Tensors_PyTorch.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 4434,
     "status": "ok",
     "timestamp": 1607146642164,
     "user": {
      "displayName": "Aakash N S",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiIWFHtan62vtW1gz2Bv2bxL3rppefcadxzEVxRKQ=s64",
      "userId": "03254185060287524023"
     },
     "user_tz": -330
    },
    "id": "WapU423CpeT5"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "my_tensor = torch.tensor([[1,2,3],[4,5,6]],requires_grad=True, device=device,dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[1., 2., 3.],\n        [4., 5., 6.]], requires_grad=True)\ncpu\ntorch.Size([2, 3])\ntorch.float32\nTrue\n"
     ]
    }
   ],
   "source": [
    "print(my_tensor)\n",
    "print(my_tensor.device)\n",
    "print(my_tensor.shape)\n",
    "print(my_tensor.dtype)\n",
    "print(my_tensor.requires_grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Other Initialization Methods\n",
    "a = torch.empty(size=(3,3))\n",
    "b = torch.zeros(size=(3,3))\n",
    "c = torch.ones(size=(3,3))\n",
    "d = torch.eye(3)\n",
    "e = torch.rand(size=(3,3))\n",
    "f = torch.arange(start=0,end=5,step=1)\n",
    "g = torch.linspace(start=0.1,end=1,steps=10)\n",
    "h = torch.empty(size = (1,5)).normal_(mean=0,std=1)\n",
    "i = torch.empty(size=(1,5)).uniform_(0,1)\n",
    "j = torch.diag(torch.ones(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[1.4013e-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]])\ntensor([[0., 0., 0.],\n        [0., 0., 0.],\n        [0., 0., 0.]])\ntensor([[1., 1., 1.],\n        [1., 1., 1.],\n        [1., 1., 1.]])\ntensor([[1., 0., 0.],\n        [0., 1., 0.],\n        [0., 0., 1.]])\ntensor([[0.6912, 0.2946, 0.0724],\n        [0.9598, 0.3494, 0.1252],\n        [0.4090, 0.4583, 0.4536]])\ntensor([0, 1, 2, 3, 4])\ntensor([0.1000, 0.2000, 0.3000, 0.4000, 0.5000, 0.6000, 0.7000, 0.8000, 0.9000,\n        1.0000])\ntensor([[-0.6392,  0.1930,  0.5493,  1.0578, -1.1686]])\ntensor([[0.5906, 0.4993, 0.7328, 0.8907, 0.9444]])\ntensor([[1., 0., 0.],\n        [0., 1., 0.],\n        [0., 0., 1.]])\n"
     ]
    }
   ],
   "source": [
    "print(a,b,c,d,e,f,g,h,i,j,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.int64\ntorch.bool\ntorch.int16\ntorch.int64\ntorch.float16\ntorch.float32\ntorch.float64\n"
     ]
    }
   ],
   "source": [
    "#Type Conversion\n",
    "tensorx = torch.arange(4)\n",
    "print(tensorx.dtype)\n",
    "print(tensorx.bool().dtype)\n",
    "print(tensorx.short().dtype)\n",
    "print(tensorx.long().dtype)\n",
    "print(tensorx.half().dtype)\n",
    "print(tensorx.float().dtype)\n",
    "print(tensorx.double().dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[5 5]\ntensor([5, 5])\n[5 5]\n"
     ]
    }
   ],
   "source": [
    "#Tensor <-> Numpy\n",
    "import numpy as np\n",
    "np_array = np.array((5,5))\n",
    "print(np_array)\n",
    "tensornp = torch.from_numpy(np_array)\n",
    "print(tensornp)\n",
    "np_array_back = tensornp.numpy()\n",
    "print(np_array_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([5, 7, 9])\ntensor([5, 7, 9])\ntensor([-3, -3, -3])\ntensor([-3, -3, -3])\ntensor([0.2500, 0.4000, 0.5000])\ntensor([0.2500, 0.4000, 0.5000])\ntensor([ 4, 10, 18])\ntensor([ 4, 10, 18])\ntensor([5, 7, 9])\ntensor([5, 7, 9])\ntensor([1, 4, 9])\ntensor([True, True, True])\n"
     ]
    }
   ],
   "source": [
    "#Math Operation\n",
    "x = torch.tensor([1,2,3])\n",
    "y = torch.tensor([4,5,6])\n",
    "\n",
    "print(x+y)\n",
    "print(x.add(y))\n",
    "\n",
    "print(x-y)\n",
    "print(x.subtract(y))\n",
    "\n",
    "print(x/y)\n",
    "print(x.true_divide(y))\n",
    "\n",
    "print(x*y)\n",
    "print(x.multiply(y))\n",
    "\n",
    "print(x+y)\n",
    "print(x.add(y))\n",
    "\n",
    "#inplace operations\n",
    "t = torch.zeros(3)\n",
    "t.add_(x)\n",
    "t+=x\n",
    "\n",
    "#exponentiations\n",
    "z = x.pow(2)\n",
    "z=x**2\n",
    "print(z)\n",
    "\n",
    "z=x<0\n",
    "z=x>0\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[0.9643, 0.7507, 0.4912],\n        [0.9863, 0.7888, 0.4897]])\ntensor([[0.9643, 0.7507, 0.4912],\n        [0.9863, 0.7888, 0.4897]])\n"
     ]
    }
   ],
   "source": [
    "x1 = torch.rand((2,5))\n",
    "x2 = torch.rand((5,3))\n",
    "x3 = torch.mm(x1,x2)\n",
    "x4 = x1.mm(x2)\n",
    "print(x3,x4,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[5.5600, 3.0721, 1.9603, 5.0415, 4.6178],\n        [6.6688, 4.0289, 2.7782, 5.6912, 5.6479],\n        [6.1679, 3.9889, 3.0391, 5.0202, 5.2341],\n        [6.8031, 3.8873, 2.5555, 6.1129, 5.6835],\n        [6.4420, 3.8293, 2.6775, 5.5868, 5.4227]])\n"
     ]
    }
   ],
   "source": [
    "#matrix Exponensiation\n",
    "matrix_exp = torch.rand(5,5)\n",
    "print(matrix_exp.matrix_power(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([ 4, 10, 18])\ntensor(32)\n"
     ]
    }
   ],
   "source": [
    "#element wise multipication\n",
    "z = x*y\n",
    "print(z)\n",
    "z = torch.dot(x,y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[[3.8725, 4.5113, 4.0529,  ..., 4.6497, 4.2341, 4.3604],\n         [4.2129, 4.4074, 4.5729,  ..., 4.6817, 4.2524, 3.4772],\n         [5.8480, 5.7207, 6.6015,  ..., 6.5405, 5.5734, 5.9854],\n         ...,\n         [5.4217, 6.4659, 5.6399,  ..., 6.4945, 5.5891, 5.4505],\n         [3.9702, 4.2997, 5.0978,  ..., 5.6961, 6.0216, 5.1740],\n         [3.8007, 4.5707, 4.7085,  ..., 5.0778, 5.1114, 4.8330]],\n\n        [[5.6484, 4.7915, 4.7952,  ..., 3.0939, 6.2386, 3.7801],\n         [5.9052, 3.9795, 5.0659,  ..., 3.7589, 6.2453, 5.1648],\n         [6.3313, 5.4387, 4.7213,  ..., 4.2219, 6.9555, 5.5326],\n         ...,\n         [5.5544, 4.7288, 4.0827,  ..., 3.4174, 5.9837, 3.7644],\n         [6.6294, 5.4354, 4.9996,  ..., 4.4789, 6.5687, 5.1400],\n         [6.1073, 4.8963, 5.1264,  ..., 4.0534, 6.3717, 4.8376]],\n\n        [[6.3848, 8.5693, 6.2869,  ..., 7.0146, 6.5459, 7.2497],\n         [5.3146, 6.3342, 4.6997,  ..., 5.3007, 4.3618, 5.3203],\n         [3.5936, 5.4167, 3.2583,  ..., 4.1700, 4.5340, 4.3366],\n         ...,\n         [2.9897, 4.2565, 3.1867,  ..., 3.5108, 4.0389, 3.8514],\n         [5.0437, 6.9224, 4.7368,  ..., 6.3339, 4.8520, 5.1988],\n         [5.2259, 6.7942, 5.5591,  ..., 5.5223, 5.0920, 5.5992]],\n\n        ...,\n\n        [[4.2658, 4.3017, 4.9685,  ..., 2.9263, 2.9415, 4.8329],\n         [4.3024, 4.1867, 5.0863,  ..., 3.5149, 3.8114, 4.9601],\n         [5.5767, 5.3183, 6.2648,  ..., 4.4010, 4.8027, 6.8305],\n         ...,\n         [4.8529, 5.4097, 5.6067,  ..., 3.2887, 4.0607, 6.0466],\n         [5.1268, 5.8020, 6.0917,  ..., 4.0716, 4.5034, 6.7840],\n         [4.2811, 4.5826, 5.5025,  ..., 3.9654, 4.7649, 5.5677]],\n\n        [[6.0686, 7.2983, 6.9359,  ..., 5.5863, 6.4303, 4.9911],\n         [5.5702, 7.4862, 6.3895,  ..., 4.9976, 6.0399, 5.4413],\n         [6.0187, 8.2339, 6.8009,  ..., 5.1629, 5.8165, 5.7525],\n         ...,\n         [4.6385, 4.9359, 4.6572,  ..., 3.7673, 4.0780, 4.6016],\n         [4.4137, 6.0689, 5.3149,  ..., 4.0367, 4.3601, 4.9159],\n         [3.7062, 5.7382, 4.6077,  ..., 3.5968, 3.5954, 2.8480]],\n\n        [[5.9954, 5.3280, 4.8842,  ..., 3.7558, 3.6506, 4.6149],\n         [6.0282, 6.8729, 5.2637,  ..., 5.8959, 3.4490, 5.4985],\n         [5.4687, 6.8205, 4.7511,  ..., 5.7684, 3.3963, 4.6937],\n         ...,\n         [5.3029, 6.5248, 5.5668,  ..., 4.5056, 3.4573, 4.1988],\n         [5.0837, 5.5158, 4.6805,  ..., 5.3495, 2.7444, 4.5532],\n         [5.7835, 6.1534, 5.1675,  ..., 5.6897, 3.2682, 4.1634]]])\n"
     ]
    }
   ],
   "source": [
    "#Batch Matrix Multiplication\n",
    "batch,n,m,p= 32,10,20,30\n",
    "tensor1 = torch.rand((batch,n,m))\n",
    "tensor2 = torch.rand((batch,m,p))\n",
    "outbmm = torch.bmm(tensor1,tensor2)\n",
    "print(outbmm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[ 0.3111, -0.4472, -0.1223, -0.6935,  0.1713],\n        [-0.4058, -0.2473,  0.4532, -0.0343,  0.4721],\n        [ 0.0692, -0.1685,  0.0615,  0.0586,  0.1585],\n        [-0.1978, -0.4518,  0.5761, -0.1747,  0.0062],\n        [ 0.1908, -0.3196, -0.0401,  0.0734, -0.0551]])\ntensor([[0.8783, 0.4504, 0.5974, 0.1450, 0.8091],\n        [0.1914, 0.6384, 0.9158, 0.7935, 0.9679],\n        [0.7414, 0.7093, 0.7453, 0.8697, 0.8016],\n        [0.5330, 0.4459, 0.9542, 0.6743, 0.7038],\n        [0.8140, 0.5719, 0.6751, 0.8816, 0.6597]])\n"
     ]
    }
   ],
   "source": [
    "#Broadcasting\n",
    "x1 = torch.rand((5,5))\n",
    "x2 = torch.rand((1,5))\n",
    "print(x1-x2)\n",
    "print(x1**x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor(6)\ntensor(1)\ntensor([0, 1, 2])\ntensor([1, 2, 3])\ntensor(2)\ntensor(2.)\ntensor([False, False, False])\ntensor([4, 5, 6])\ntensor([1, 2, 3])\n"
     ]
    }
   ],
   "source": [
    "sum_x = torch.sum(x,dim=0)\n",
    "values,indices = torch.max(x,dim=0)\n",
    "values,indices = torch.min(x,dim=0)\n",
    "abs_x = torch.abs(x)\n",
    "z = torch.argmax(x,dim=0)\n",
    "mean_x= torch.mean(x.float(),dim=0)\n",
    "eq = torch.eq(x,y)\n",
    "so,indices= torch.sort(y,dim=0,descending=False)\n",
    "cl = torch.clamp(x,min=0)\n",
    "print(sum_x,values,indices,abs_x,z,mean_x,eq,so,cl,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([10, 25])\ntorch.Size([25])\ntensor([0.2201, 0.4017, 0.0419, 0.3441, 0.2275, 0.6212, 0.4045, 0.7572, 0.5486,\n        0.6743])\ntensor([0.0419, 0.4315, 0.2126, 0.4223, 0.7176, 0.1342, 0.8823, 0.1316, 0.5120,\n        0.9428])\ntensor([3, 4, 7])\n"
     ]
    }
   ],
   "source": [
    "batch_size= 10\n",
    "features = 25\n",
    "x = torch.rand(batch_size,features)\n",
    "\n",
    "print(x.shape)\n",
    "print(x[0].shape)\n",
    "print(x[:,0])\n",
    "print(x[2,0:10])\n",
    "\n",
    "x= torch.arange(10)\n",
    "indices= [3,4,7]\n",
    "print(x[indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([3, 4, 5, 6, 7, 8, 9])\ntensor([9])\ntensor([3, 4, 5, 6, 7, 8])\ntensor([0, 2, 4, 6, 8])\n"
     ]
    }
   ],
   "source": [
    "x= torch.arange(10)\n",
    "print(x[(x>2) | (x>8)])\n",
    "print(x[(x>2) & (x>8)])\n",
    "print(x[(x>2) ^ (x>8)])\n",
    "print(x[x.remainder(2)==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([ 0,  2,  4,  6,  8, 10,  6,  7,  8,  9])\ntensor([0, 0, 1, 2, 2, 3, 4])\n1\n10\n"
     ]
    }
   ],
   "source": [
    "print(torch.where(x>5,x,x*2))\n",
    "print(torch.tensor([0,0,1,2,2,3,4]))\n",
    "print(x.ndimension())\n",
    "print(x.numel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[0, 1, 2],\n        [3, 4, 5],\n        [6, 7, 8]])\ntensor([[0, 1, 2],\n        [3, 4, 5],\n        [6, 7, 8]])\n"
     ]
    }
   ],
   "source": [
    "#Reshaping\n",
    "x= torch.arange(9)\n",
    "x_3x3 = x.view(3,3)\n",
    "print(x_3x3)\n",
    "x_3x3 = x.reshape(3,3)\n",
    "print(x_3x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([64, 2, 5])  -->  torch.Size([64, 5, 2])\ntorch.Size([1, 10])\ntorch.Size([10, 1])\ntorch.Size([1, 1, 10]) torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "batch = 64\n",
    "x=torch.rand(batch,2,5)\n",
    "\n",
    "z = x.permute(0,2,1)\n",
    "print(x.shape,' --> ',z.shape)\n",
    "\n",
    "x = torch.arange(10)\n",
    "print(x.unsqueeze(0).shape)\n",
    "print(x.unsqueeze(1).shape)\n",
    "x =torch.arange(10).unsqueeze(0).unsqueeze((1))\n",
    "print(x.shape,x.squeeze(1).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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