Learning about tensors
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--index-url https://download.pytorch.org/whl/cu117
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--index-url https://download.pytorch.org/whl/cu117
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torchvision
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torchvision
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--index-url https://download.pytorch.org/whl/cu117
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--index-url https://download.pytorch.org/whl/cu117
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torchaudio
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torchaudio
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numpy
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312
tensors.ipynb
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312
tensors.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import numpy as np\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Tensors are a specialized data structure that are very similar to arrays and matrices. In PyTorch, \n",
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"we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters.\n",
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"Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators. In fact, tensors and NumPy arrays can often share the same underlying memory, eliminating the need to copy data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Ones Tensor: \n",
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" tensor([[1., 1.],\n",
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" [1., 1.]]) \n",
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"\n",
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"Random Tensor: \n",
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" tensor([[0.3247, 0.4553],\n",
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" [0.8209, 0.3013]]) \n",
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"\n",
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"Random Tensor: \n",
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" tensor([[0.5109, 0.3653, 0.7545],\n",
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" [0.7229, 0.7191, 0.2993]])\n",
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"\n",
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"Ones Tensor: \n",
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" tensor([[1., 1., 1.],\n",
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" [1., 1., 1.]])\n",
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"\n",
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"Zeros Tensor: \n",
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" tensor([[0., 0., 0.],\n",
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" [0., 0., 0.]])\n"
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]
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}
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],
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"source": [
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"# Initializing a tensor \n",
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"\n",
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"# Directly from data\n",
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"data = [[1,2], [3,4]]\n",
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"x_data = torch.Tensor(data)\n",
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"\n",
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"# From NumPy array\n",
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"np_array = np.array(data)\n",
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"x_np = torch.from_numpy(np_array)\n",
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"\n",
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"# From another tensor\n",
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"x_ones = torch.ones_like(x_data) # retains the properties of x_data\n",
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"print(f\"Ones Tensor: \\n {x_ones} \\n\")\n",
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"x_rand = torch.rand_like(x_data, dtype=torch.float)\n",
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"print(f\"Random Tensor: \\n {x_rand} \\n\")\n",
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"\n",
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"# Random values or constant values\n",
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"shape = (2,3,)\n",
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"rand_tensor = torch.rand(shape)\n",
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"ones_tensor = torch.ones(shape)\n",
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"zeros_tensor = torch.zeros(shape)\n",
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"\n",
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"print(f\"Random Tensor: \\n {rand_tensor}\\n\")\n",
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"print(f\"Ones Tensor: \\n {ones_tensor}\\n\")\n",
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"print(f\"Zeros Tensor: \\n {zeros_tensor}\")\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Tensor attributes describe their shape, datatype, and the device on which they are stored."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Shape of tensor: torch.Size([3, 4])\n",
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"Datatype of tensor: torch.float32\n",
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"Device tensor is stored on: cpu\n"
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]
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}
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],
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"source": [
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"tensor = torch.rand(3,4)\n",
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"\n",
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"print(f\"Shape of tensor: {tensor.shape}\")\n",
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"print(f\"Datatype of tensor: {tensor.dtype}\")\n",
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"print(f\"Device tensor is stored on: {tensor.device}\")\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Over 100 tensor operations, including arithmetic, linear algebra, matrix manipulation (transposing, indexing, slicing), sampling and more.\n",
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"Each of these operations can be run on the GPU (at typically higher speeds than on a CPU). If you’re using Colab, allocate a GPU by going to Runtime > Change runtime type > GPU.\n",
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"By default, tensors are created on the CPU. We need to explicitly move tensors to the GPU using .to method (after checking for GPU availability). Keep in mind that copying large tensors across devices can be expensive in terms of time and memory!\n",
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"\n",
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"Example: We move our tensor to the GPU if available\n",
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"\n",
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"\n",
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"```python\n",
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"\n",
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"if torch.cuda.is_available():\n",
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" tensor = tensor.to(\"cuda\")\n",
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"\n",
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"```\n",
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"\n"
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]
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},
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"First row: tensor([1., 1., 1., 1.])\n",
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"first column: tensor([1., 1., 1., 1.])\n",
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"Last column: tensor([1., 1., 1., 1.])\n",
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"tensor([[1., 0., 1., 1.],\n",
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" [1., 0., 1., 1.],\n",
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" [1., 0., 1., 1.],\n",
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" [1., 0., 1., 1.]])\n"
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]
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}
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],
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"source": [
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"# Indexing and slicing tensors\n",
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"tensor = torch.ones(4,4)\n",
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"print(f\"First row: {tensor[0]}\")\n",
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"print (f\"first column: {tensor[:, 0]}\")\n",
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"print(f\"Last column: {tensor[...,-1]}\")\n",
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"tensor[:,1] = 0\n",
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"print(tensor)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
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" [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
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" [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
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" [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.]])\n"
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]
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}
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],
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"source": [
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"# Joining tensors\n",
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"t1 = torch.cat([tensor, tensor, tensor], dim=1)\n",
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"print(t1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[1., 0., 1., 1.],\n",
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" [1., 0., 1., 1.],\n",
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" [1., 0., 1., 1.],\n",
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" [1., 0., 1., 1.]])"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Arithmetic operations \n",
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"# # computes the matrix multiplication between two tensors\n",
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"\n",
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"# 'tensor.T' return the transpose of a tensor\n",
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"y1 = tensor @ tensor.T\n",
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"y2 = tensor.matmul(tensor.T)\n",
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"\n",
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"y3 = torch.rand_like(y1)\n",
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"torch.matmul(tensor, tensor.T, out=y3)\n",
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"\n",
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"# Computes the element-wise product\n",
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"z1 = tensor * tensor\n",
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"z2 = tensor.mul(tensor)\n",
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"\n",
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"z3 = torch.rand_like(tensor)\n",
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"torch.mul(tensor, tensor, out=z3)\n",
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"\n",
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"\n"
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]
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},
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"12.0 <class 'float'>\n"
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]
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}
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],
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"source": [
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"# Gets the python value from a tensor\n",
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"agg = tensor.sum()\n",
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"agg_item = agg.item()\n",
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"print(agg_item, type(agg_item))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Operations that store the result into the operand are called in-place. They are denoted by a _ suffix"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[1., 0., 1., 1.],\n",
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" [1., 0., 1., 1.],\n",
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" [1., 0., 1., 1.],\n",
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" [1., 0., 1., 1.]])\n",
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"\n",
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"tensor([[6., 5., 6., 6.],\n",
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" [6., 5., 6., 6.],\n",
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" [6., 5., 6., 6.],\n",
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" [6., 5., 6., 6.]])\n"
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]
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}
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],
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"source": [
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"# in-place operations\n",
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"print(f\"{tensor}\\n\")\n",
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"tensor.add_(5)\n",
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"print(tensor)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.10"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "f4b5116e1c1eac4da82e4f519e811a9a213a412fad4fdb2c86d0bd3e5d22b3b4"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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