{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from utils import Scalar, draw_graph\n", "from linear_model import Linear, mse" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "%3\n", "\n", "\n", "4541826048backward\n", "\n", "x2= 2.00\n", "\n", "\n", "4541826576backward\n", "\n", "grad= 4.50\n", "\n", "value= 4.50\n", "\n", "-\n", "\n", "\n", "4541825664backward\n", "\n", "y2= 4.50\n", "\n", "\n", "4541826576backward->4541825664backward\n", "\n", "\n", "\n", "4541826528backward\n", "\n", "grad=-4.50\n", "\n", "value= 0.00\n", "\n", "+\n", "\n", "\n", "4541826576backward->4541826528backward\n", "\n", "\n", "-4.50\n", "\n", "\n", "4541826624backward\n", "\n", "grad= 1.00\n", "\n", "value= 10.62\n", "\n", "mse\n", "\n", "\n", "4541826624backward->4541826576backward\n", "\n", "\n", " 4.50\n", "\n", "\n", "4541826192backward\n", "\n", "grad= 1.00\n", "\n", "value= 1.00\n", "\n", "-\n", "\n", "\n", "4541826624backward->4541826192backward\n", "\n", "\n", " 1.00\n", "\n", "\n", "4541825616backward\n", "\n", "y1= 1.00\n", "\n", "\n", "4541826144backward\n", "\n", "grad=-10.50\n", "\n", "value= 0.00\n", "\n", "a\n", "\n", "\n", "4541826192backward->4541825616backward\n", "\n", "\n", "\n", "4541826336backward\n", "\n", "grad=-1.00\n", "\n", "value= 0.00\n", "\n", "+\n", "\n", "\n", "4541826192backward->4541826336backward\n", "\n", "\n", "-1.00\n", "\n", "\n", "4541825712backward\n", "\n", "x1= 1.50\n", "\n", "\n", "4541826288backward\n", "\n", "grad=-5.50\n", "\n", "value= 0.00\n", "\n", "b\n", "\n", "\n", "4541826336backward->4541826288backward\n", "\n", "\n", "-1.00\n", "\n", "\n", "4541825856backward\n", "\n", "grad=-1.00\n", "\n", "value= 0.00\n", "\n", "*\n", "\n", "\n", "4541826336backward->4541825856backward\n", "\n", "\n", "-1.00\n", "\n", "\n", "4541825856backward->4541826144backward\n", "\n", "\n", "-1.50\n", "\n", "\n", "4541825856backward->4541825712backward\n", "\n", "\n", "\n", "4541826480backward\n", "\n", "grad=-4.50\n", "\n", "value= 0.00\n", "\n", "*\n", "\n", "\n", "4541826480backward->4541826048backward\n", "\n", "\n", "\n", "4541826480backward->4541826144backward\n", "\n", "\n", "-9.00\n", "\n", "\n", "4541826528backward->4541826288backward\n", "\n", "\n", "-4.50\n", "\n", "\n", "4541826528backward->4541826480backward\n", "\n", "\n", "-4.50\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x1 = Scalar(1.5, label='x1', requires_grad=False)\n", "y1 = Scalar(1.0, label='y1', requires_grad=False)\n", "x2 = Scalar(2.0, label='x2', requires_grad=False)\n", "y2 = Scalar(4.5, label='y2', requires_grad=False)\n", "# 反向传播\n", "model = Linear()\n", "loss = mse([model.error(x1, y1), model.error(x2, y2)])\n", "loss.backward()\n", "draw_graph(loss, 'backward')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "%3\n", "\n", "\n", "4541826048backward\n", "\n", "x2= 2.00\n", "\n", "\n", "4541826096backward\n", "\n", "grad= 1.00\n", "\n", "value= 10.62\n", "\n", "mse\n", "\n", "\n", "4541773376backward\n", "\n", "grad= 4.50\n", "\n", "value= 4.50\n", "\n", "-\n", "\n", "\n", "4541826096backward->4541773376backward\n", "\n", "\n", " 4.50\n", "\n", "\n", "4541775776backward\n", "\n", "grad= 1.00\n", "\n", "value= 1.00\n", "\n", "-\n", "\n", "\n", "4541826096backward->4541775776backward\n", "\n", "\n", " 1.00\n", "\n", "\n", "4541773424backward\n", "\n", "grad=-4.50\n", "\n", "value= 0.00\n", "\n", "+\n", "\n", "\n", "4541773376backward->4541773424backward\n", "\n", "\n", "-4.50\n", "\n", "\n", "4541825664backward\n", "\n", "y2= 4.50\n", "\n", "\n", "4541773376backward->4541825664backward\n", "\n", "\n", "\n", "4541825616backward\n", "\n", "y1= 1.00\n", "\n", "\n", "4541774528backward\n", "\n", "grad=-5.50\n", "\n", "value= 0.00\n", "\n", "b\n", "\n", "\n", "4541773424backward->4541774528backward\n", "\n", "\n", "-4.50\n", "\n", "\n", "4541773616backward\n", "\n", "grad=-4.50\n", "\n", "value= 0.00\n", "\n", "*\n", "\n", "\n", "4541773424backward->4541773616backward\n", "\n", "\n", "\n", "4541773472backward\n", "\n", "grad=-1.00\n", "\n", "value= 0.00\n", "\n", "*\n", "\n", "\n", "4541825712backward\n", "\n", "x1= 1.50\n", "\n", "\n", "4541773472backward->4541825712backward\n", "\n", "\n", "\n", "4541775728backward\n", "\n", "a= 0.00\n", "\n", "\n", "4541773472backward->4541775728backward\n", "\n", "\n", "\n", "4541774576backward\n", "\n", "grad=-1.00\n", "\n", "value= 0.00\n", "\n", "+\n", "\n", "\n", "4541774576backward->4541773472backward\n", "\n", "\n", "\n", "4541774576backward->4541774528backward\n", "\n", "\n", "-1.00\n", "\n", "\n", "4541773616backward->4541826048backward\n", "\n", "\n", "\n", "4541773616backward->4541775728backward\n", "\n", "\n", "\n", "4541775776backward->4541825616backward\n", "\n", "\n", "\n", "4541775776backward->4541774576backward\n", "\n", "\n", "-1.00\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 反向传播\n", "model = Linear()\n", "# 冻结参数a\n", "model.a.requires_grad = False\n", "loss = mse([model.error(x1, y1), model.error(x2, y2)])\n", "loss.backward()\n", "draw_graph(loss, 'backward')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "%3\n", "\n", "\n", "4541826048backward\n", "\n", "x2= 2.00\n", "\n", "\n", "4541825568backward\n", "\n", "grad= 4.50\n", "\n", "value= 4.50\n", "\n", "-\n", "\n", "\n", "4541825664backward\n", "\n", "y2= 4.50\n", "\n", "\n", "4541825568backward->4541825664backward\n", "\n", "\n", "\n", "4541825376backward\n", "\n", "grad=-4.50\n", "\n", "value= 0.00\n", "\n", "+\n", "\n", "\n", "4541825568backward->4541825376backward\n", "\n", "\n", "\n", "4541825616backward\n", "\n", "y1= 1.00\n", "\n", "\n", "4541827680backward\n", "\n", "grad= 1.00\n", "\n", "value= 1.00\n", "\n", "-\n", "\n", "\n", "4541827680backward->4541825616backward\n", "\n", "\n", "\n", "4541827008backward\n", "\n", "grad=-1.00\n", "\n", "value= 0.00\n", "\n", "+\n", "\n", "\n", "4541827680backward->4541827008backward\n", "\n", "\n", "\n", "4541825136backward\n", "\n", "a= 0.00\n", "\n", "\n", "4541825184backward\n", "\n", "grad=-4.50\n", "\n", "value= 0.00\n", "\n", "*\n", "\n", "\n", "4541825184backward->4541826048backward\n", "\n", "\n", "\n", "4541825184backward->4541825136backward\n", "\n", "\n", "\n", "4541825712backward\n", "\n", "x1= 1.50\n", "\n", "\n", "4541826432backward\n", "\n", "b= 0.00\n", "\n", "\n", "4541827008backward->4541826432backward\n", "\n", "\n", "\n", "4541825472backward\n", "\n", "grad=-1.00\n", "\n", "value= 0.00\n", "\n", "*\n", "\n", "\n", "4541827008backward->4541825472backward\n", "\n", "\n", "\n", "4541825376backward->4541825184backward\n", "\n", "\n", "\n", "4541825376backward->4541826432backward\n", "\n", "\n", "\n", "4541825904backward\n", "\n", "grad= 1.00\n", "\n", "value= 10.62\n", "\n", "mse\n", "\n", "\n", "4541825904backward->4541825568backward\n", "\n", "\n", "\n", "4541825904backward->4541827680backward\n", "\n", "\n", "\n", "4541825472backward->4541825136backward\n", "\n", "\n", "\n", "4541825472backward->4541825712backward\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 反向传播\n", "model = Linear()\n", "# 冻结参数a和参数b\n", "model.a.requires_grad = False\n", "model.b.requires_grad = False\n", "loss = mse([model.error(x1, y1), model.error(x2, y2)])\n", "loss.backward()\n", "draw_graph(loss, 'backward')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }