extra_torch_tensor.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original Notebook\n",
    "\n",
    "### Introduction to PyTorch Tensor\n",
    "\n",
    "**Reference**: [\"What is PyTorch?\"](https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py) by [Soumith Chintala](http://soumith.ch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "What is PyTorch?\n",
    "================\n",
    "\n",
    "It’s a Python-based scientific computing package targeted at two sets of\n",
    "audiences:\n",
    "\n",
    "-  A replacement for NumPy to use the power of GPUs\n",
    "-  a deep learning research platform that provides maximum flexibility\n",
    "   and speed\n",
    "\n",
    "Getting Started\n",
    "---------------\n",
    "\n",
    "Tensors\n",
    "^^^^^^^\n",
    "\n",
    "Tensors are similar to NumPy’s ndarrays, with the addition being that\n",
    "Tensors can also be used on a GPU to accelerate computing.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"><h4>Note</h4><p>An uninitialized matrix is declared,\n",
    "    but does not contain definite known\n",
    "    values before it is used. When an\n",
    "    uninitialized matrix is created,\n",
    "    whatever values were in the allocated\n",
    "    memory at the time will appear as the initial values.</p></div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a 5x3 matrix, uninitialized:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 0., 0.],\n",
      "        [0., 0., 0.],\n",
      "        [0., 0., 0.],\n",
      "        [0., 0., 0.],\n",
      "        [0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.empty(5, 3)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Tensor"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a randomly initialized matrix:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1698, 0.9210, 0.6316],\n",
      "        [0.3320, 0.5131, 0.0979],\n",
      "        [0.7772, 0.0237, 0.9043],\n",
      "        [0.8486, 0.5272, 0.0556],\n",
      "        [0.6724, 0.9832, 0.3684]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.rand(5, 3)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a matrix filled zeros and of dtype long:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0, 0, 0],\n",
      "        [0, 0, 0],\n",
      "        [0, 0, 0],\n",
      "        [0, 0, 0],\n",
      "        [0, 0, 0]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.zeros(5, 3, dtype=torch.long)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a tensor directly from data:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([5.5000, 3.0000])\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([5.5, 3])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or create a tensor based on an existing tensor. These methods\n",
    "will reuse properties of the input tensor, e.g. dtype, unless\n",
    "new values are provided by user\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 1., 1.],\n",
      "        [1., 1., 1.],\n",
      "        [1., 1., 1.],\n",
      "        [1., 1., 1.],\n",
      "        [1., 1., 1.]], dtype=torch.float64)\n",
      "tensor([[ 0.5752,  1.0416,  1.3851],\n",
      "        [ 1.3198,  0.5191,  0.9312],\n",
      "        [ 0.7598, -1.3322, -0.6987],\n",
      "        [-1.6433,  0.9256, -0.1461],\n",
      "        [-0.0300, -0.1440,  0.7353]])\n"
     ]
    }
   ],
   "source": [
    "x = x.new_ones(5, 3, dtype=torch.double)      # new_* methods take in sizes\n",
    "print(x)\n",
    "\n",
    "x = torch.randn_like(x, dtype=torch.float)    # override dtype!\n",
    "print(x)                                      # result has the same size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get its size:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5, 3])\n"
     ]
    }
   ],
   "source": [
    "print(x.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"><h4>Note</h4><p>``torch.Size`` is in fact a tuple, so it supports all tuple operations.</p></div>\n",
    "\n",
    "Operations\n",
    "^^^^^^^^^^\n",
    "There are multiple syntaxes for operations. In the following\n",
    "example, we will take a look at the addition operation.\n",
    "\n",
    "Addition: syntax 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.1527,  1.2403,  1.4093],\n",
      "        [ 1.7279,  0.7558,  1.0488],\n",
      "        [ 0.9757, -0.4742, -0.0138],\n",
      "        [-1.4919,  1.5009,  0.1834],\n",
      "        [ 0.2617,  0.2694,  1.3259]])\n"
     ]
    }
   ],
   "source": [
    "y = torch.rand(5, 3)\n",
    "print(x + y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Addition: syntax 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.1527,  1.2403,  1.4093],\n",
      "        [ 1.7279,  0.7558,  1.0488],\n",
      "        [ 0.9757, -0.4742, -0.0138],\n",
      "        [-1.4919,  1.5009,  0.1834],\n",
      "        [ 0.2617,  0.2694,  1.3259]])\n"
     ]
    }
   ],
   "source": [
    "print(torch.add(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Addition: providing an output tensor as argument\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.1527,  1.2403,  1.4093],\n",
      "        [ 1.7279,  0.7558,  1.0488],\n",
      "        [ 0.9757, -0.4742, -0.0138],\n",
      "        [-1.4919,  1.5009,  0.1834],\n",
      "        [ 0.2617,  0.2694,  1.3259]])\n"
     ]
    }
   ],
   "source": [
    "result = torch.empty(5, 3)\n",
    "torch.add(x, y, out=result)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Addition: in-place\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.1527,  1.2403,  1.4093],\n",
      "        [ 1.7279,  0.7558,  1.0488],\n",
      "        [ 0.9757, -0.4742, -0.0138],\n",
      "        [-1.4919,  1.5009,  0.1834],\n",
      "        [ 0.2617,  0.2694,  1.3259]])\n"
     ]
    }
   ],
   "source": [
    "# adds x to y\n",
    "y.add_(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"><h4>Note</h4><p>Any operation that mutates a tensor in-place is post-fixed with an ``_``.\n",
    "    For example: ``x.copy_(y)``, ``x.t_()``, will change ``x``.</p></div>\n",
    "\n",
    "You can use standard NumPy-like indexing with all bells and whistles!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 1.0416,  0.5191, -1.3322,  0.9256, -0.1440])\n"
     ]
    }
   ],
   "source": [
    "print(x[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resizing: If you want to resize/reshape tensor, you can use ``torch.view``:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])\n"
     ]
    }
   ],
   "source": [
    "x = torch.randn(4, 4)\n",
    "y = x.view(16)\n",
    "z = x.view(-1, 8)  # the size -1 is inferred from other dimensions\n",
    "print(x.size(), y.size(), z.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have a one element tensor, use ``.item()`` to get the value as a\n",
    "Python number\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.0556])\n",
      "1.0555715560913086\n"
     ]
    }
   ],
   "source": [
    "x = torch.randn(1)\n",
    "print(x)\n",
    "print(x.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read later:**\n",
    "\n",
    "\n",
    "  100+ Tensor operations, including transposing, indexing, slicing,\n",
    "  mathematical operations, linear algebra, random numbers, etc.,\n",
    "  are described\n",
    "  `here <https://pytorch.org/docs/torch>`_.\n",
    "\n",
    "NumPy Bridge\n",
    "------------\n",
    "\n",
    "Converting a Torch Tensor to a NumPy array and vice versa is a breeze.\n",
    "\n",
    "The Torch Tensor and NumPy array will share their underlying memory\n",
    "locations (if the Torch Tensor is on CPU), and changing one will change\n",
    "the other.\n",
    "\n",
    "Converting a Torch Tensor to a NumPy Array\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1., 1., 1., 1., 1.])\n"
     ]
    }
   ],
   "source": [
    "a = torch.ones(5)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "b = a.numpy()\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See how the numpy array changed in value.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2., 2., 2., 2., 2.])\n",
      "[2. 2. 2. 2. 2.]\n"
     ]
    }
   ],
   "source": [
    "a.add_(1)\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting NumPy Array to Torch Tensor\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "See how changing the np array changed the Torch Tensor automatically\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2. 2. 2. 2. 2.]\n",
      "tensor([2., 2., 2., 2., 2.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones(5)\n",
    "b = torch.from_numpy(a)\n",
    "np.add(a, 1, out=a)\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the Tensors on the CPU except a CharTensor support converting to\n",
    "NumPy and back.\n",
    "\n",
    "CUDA Tensors\n",
    "------------\n",
    "\n",
    "Tensors can be moved onto any device using the ``.to`` method.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let us run this cell only if CUDA is available\n",
    "# We will use ``torch.device`` objects to move tensors in and out of GPU\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")          # a CUDA device object\n",
    "    y = torch.ones_like(x, device=device)  # directly create a tensor on GPU\n",
    "    x = x.to(device)                       # or just use strings ``.to(\"cuda\")``\n",
    "    z = x + y\n",
    "    print(z)\n",
    "    print(z.to(\"cpu\", torch.double))       # ``.to`` can also change dtype together!"
   ]
  }
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