1. pytorch_basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2], [3, 4]]\n"
     ]
    }
   ],
   "source": [
    "arr = [[1,2], [3,4]]\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  2\n",
       " 3  4\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.Tensor(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create matrices with default values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.],\n",
       "       [ 1.,  1.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.ones((2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.91085131,  0.77587517],\n",
       "       [ 0.01418388,  0.72673482]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.2396  0.0187\n",
       " 0.7332  0.6539\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.rand(2,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seeding (to keep random values same)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.4236548 ,  0.64589411],\n",
       "       [ 0.43758721,  0.891773  ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5488135 ,  0.71518937],\n",
       "       [ 0.60276338,  0.54488318]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seed\n",
    "np.random.seed(0)\n",
    "np.random.rand(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.5488  0.5928\n",
       " 0.7152  0.8443\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#torch seed\n",
    "torch.manual_seed(0)\n",
    "torch.rand(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.5488  0.5928\n",
       " 0.7152  0.8443\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "torch.rand(2,2)\n",
    "#same random array again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.6028  0.8579\n",
       " 0.5449  0.8473\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#no seed\n",
    "torch.rand(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy and Torch bridge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy to torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np_array = np.ones((2,2 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.]\n",
      " [ 1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "print(np_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(np_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1  1\n",
      " 1  1\n",
      "[torch.DoubleTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#convert to tensor\n",
    "torch_tensor = torch.from_numpy(np_array)\n",
    "print(torch_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "can't convert a given np.ndarray to a tensor - it has an invalid type. The only supported types are: double, float, int64, int32, and uint8.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-5e33c05650e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Data types\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mnp_array_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp_array_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m: can't convert a given np.ndarray to a tensor - it has an invalid type. The only supported types are: double, float, int64, int32, and uint8."
     ]
    }
   ],
   "source": [
    "#Data types\n",
    "np_array_new = np.ones((2, 2), dtype=np.int8)\n",
    "torch.from_numpy(np_array_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy can convert some data types, but not all\n",
    "Conversions are supported for:\n",
    "1. double\n",
    "2. float\n",
    "3. int64, int32, unit8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.LongTensor of size 2x2]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_new = np.ones((2, 2), dtype=np.int64)\n",
    "torch.from_numpy(np_array_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.IntTensor of size 2x2]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_new = np.ones((2, 2), dtype=np.int32)\n",
    "torch.from_numpy(np_array_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.ByteTensor of size 2x2]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_new = np.ones((2, 2), dtype=np.uint8)\n",
    "torch.from_numpy(np_array_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.DoubleTensor of size 2x2]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_new = np.ones((2, 2), dtype=np.float64)\n",
    "torch.from_numpy(np_array_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_new = np.ones((2, 2), dtype=np.float32)\n",
    "torch.from_numpy(np_array_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.DoubleTensor of size 2x2]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_new = np.ones((2, 2), dtype=np.double)\n",
    "torch.from_numpy(np_array_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Torch to Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "torch_tensor = torch.ones(2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.FloatTensor"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(torch_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "torch_to_numpy = torch_tensor.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(torch_to_numpy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Switching between GPU and CPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#CPU\n",
    "tensor_cpu = torch.ones(2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#CPU to GPU\n",
    "if torch.cuda.is_available():\n",
    "    tensor_cpu.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GPU to CPU\n",
    "tensor_cpu.cpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensor operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resize tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1  1\n",
      " 1  1\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = torch.ones(2, 2)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 2])\n"
     ]
    }
   ],
   "source": [
    "print(a.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1\n",
       " 1\n",
       " 1\n",
       " 1\n",
       "[torch.FloatTensor of size 4]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.view(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.view(4).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element wise addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1  1\n",
      " 1  1\n",
      "[torch.FloatTensor of size 2x2]\n",
      " \n",
      " 1  1\n",
      " 1  1\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = torch.ones(2,2)\n",
    "b = torch.ones(2,2)\n",
    "\n",
    "print(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normal addition \n",
      " 2  2\n",
      " 2  2\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n",
      "with torch.add \n",
      " 2  2\n",
      " 2  2\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Element wise Addition\n",
    "\n",
    "c = a + b\n",
    "print(\"normal addition\", c)\n",
    "\n",
    "#OR\n",
    "\n",
    "c = torch.add(a, b)\n",
    "print(\"with torch.add\", c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old c tensor \n",
      " 2  2\n",
      " 2  2\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n",
      "New c tensor \n",
      " 3  3\n",
      " 3  3\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# in-place addition\n",
    "print(\"Old c tensor\", c)\n",
    "\n",
    "# _ does inplace addition\n",
    "c.add_(a)\n",
    "\n",
    "print(\"New c tensor\", c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element wise-subtraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original tensors \n",
      " 1  1\n",
      " 1  1\n",
      "[torch.FloatTensor of size 2x2]\n",
      " \n",
      " 1  1\n",
      " 1  1\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n",
      "Subtracted normally \n",
      " 0  0\n",
      " 0  0\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = torch.ones(2,2)\n",
    "b = torch.ones(2,2)\n",
    "\n",
    "print(\"original tensors\", a, b)\n",
    "\n",
    "print(\"Subtracted normally\", a - b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0  0\n",
      " 0  0\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n",
      "\n",
      " 1  1\n",
      " 1  1\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(a.sub(b))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0  0\n",
      " 0  0\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n",
      "\n",
      " 0  0\n",
      " 0  0\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#inplace\n",
    "print(a.sub_(b))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Element wise multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0  0\n",
      " 0  0\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = torch.ones(2,2)\n",
    "b = torch.zeros(2,2)\n",
    "\n",
    "print(a * b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0  0\n",
      " 0  0\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n",
      "\n",
      " 1  1\n",
      " 1  1\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(torch.mul(a, b))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0  0\n",
      " 0  0\n",
      "[torch.FloatTensor of size 2x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#In Place\n",
    "print(a.mul_(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element-wise Division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0  0\n",
       " 0  0\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.ones(2, 2)\n",
    "b = torch.zeros(2, 2)\n",
    "\n",
    "b/a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0  0\n",
       " 0  0\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.div(b, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0  0\n",
       " 0  0\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.div_(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensor Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9 , 10])\n",
    "a.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 5.5000\n",
       "[torch.FloatTensor of size 1]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.mean(dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "dimension out of range (expected to be in range of [-1, 0], but got 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-81aec0cf1c00>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m: dimension out of range (expected to be in range of [-1, 0], but got 1)"
     ]
    }
   ],
   "source": [
    "a.mean(dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = torch.Tensor([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
    "b.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 2.5000\n",
       " 6.5000\n",
       "[torch.FloatTensor of size 2]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.mean(dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensor Standard Deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1.5811\n",
       "[torch.FloatTensor of size 1]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.Tensor([1, 2, 3, 4, 5])\n",
    "a.std(dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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