0_tensor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torch'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-eb42ca6e4af3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
     ]
    }
   ],
   "source": [
    "!pip install torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.1210e-44, 0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00]])\n"
     ]
    }
   ],
   "source": [
    "# 初期化しないと値はバラバラに\n",
    "x = torch.empty(5, 3)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4738, 0.5964, 0.6824],\n",
      "        [0.7134, 0.3003, 0.0326],\n",
      "        [0.1522, 0.4193, 0.7227],\n",
      "        [0.1430, 0.1306, 0.5046],\n",
      "        [0.8452, 0.8657, 0.8715]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.rand(5, 3)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": "code",
   "execution_count": 16,
   "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": "code",
   "execution_count": 24,
   "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([[-0.2047,  1.8272, -1.0836],\n",
      "        [-1.7062,  1.3481, -2.3612],\n",
      "        [-0.7970,  0.1375,  0.1307],\n",
      "        [ 0.7806, -0.7434,  0.7792],\n",
      "        [ 1.7760, -0.7863,  0.2050]])\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": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5, 3])\n",
      "torch.Size([5, 3])\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(x.size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-0.1761,  2.0925, -1.0603],\n",
      "        [-1.3685,  1.7579, -1.7458],\n",
      "        [-0.6246,  1.0586,  0.7204],\n",
      "        [ 1.2610, -0.5802,  0.9026],\n",
      "        [ 2.6919, -0.1647,  0.4457]])\n"
     ]
    }
   ],
   "source": [
    "y = torch.rand(5, 3)\n",
    "print(x + y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-0.1761,  2.0925, -1.0603],\n",
      "        [-1.3685,  1.7579, -1.7458],\n",
      "        [-0.6246,  1.0586,  0.7204],\n",
      "        [ 1.2610, -0.5802,  0.9026],\n",
      "        [ 2.6919, -0.1647,  0.4457]])\n"
     ]
    }
   ],
   "source": [
    "result = torch.empty(5, 3)\n",
    "torch.add(x, y, out=result) # output先を指定\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-0.2047,  1.8272, -1.0836],\n",
      "        [-1.7062,  1.3481, -2.3612],\n",
      "        [-0.7970,  0.1375,  0.1307],\n",
      "        [ 0.7806, -0.7434,  0.7792],\n",
      "        [ 1.7760, -0.7863,  0.2050]])\n",
      "tensor([[0.0286, 0.2654, 0.0233],\n",
      "        [0.3377, 0.4098, 0.6153],\n",
      "        [0.1724, 0.9212, 0.5897],\n",
      "        [0.4804, 0.1632, 0.1234],\n",
      "        [0.9159, 0.6216, 0.2407]])\n",
      "tensor([[-0.1761,  2.0925, -1.0603],\n",
      "        [-1.3685,  1.7579, -1.7458],\n",
      "        [-0.6246,  1.0586,  0.7204],\n",
      "        [ 1.2610, -0.5802,  0.9026],\n",
      "        [ 2.6919, -0.1647,  0.4457]])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(x)\n",
    "print(y)\n",
    "y.add_(x) # add_  値自体を置き換える\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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)   # viewでreshape    x自体は変化しないことに注意\n",
    "z = x.view(-1, 8) # -1はよしなにやってねサイン\n",
    "\n",
    "print(x.shape, y.shape, z.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.1499,  0.7208, -1.1816, -0.3610],\n",
      "        [ 1.4137,  0.4858, -0.2342, -0.3933],\n",
      "        [-0.8618,  1.0866,  2.3717,  0.9323],\n",
      "        [ 0.1567,  1.2658,  0.4764,  0.8672]])\n",
      "tensor([ 0.0000,  0.0000, -1.1816, -0.3610,  1.4137,  0.4858, -0.2342, -0.3933,\n",
      "        -0.8618,  1.0866,  2.3717,  0.9323,  0.1567,  1.2658,  0.4764,  0.8672])\n",
      "tensor([[ 0.0000,  0.0000, -1.1816, -0.3610],\n",
      "        [ 1.4137,  0.4858, -0.2342, -0.3933],\n",
      "        [-0.8618,  1.0866,  2.3717,  0.9323],\n",
      "        [ 0.1567,  1.2658,  0.4764,  0.8672]])\n"
     ]
    }
   ],
   "source": [
    "# view()して作ったテンソルも同じメモリ空間を指しているので、yに値を入れるとxも変化する\n",
    "x = torch.randn(4, 4)\n",
    "print(x)\n",
    "y = x.view(16)   \n",
    "y[0:2] = 0\n",
    "\n",
    "print(y)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.0962])\n",
      "0.09616874158382416\n"
     ]
    }
   ],
   "source": [
    "x = torch.randn(1)\n",
    "print(x)\n",
    "print(x.item()) # 普通のpythonの値を取り出す   値が一つだけの時しか使えない"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1., 1., 1., 1., 1.]) <class 'torch.Tensor'>\n",
      "[1. 1. 1. 1. 1.] <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "a = torch.ones(5)\n",
    "print(a, type(a))\n",
    "\n",
    "b = a.numpy() # numpyのarrayに変換\n",
    "print(b, type(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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) # メモリを共有してるのでaを変えるとbも変化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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) # numpy -> torch\n",
    "np.add(a, 1, out=a)\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GPUがある場合\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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