compute_gradient.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Linear, self).__init__()\n",
    "        \n",
    "        self.a = torch.rand(1, requires_grad=True)\n",
    "        self.b = torch.rand(1, requires_grad=True)\n",
    "        \n",
    "        print(\"a:{}\".format(self.a))\n",
    "        print(\"b:{}\".format(self.b))\n",
    "        \n",
    "    def forward(self, feature):\n",
    "        return self.a*feature+self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature= torch.tensor([1])\n",
    "label = torch.tensor([1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a:tensor([0.0222], requires_grad=True)\n",
      "b:tensor([0.0577], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "linear = Linear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.0799], grad_fn=<AddBackward0>)\n"
     ]
    }
   ],
   "source": [
    "print(linear(feature))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = torch.square(linear(feature) - label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.8402])\n"
     ]
    }
   ],
   "source": [
    "optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.8402])\n"
     ]
    }
   ],
   "source": [
    "print(linear.b.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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