Argmax_Reduction_ops.ipynb

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    {
      "cell_type": "code",
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      "source": [
        "import numpy as np\n",
        "import torch"
      ],
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      "outputs": []
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        "t = torch.tensor([\n",
        "        [0,1,0],\n",
        "        [2,0,2],\n",
        "        [0,3,0]\n",
        "\n",
        "] , dtype = torch.float32)"
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        "t.sum()"
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              "tensor(8.)"
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        "t.numel()"
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            "text/plain": [
              "9"
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        "t.sum().numel()"
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              "1"
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        "t.sum().numel() < t.numel()"
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            "text/plain": [
              "True"
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        "t = torch.tensor([\n",
        "                  [1,1,1,1],\n",
        "                  [2,3,4,5],\n",
        "                  [11,23,8,9]\n",
        "] , dtype = torch.float32)\n",
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              "tensor(23.)"
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        "t.argmax()"
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              "tensor(9)"
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        "t.flatten()"
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            "text/plain": [
              "tensor([ 1.,  1.,  1.,  1.,  2.,  3.,  4.,  5., 11., 23.,  8.,  9.])"
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              "torch.return_types.max(values=tensor([11., 23.,  8.,  9.]), indices=tensor([2, 2, 2, 2]))"
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          },
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            "text/plain": [
              "torch.return_types.max(values=tensor([ 1.,  5., 23.]), indices=tensor([0, 3, 1]))"
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