numpy_pytorch.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87665a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f820f656",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [[1,2,3], [4,5,6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ff52f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2af46c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array(data)\n",
    "tns = torch.tensor(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ea46236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b220c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 2, 3],\n",
       "        [4, 5, 6]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0bccfd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.ndim, tns.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cfbd715c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'torch.LongTensor'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tns.type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9923bb7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 3D tensor of size 2x2x2.\n",
    "data1 = [[[1., 2.], [3., 4.]],\n",
    "        [[5., 6.], [7., 8.]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43202a55",
   "metadata": {},
   "outputs": [],
   "source": [
    "tns1 = torch.tensor(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0a210bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[1., 2.],\n",
       "         [3., 4.]],\n",
       "\n",
       "        [[5., 6.],\n",
       "         [7., 8.]]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tns1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af81e56e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tns1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "595a7b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 2.],\n",
       "        [3., 4.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index into N and get a matrix\n",
    "tns1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dba28ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3.)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tns1[0][1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ed80c59c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a Python number from tensor\n",
    "tns1[0][1][0].item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5bed0bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3., 4.],\n",
       "        [7., 8.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tns1[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b198dd6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([5., 6.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tns1[1, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39ec7e47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[11., 12.],\n",
       "         [13., 14.]],\n",
       "\n",
       "        [[15., 16.],\n",
       "         [17., 18.]]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add 10 to each element\n",
    "tns1 + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "82061f4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[10., 20.],\n",
       "         [30., 40.]],\n",
       "\n",
       "        [[50., 60.],\n",
       "         [70., 80.]]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiply 10 to each element\n",
    "tns1 * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4bad0ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'torch.FloatTensor'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tns1.type()"
   ]
  }
 ],
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