Chapter1_Tensors/Tensor_Creation_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensor Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2017, 9, 26)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import date\n",
    "date.today()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "author = \"kyubyong. https://github.com/Kyubyong/pytorch_exercises\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.2.0_3'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.13.0'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE on notation\n",
    "\n",
    "    _x, _y, _z, ...: NumPy 0-d or 1-d arrays\n",
    "    _X, _Y, _Z, ...: NumPy 2-d or higer dimensional arrays\n",
    "    x, y, z, ...: 0-d or 1-d tensors\n",
    "    X, Y, Z, ...: 2-d or higher dimensional tensors\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From Python list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1. Convert a python list `a` into an int32 tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1  2  3\n",
      " 4  5  6\n",
      "[torch.IntTensor of size 2x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = [[1, 2, 3], [4, 5, 6]]\n",
    "X = torch.IntTensor(a)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q2. Create a float32 tensor of shape (3, 2), filled with 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 10  10\n",
      " 10  10\n",
      " 10  10\n",
      "[torch.FloatTensor of size 3x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.FloatTensor(3, 2).fill_(10)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From Numpy Array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q3. Convert a NumPy array _x into a tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1\n",
      " 2\n",
      " 3\n",
      "[torch.LongTensor of size 3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_x = np.array([1, 2, 3])\n",
    "x = torch.from_numpy(_x)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ones and zeros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q4. Create a 3-by-3 2-D tensor with ones on the diagonal and zeros elsewhere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1  0  0\n",
      " 0  1  0\n",
      " 0  0  1\n",
      "[torch.FloatTensor of size 3x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.eye(3)\n",
    "print(X)\n",
    "\n",
    "assert np.array_equal(X.numpy(), np.eye(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q5. Create a tensor with shape of (3, 2) filled with 1's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1  1\n",
      " 1  1\n",
      " 1  1\n",
      "[torch.FloatTensor of size 3x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.ones(3, 2)\n",
    "print(X)\n",
    "\n",
    "assert np.array_equal(X.numpy(), np.ones([3, 2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q6. Create a tensor with shape of (3, 2) filled with 0's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0  0\n",
      " 0  0\n",
      " 0  0\n",
      "[torch.FloatTensor of size 3x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.zeros(3, 2)\n",
    "print(X)\n",
    "\n",
    "assert np.array_equal(X.numpy(), np.zeros([3, 2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q7. Create a 1D tensor which looks like 2, 4, 6, 8, ..., 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    2.0e+00\n",
      "    4.0e+00\n",
      "    6.0e+00\n",
      "    8.0e+00\n",
      "    1.0e+01\n",
      "    1.2e+01\n",
      "    1.4e+01\n",
      "    1.6e+01\n",
      "    1.8e+01\n",
      "    2.0e+01\n",
      "    2.2e+01\n",
      "    2.4e+01\n",
      "    2.6e+01\n",
      "    2.8e+01\n",
      "    3.0e+01\n",
      "    3.2e+01\n",
      "    3.4e+01\n",
      "    3.6e+01\n",
      "    3.8e+01\n",
      "    4.0e+01\n",
      "    4.2e+01\n",
      "    4.4e+01\n",
      "    4.6e+01\n",
      "    4.8e+01\n",
      "    5.0e+01\n",
      "    5.2e+01\n",
      "    5.4e+01\n",
      "    5.6e+01\n",
      "    5.8e+01\n",
      "    6.0e+01\n",
      "    6.2e+01\n",
      "    6.4e+01\n",
      "    6.6e+01\n",
      "    6.8e+01\n",
      "    7.0e+01\n",
      "    7.2e+01\n",
      "    7.4e+01\n",
      "    7.6e+01\n",
      "    7.8e+01\n",
      "    8.0e+01\n",
      "    8.2e+01\n",
      "    8.4e+01\n",
      "    8.6e+01\n",
      "    8.8e+01\n",
      "    9.0e+01\n",
      "    9.2e+01\n",
      "    9.4e+01\n",
      "    9.6e+01\n",
      "    9.8e+01\n",
      "    1.0e+02\n",
      "[torch.FloatTensor of size 50]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = torch.arange(2, 101, 2) # Unlike numpy api, torch arange function requires the start argument.\n",
    "print(x)\n",
    "\n",
    "assert np.array_equal(x.numpy(), np.arange(2, 101, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q8. Create a 1D tensor of 50 evenly spaced elements between 3. and 10., inclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     3.0\n",
      "     3.1\n",
      "     3.3\n",
      "     3.4\n",
      "     3.6\n",
      "     3.7\n",
      "     3.9\n",
      "     4.0\n",
      "     4.1\n",
      "     4.3\n",
      "     4.4\n",
      "     4.6\n",
      "     4.7\n",
      "     4.9\n",
      "     5.0\n",
      "     5.1\n",
      "     5.3\n",
      "     5.4\n",
      "     5.6\n",
      "     5.7\n",
      "     5.9\n",
      "     6.0\n",
      "     6.1\n",
      "     6.3\n",
      "     6.4\n",
      "     6.6\n",
      "     6.7\n",
      "     6.9\n",
      "     7.0\n",
      "     7.1\n",
      "     7.3\n",
      "     7.4\n",
      "     7.6\n",
      "     7.7\n",
      "     7.9\n",
      "     8.0\n",
      "     8.1\n",
      "     8.3\n",
      "     8.4\n",
      "     8.6\n",
      "     8.7\n",
      "     8.9\n",
      "     9.0\n",
      "     9.1\n",
      "     9.3\n",
      "     9.4\n",
      "     9.6\n",
      "     9.7\n",
      "     9.9\n",
      "    10.0\n",
      "[torch.FloatTensor of size 50]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = torch.linspace(3, 10, 50)\n",
    "print(x)\n",
    "\n",
    "assert np.allclose(x.numpy(), np.linspace(3., 10, 50))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q9. Create a 1-D tensor of 50 element spaced evenly on a log scale between 3. and 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YqFGPMmnSXAoLvwIKeeihy2jQoB79+18T69J8Q0f6InLMTJnyIYWFuUBDoCmFhUN5550Z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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ca8310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = torch.logspace(3, 10, 50)\n",
    "\n",
    "assert np.allclose(x.numpy(), np.logspace(3., 10., 50))\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(range(len(_x)), _x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q10. Get the diagonal of X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  0\n",
      "  5\n",
      " 10\n",
      "[torch.FloatTensor of size 3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.Tensor([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])\n",
    "y = X.diag()\n",
    "print(y)\n",
    "\n",
    "assert np.array_equal(y.numpy(), np.diag(X.numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q11. Get the 1th diagonal of X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  1\n",
      "  6\n",
      " 11\n",
      "[torch.FloatTensor of size 3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.Tensor([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])\n",
    "y = X.diag(1)\n",
    "print(y)\n",
    "\n",
    "assert np.array_equal(y.numpy(), np.diag(X.numpy(), 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q12. Get the sum of the elements of the diagonal of X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.0\n"
     ]
    }
   ],
   "source": [
    "X = torch.Tensor([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])\n",
    "y = X.trace()\n",
    "print(y)\n",
    "\n",
    "assert np.array_equal(y, np.trace(X.numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q13. Return the lower triangular part of X, the other elements are set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1  0  0\n",
      " 4  5  0\n",
      " 7  8  9\n",
      "[torch.FloatTensor of size 3x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.Tensor([[1,2,3], [4,5,6], [7,8,9]])\n",
    "Y = X.tril()\n",
    "print(Y)\n",
    "\n",
    "assert np.array_equal(Y.numpy(), np.tril(X.numpy()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q14. Return the upper triangular part of X, the other elements are set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1  2  3\n",
      " 0  5  6\n",
      " 0  0  9\n",
      "[torch.FloatTensor of size 3x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.Tensor([[1,2,3], [4,5,6], [7,8,9]])\n",
    "Y = X.triu()\n",
    "print(Y)\n",
    "\n",
    "assert np.array_equal(Y.numpy(), np.triu(X.numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save and Load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q15. Save X to `temp.pt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = torch.randn(1, 10)\n",
    "torch.save(X, 'temp.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q16. Load the `temp.pt` you just saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0.0362 -0.9936  2.7210  0.7300  0.4732  0.3571 -0.7472 -1.1666 -1.0121  0.6474\n",
      "[torch.FloatTensor of size 1x10]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X2 = torch.load('temp.pt')\n",
    "print(X2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q17. Print X2 such that all elements are displayed with precision=1 (without actually changing the values of X2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Columns 0 to 7 \n",
      " 3.6e-02 -9.9e-01  2.7e+00  7.3e-01  4.7e-01  3.6e-01 -7.5e-01 -1.2e+00\n",
      "\n",
      "Columns 8 to 9 \n",
      "-1.0e+00  6.5e-01\n",
      "[torch.FloatTensor of size 1x10]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "torch.set_printoptions(precision=1)\n",
    "print(X2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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