00_Pytorch_Fundamentals_Exercises.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/exercises/00_pytorch_fundamentals_exercises.ipynb","timestamp":1713625750178}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# 00. PyTorch Fundamentals Exercises\n","\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","  * The documentation on [`torch.cuda`](https://pytorch.org/docs/master/notes/cuda.html#cuda-semantics).\n","\n"],"metadata":{"id":"AzDBM_v4iMe7"}},{"cell_type":"code","source":["# No code solution (reading)"],"metadata":{"id":"bGD0oD8Kizak"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 2. Create a random tensor with shape `(7, 7)`.\n"],"metadata":{"id":"__iXqqz-ioUJ"}},{"cell_type":"code","source":["# Import torch\n","import torch\n","tensor_0 = torch.rand([7, 7])\n","tensor_0, tensor_0.shape, tensor_0.device\n","# Create random tensor\n"],"metadata":{"id":"6pUq9Dc8i2L7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1713633599864,"user_tz":600,"elapsed":531,"user":{"displayName":"Its Erphan","userId":"06973834403098047756"}},"outputId":"ff2c7de7-52a2-4ed1-a95c-e027d802d980"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(tensor([[0.9768, 0.9709, 0.1258, 0.1688, 0.3315, 0.0756, 0.6617],\n","         [0.6780, 0.4275, 0.9751, 0.4111, 0.0301, 0.0474, 0.3020],\n","         [0.5317, 0.3547, 0.2851, 0.6157, 0.1511, 0.8606, 0.7691],\n","         [0.2924, 0.9222, 0.6503, 0.3631, 0.5752, 0.7682, 0.3204],\n","         [0.2763, 0.9067, 0.6853, 0.6836, 0.7671, 0.1264, 0.7975],\n","         [0.1959, 0.5670, 0.6929, 0.7259, 0.3529, 0.2529, 0.6002],\n","         [0.6284, 0.0841, 0.9417, 0.8407, 0.2947, 0.3710, 0.8276]]),\n"," torch.Size([7, 7]),\n"," device(type='cpu'))"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["### 3. Perform a matrix multiplication on the tensor from 2 with another random tensor with shape `(1, 7)` (hint: you may have to transpose the second tensor)."],"metadata":{"id":"9-XxvRLfiqkR"}},{"cell_type":"code","source":["# Create another random tensor\n","\n","tensor_1 = torch.rand([1, 7])\n","\n","print(torch.matmul(tensor_0, tensor_1.reshape([7, 1])))\n","print(tensor_1.reshape([7, 1]).shape)\n","\n","# Perform matrix multiplication\n"],"metadata":{"id":"NcLqR0Sbi_vT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1713633938523,"user_tz":600,"elapsed":563,"user":{"displayName":"Its Erphan","userId":"06973834403098047756"}},"outputId":"8b5e33c5-51be-4213-c2d0-d99a5d7a5696"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[1.4838],\n","        [1.5761],\n","        [1.7222],\n","        [1.9459],\n","        [1.7197],\n","        [1.4396],\n","        [1.9998]])\n","torch.Size([7, 1])\n"]}]},{"cell_type":"markdown","source":["### 4. Set the random seed to `0` and do 2 & 3 over again.\n","\n","The output should be:\n","```\n","(tensor([[1.8542],\n","         [1.9611],\n","         [2.2884],\n","         [3.0481],\n","         [1.7067],\n","         [2.5290],\n","         [1.7989]]), torch.Size([7, 1]))\n","```"],"metadata":{"id":"eiutdKUFiryU"}},{"cell_type":"code","source":["# Set manual seed\n","RANDOM_SEED = 42\n","torch.manual_seed(RANDOM_SEED)\n","# Create two random tensors\n","torch.manual_seed(RANDOM_SEED)\n","tensor_0 = torch.rand([7, 7])\n","torch.manual_seed(RANDOM_SEED)\n","tensor_1 = torch.rand([1, 7])\n","\n","# Matrix multiply tensors\n","print(f\"First tensor: {tensor_0}\")\n","print(f\"Second tensor: {tensor_1}\")\n","print(f\"Matrix Multiply is equal to: {torch.matmul(tensor_0, tensor_1.reshape([7, 1]))}\")"],"metadata":{"id":"D-lOWI_1jRMm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1713634151109,"user_tz":600,"elapsed":345,"user":{"displayName":"Its Erphan","userId":"06973834403098047756"}},"outputId":"6e3ef4b8-ec65-4e03-ac0b-ca8fbce50d4d"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["First tensor: tensor([[0.8823, 0.9150, 0.3829, 0.9593, 0.3904, 0.6009, 0.2566],\n","        [0.7936, 0.9408, 0.1332, 0.9346, 0.5936, 0.8694, 0.5677],\n","        [0.7411, 0.4294, 0.8854, 0.5739, 0.2666, 0.6274, 0.2696],\n","        [0.4414, 0.2969, 0.8317, 0.1053, 0.2695, 0.3588, 0.1994],\n","        [0.5472, 0.0062, 0.9516, 0.0753, 0.8860, 0.5832, 0.3376],\n","        [0.8090, 0.5779, 0.9040, 0.5547, 0.3423, 0.6343, 0.3644],\n","        [0.7104, 0.9464, 0.7890, 0.2814, 0.7886, 0.5895, 0.7539]])\n","Second tensor: tensor([[0.8823, 0.9150, 0.3829, 0.9593, 0.3904, 0.6009, 0.2566]])\n","Matrix Multiply is equal to: tensor([[3.2618],\n","        [3.4084],\n","        [2.4866],\n","        [1.4525],\n","        [1.7079],\n","        [2.7291],\n","        [2.9204]])\n"]}]},{"cell_type":"markdown","source":["### 5. Speaking of random seeds, we saw how to set it with `torch.manual_seed()` but is there a GPU equivalent? (hint: you'll need to look into the documentation for `torch.cuda` for this one)\n","  * If there is, set the GPU random seed to `1234`."],"metadata":{"id":"ezY6ks9Cis37"}},{"cell_type":"code","source":["# Set random seed on the GPU\n","RANDOM_SEED = 42\n","torch.cuda.manual_seed(RANDOM_SEED)\n"],"metadata":{"id":"_LKWcfSTjp00","executionInfo":{"status":"ok","timestamp":1713634291407,"user_tz":600,"elapsed":357,"user":{"displayName":"Its Erphan","userId":"06973834403098047756"}}},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":["\n","### 6. Create two random tensors of shape `(2, 3)` and send them both to the GPU (you'll need access to a GPU for this). Set `torch.manual_seed(1234)` when creating the tensors (this doesn't have to be the GPU random seed). The output should be something like:\n","\n","```\n","Device: cuda\n","(tensor([[0.0290, 0.4019, 0.2598],\n","         [0.3666, 0.0583, 0.7006]], device='cuda:0'),\n"," tensor([[0.0518, 0.4681, 0.6738],\n","         [0.3315, 0.7837, 0.5631]], device='cuda:0'))\n","```"],"metadata":{"id":"Ir9qSaj6it4n"}},{"cell_type":"code","source":["# Set random seed\n","# Check for access to GPU\n","# Create two random tensors on GPU\n","\n","#Collab Doesn't let me use GPU atm..."],"metadata":{"id":"azXExiFZj5nm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["\n","### 7. Perform a matrix multiplication on the tensors you created in 6 (again, you may have to adjust the shapes of one of the tensors).\n","\n","The output should look like:\n","```\n","(tensor([[0.3647, 0.4709],\n","         [0.5184, 0.5617]], device='cuda:0'), torch.Size([2, 2]))\n","```"],"metadata":{"id":"5TlAxeiSiu1y"}},{"cell_type":"code","source":["# Perform matmul on tensor_A and tensor_B\n","RANDOM_SEED = 1234\n","torch.manual_seed(RANDOM_SEED)\n","x = torch.rand([2, 3])\n","torch.manual_seed(RANDOM_SEED)\n","y = torch.rand([2, 3])\n","MM = torch.matmul(x, y.reshape([3, 2]))\n","print(f\"MM : {MM},{MM.device}, {MM.shape} \")\n"],"metadata":{"id":"fAeG7ox0lHEO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1713634709201,"user_tz":600,"elapsed":339,"user":{"displayName":"Its Erphan","userId":"06973834403098047756"}},"outputId":"e37745bc-b89a-48b5-faec-c49d2ad5be72"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["MM : tensor([[0.1204, 0.3411],\n","        [0.0666, 0.6596]]),cpu, torch.Size([2, 2]) \n"]}]},{"cell_type":"markdown","source":["### 8. Find the maximum and minimum values of the output of 7."],"metadata":{"id":"G7qfa5CSivwg"}},{"cell_type":"code","source":["# Find max\n","print(torch.max(MM))\n","# Find min\n","print(torch.min(MM))"],"metadata":{"id":"Fu8_3mZpllOd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1713634742913,"user_tz":600,"elapsed":1,"user":{"displayName":"Its Erphan","userId":"06973834403098047756"}},"outputId":"273f4fcd-2454-4cc9-e987-e7c05b89200e"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor(0.6596)\n","tensor(0.0666)\n"]}]},{"cell_type":"markdown","source":["### 9. Find the maximum and minimum index values of the output of 7."],"metadata":{"id":"wrTj5FgNiw47"}},{"cell_type":"code","source":["# Find arg max\n","\n","print(torch.argmax(MM))\n","# Find arg min\n","print(torch.argmin(MM))"],"metadata":{"id":"CCEKt4K2lsfQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1713634788096,"user_tz":600,"elapsed":4,"user":{"displayName":"Its Erphan","userId":"06973834403098047756"}},"outputId":"5e7b11c0-99a1-495a-89b6-9188effd4046"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor(3)\n","tensor(2)\n"]}]},{"cell_type":"markdown","source":["\n","### 10. Make a random tensor with shape `(1, 1, 1, 10)` and then create a new tensor with all the `1` dimensions removed to be left with a tensor of shape `(10)`. Set the seed to `7` when you create it and print out the first tensor and it's shape as well as the second tensor and it's shape.\n","\n","The output should look like:\n","\n","```\n","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","```"],"metadata":{"id":"hmeybz4uixy7"}},{"cell_type":"code","source":["# Set seed\n","RANDOM_SEED = 7\n","\n","\n","# Create random tensor\n","torch.manual_seed(RANDOM_SEED)\n","tensor_0 = torch.rand([1, 1, 1, 10])\n","\n","# Remove single dimensions\n","tensor_0_squeezed = tensor_0.squeeze()\n","\n","# Print out tensors and their shapes\n","print(f\"The first tensor: {tensor_0}, and its shape:{tensor_0.shape}\")\n","\n","print(f\"\\nThe second tensor: {tensor_0_squeezed}, and its shape:{tensor_0_squeezed.shape}\")"],"metadata":{"id":"TQ9zbRzVl1jV","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1713634985390,"user_tz":600,"elapsed":5,"user":{"displayName":"Its Erphan","userId":"06973834403098047756"}},"outputId":"69ec90d0-b6bb-4502-de12-1fce748260ae"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["The first tensor: tensor([[[[0.5349, 0.1988, 0.6592, 0.6569, 0.2328, 0.4251, 0.2071, 0.6297,\n","           0.3653, 0.8513]]]]), and its shape:torch.Size([1, 1, 1, 10])\n","\n","The second tensor: tensor([0.5349, 0.1988, 0.6592, 0.6569, 0.2328, 0.4251, 0.2071, 0.6297, 0.3653,\n","        0.8513]), and its shape:torch.Size([10])\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"zB5SIT16mr9f"},"execution_count":null,"outputs":[]}]}