02.01 数据操作.ipynb · Levi/dive_into_deeplearning

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02.01 数据操作.ipynb 9.70 KB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37d3d9cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "张量和向量的区别是什么？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "963a480e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
       " torch.Size([12]),\n",
       " tensor([[ 0,  1,  2,  3],\n",
       "         [ 4,  5,  6,  7],\n",
       "         [ 8,  9, 10, 11]]),\n",
       " torch.Size([3, 4]))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "x = torch.arange(12)\n",
    "X = x.reshape(3,4)\n",
    "x, x.shape, X, X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed86d285",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 3, 4])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.zeros(2,3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "52163c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[1., 1., 1., 1.],\n",
       "         [1., 1., 1., 1.],\n",
       "         [1., 1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1., 1.],\n",
       "         [1., 1., 1., 1.],\n",
       "         [1., 1., 1., 1.]]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.ones(2,3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "65e70636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.7069, -0.8558, -0.4252,  1.0165],\n",
       "        [-0.1231,  1.6103,  0.0955,  1.0120],\n",
       "        [-0.8215, -0.1924, -1.2083, -0.3637]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.randn(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e0fbb339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 2, 3],\n",
       "        [4, 5, 6],\n",
       "        [7, 8, 9]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor([[1,2,3], [4,5,6],[7,8,9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6f7626ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([ 3.,  4.,  6., 10.]),\n",
       " tensor([-1.,  0.,  2.,  6.]),\n",
       " tensor([ 2.,  4.,  8., 16.]),\n",
       " tensor([0.5000, 1.0000, 2.0000, 4.0000]),\n",
       " tensor([ 1.,  4., 16., 64.]))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.tensor([1.0, 2, 4, 8])\n",
    "y = torch.tensor([2, 2, 2, 2])\n",
    "X+y, X-y, X*y, X/y, X**y  #**是求幂运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "af6562e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.,  1.,  2.,  3.],\n",
       "         [ 4.,  5.,  6.,  7.],\n",
       "         [ 8.,  9., 10., 11.]]),\n",
       " tensor([[2., 1., 4., 3.],\n",
       "         [1., 2., 3., 4.],\n",
       "         [4., 3., 2., 1.]]),\n",
       " tensor([[ 0.,  1.,  2.,  3.,  2.,  1.,  4.,  3.],\n",
       "         [ 4.,  5.,  6.,  7.,  1.,  2.,  3.,  4.],\n",
       "         [ 8.,  9., 10., 11.,  4.,  3.,  2.,  1.]]))"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.arange(12, dtype=torch.float32).reshape(3,4)\n",
    "Y = torch.tensor([[2.0, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])\n",
    "X,Y, torch.cat((X,Y),dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9b56db29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[False, False, False, False],\n",
       "        [False, False, False, False],\n",
       "        [False, False, False, False]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X < Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f21b99db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]), tensor(66))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, x.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "276dd52e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0],\n",
       "         [1],\n",
       "         [2]]),\n",
       " tensor([0, 1]),\n",
       " tensor([[0, 1],\n",
       "         [1, 2],\n",
       "         [2, 3]]))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.arange(3).reshape(3,1)\n",
    "b = torch.arange(2)\n",
    "a, b, a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7c2819fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.,  1.,  2.,  3.],\n",
       "         [ 4.,  5.,  6.,  7.],\n",
       "         [ 8.,  9., 10., 11.]]),\n",
       " tensor([ 8.,  9., 10., 11.]),\n",
       " tensor([[ 4.,  5.,  6.,  7.],\n",
       "         [ 8.,  9., 10., 11.]]))"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,X[-1],X[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b85917f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.,  1.,  2.,  3.],\n",
       "        [ 4.,  5.,  9.,  7.],\n",
       "        [ 8.,  9., 10., 11.]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1,2] = 9\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "369e2b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.,  0.],\n",
       "        [ 8.,  9., 10., 11.]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:2, :] = 0\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "65a60f95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140361022024976, 140361022024736)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addr = id(Y)\n",
    "Y = Y + X\n",
    "addr, id(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a4367a24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id(Z): 140361022023376\n",
      "id(Z): 140361022023376\n"
     ]
    }
   ],
   "source": [
    "Z = torch.zeros_like(Y)\n",
    "print('id(Z):', id(Z))\n",
    "Z[:] = X+Y\n",
    "print('id(Z):', id(Z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "16cfbb9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140361642321296, 140361642321296)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before = id(X)\n",
    "X += Y\n",
    "before, id(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4a2399eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 2.,  1.,  4.,  3.],\n",
       "         [ 1.,  2.,  3.,  4.],\n",
       "         [44., 48., 52., 56.]]),\n",
       " array([[ 2.,  1.,  4.,  3.],\n",
       "        [ 1.,  2.,  3.,  4.],\n",
       "        [44., 48., 52., 56.]], dtype=float32),\n",
       " tensor([[ 2.,  1.,  4.,  3.],\n",
       "         [ 1.,  2.,  3.,  4.],\n",
       "         [44., 48., 52., 56.]]))"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = X.numpy()\n",
    "B = torch.tensor(A)\n",
    "X,A,B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "58607285",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([3.5000]), 3.5, 3.5, 3)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.tensor([3.5])\n",
    "a, a.item(), float(a), int(a)"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
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 "nbformat": 4,
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authored 2022-07-10 20:19 . 重读第10章注意力机制

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