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"execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "1VxEOik46Y4i", - "outputId": "f3141076-29bc-4600-c1c3-1586b1fe2292" + "outputId": "f3141076-29bc-4600-c1c3-1586b1fe2292", + "ExecuteTime": { + "end_time": "2024-10-03T13:53:53.168444Z", + "start_time": "2024-10-03T13:53:51.534003Z" + } }, + "source": [ + "import torch\n", + "torch.__version__" + ], "outputs": [ { "data": { "text/plain": [ - "'1.13.1+cu116'" + "'2.4.1'" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "import torch\n", - "torch.__version__" - ] + "execution_count": 2 }, { "cell_type": "markdown", @@ -1719,10 +1723,10 @@ "evalue": "mat1 and mat2 shapes cannot be multiplied (3x2 and 3x2)", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/daniel/code/pytorch/pytorch-course/pytorch-deep-learning/00_pytorch_fundamentals.ipynb Cell 75\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m tensor_A \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mtensor([[\u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m],\n\u001b[1;32m 3\u001b[0m [\u001b[39m3\u001b[39m, \u001b[39m4\u001b[39m],\n\u001b[1;32m 4\u001b[0m [\u001b[39m5\u001b[39m, \u001b[39m6\u001b[39m]], dtype\u001b[39m=\u001b[39mtorch\u001b[39m.\u001b[39mfloat32)\n\u001b[1;32m 6\u001b[0m tensor_B \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mtensor([[\u001b[39m7\u001b[39m, \u001b[39m10\u001b[39m],\n\u001b[1;32m 7\u001b[0m [\u001b[39m8\u001b[39m, \u001b[39m11\u001b[39m], \n\u001b[1;32m 8\u001b[0m [\u001b[39m9\u001b[39m, \u001b[39m12\u001b[39m]], dtype\u001b[39m=\u001b[39mtorch\u001b[39m.\u001b[39mfloat32)\n\u001b[0;32m---> 10\u001b[0m torch\u001b[39m.\u001b[39;49mmatmul(tensor_A, tensor_B)\n", - "\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (3x2 and 3x2)" + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mRuntimeError\u001B[0m Traceback (most recent call last)", + "\u001B[1;32m/home/daniel/code/pytorch/pytorch-course/pytorch-deep-learning/00_pytorch_fundamentals.ipynb Cell 75\u001B[0m in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[1;32m 2\u001B[0m tensor_A \u001B[39m=\u001B[39m torch\u001B[39m.\u001B[39mtensor([[\u001B[39m1\u001B[39m, \u001B[39m2\u001B[39m],\n\u001B[1;32m 3\u001B[0m [\u001B[39m3\u001B[39m, \u001B[39m4\u001B[39m],\n\u001B[1;32m 4\u001B[0m [\u001B[39m5\u001B[39m, \u001B[39m6\u001B[39m]], dtype\u001B[39m=\u001B[39mtorch\u001B[39m.\u001B[39mfloat32)\n\u001B[1;32m 6\u001B[0m tensor_B \u001B[39m=\u001B[39m torch\u001B[39m.\u001B[39mtensor([[\u001B[39m7\u001B[39m, \u001B[39m10\u001B[39m],\n\u001B[1;32m 7\u001B[0m [\u001B[39m8\u001B[39m, \u001B[39m11\u001B[39m], \n\u001B[1;32m 8\u001B[0m [\u001B[39m9\u001B[39m, \u001B[39m12\u001B[39m]], dtype\u001B[39m=\u001B[39mtorch\u001B[39m.\u001B[39mfloat32)\n\u001B[0;32m---> 10\u001B[0m torch\u001B[39m.\u001B[39;49mmatmul(tensor_A, tensor_B)\n", + "\u001B[0;31mRuntimeError\u001B[0m: mat1 and mat2 shapes cannot be multiplied (3x2 and 3x2)" ] } ], @@ -1910,6 +1914,7 @@ "You can create your own matrix multiplication visuals like this at http://matrixmultiplication.xyz/.\n", "\n", "> **Note:** A matrix multiplication like this is also referred to as the [**dot product**](https://www.mathsisfun.com/algebra/vectors-dot-product.html) of two matrices.\n", + "> Refer to this excellent course to learn more about matrix multiplication https://www.khanacademy.org/math/linear-algebra\n", "\n" ] }, @@ -3690,10 +3695,10 @@ "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/daniel/code/pytorch/pytorch-course/pytorch-deep-learning/00_pytorch_fundamentals.ipynb Cell 157\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# If tensor is on GPU, can't transform it to NumPy (this will error)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m tensor_on_gpu\u001b[39m.\u001b[39;49mnumpy()\n", - "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first." + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", + "\u001B[1;32m/home/daniel/code/pytorch/pytorch-course/pytorch-deep-learning/00_pytorch_fundamentals.ipynb Cell 157\u001B[0m in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[39m# If tensor is on GPU, can't transform it to NumPy (this will error)\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m tensor_on_gpu\u001B[39m.\u001B[39;49mnumpy()\n", + "\u001B[0;31mTypeError\u001B[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first." ] } ], diff --git a/03_pytorch_computer_vision.ipynb b/03_pytorch_computer_vision.ipynb index afcb1a90..86209fe7 100644 --- a/03_pytorch_computer_vision.ipynb +++ b/03_pytorch_computer_vision.ipynb @@ -4549,6 +4549,7 @@ "\n", "## Extra-curriculum\n", "* **Watch:** [MIT's Introduction to Deep Computer Vision](https://www.youtube.com/watch?v=iaSUYvmCekI&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI&index=3) lecture. This will give you a great intuition behind convolutional neural networks.\n", + "* Complete https://www.kaggle.com/learn/intro-to-machine-learning\n", "* Spend 10-minutes clicking thorugh the different options of the [PyTorch vision library](https://pytorch.org/vision/stable/index.html), what different modules are available?\n", "* Lookup \"most common convolutional neural networks\", what architectures do you find? Are any of them contained within the [`torchvision.models`](https://pytorch.org/vision/stable/models.html) library? What do you think you could do with these?\n", "* For a large number of pretrained PyTorch computer vision models as well as many different extensions to PyTorch's computer vision functionalities check out the [PyTorch Image Models library `timm`](https://github.com/rwightman/pytorch-image-models/) (Torch Image Models) by Ross Wightman." diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 00000000..7d3f3662 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,647 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "filelock" +version = "3.16.1" +description = "A platform independent file lock." +optional = false +python-versions = ">=3.8" +files = [ + {file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"}, + {file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"}, +] + +[package.extras] 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