbi_vT",
+        "outputId": "1ca6c5b2-d54e-4847-8ae8-b4171ee48654",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": 3,
-      "outputs": []
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "tensor([[1.5162, 1.4619, 1.2879, 0.9661, 1.1961, 1.2263, 1.2790]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -113,18 +152,44 @@
       "cell_type": "code",
       "source": [
         "# Set manual seed\n",
-        "\n",
-        "\n",
+        "random_seed=0\n",
         "# Create two random tensors\n",
+        "torch.manual_seed(random_seed)\n",
+        "tensor_a=torch.rand(7,7)\n",
+        "torch.manual_seed(random_seed)\n",
+        "tensor_b=torch.rand(1,7)\n",
         "\n",
-        "\n",
-        "# Matrix multiply tensors\n"
+        "# Matrix multiply tensors\n",
+        "mul= torch.mm(tensor_a,tensor_b.T)\n",
+        "mul, mul.shape"
       ],
       "metadata": {
-        "id": "D-lOWI_1jRMm"
+        "id": "D-lOWI_1jRMm",
+        "outputId": "d563b732-f901-40af-ab64-e34849ca543c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": 4,
-      "outputs": []
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor([[1.5985],\n",
+              "         [1.1173],\n",
+              "         [1.2741],\n",
+              "         [1.6838],\n",
+              "         [0.8279],\n",
+              "         [1.0347],\n",
+              "         [1.2498]]),\n",
+              " torch.Size([7, 1]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -144,7 +209,7 @@
       "metadata": {
         "id": "_LKWcfSTjp00"
       },
-      "execution_count": 5,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -169,17 +234,48 @@
       "cell_type": "code",
       "source": [
         "# Set random seed\n",
-        "\n",
-        "\n",
+        "torch.cuda.manual_seed(1234)\n",
+        "torch.cuda.set_device(0)\n",
         "# Check for access to GPU\n",
-        "\n",
-        "# Create two random tensors on GPU\n"
+        "device= \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "print(f\"Device: {device}\")\n",
+        "# Create two random tensors on GPU\n",
+        "torch.cuda.manual_seed(1234)\n",
+        "tensor_c = torch.rand(2,3).to(device)\n",
+        "torch.cuda.manual_seed(1234)\n",
+        "tensor_d = torch.rand(2,3).to(device)\n",
+        "tensor_c, tensor_d\n"
       ],
       "metadata": {
-        "id": "azXExiFZj5nm"
+        "id": "azXExiFZj5nm",
+        "outputId": "a2f6555a-d7be-4c8d-a4c5-e296f5a753aa",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": 6,
-      "outputs": []
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Device: cuda\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor([[0.7529, 0.8579, 0.6870],\n",
+              "         [0.0051, 0.1757, 0.7497]], device='cuda:0'),\n",
+              " tensor([[0.6047, 0.1100, 0.2121],\n",
+              "         [0.9704, 0.8369, 0.2820]], device='cuda:0'))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 58
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -200,13 +296,32 @@
     {
       "cell_type": "code",
       "source": [
-        "# Perform matmul on tensor_A and tensor_B\n"
+        "# Perform matmul on tensor_A and tensor_B\n",
+        "mul2=torch.mm(tensor_c,tensor_d.T)\n",
+        "mul2,mul2.shape"
       ],
       "metadata": {
-        "id": "fAeG7ox0lHEO"
+        "id": "fAeG7ox0lHEO",
+        "outputId": "b8f0cf80-35bb-41d4-b94b-cf822b384024",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": 7,
-      "outputs": []
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor([[1.1941, 0.6911],\n",
+              "         [0.3175, 0.2087]], device='cuda:0'),\n",
+              " torch.Size([2, 2]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 47
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -221,14 +336,31 @@
       "cell_type": "code",
       "source": [
         "# Find max\n",
-        "\n",
-        "# Find min\n"
+        "max= torch.max(mul2)\n",
+        "# Find min\n",
+        "min= torch.min(mul2)\n",
+        "max,min"
       ],
       "metadata": {
-        "id": "Fu8_3mZpllOd"
+        "id": "Fu8_3mZpllOd",
+        "outputId": "479d6132-727d-4980-a819-82d0466ab10e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": 8,
-      "outputs": []
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor(1.1941, device='cuda:0'), tensor(0.2087, device='cuda:0'))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -243,15 +375,34 @@
       "cell_type": "code",
       "source": [
         "# Find arg max\n",
+        "max2= torch.argmax(mul2)\n",
         "\n",
-        "\n",
-        "# Find arg min\n"
+        "# Find arg min\n",
+        "min2=torch.argmin(mul2)\n",
+        "max2,min2"
       ],
       "metadata": {
-        "id": "CCEKt4K2lsfQ"
+        "id": "CCEKt4K2lsfQ",
+        "outputId": "c8fb459d-2b91-4b80-a210-b46275657147",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": 9,
-      "outputs": []
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor(0, device='cuda:0'),\n",
+              " tensor(3, device='cuda:0'),\n",
+              " tensor(0.2087, device='cuda:0'))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -276,20 +427,50 @@
       "cell_type": "code",
       "source": [
         "# Set seed\n",
-        "\n",
+        "torch.cuda.manual_seed(7)\n",
         "\n",
         "# Create random tensor\n",
-        "\n",
+        "tensor_f = torch.rand(1,1,1,10)\n",
         "\n",
         "# Remove single dimensions\n",
+        "tensor_g=tensor_f.squeeze()\n",
         "\n",
-        "\n",
-        "# Print out tensors and their shapes\n"
+        "# Print out tensors and their shapes\n",
+        "print(f\"tensor_f: {tensor_f}\")\n",
+        "print(f\"tensfor_f_shape: {tensor_f.shape}\")\n",
+        "print(f\"tensor_g:{tensor_g}\")\n",
+        "print(f\"tensor_g_shape: {tensor_g.shape}\")"
       ],
       "metadata": {
-        "id": "TQ9zbRzVl1jV"
+        "id": "TQ9zbRzVl1jV",
+        "outputId": "a7ee7640-b73a-4874-8682-5b4e86b0c5c4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": 10,
+      "execution_count": 69,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor_f: tensor([[[[0.9537, 0.8426, 0.0784, 0.3756, 0.5226, 0.5730, 0.6186, 0.6962,\n",
+            "           0.5300, 0.2560]]]])\n",
+            "tensfor_f_shape: torch.Size([1, 1, 1, 10])\n",
+            "tensor_g:tensor([0.9537, 0.8426, 0.0784, 0.3756, 0.5226, 0.5730, 0.6186, 0.6962, 0.5300,\n",
+            "        0.2560])\n",
+            "tensor_g_shape: torch.Size([10])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "0HaL7IBHbRVU"
+      },
+      "execution_count": null,
       "outputs": []
     }
   ]