Normal operation on small tensor

test-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.1\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.2906, 0.7618, 0.0318, 0.6305, 0.3093],\n",
      "        [0.3360, 0.3016, 0.7235, 0.1689, 0.7401],\n",
      "        [0.4158, 0.5294, 0.1691, 0.4574, 0.1047],\n",
      "        [0.1103, 0.1274, 0.8512, 0.3206, 0.2296],\n",
      "        [0.4130, 0.5525, 0.9446, 0.6805, 0.6599],\n",
      "        [0.4207, 0.9097, 0.1605, 0.9655, 0.4070],\n",
      "        [0.5831, 0.1240, 0.9495, 0.3958, 0.5785],\n",
      "        [0.9375, 0.8432, 0.1809, 0.7320, 0.1422],\n",
      "        [0.2422, 0.8776, 0.7639, 0.4937, 0.0692],\n",
      "        [0.7932, 0.7240, 0.3930, 0.9832, 0.7180]])\n"
     ]
    }
   ],
   "source": [
    "a = torch.rand(10, 5)\n",
    "b = a.clone()\n",
    "c = a.clone()\n",
    "d = a.clone()\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.2906,  0.7618,  0.0318,  0.6305,  0.3093],\n",
      "        [ 0.3360,  0.3016,  0.7235,  0.1689,  0.7401],\n",
      "        [ 0.4158,  0.5294,  0.1691,  0.4574,  0.1047],\n",
      "        [ 0.1103,  0.1274,  0.8512,  0.3206,  0.2296],\n",
      "        [ 0.4130,  0.5525,  0.9446,  0.6805,  0.6599],\n",
      "        [ 0.4207,  0.9097,  0.1605,  0.9655,  0.4070],\n",
      "        [ 0.5831,  0.1240,  0.9495,  0.3958,  0.5785],\n",
      "        [ 0.9375,  0.8432,  0.1809,  0.7320,  0.1422],\n",
      "        [ 0.2422,  0.8776,  0.7639,  0.4937,  0.0692],\n",
      "        [-0.4388, -0.2975,  0.6762,  0.7267, -0.8753]])\n"
     ]
    }
   ],
   "source": [
    "a[9:10].normal_(0, 1)\n",
    "print(a)\n",
    "# Note that last row has changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.2906,  0.7618,  0.0318,  0.6305,  0.3093],\n",
      "        [ 0.3360,  0.3016,  0.7235,  0.1689,  0.7401],\n",
      "        [ 0.4158,  0.5294,  0.1691,  0.4574,  0.1047],\n",
      "        [ 0.1103,  0.1274,  0.8512,  0.3206,  0.2296],\n",
      "        [ 0.4130,  0.5525,  0.9446,  0.6805,  0.6599],\n",
      "        [ 0.4207,  0.9097,  0.1605,  0.9655,  0.4070],\n",
      "        [ 0.5831,  0.1240,  0.9495,  0.3958,  0.5785],\n",
      "        [ 0.9375,  0.8432,  0.1809,  0.7320,  0.1422],\n",
      "        [ 0.2422,  0.8776,  0.7639,  0.4937,  0.0692],\n",
      "        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000]])\n"
     ]
    }
   ],
   "source": [
    "b[9:10] = -1\n",
    "print(b)\n",
    "# Note that last row has changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.2906,  0.7618,  0.0318,  0.6305,  0.3093],\n",
      "        [ 0.3360,  0.3016,  0.7235,  0.1689,  0.7401],\n",
      "        [ 0.4158,  0.5294,  0.1691,  0.4574,  0.1047],\n",
      "        [ 0.1103,  0.1274,  0.8512,  0.3206,  0.2296],\n",
      "        [ 0.4130,  0.5525,  0.9446,  0.6805,  0.6599],\n",
      "        [ 0.4207,  0.9097,  0.1605,  0.9655,  0.4070],\n",
      "        [ 0.5831,  0.1240,  0.9495,  0.3958,  0.5785],\n",
      "        [ 0.9375,  0.8432,  0.1809,  0.7320,  0.1422],\n",
      "        [ 0.2422,  0.8776,  0.7639,  0.4937,  0.0692],\n",
      "        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000]])\n"
     ]
    }
   ],
   "source": [
    "c[9:10].copy_(torch.rand(1, 5).fill_(-1))\n",
    "print(c)\n",
    "# Note that last row has changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.2906,  0.7618,  0.0318,  0.6305,  0.3093],\n",
      "        [ 0.3360,  0.3016,  0.7235,  0.1689,  0.7401],\n",
      "        [ 0.4158,  0.5294,  0.1691,  0.4574,  0.1047],\n",
      "        [ 0.1103,  0.1274,  0.8512,  0.3206,  0.2296],\n",
      "        [ 0.4130,  0.5525,  0.9446,  0.6805,  0.6599],\n",
      "        [ 0.4207,  0.9097,  0.1605,  0.9655,  0.4070],\n",
      "        [ 0.5831,  0.1240,  0.9495,  0.3958,  0.5785],\n",
      "        [ 0.9375,  0.8432,  0.1809,  0.7320,  0.1422],\n",
      "        [ 0.2422,  0.8776,  0.7639,  0.4937,  0.0692],\n",
      "        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000]])\n"
     ]
    }
   ],
   "source": [
    "d[9:10].fill_(-1)\n",
    "print(d)\n",
    "# Note that last row has changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}