Untitled1.ipynb

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      "tensor([[0.1660, 0.0763, 0.7568],\n",
      "        [0.8072, 0.1441, 0.2060]])\n",
      "tensor([[1., 1., 1.],\n",
      "        [1., 1., 1.]])\n",
      "tensor([[0.1660, 0.0763, 0.7568],\n",
      "        [0.8072, 0.1441, 0.2060]])\n",
      "tensor([[2.7183, 2.7183, 2.7183],\n",
      "        [2.7183, 2.7183, 2.7183]])\n",
      "tensor([[0.9991, 0.9991],\n",
      "        [1.1573, 1.1573]])\n",
      "tensor([1, 2])\n",
      "===============\n",
      "tensor([0.3000, 0.5000])\n",
      "tensor([2, 2])\n",
      "===============\n",
      "tensor([[0.1000, 0.3000, 0.6000],\n",
      "        [0.3000, 0.2000, 0.5000]])\n",
      "tensor([0.6000, 0.5000])\n",
      "-----------\n",
      "-----------\n",
      "tensor(0)\n",
      "tensor(0)\n",
      "tensor([2, 3])\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "element 0 of tensors does not require grad and does not have a grad_fn",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [11], line 55\u001b[0m\n\u001b[1;32m     53\u001b[0m x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m])\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28mprint\u001b[39m(x)\n\u001b[0;32m---> 55\u001b[0m \u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# 定义一个计算图\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m mx\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mrecord():\n",
      "File \u001b[0;32m~/.virtualenvs/heracles/lib/python3.9/site-packages/torch/_tensor.py:492\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    482\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    483\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m    484\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m    485\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    490\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m    491\u001b[0m     )\n\u001b[0;32m--> 492\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.virtualenvs/heracles/lib/python3.9/site-packages/torch/autograd/__init__.py:251\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    246\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m    248\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 251\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m    252\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    259\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: element 0 of tensors does not require grad and does not have a grad_fn"
     ]
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   "source": [
    "# from numpy import np\n",
    "import torch\n",
    "\n",
    "torch.tensor(((1,2,3),(5,6,7)))\n",
    "x = torch.rand((2, 3)) \n",
    "# nd.ones((2,3))\n",
    "print(x)\n",
    "y = torch.full((2, 3), 1.0)\n",
    "print(y)\n",
    "(x.shape, x.size, x.dtype)\n",
    "print(x*y)\n",
    "\n",
    "print(y.exp())\n",
    "\n",
    "y.exp()\n",
    "print(torch.matmul(x, y.T))\n",
    "\n",
    "y = torch.tensor([1, 2])\n",
    "y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
    "y_hat = y_hat[[0, 1],y]\n",
    "print(y)\n",
    "print('===============')\n",
    "print(y_hat)\n",
    "\n",
    "\n",
    "y = torch.tensor([2, 2])\n",
    "print(y)\n",
    "y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
    "print('===============')\n",
    "print(y_hat)\n",
    "\n",
    "y_hat = y_hat[[0, 1], y]\n",
    "print(y_hat)\n",
    "print('-----------')\n",
    "y_hat = y_hat.argmax(axis=0)\n",
    "print('-----------')\n",
    "print(y_hat)\n",
    "\n",
    "def accuracy(y_hat, y):  #@save\n",
    "    \"\"\"计算预测正确的数量\"\"\"\n",
    "    if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n",
    "        y_hat = y_hat.argmax(axis=1)\n",
    "        print('-----------')\n",
    "        print(y_hat)\n",
    "    cmp = y_hat.to(y.dtype) == y \n",
    "    print(y_hat.to(y.dtype))\n",
    "    return float(cmp.to(y.dtype).sum())\n",
    "\n",
    "accuracy(y_hat, y)\n",
    "\n",
    " \n",
    "# 创建一个变量并关联梯度\n",
    "x = torch.tensor([2, 3])\n",
    "print(x)\n",
    "x.backward()\n",
    "\n",
    "# 定义一个计算图\n",
    "with mx.autograd.record():\n",
    "    y = x ** 2  # 前向传播\n",
    "    # 计算损失函数（这里只是一个示例，实际情况可能更加复杂）\n",
    "    loss = y.sum()\n",
    "\n",
    "# 自动计算梯度\n",
    "loss.backward()\n",
    "\n",
    "# 访问梯度\n",
    "print('----')\n",
    "print(x.grad)\n",
    "print('----')\n",
    "\n",
    "# 创建一个二维张量\n",
    "x = torch.tensor([[1, 2, 3],\n",
    "                 [4, 5, 6],[7, 8, 9]])\n",
    "\n",
    "# 在第二个维度上求和并保持维度\n",
    "sum_result = x.sum(0, keepdims=False)\n",
    "\n",
    "print(\"原始张量:\\n\", x)\n",
    "print(\"求和结果:\\n\", sum_result)\n",
    "\n",
    "sum_result = x.sum(1, keepdims=True)\n",
    "\n",
    "\n",
    "print(\"原始张量:\\n\", x)\n",
    "print(\"求和结果:\\n\", sum_result)\n",
    "\n",
    "x = torch.tensor([[6, 4, 2],\n",
    "                 [3, 4, 1]])\n",
    "\n",
    "# 在第一个轴上找到最大值所在的索引\n",
    "max_indices_axis0 = x.argmax(axis=0)\n",
    "\n",
    "# 在第二个轴上找到最大值所在的索引\n",
    "max_indices_axis1 = x.argmax(axis=1)\n",
    "\n",
    "print(\"原始张量:\\n\", x)\n",
    "print(\"在第一个轴上的最大值索引:\\n\", max_indices_axis0)\n",
    "print(\"在第二个轴上的最大值索引:\\n\", max_indices_axis1)\n",
    "\n",
    "\n",
    "y = np.array([0, 2])\n",
    "y_hat = np.array([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
    "y_hat[[0, 1], y]\n",
    " \n",
    "print(y_hat)"
   ]
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