L02-Kata-SGD.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from fastai.basics import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.ones(n,2) \n",
    "x[:,0].uniform_(-1.,1)\n",
    "x[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = tensor(3.,2); a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = x@a + torch.rand(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(x[:,0], y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mse(y_hat, y): return ((y_hat-y)**2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = tensor(-1.,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = x@a\n",
    "mse(y_hat, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(x[:,0],y)\n",
    "plt.scatter(x[:,0],y_hat);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = nn.Parameter(a); a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update():\n",
    "    y_hat = x@a\n",
    "    loss = mse(y, y_hat)\n",
    "    if t % 10 == 0: print(loss)\n",
    "    loss.backward()\n",
    "    with torch.no_grad():\n",
    "        a.sub_(lr * a.grad)\n",
    "        a.grad.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 1e-3\n",
    "for t in range(100): update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(x[:,0],y)\n",
    "plt.scatter(x[:,0],x@a);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import animation, rc\n",
    "rc('animation', html='jshtml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = nn.Parameter(tensor(-1.,1))\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.scatter(x[:,0], y, c='orange')\n",
    "line, = plt.plot(x[:,0], x@a)\n",
    "plt.close()\n",
    "\n",
    "def animate(i):\n",
    "    update()\n",
    "    line.set_ydata(x@a)\n",
    "    return line,\n",
    "\n",
    "animation.FuncAnimation(fig, animate, np.arange(0, 100), interval=20)"
   ]
  }
 ],
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