two_man_sgd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac05962d-65aa-427a-9d2c-193550e7a93b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6484a61d-7403-4695-ba19-67795cfe8458",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run imports.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "faaf3452-05a0-43ed-a063-2e3c625f75fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "ds = cifar10.CIFAR10()\n",
    "# ds = mnist.MNIST()\n",
    "net = cifar10.Network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0aca950d-3ce1-4d89-bb50-53ab041908b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds.calc_performance(net, tqdm=tqdm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "deluxe-cloud",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import mnist\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "import numpy as np\n",
    "import util\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "permanent-moore",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mnist import MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "controlled-solution",
   "metadata": {},
   "outputs": [],
   "source": [
    "def intercept(v1, g1, v2, g2):\n",
    "    '''\n",
    "    from: https://math.stackexchange.com/questions/1036959/midpoint-of-the-shortest-distance-between-2-rays-in-3d\n",
    "    '''\n",
    "    def dot(a, b):\n",
    "        return (a*b).sum(dim=-1)\n",
    "    \n",
    "    a, b, c, d = v1, g1, v2, g2\n",
    "    \n",
    "    bd = dot(b, d)\n",
    "    ad = dot(a, d)\n",
    "    bc = dot(b, c)\n",
    "    cd = dot(c, d) \n",
    "    ab = dot(a, b)\n",
    "    \n",
    "    s = (bd*(ad-bc)-ad*cd)/(bd**2.-1.)\n",
    "    t = (bd*(cd-ad)-bc*ab)/(bd**2.-1.)\n",
    "    \n",
    "    return (a+c+b*t[:, None]+d*s[:, None])/2.\n",
    "\n",
    "def intercept(v1, g1, v2, g2):\n",
    "    '''\n",
    "    from: https://math.stackexchange.com/questions/3242011/finding-the-closest-two-points-on-two-lines-in-n-dimensions\n",
    "    '''\n",
    "    \n",
    "    bs = v1.shape[:-1]\n",
    "    def dot(a, b):\n",
    "        return (a*b).sum(dim=-1)\n",
    "    \n",
    "    g1 = g1/g1.norm(dim=-1, keepdim=True)\n",
    "    g2 = g2/g2.norm(dim=-1, keepdim=True)\n",
    "    \n",
    "    a, b, c, d = v1, g1, v2, g2\n",
    "    \n",
    "    # Ax = B\n",
    "    A = torch.zeros(*bs, 2, 2)\n",
    "    B = torch.zeros(*bs, 2)\n",
    "    A[..., 0, 0] = b.norm(dim=-1)**2.\n",
    "    A[..., 0, 1] = -dot(b, d)\n",
    "    A[..., 1, 0] = -dot(b, d)\n",
    "    A[..., 1, 1] = d.norm(dim=-1)**2.\n",
    "    \n",
    "    \n",
    "    B[..., 0] = -dot(a-c, b)\n",
    "    B[..., 1] = dot(a-c, d)\n",
    "    \n",
    "#     print(A)\n",
    "#     print(torch.matrix_rank(A))\n",
    "#     print(A.det())\n",
    "#     print(A.inverse())\n",
    "    \n",
    "    st = (A.inverse()@B[..., None])[..., 0]\n",
    "    s, t = st[..., 0], st[..., 1]\n",
    "    \n",
    "    # TODO clip s, t to 0 as lower limit\n",
    "    \n",
    "#     if s<0 or t<0:\n",
    "#         print('st ', s,t)\n",
    "    \n",
    "    return (a+c+b*s[..., None]+d*t[..., None])/2., s, t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "infrared-mission",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "loving-inventory",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn_quad(x):\n",
    "    return x.norm(dim=-1)**2\n",
    "\n",
    "def fg_fn(x, fn):\n",
    "    x = x.detach().requires_grad_()\n",
    "    loss = fn(x)\n",
    "    loss.backward()\n",
    "    return loss, x.grad\n",
    "\n",
    "fn = rosenbrock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "designed-intermediate",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_sgd_trajectory(x, steps, lr=1e-1):\n",
    "    traj = [] # trajectory\n",
    "    traj_grad = []\n",
    "\n",
    "    x.requires_grad_()\n",
    "\n",
    "    opt = torch.optim.SGD([x], lr=lr)\n",
    "    for _ in range(steps):\n",
    "        loss = fn(x)\n",
    "\n",
    "        opt.zero_grad()\n",
    "        loss.backward()\n",
    "        traj.append(x.detach().clone())\n",
    "        traj_grad.append(x.grad.detach().clone())\n",
    "        opt.step()\n",
    "\n",
    "    traj = torch.stack(traj)\n",
    "    traj_grad = torch.stack(traj_grad)\n",
    "    return traj, traj_grad\n",
    "\n",
    "def calc_eps_intercept_trajectory(x, steps, eps=1e-1):\n",
    "    traj = [] # trajectory\n",
    "    traj_grad = []\n",
    "\n",
    "    x1 = x\n",
    "    \n",
    "    for _ in range(steps):\n",
    "        \n",
    "        x2 = x1 + eps*torch.randn_like(x1)\n",
    "        f1, g1 = fg_fn(x1, fn)\n",
    "        f2, g2 = fg_fn(x2, fn)\n",
    "        x3, _, _ = intercept(x1.detach(), -g1.detach(), x2.detach(), -g2.detach())\n",
    "        \n",
    "        traj.append(torch.stack([x1, x2]).detach().clone())\n",
    "        traj_grad.append(torch.stack([g1, g2]).detach().clone())\n",
    "        \n",
    "        x1 = x3\n",
    "\n",
    "    traj = torch.stack(traj)\n",
    "    traj_grad = torch.stack(traj_grad)\n",
    "    return traj, traj_grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "raising-government",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_contour(fn, xmin, ymin, xmax, ymax):\n",
    "    \n",
    "    x1 = torch.linspace(xmin, xmax, 100)\n",
    "    x2 = torch.linspace(ymin, ymax, 100)\n",
    "    x1, x2 = torch.meshgrid(x1, x2)\n",
    "    x = torch.stack([x1, x2], dim=-1)\n",
    "    z = fn(x)\n",
    "\n",
    "    plt.contourf(x1, x2, z, levels=100)\n",
    "    plt.colorbar()\n",
    "\n",
    "def plot_sgd_traj(traj, traj_grad, s=10, glen=.01):\n",
    "    plt.scatter(*traj.T.numpy(), c='g', s=s)\n",
    "\n",
    "    for v, g in zip(traj, traj_grad):\n",
    "        a = torch.stack([v, v-glen*g])\n",
    "        plt.plot(*a.numpy().T, c='r')\n",
    "        \n",
    "def plot_eps_intercept_traj(traj, traj_grad, s=10, glen=.01):\n",
    "    plt.scatter(*traj[:, 0].T.numpy(), c='g', s=s)\n",
    "    plt.scatter(*traj[:, 1].T.numpy(), c='b', s=s)\n",
    "\n",
    "    for vs, gs in zip(traj, traj_grad):\n",
    "        for v, g in zip(vs, gs):\n",
    "            a = torch.stack([v, v-glen*g])\n",
    "            plt.plot(*a.numpy().T, c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8bd72fe-b1d6-46bd-ace9-eb2a2e61e8e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8c60dea-5624-4a3e-b643-f11ac73f8588",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "200f79e0-b268-425b-9c84-a0b2ed5dcfac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "similar-netherlands",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "a = 7\n",
    "\n",
    "x = torch.tensor([5., 5.])\n",
    "\n",
    "# plot_contour(fn, -a, -a, a, a)\n",
    "# traj, traj_grad = calc_sgd_trajectory(x, 10, lr=1e-6)\n",
    "# plot_sgd_traj(traj, traj_grad, s=10, glen=1e-5)\n",
    "# plt.show()\n",
    "\n",
    "plot_contour(fn, -a, -a, a, a)\n",
    "traj, traj_grad = calc_eps_intercept_trajectory(x, 2, eps=1e-1)\n",
    "plot_eps_intercept_traj(traj, traj_grad, s=10, glen=1e-4)\n",
    "plt.xlim(-a, a); plt.ylim(-a, a)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "southern-asthma",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "assured-framework",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "exclusive-generation",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "normal-letter",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "emotional-toolbox",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "genuine-chance",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(1)\n",
    "v1 = torch.randn(16, 2)\n",
    "g1 = torch.randn(16, 2)\n",
    "v2 = torch.randn(16, 2)\n",
    "g2 = torch.randn(16, 2)\n",
    "\n",
    "# v1 = torch.tensor([[0, 0.]])\n",
    "# g1 = torch.tensor([[1, 0.]])\n",
    "# v2 = torch.tensor([[0, -2.]])\n",
    "# g2 = torch.tensor([[1, 2.]])/10.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "hybrid-ghana",
   "metadata": {},
   "outputs": [],
   "source": [
    "it = intercept(v1, g1, v2, g2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "sorted-dimension",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "scatter() got multiple values for argument 'marker'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-452eef4be97b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mg2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\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      9\u001b[0m     \u001b[0;31m# plt.scatter(*it1[i], marker='x', c='g', s=100)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: scatter() got multiple values for argument 'marker'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for i in range(len(v1)):\n",
    "    plt.figure(figsize=(5, 5))\n",
    "#     plt.quiver(*v1[i], *g1[i], scale=5)\n",
    "#     plt.quiver(*v2[i], *g2[i], scale=5)\n",
    "    \n",
    "    plt.plot(*torch.stack([v1[i], v1[i]+g1[i]]).T)\n",
    "    plt.plot(*torch.stack([v2[i], v2[i]+g2[i]]).T)\n",
    "    plt.scatter(*it[i], marker='x', c='r', s=100)\n",
    "    # plt.scatter(*it1[i], marker='x', c='g', s=100)\n",
    "\n",
    "    plt.xlim(-4, 4); plt.ylim(-4, 4)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "actual-knowing",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "amazing-relations",
   "metadata": {},
   "outputs": [],
   "source": [
    "def step_intercept_epsilon(x1, fn, eps=1e-2):\n",
    "    x2 = x1 + eps*torch.randn_like(x1)\n",
    "    f1, g1 = fg_fn(x1, fn)\n",
    "    f2, g2 = fg_fn(x2, fn)\n",
    "    x3 = intercept(x1.detach(), -g1.detach(), x2.detach(), -g2.detach())\n",
    "    return x3\n",
    "\n",
    "def step(x):\n",
    "    fx = func(x)\n",
    "    gx = grad(x)\n",
    "    \n",
    "    t = 1e-5\n",
    "    while func(x-t*gx)<fx:\n",
    "        t *= 2\n",
    "    v1 = x-t/2.*gx\n",
    "    v2 = x-t*gx\n",
    "    v2 = v2+torch.randn_like(v2)*.1\n",
    "    \n",
    "    g1 = grad(v1)\n",
    "    g2 = grad(v2)\n",
    "    \n",
    "    v3 = intercept(v1, -g1, v2, -g2)\n",
    "    \n",
    "    # plt.plot(*torch.stack([v1, (v1-g1/10.)]).T)\n",
    "    # plt.plot(*torch.stack([v2, (v2-g2/10.)]).T)\n",
    "\n",
    "    # plt.scatter(*v3, marker='x', c='r', s=100)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "needed-judge",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = (torch.randn(100)*100).requires_grad_()\n",
    "x2 = (torch.randn(2)*100).requires_grad_()\n",
    "\n",
    "# f1, g1 = fg_quad(x1)\n",
    "# f2, g2 = fg_quad(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "historic-metadata",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = (torch.randn(100)).requires_grad_()\n",
    "x3 = step_intercept_epsilon(x1, fn_quad, eps=1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "widespread-berkeley",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ffa6d5c4-eef7-4135-8d01-ac83484de9a1",
   "metadata": {},
   "source": [
    "# TODO the more parralel the two grad directions, the less you trust my intersection estimate\n",
    "# use convex combination of (intersection estimate) and (mean of the two base points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "welcome-hunter",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Network(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.seq = nn.Sequential(\n",
    "            nn.Conv2d(1, 8, 3, padding=1),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(8, 8, 3, padding=1),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(8, 8, 3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(8, 8, 3, padding=1),\n",
    "            nn.MaxPool2d(3),\n",
    "        )\n",
    "        self.classification = nn.Sequential(\n",
    "            nn.Linear(32, 10),\n",
    "            nn.LogSoftmax(dim=-1),\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        x = self.seq(x)\n",
    "        x = x.reshape(len(x), -1)\n",
    "        x = self.classification(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dried-judges",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "ds = mnist.MNIST()\n",
    "dataset = mnist\n",
    "\n",
    "ds = cifar10.CIFAR10()\n",
    "dataset = cifar10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "systematic-copyright",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2306\n"
     ]
    }
   ],
   "source": [
    "net = dataset.Network()\n",
    "opt = torch.optim.Adam(net.parameters(), lr=1e-2)\n",
    "print(np.sum([p.numel() for p in net.parameters()]))\n",
    "\n",
    "# net(torch.randn(1,1,28,28))\n",
    "\n",
    "v = nn.utils.parameters_to_vector(net.parameters()).detach().requires_grad_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97496ef5-1772-49e2-b183-2a6511fea1d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6a09bc0-d1d7-4b8a-b470-43f6e8ed5dbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4333824e-338d-40ee-97f5-8e6bdc0656a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3e7a6fc-9043-41c1-9685-97f87ffd5a57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4121646b-c3b2-45a0-b7e0-0dcf9519e20c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "917e1003-5615-4c14-9506-76f305113160",
   "metadata": {
    "tags": []
   },
   "source": [
    "## MNIST Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "8cae0672-7079-40c7-a5d9-0cb15b054e0b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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    },
    {
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    },
    {
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       "model_id": "9bd0ceb62cf04c518ace947d7e4b143c",
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    {
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       "model_id": "3a418b42cc3a471cb0d034b93b040ad1",
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     },
     "metadata": {},
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    },
    {
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    },
    {
     "data": {
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       "model_id": "495edd5253db4275ab7b432f267603a9",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "f1017aa17f064bffa8922e891ca8ee2b",
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    },
    {
     "data": {
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       "model_id": "7d7eea3198e14de48f897e0aa9a7da91",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4df42438ecb4acc88f83995198c495a",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "d3dd21c8cc434d4d8e2aa48b8d39f816",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "net = dataset.Network()\n",
    "opt = torch.optim.Adam(net.parameters(), lr=1e-2)\n",
    "\n",
    "accu = util.Accumulator()\n",
    "\n",
    "for epoch_idx in range(10):\n",
    "    for x, y in tqdm(ds.dl_train):\n",
    "        yp = net(x)\n",
    "        loss = ds.loss_fn(yp, y)\n",
    "        acc = (yp.argmax(dim=-1)==y).sum().item()/len(x)\n",
    "\n",
    "        opt.zero_grad()\n",
    "        loss.backward()\n",
    "        w = torch.cat([p.flatten().detach() for p in net.parameters()])\n",
    "        wg = torch.cat([p.grad.flatten().detach() for p in net.parameters()])\n",
    "        opt.step()\n",
    "        \n",
    "        wnorm = nn.utils.parameters_to_vector(net.parameters()).norm()\n",
    "        \n",
    "        accu.append(loss_batch=loss.item(), acc_batch=acc, wnorm=wnorm.item(), w=w.numpy(), wg=wg.numpy())\n",
    "        \n",
    "data['adam'] = accu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "d254535f-b083-4fac-8be6-32edd508ae35",
   "metadata": {},
   "outputs": [],
   "source": [
    "w_opt = torch.from_numpy(accu['w'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "b5148aa8-ea5b-41d0-ac6c-3424749b6cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bea0898a60c749548c62acac13eb2065",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise = []\n",
    "losses = []\n",
    "for x, y in tqdm(ds.dl_train):\n",
    "    eps = torch.randn(1).exp()*torch.randn_like(w_opt)\n",
    "    w = eps+w_opt\n",
    "    wnorm = nn.utils.parameters_to_vector(net.parameters()).norm()\n",
    "    nn.utils.vector_to_parameters(w, net.parameters())\n",
    "    yp = net(x)\n",
    "    loss = ds.loss_fn(yp, y)\n",
    "    noise.append(eps.norm().item())\n",
    "    losses.append(loss.item())\n",
    "noise = torch.tensor(noise)\n",
    "losses = torch.tensor(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "8f480608-8dca-44f4-bab6-65d5560c6c14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(noise, losses)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9126b08a-ccaf-4bab-96a6-106c13dcd59f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "5f043712-7515-48dd-be02-67a93b250261",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = torch.from_numpy(accu['w'])\n",
    "wg = torch.from_numpy(accu['wg'])\n",
    "w_opt = w[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "fa0fbdeb-c190-47bd-8c2d-6c53d0a2c6df",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def calc_loss_grad(w):\n",
    "    x, y = next(iter(ds.dl_train))\n",
    "    nn.utils.vector_to_parameters(w, net.parameters())\n",
    "    yp = net(x)\n",
    "    loss = ds.loss_fn(yp, y)\n",
    "    opt.zero_grad()\n",
    "    loss.backward()\n",
    "    return loss.item(), torch.cat([p.grad.flatten().detach() for p in net.parameters()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "ebea90f5-3572-4e46-86a3-bf54b2ed5596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3053641319274902 tensor(0.0004) tensor([-7.0633e-05,  7.7751e-05, -1.4290e-04,  ...,  1.4163e-02,\n",
      "        -1.6070e-02,  6.0816e-03])\n"
     ]
    }
   ],
   "source": [
    "loss, g = calc_loss_grad(w[0])\n",
    "print(loss, g.abs().mean(), g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "d9584228-8057-4b4a-acf8-d07648c527e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.280420184135437 tensor(0.0282) tensor([ 0.0315,  0.0406,  0.0264,  ..., -0.0025,  0.0205,  0.0014])\n"
     ]
    }
   ],
   "source": [
    "loss, g = calc_loss_grad(w[-1])\n",
    "print(loss, g.abs().mean(), g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "626ee0cd-d307-4f46-be34-7dde6673d5d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "id": "a460c466-1daf-4c91-853a-21e6fb02d63e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = w[30]\n",
    "# b = a+1e-2*-wg[30]\n",
    "b = a+3.*(w[31]-a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "id": "84b766d7-d471-41ed-bbc0-feab1e0cce3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c7f5cddd8a845bba4931641bf1dc82f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9774254330182268 1.9975853085517883 2.0177451840853498\n"
     ]
    }
   ],
   "source": [
    "temp = np.array([calc_loss_grad(a)[0] for _ in tqdm(range(50))])\n",
    "print(temp.mean()-temp.std(), temp.mean(), temp.mean()+temp.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "id": "eba183b5-88fa-4815-b7dd-a606f874c81d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac45a540237846fb92e2376d129d39a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9700348463181045 1.9931335031986237 2.016232160079143\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1966b3463754d2b9763f8e04966183f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9681223449958314 1.9887425899505615 2.009362834905292\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "688b428b25084cea81fa3c2964533f45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9552130600634972 1.9830652475357056 2.0109174350079138\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce917522cc1348c381a290b8ea8268bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9476587615547531 1.972130012512207 1.9966012634696608\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4e124f0a9854c5ebc3758c5b7ac7d3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9322340289027287 1.9592256247997284 1.9862172206967281\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    temp = np.array([calc_loss_grad(w[30+i])[0] for _ in tqdm(range(20))])\n",
    "    print(temp.mean()-temp.std(), temp.mean(), temp.mean()+temp.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "id": "ecf49cea-7c3a-4aee-898f-e7a472e3a54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.manual_seed(1)\n",
    "la, ga = calc_loss_grad(a)\n",
    "# torch.manual_seed(1)\n",
    "lb, gb = calc_loss_grad(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "id": "8f987d91-7293-41fa-ba7e-492841827478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.9633780717849731, 2.0633890628814697)"
      ]
     },
     "execution_count": 745,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la, lb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "id": "f93dafa3-a0ae-4893-b2ee-4e3503e320b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([ 0.0064,  0.0067,  0.0034,  ...,  0.0102, -0.0131,  0.0185]),\n",
       " tensor([-0.0004, -0.0132, -0.0276,  ...,  0.0037,  0.0201,  0.0337]))"
      ]
     },
     "execution_count": 725,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga, gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "id": "3e08f1ee-4a96-494d-b1b7-58baf50eaf35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.1885, -0.0331, -0.0825,  ...,  0.0186, -0.1911, -0.2560]),\n",
       " tensor([-0.2095, -0.0467, -0.0828,  ...,  0.0062, -0.1914, -0.2693]))"
      ]
     },
     "execution_count": 726,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "id": "af913c65-7385-4e22-a4e7-760f80df0e28",
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpolate_weight_grad(a, ga, b, gb, avg=False):\n",
    "    mask1 = torch.logical_and(-ga>0, -gb<0)\n",
    "    mask1 = torch.logical_and(a<b, mask1)\n",
    "    mask2 = torch.logical_and(-gb>0, -ga<0)\n",
    "    mask2 = torch.logical_and(b<a, mask2)\n",
    "    mask = torch.logical_or(mask1, mask2)\n",
    "    # mask = mask1\n",
    "    print(mask.sum())\n",
    "    c = a.clone()\n",
    "    \n",
    "    if avg:\n",
    "        c[mask] = (a[mask]+b[mask])/2.\n",
    "    else:\n",
    "        ta = ga[mask].abs()\n",
    "        tb = gb[mask].abs()\n",
    "        z = ta+tb\n",
    "        c[mask] = tb/z*a[mask]+ta/z*b[mask]\n",
    "    return c, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "id": "8b8850b8-8704-483d-aa8d-1d2178f5397d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(462)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5434b3908944a7898f20087618dfa92",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9580971010091537 1.9792889404296874 2.000480779850221\n",
      "tensor(462)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d25ad4228a44d05a9ed8a98d42e432f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9593507528120728 1.980455641746521 2.001560530680969\n"
     ]
    }
   ],
   "source": [
    "c, mask = interpolate_weight_grad(a, ga, b, gb, avg=True)\n",
    "torch.manual_seed(0)\n",
    "temp = np.array([calc_loss_grad(c)[0] for _ in tqdm(range(50))])\n",
    "print(temp.mean()-temp.std(), temp.mean(), temp.mean()+temp.std())\n",
    "torch.manual_seed(0)\n",
    "c, mask = interpolate_weight_grad(a, ga, b, gb, avg=False)\n",
    "temp = np.array([calc_loss_grad(c)[0] for _ in tqdm(range(50))])\n",
    "print(temp.mean()-temp.std(), temp.mean(), temp.mean()+temp.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a4f3ae3-d6fa-4585-8318-ffee09138790",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3fb1d8b-bc2f-49f0-b8c3-e79fc8e72e11",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee81974b-2718-4e8d-ad3c-58e13d8d6467",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e8a2c33-2938-4223-8dab-9937b0de26e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79a36e9f-5b2d-4247-a24d-511142c866d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adc9cb96-92a8-40ea-9f69-35b92194efbd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2c2568ba-2c5d-49f8-b44a-8ce2b4e598fc",
   "metadata": {},
   "source": [
    "# Learned Lessons\n",
    "\n",
    "So basically, I think the extrapolative intercept thing works better than SGD, its just that the learning rate does not adapt fast enough like in Adam.\n",
    "(Adam takes multiple 1e-5 gradients and bumps up the LR a lot for that weight and takes it to parts of weight space where gradients are not that low)\n",
    "\n",
    "Another thing I can do to improve ON TOP OF Adam is to, rather than find the best LR adaptation, \n",
    "we can \"overshoot\" the optimum weight and get a (weight, gradient) pair on the \"other side\" of the optimum.\n",
    "Then we can teleport some parts of the weight to that optimum knowing the two pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9000823-4287-4e38-b78c-6d22f2b8d89c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe1b8ea1-b984-49cf-bd6a-7970749ef2a2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a2819c6-556e-4aa4-a83d-a720ab9700c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdc7c45e-0bb1-4abd-befc-96ec369dcfe3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "302381cc-8070-4072-8a83-703489ae4590",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bae4ce6a-7979-4bd3-b18a-00e81c1f3b4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dfa4eca-fda8-4310-ad9d-4818fb058e6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32dceebe-2c25-4c8c-b2e1-951971ee9fba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ab0bd95-ce83-4707-8592-85bbb56ec629",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "85e52821-754d-4944-ade0-ba6ddce740e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# w_opt-wn, w_opt-w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73bc8431-1d83-4a97-8efe-cc8c48ae7ab4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "497cf169-216c-4287-a79a-938b1bcb07e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e38e75a0-3424-4eb9-8602-7c174e772d0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31566590-2009-4490-974e-76fe706f527a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93fbaf24-08c6-40c8-9de2-1e02ada0b10f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df6b41bf-f581-4364-a547-c82bd0dbbd81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "128a39ee-8f73-4cbb-8465-c144501db253",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ac2b26d-2a3a-40a2-a95c-b2abc7015771",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bbf60b0-6778-43e4-bced-3c30c5e1f1a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3ddc977-6e39-414b-b745-7c8c986dce9a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "282b3f61-d85b-47de-b0a8-1f328946d829",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "96a71b9b-1ba2-4f47-b87d-f207e2cfc803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "053ec915dd8a45f5a1b52005d61b3d42",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ws, wgs = [], []\n",
    "for x, y in tqdm(ds.dl_train):\n",
    "    yp = net(x)\n",
    "    loss = ds.loss_fn(yp, y)\n",
    "    opt.zero_grad()\n",
    "    loss.backward()\n",
    "    w = torch.cat([p.flatten().detach() for p in net.parameters()])\n",
    "    wg = torch.cat([p.grad.flatten().detach() for p in net.parameters()])\n",
    "    ws.append(w)\n",
    "    wgs.append(wg)\n",
    "ws = torch.stack(ws)\n",
    "wgs = torch.stack(wgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c68d16ae-65af-453f-b0de-3a51b0f1d499",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50b87a4e-fe01-4990-91ab-08b584e39555",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3b0a7eb-3a93-40b9-b942-9220a9679a3c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d993640d-f23a-41c9-9967-4e3c0f5251f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62ea688a-cb4b-459b-abc0-8ab07a662766",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4bf6daf-984d-47f6-b3c8-57137a792d80",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be2d26d7-c8d4-47f6-b723-ab1e4f1c3302",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bd20213-d4ba-48e3-85ac-059111403788",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d70ab89-eae1-4474-9659-954d481d424e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7806a6d6-f762-4ca4-9e57-aaf12d336c06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3803ec6d-6c03-4a63-a900-9338969c4e07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b331968-020f-45b9-b2ee-a7ad67b145c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b78a8c44-0146-48e9-9fed-cdb500444de8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d12d9b16-46f5-4898-8423-961894754e91",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2727224d-2fe5-4783-9cf9-bfe6f18b85ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66dd8982-8fd1-4fce-b264-6e0af454a612",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10962dd6-75cb-4d75-a53e-932c54fab4f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f962bbd9-16c2-4116-8aba-e2122a0e2484",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a71a2eb-f2fb-4e6d-b236-a737a712d1d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "51802a9f-687d-4d30-89f2-2c8ad6aa0621",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.manifold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d3b3c9b4-a746-4599-bc02-415d9df50d31",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akarshkumar0101/miniconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/Users/akarshkumar0101/miniconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "tsne = sklearn.manifold.TSNE()\n",
    "x = tsne.fit_transform(ws.numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9dd665c4-2616-4ec9-93cc-7ae4e1bb4922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "85d16412-fa5e-4908-acfe-8ca987999132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 22.921944, -16.453672], dtype=float32),\n",
       " array([ 22.634884, -16.115044], dtype=float32))"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0], x[250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "28bca7a5-c531-420a-852f-1615f005631b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.0014,  0.1032, -0.1584,  ...,  0.1078, -0.1675, -0.1584]),\n",
       " tensor([-0.0014,  0.1032, -0.1584,  ...,  0.1078, -0.1675, -0.1584]))"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws[0], ws[250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "a8434f1e-aa06-48ce-b332-db53baa2babf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([500, 2306])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "59fdbbb9-ea4a-4eed-a398-0335ff5aea57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcbb0001370>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow((ws[:, None, :]-ws[None, :, :]).norm(dim=-1).numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbc0408c-550f-430d-be0f-d042fc1b4456",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35382ca4-f915-4895-98b2-1086bbc795e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad4ffc8c-6567-43b7-a04e-35f3d0163446",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccceb890-3542-40a4-a8f2-0ec25bf04264",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b282510-abe9-4ef7-96db-401ba454c49d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "ae9cc86d-8d6c-4cd2-b7f9-9ae4080a5f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fccdaa08a90>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,30))\n",
    "# plt.scatter(*x.T, c=accu['loss_batch'])\n",
    "plt.scatter(*x[:250].T, c='r')\n",
    "plt.scatter(*x[250:].T, c='b')\n",
    "# plt.plot(*x.T)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d92d11f-cbeb-4e8f-8cc0-afefeb032ede",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5ee26ca-4037-4854-bce7-103410705e54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e221f83-70a4-4c06-a03a-74580ed84c64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aef6e7f5-71ed-477b-b704-d575c2c8a6e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc16a78f-d7bd-4b47-9481-d002f30f6c76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6e9b2d4-1866-4f5e-97ee-f58afc19bf42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5228c5aa-dacf-4e3d-bb73-c97304e7d9ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e3af569-655f-4a88-8491-50166ab4d9f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2e3e40a3-54f5-45ad-8fd8-317e1b07587c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## MNIST SGD w/ Momentum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b182ee67-7b38-4df5-8ff5-75c3a128e14a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6fd53eac0d824b0eb6e88c558af9a651",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "net = dataset.Network()\n",
    "opt = torch.optim.SGD(net.parameters(), lr=1e-1, momentum=0.9)\n",
    "\n",
    "accu = util.Accumulator()\n",
    "\n",
    "for epoch_idx in range(1):\n",
    "    for x, y in tqdm(ds.dl_train):\n",
    "        yp = net(x)\n",
    "        loss = ds.loss_fn(yp, y)\n",
    "        acc = (yp.argmax(dim=-1)==y).sum().item()/len(x)\n",
    "\n",
    "        opt.zero_grad()\n",
    "        loss.backward()\n",
    "        w = torch.cat([p.flatten().detach() for p in net.parameters()])\n",
    "        wg = torch.cat([p.grad.flatten().detach() for p in net.parameters()])\n",
    "        opt.step()\n",
    "\n",
    "        wnorm = nn.utils.parameters_to_vector(net.parameters()).norm()\n",
    "        \n",
    "        accu.append(loss_batch=loss.item(), acc_batch=acc, wnorm=wnorm.item(), w=w.numpy(), wg=wg.numpy())\n",
    "        \n",
    "data['sgdm'] = accu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "4099a32e-a3e5-49d0-beef-709354a956aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "util.plot_accumulators(data, keys=['loss_batch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9872be5-8956-45bf-b743-46229758bd2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f33533d4-7452-4b08-9310-21a4f5305a1f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3329357a-6825-48f6-81db-eec44e84aae4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e369da1-5588-47ba-a974-5736d0c3468a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b3225cd-33ab-4585-afcf-7124346ed8e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "720806c9-7ea8-458e-b6a6-e03e4f1edc37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b108c04c-20e2-47f2-8078-cf375ba53dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = torch.from_numpy(data['adam']['wg'])\n",
    "plt.plot(a.abs().mean(dim=-1).numpy())\n",
    "a = torch.from_numpy(data['sgdm']['wg'])\n",
    "plt.plot(a.abs().mean(dim=-1).numpy())\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "dbd5013e-e501-4bf1-8139-192992e73b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.quiver.Quiver at 0x7f8809cd9670>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1, 1, 20)\n",
    "y = np.linspace(-1, 1, 20)\n",
    "x, y = np.meshgrid(x, y, indexing='ij')\n",
    "\n",
    "plt.quiver(x, y, y, np.zeros_like(x), color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "693e098d-3c6a-4fc3-889e-ab86a0b16aa7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ec8254b-d97c-4816-a872-e4338dad0687",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d89e72dd-4f18-4cd4-bb2f-2c7b0bdb0f3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "fed87f6a-21c0-42cc-be7e-e66f684d494b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8806f6c130>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = torch.from_numpy(data['adam']['w'])\n",
    "wg = torch.from_numpy(data['adam']['wg'])\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "i = 0\n",
    "plt.scatter(w[:, i], -wg[:, i], c=np.arange(50))\n",
    "plt.plot(w[:, i], -wg[:, i])\n",
    "plt.grid()\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff99f925-57af-4f18-9d1e-f5f27872e3b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "718b64a7-1500-4ae0-815b-6eb1c104aefa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc4f5756-3072-4990-8b8b-299951cb1250",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e92ccded-ec0a-40d7-9e44-3a4a86ce008b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(211)\n",
    "plt.plot(data['adam']['w'][:, ::1000])\n",
    "plt.subplot(212)\n",
    "plt.plot(data['adam']['wg'][:, ::1000], linestyle='-')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(211)\n",
    "plt.plot(data['sgdm']['w'][:, ::1000])\n",
    "plt.subplot(212)\n",
    "plt.plot(data['sgdm']['wg'][:, ::1000], linestyle='-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec0c2ffd-4b9b-402c-91a7-4b9382b0e1e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e80894d-5a57-4e1d-b57f-0b1ffbd7324f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc37423d-ad02-4439-bd20-472d2614cb0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6218dce7-df93-45ae-8c98-e6bd1cd28167",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35bdd268-c430-4737-8783-5b24db8998ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "420e5bb6-ba06-4260-8290-8b3d652dc16e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "hybrid-rubber",
   "metadata": {},
   "outputs": [],
   "source": [
    "vo = nn.utils.parameters_to_vector(net.parameters()).detach().requires_grad_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "stuck-portfolio",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:03<00:00,  5.90it/s]\n",
      "100%|██████████| 20/20 [00:03<00:00,  5.08it/s]\n"
     ]
    }
   ],
   "source": [
    "fo, g = fg_fn_nn(vo, x, y)\n",
    "fs = []\n",
    "\n",
    "for t in tqdm(torch.logspace(-1, -3, 20)):\n",
    "    f, _ = fg_fn_nn(vo+g*t, x, y)\n",
    "    fs.append([t,f.item()])\n",
    "fs.append([0, fo.item()])\n",
    "for t in tqdm(-torch.logspace(-3, -1, 20)):\n",
    "    f, _ = fg_fn_nn(vo+g*t, x, y)\n",
    "    fs.append([t, f.item()])\n",
    "fs = np.array(fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "blank-system",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1917508840560913, 3.0)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(*fs.T)\n",
    "plt.ylim(fs[:, 1].min(), 3)\n",
    "# plt.xscale('symlog')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "still-edgar",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "everyday-updating",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interstate-honey",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "private-colorado",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "wicked-player",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "burning-cabinet",
   "metadata": {},
   "outputs": [],
   "source": [
    "def step_epsilon(x1, x, y, net, opt, eps=1e-2):\n",
    "    f1, g1 = fg_fn_nn(x1, x, y, net, opt)\n",
    "    \n",
    "    g1n = g1/g1.norm(dim=-1, keepdim=True)\n",
    "    x2 = x1 + eps*torch.randn_like(x1)-g1n*eps\n",
    "\n",
    "    f2, g2 = fg_fn_nn(x2, x, y, net, opt)\n",
    "    x3, s, t = intercept(x1.detach(), -g1.detach(), x2.detach(), -g2.detach())\n",
    "    return f1, x3, s, t\n",
    "\n",
    "def step_smart(x1, x, y, t=1e-5):\n",
    "    f1, g1 = fg_fn_nn(x1, x, y)\n",
    "    \n",
    "    ft, gt = fg_fn_nn(x1-t*g1, x, y)\n",
    "    if ft<f1:\n",
    "        while ft<f1:\n",
    "            t *= 2.\n",
    "            ft, gt = fg_fn_nn(x1-t*g1, x, y)\n",
    "    elif ft>f1:\n",
    "        while ft>f1:\n",
    "            t /= 2.\n",
    "            ft, gt = fg_fn_nn(x1-t*g1, x, y)\n",
    "        t*=2.\n",
    "        \n",
    "    x2 = x1-t*g1\n",
    "    x1 = x1-t/2.*g1\n",
    "    \n",
    "    f1, g1 = fg_fn_nn(x1, x, y)\n",
    "    f2, g2 = fg_fn_nn(x2, x, y)\n",
    "    x3 = intercept(x1.detach(), -g1.detach(), x2.detach(), -g2.detach())\n",
    "    return f1, x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "elect-bristol",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fg_fn_nn(v, x, y, net, opt):\n",
    "    nn.utils.vector_to_parameters(v, net.parameters())\n",
    "    opt.zero_grad()\n",
    "    yp = net(x)\n",
    "    loss = ds.loss_fn(yp, y)\n",
    "    loss.backward()\n",
    "    \n",
    "    g = torch.cat([p.grad.flatten() for p in net.parameters()])\n",
    "    return loss, g"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd05ea41-5979-4388-973d-c160a4f1984e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## MNIST Two-Man-SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "raised-sensitivity",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60cde731e66745158f171f58b7159010",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step size 0.0236\n",
      " 2.307,  3.772\n",
      "step size 0.0378\n",
      " 2.310,  3.770\n",
      "step size 0.0648\n",
      " 2.309,  3.770\n",
      " 2.305,  3.770\n",
      "step size 0.0353\n",
      " 2.310,  3.769\n",
      " 2.310,  3.770\n",
      "step size 0.0310\n",
      " 2.310,  3.770\n",
      "step size 0.0327\n",
      " 2.302,  3.770\n",
      "step size 0.0424\n",
      " 2.308,  3.768\n",
      " 2.309,  3.770\n",
      "step size 0.0257\n",
      " 2.308,  3.769\n",
      "step size 0.0342\n",
      " 2.307,  3.767\n",
      " 2.304,  3.767\n",
      "step size 0.0372\n",
      " 2.307,  3.765\n",
      "step size 0.0339\n",
      " 2.303,  3.767\n",
      "step size 0.0485\n",
      " 2.309,  3.766\n",
      "step size 0.0457\n",
      " 2.310,  3.766\n",
      "step size 0.0527\n",
      " 2.306,  3.765\n",
      "step size 0.0242\n",
      " 2.301,  3.765\n",
      "step size 0.0418\n",
      " 2.302,  3.767\n",
      "step size 0.0252\n",
      " 2.307,  3.765\n",
      "step size 0.0361\n",
      " 2.304,  3.766\n",
      "step size 0.0282\n",
      " 2.305,  3.766\n",
      "step size 0.0248\n",
      " 2.304,  3.766\n",
      "step size 0.0478\n",
      " 2.304,  3.767\n",
      " 2.305,  3.767\n",
      "step size 0.0452\n",
      " 2.302,  3.768\n",
      " 2.299,  3.768\n",
      "step size 0.0386\n",
      " 2.305,  3.769\n",
      "step size 0.0517\n",
      " 2.300,  3.771\n",
      "step size 0.0255\n",
      " 2.303,  3.771\n",
      "step size 0.0312\n",
      " 2.301,  3.772\n",
      " 2.298,  3.772\n",
      " 2.301,  3.772\n",
      "step size 0.0290\n",
      " 2.301,  3.774\n",
      "step size 0.0578\n",
      " 2.296,  3.779\n",
      "step size 0.0481\n",
      " 2.299,  3.783\n",
      "step size 0.0251\n",
      " 2.299,  3.779\n",
      "step size 0.0718\n",
      " 2.297,  3.785\n",
      " 2.294,  3.785\n",
      "step size 0.0619\n",
      " 2.289,  3.793\n",
      "step size 0.0321\n",
      " 2.288,  3.796\n",
      "step size 0.0773\n",
      " 2.288,  3.804\n",
      "step size 0.0612\n",
      " 2.285,  3.804\n",
      "step size 0.0782\n",
      " 2.278,  3.816\n",
      "step size 0.0860\n",
      " 2.262,  3.828\n",
      "step size 0.0419\n",
      " 2.250,  3.836\n",
      "step size 0.0389\n",
      " 2.238,  3.843\n",
      "step size 0.0940\n",
      " 2.226,  3.864\n",
      "step size 0.1426\n",
      " 2.238,  3.864\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "net = dataset.Network()\n",
    "opt = torch.optim.Adam(net.parameters(), lr=1e-2)\n",
    "v = nn.utils.parameters_to_vector(net.parameters()).detach().requires_grad_()\n",
    "\n",
    "accu = util.Accumulator()\n",
    "\n",
    "eps = 1e-3\n",
    "\n",
    "for epoch_idx in range(1):\n",
    "    for x, y in tqdm(ds.dl_train):\n",
    "        f1, v2, s, t = step_epsilon(v, x, y, net, opt, eps)\n",
    "        step_size = 0.\n",
    "        \n",
    "        # if 'step_size' in accu.keys() and (v2-v).norm()>accu['step_size'].mean()+accu['step_size'].std():\n",
    "            # continue\n",
    "        \n",
    "        pv = v\n",
    "        if s>0 and t>0:\n",
    "            print(f'step size {(v2-v).norm():.04f}')\n",
    "            step_size = (v2-v).norm().item()\n",
    "            v = v2\n",
    "        \n",
    "        \n",
    "        # f1, v = step_smart(v, x, y)\n",
    "        # print('mean abs weight ', x1.abs().mean().item())\n",
    "        \n",
    "        w = nn.utils.parameters_to_vector(net.parameters()).detach()\n",
    "        yp = net(x)\n",
    "        loss = ds.loss_fn(yp, y)\n",
    "        acc = (yp.argmax(dim=-1)==y).sum().item()/len(x)\n",
    "        print(f'{loss.item(): .03f}, {v.norm(dim=-1): .03f}')\n",
    "        \n",
    "        if 'loss_batch' in accu.keys() and loss.item()>accu['loss_batch'][-1].item():\n",
    "            # goback\n",
    "            v = pv\n",
    "\n",
    "        \n",
    "        if 'loss_batch' in accu.keys() and loss.item()>accu['loss_batch'][-1]+.2:\n",
    "            print('DETECTED SPIKE')\n",
    "            for i, (x, y) in enumerate(ds.dl_train):\n",
    "                yp = net(x)\n",
    "                loss = ds.loss_fn(yp, y)\n",
    "                print(loss.item())\n",
    "                if i==5:\n",
    "                    break\n",
    "            print('\\n\\n\\n\\n')\n",
    "        \n",
    "        wnorm = nn.utils.parameters_to_vector(net.parameters()).norm()\n",
    "        accu.append(loss_batch=loss.item(), acc_batch=acc, wnorm=wnorm.item(), \n",
    "                    step_size=step_size, w=w.numpy())\n",
    "        \n",
    "data['2man'] = accu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61e97a10-b787-4d66-938d-6df5cec16994",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da19920d-a844-4cde-9d70-f40511984c04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0da32ad0-defa-4362-9619-97f77968ad5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bc00b51-fd8b-407a-82cf-93564d748a69",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be4db839-d9c8-4438-8740-fc0813ea4c5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b092da4-369f-42ff-bfd4-118ce5d97b0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "922e82a1-28ba-4c2f-a560-67e96476d242",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54935e42-fb32-463d-bb1f-bae6c30576c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce571ba9-564e-4423-9669-44d1946147ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b74ea635-db67-42e0-a9ed-18c613e0df4e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dd30adb-1d0b-4b91-86a3-01244747da36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b6a435d-4897-412e-a3dd-1a32fdf4528c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "5bb1339b-eff4-42e2-b818-cf41006dd7a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8ef2fc94f0>]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(accu['loss_batch'])\n",
    "plt.show()\n",
    "plt.plot(accu['step_size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8cfb34e-b2f1-4176-8aa0-38bb5a132902",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "789a6f7a-b844-441e-ad75-9ec47d20e7ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "872b9ebc-d319-4498-ad6b-bc7e48389d74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33e13030-7db4-4a22-9362-535b6a19112d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "bca0995a-ffb6-4d01-a545-32dae88cb2f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "util.plot_accumulators(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ae173a4-bb51-42dd-b0e3-2a91dbc00038",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e989ad48-98d0-4fce-9115-c9e5524c2ecc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "407ed8d2-0844-4efc-88bd-57ac8d6256fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8ef89c7e20>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['adam']['loss_batch'], label='Adam')\n",
    "plt.plot(data['sgdm']['loss_batch'], label='SGD+momentum')\n",
    "plt.plot(np.arange(len(data['2man']['loss_batch']))*2, \n",
    "         data['2man']['loss_batch'], label='2-Man (scaled)')\n",
    "plt.title('CIFAR Performance'); plt.ylabel('Loss'); plt.xlabel('Batches')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cb3568b9-2416-40c4-931a-67380562b4cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f90acee8580>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(data['sgdm_meter_loss'].data)[:, 0], label='SGD+momentum')\n",
    "plt.plot(np.array(data['adam_meter_loss'].data)[:, 0], label='Adam')\n",
    "plt.plot(np.arange(250)*2, np.array(data['2mansgd_meter_loss'].data)[:, 0], label='2-man (scaled)')\n",
    "plt.title('CIFAR Performance'); plt.ylabel('Loss'); plt.xlabel('Batches')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c5b1053-5da8-4e0a-86cc-863135f6a315",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "13759e78-3589-4573-afa9-80c5c6bc2bea",
   "metadata": {},
   "source": [
    "# Combining Adam/SGD with momentum to the gradients (delta weights) for 2 man sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2d62e619-748e-4120-a409-fbfc4d5a1132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2306"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum([p.numel() for p in net.parameters()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7caf0579-9ea9-4df2-aedd-89a6beb8b661",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_grad(g, net):\n",
    "    i = 0\n",
    "    for p in net.parameters():\n",
    "        if g is None:\n",
    "            p.data = None\n",
    "        else:\n",
    "            gdata = g[i: i+p.numel()].reshape(p.shape)\n",
    "            p.grad = gdata.clone()\n",
    "    \n",
    "set_grad(torch.zeros(2306), net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3c28d4d-3325-4127-9ef6-c3da9e60f922",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "73e73f48-7dbf-43a4-b948-a061aaef6e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c034b5c8982542f4940016d4c1d19580",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3124072551727295\n",
      "2.305079698562622\n",
      "2.300426959991455\n",
      "2.3082163333892822\n",
      "2.3035242557525635\n",
      "2.30521821975708\n",
      "2.3046319484710693\n",
      "2.300846815109253\n",
      "2.307983875274658\n",
      "2.3099865913391113\n",
      "2.3053438663482666\n",
      "2.3066904544830322\n",
      "2.3061087131500244\n",
      "2.2977609634399414\n",
      "2.299973726272583\n",
      "2.3055291175842285\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/fk/0sthbc6s2zbd1kyry1yjdxzw0000gn/T/ipykernel_82218/76558129.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\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     13\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl_train\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---> 14\u001b[0;31m         \u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep_epsilon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\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     15\u001b[0m         \u001b[0mnv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/fk/0sthbc6s2zbd1kyry1yjdxzw0000gn/T/ipykernel_82218/3977236183.py\u001b[0m in \u001b[0;36mstep_epsilon\u001b[0;34m(x1, x, y, net, opt, eps)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstep_epsilon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-2\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----> 2\u001b[0;31m     \u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg_fn_nn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt\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      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mg1n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mx2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mg1n\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/fk/0sthbc6s2zbd1kyry1yjdxzw0000gn/T/ipykernel_82218/4041098888.py\u001b[0m in \u001b[0;36mfg_fn_nn\u001b[0;34m(v, x, y, net, opt)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvector_to_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\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      3\u001b[0m     \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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----> 4\u001b[0;31m     \u001b[0myp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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      5\u001b[0m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mloss\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[0;32m~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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~/ml-ideas/cifar10.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    100\u001b[0m         )\n\u001b[1;32m    101\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\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--> 102\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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    103\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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[1;32m    104\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassification\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\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    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\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    142\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/pooling.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         return F.max_pool2d(input, self.kernel_size, self.stride,\n\u001b[0m\u001b[1;32m    163\u001b[0m                             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdilation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mceil_mode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m                             self.return_indices)\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/torch/_jit_internal.py\u001b[0m in \u001b[0;36mfn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    420\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mif_true\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\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;32m--> 422\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mif_false\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mif_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mif_false\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[0;32m~/miniconda3/lib/python3.9/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36m_max_pool2d\u001b[0;34m(input, kernel_size, stride, padding, dilation, ceil_mode, return_indices)\u001b[0m\n\u001b[1;32m    717\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstride\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    718\u001b[0m         \u001b[0mstride\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\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--> 719\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_pool2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdilation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mceil_mode\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    720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "net = dataset.Network()\n",
    "# opt = torch.optim.Adam(net.parameters(), lr=1e-2)\n",
    "opt = torch.optim.SGD(net.parameters(), lr=1, momentum=0.9)\n",
    "# opt = torch.optim.SGD(net.parameters(), lr=1, momentum=0)\n",
    "v = nn.utils.parameters_to_vector(net.parameters()).detach().requires_grad_()\n",
    "\n",
    "meter_acc = metrics.AverageMeter(keep_verbose_stats=True)\n",
    "meter_loss = metrics.AverageMeter(keep_verbose_stats=True)\n",
    "\n",
    "eps = 1e-2\n",
    "\n",
    "for epoch_idx in range(5):\n",
    "    for x, y in tqdm(ds.dl_train):\n",
    "        f1, v2, s, t = step_epsilon(v, x, y, net, opt, eps)\n",
    "        nv = v\n",
    "        if s>0 and t>0:\n",
    "            nv = v2\n",
    "        g = -(nv-v)\n",
    "        \n",
    "        set_grad(g, net)\n",
    "        opt.step()\n",
    "        opt.zero_grad()\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        yp = net(x)\n",
    "        loss = ds.loss_fn(yp, y)\n",
    "        acc = (yp.argmax(dim=-1)==y).sum().item()/len(x)\n",
    "        print(loss.item())\n",
    "\n",
    "        meter_acc.update(acc, len(x))\n",
    "        meter_loss.update(loss.item(), len(x))\n",
    "    \n",
    "data['2mansgd_meter_loss'] = meter_loss\n",
    "data['2mansgd_meter_acc'] = meter_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9b007f9-44b9-41eb-ac2e-aa8095c0335b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1c8cc81-e48f-40e8-bb26-c13901602d42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64dcffa2-2d33-43db-a5ba-15c0291b68bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06e4e939-75a9-4766-8158-b450b1d7a78a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "metallic-gibraltar",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "worldwide-membrane",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "18bc4a15-a37a-4ffa-9d6e-35accf32d50e",
   "metadata": {},
   "source": [
    "# TODO: sample noise not randomly, but towards negative grad\n",
    "\n",
    "# TODO: ignore intersection if line s or t values are negative\n",
    "# (this means go away from the negative grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "choice-education",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "assured-yemen",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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