computation graph

6 files changed, 774 insertions, 47 deletions

diff --git a/basics/np_basics/axis_demo.py b/basics/np_basics/axis_demo.py
deleted file mode 100644
index e69de29..0000000
--- a/basics/np_basics/axis_demo.py
+++ /dev/null
diff --git a/fun_math/monte_carlo/random_cut.py b/fun_math/monte_carlo/random_cut.py
index e69de29..7b63624 100644
--- a/fun_math/monte_carlo/random_cut.py
+++ b/fun_math/monte_carlo/random_cut.py
@@ -0,0 +1,10 @@
+import random
+import math
+freq = 0
+N = 100000
+for i in range(N):
+    p1 = random.random()
+    p2 = random.random()
+    if math.fabs(p1-p2) <= 0.5:
+        freq += 1
+print(freq/N)
\ No newline at end of file
diff --git a/learn_torch/grad/03_computation_graph.ipynb b/learn_torch/grad/03_computation_graph.ipynb
new file mode 100644
index 0000000..56ccef0
--- /dev/null
+++ b/learn_torch/grad/03_computation_graph.ipynb
@@ -0,0 +1,764 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6883ffaa",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:05:49.628059Z",
+     "start_time": "2023-01-01T04:05:47.517897Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "from IPython.display import Image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4670f091",
+   "metadata": {},
+   "source": [
+    "## 1. 基础回顾"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55d18176",
+   "metadata": {},
+   "source": [
+    "$$\n",
+    "\\begin{split}\n",
+    "&y=f(x)=6x^2+2x+4\\\\\n",
+    "&\\frac{dy}{dx}=12x+2\n",
+    "\\end{split}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "db02a116",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:05:50.425308Z",
+     "start_time": "2023-01-01T04:05:50.418195Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = torch.tensor(3., requires_grad=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "530543e4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:06:14.186094Z",
+     "start_time": "2023-01-01T04:06:14.180215Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(3.)\n",
+      "None\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x.data)\n",
+    "print(x.grad)\n",
+    "print(x.is_leaf)\n",
+    "print(x.requires_grad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "baaefac5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:06:42.131133Z",
+     "start_time": "2023-01-01T04:06:42.127261Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = 6*x**2 + 2*x + 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "051fdc65",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:06:50.780036Z",
+     "start_time": "2023-01-01T04:06:50.774021Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(64.)\n",
+      "None\n",
+      "False\n",
+      "True\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/chunhuizhang/anaconda3/envs/py3.7/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/build/aten/src/ATen/core/TensorBody.h:485.)\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y.data)\n",
+    "print(y.grad)\n",
+    "print(y.is_leaf)\n",
+    "print(y.requires_grad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "48e0eb21",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:07:40.649907Z",
+     "start_time": "2023-01-01T04:07:40.646797Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y.backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8c89986d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:07:53.472977Z",
+     "start_time": "2023-01-01T04:07:53.464727Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(3.)\n",
+      "tensor(38.)\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x.data)\n",
+    "print(x.grad)\n",
+    "print(x.is_leaf)\n",
+    "print(x.requires_grad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "01eade09",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:08:21.831569Z",
+     "start_time": "2023-01-01T04:08:21.826374Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(64.)\n",
+      "None\n",
+      "False\n",
+      "True\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/chunhuizhang/anaconda3/envs/py3.7/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/build/aten/src/ATen/core/TensorBody.h:485.)\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y.data)\n",
+    "print(y.grad)\n",
+    "print(y.is_leaf)\n",
+    "print(y.requires_grad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cca89e01",
+   "metadata": {},
+   "source": [
+    "## 2. partial derivaties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "524fef0c",
+   "metadata": {},
+   "source": [
+    "$$\n",
+    "\\begin{split}\n",
+    "&f(u, v)=u^3+v^2+4uv\\\\\n",
+    "&\\frac{\\partial f}{\\partial u}=3u^2+4v\\\\\n",
+    "&\\frac{\\partial f}{\\partial v}=2v+4u\n",
+    "\\end{split}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "56c703b3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:09:16.735453Z",
+     "start_time": "2023-01-01T04:09:16.732186Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "u = torch.tensor(3., requires_grad=True)\n",
+    "v = torch.tensor(4., requires_grad=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "27f88b2c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:09:30.103390Z",
+     "start_time": "2023-01-01T04:09:30.099559Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "f = u**3 + v**2 + 4*u*v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c338966b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:09:36.390914Z",
+     "start_time": "2023-01-01T04:09:36.387331Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "f.backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "80ce1a29",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:09:41.727093Z",
+     "start_time": "2023-01-01T04:09:41.718168Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(43.)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "u.grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "02aa179e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:09:46.383983Z",
+     "start_time": "2023-01-01T04:09:46.377328Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(20.)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86945673",
+   "metadata": {},
+   "source": [
+    "## 3. 计算图（computation graph）"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d3b3f0c",
+   "metadata": {},
+   "source": [
+    "$$\n",
+    "L=f(g(h(x)))\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6cd2b2bf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:10:38.127984Z",
+     "start_time": "2023-01-01T04:10:38.119531Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch \n",
+    "\n",
+    "a = torch.randn((3,3), requires_grad = True)\n",
+    "\n",
+    "w1 = torch.randn((3,3), requires_grad = True)\n",
+    "w2 = torch.randn((3,3), requires_grad = True)\n",
+    "w3 = torch.randn((3,3), requires_grad = True)\n",
+    "w4 = torch.randn((3,3), requires_grad = True)\n",
+    "\n",
+    "# b: 3*3\n",
+    "b = w1*a\n",
+    "# c: 3*3 \n",
+    "c = w2*a\n",
+    "\n",
+    "d = w3*b + w4*c \n",
+    "\n",
+    "L = (10 - d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "7a3ab7ec",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T03:19:09.927726Z",
+     "start_time": "2023-01-01T03:19:09.922594Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image('./imgs/comp_graph.PNG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "eb382ae2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:12:51.675612Z",
+     "start_time": "2023-01-01T04:12:51.671184Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.is_leaf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "065c3989",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:12:59.542163Z",
+     "start_time": "2023-01-01T04:12:59.537221Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w1.is_leaf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5a678cb6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:13:05.135500Z",
+     "start_time": "2023-01-01T04:13:05.130354Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w4.is_leaf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0a8da2da",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:13:59.414916Z",
+     "start_time": "2023-01-01T04:13:59.408129Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[11.6027,  9.5202,  9.5188],\n",
+       "        [ 9.6397, 10.4312, 10.2627],\n",
+       "        [ 9.5411, 10.0712, 10.2101]], grad_fn=<RsubBackward1>)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cb793398",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:13:36.725457Z",
+     "start_time": "2023-01-01T04:13:36.606386Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "grad can be implicitly created only for scalar outputs",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/1j/fv7y5kz53592pt9xx4tqfrl80000gn/T/ipykernel_76273/1281551185.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/envs/py3.7/lib/python3.7/site-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    486\u001b[0m             )\n\u001b[1;32m    487\u001b[0m         torch.autograd.backward(\n\u001b[0;32m--> 488\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m         )\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/py3.7/lib/python3.7/site-packages/torch/autograd/__init__.py\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    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0mgrad_tensors_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_tensor_or_tensors_to_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m     \u001b[0mgrad_tensors_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_make_grads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_grads_batched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    191\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mretain_graph\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0mretain_graph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/py3.7/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36m_make_grads\u001b[0;34m(outputs, grads, is_grads_batched)\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequires_grad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"grad can be implicitly created only for scalar outputs\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m                 \u001b[0mnew_grads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemory_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreserve_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: grad can be implicitly created only for scalar outputs"
+     ]
+    }
+   ],
+   "source": [
+    "L.backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "fd5555c3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:14:26.120104Z",
+     "start_time": "2023-01-01T04:14:26.114701Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch \n",
+    "\n",
+    "a = torch.randn((3,3), requires_grad = True)\n",
+    "\n",
+    "w1 = torch.randn((3,3), requires_grad = True)\n",
+    "w2 = torch.randn((3,3), requires_grad = True)\n",
+    "w3 = torch.randn((3,3), requires_grad = True)\n",
+    "w4 = torch.randn((3,3), requires_grad = True)\n",
+    "\n",
+    "# b: 3*3\n",
+    "b = w1*a\n",
+    "# c: 3*3 \n",
+    "c = w2*a\n",
+    "\n",
+    "d = w3*b + w4*c \n",
+    "d.retain_grad()\n",
+    "L = (10 - d).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fb2e3a7c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:14:34.023377Z",
+     "start_time": "2023-01-01T04:14:34.018518Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(89.2221, grad_fn=<SumBackward0>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "40ba6c78",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:14:46.039251Z",
+     "start_time": "2023-01-01T04:14:46.034221Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "L.backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f04f54ef",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:14:51.288832Z",
+     "start_time": "2023-01-01T04:14:51.283356Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-1.0138, -0.1304, -0.0858],\n",
+       "        [ 0.7689,  0.0097, -0.0731],\n",
+       "        [ 0.6819,  0.8873, -0.4642]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68fb7800",
+   "metadata": {},
+   "source": [
+    "$$\n",
+    "\\begin{split}\n",
+    "\\frac{\\partial L}{\\partial a}&=\\frac{\\partial L}{\\partial d}\\left(\\frac{\\partial d}{\\partial b}\\frac{\\partial b}{\\partial a} + \\frac{\\partial d}{\\partial c}\\frac{\\partial c}{\\partial a}\\right)\\\\\n",
+    "&=\\frac{\\partial L}{\\partial d}\\frac{\\partial d}{\\partial b}\\frac{\\partial b}{\\partial a}+\\frac{\\partial L}{\\partial d}\\frac{\\partial d}{\\partial c}\\frac{\\partial c}{\\partial a}\\\\\n",
+    "&=(-1)\\cdot w_3\\cdot w_1+(-1)\\cdot w_4\\cdot w_2\n",
+    "\\end{split}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d935f7ea",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T04:16:37.559424Z",
+     "start_time": "2023-01-01T04:16:37.553668Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-1.0138, -0.1304, -0.0858],\n",
+       "        [ 0.7689,  0.0097, -0.0731],\n",
+       "        [ 0.6819,  0.8873, -0.4642]], grad_fn=<SubBackward0>)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-w3*w1-w4*w2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "112793a6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T03:34:06.280890Z",
+     "start_time": "2023-01-01T03:34:06.272605Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image('./imgs/comp_graph_grad.PNG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "46625a93",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-01-01T03:27:52.848832Z",
+     "start_time": "2023-01-01T03:27:52.842742Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9fe2349",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
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