CIFAR10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CIFAR10.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sverdoot/optimizer-SUG-torch/blob/master/CIFAR10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EToBGXtFYWgd",
        "colab_type": "code",
        "outputId": "de0ec54c-d917-4b1a-948d-9a275cc3b9ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbKsgAwNYVjZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.chdir(\"/content/drive/My Drive/Colab Notebooks/Optimization project\")\n",
        "os.getcwd()\n",
        "\n",
        "file_path = \"./CIFAR10\"\n",
        "#directory = os.path.dirname(file_path)\n",
        "\n",
        "try:\n",
        "    os.stat(file_path)\n",
        "except:\n",
        "    os.mkdir(file_path)       "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjtZDmg7YmpZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "from torch.autograd import Variable\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pA8A5BaJ-1-A",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "from torch.optim import Optimizer\n",
        "import math\n",
        "import copy\n",
        "\n",
        "class SUG(Optimizer):\n",
        "    def __init__(self, params, l_0, d_0=0, prob=1., eps=1e-4, momentum=0, dampening=0,\n",
        "                 weight_decay=0, nesterov=False):\n",
        "        if l_0 < 0.0:\n",
        "            raise ValueError(\"Invalid Lipsitz constant of gradient: {}\".format(l_0))\n",
        "        if d_0 < 0.0:\n",
        "            raise ValueError(\"Invalid disperion of gradient: {}\".format(d_0))\n",
        "        if momentum < 0.0:\n",
        "            raise ValueError(\"Invalid momentum value: {}\".format(momentum))\n",
        "        if weight_decay < 0.0:\n",
        "            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n",
        "\n",
        "        defaults = dict(L=l_0, momentum=momentum, dampening=dampening,\n",
        "                        weight_decay=weight_decay, nesterov=nesterov)\n",
        "        if nesterov and (momentum <= 0 or dampening != 0):\n",
        "            raise ValueError(\"Nesterov momentum requires a momentum and zero dampening\")\n",
        "        self.Lips = l_0\n",
        "        self.prev_Lips = l_0\n",
        "        self.D_0 = d_0\n",
        "        self.eps = eps\n",
        "        self.prob = prob\n",
        "        self.start_param = params\n",
        "        self.upd_sq_grad_norm = None\n",
        "        self.sq_grad_norm = None\n",
        "        self.loss = torch.tensor(0.)\n",
        "        self.cur_loss = 0\n",
        "        self.closure = None\n",
        "        super(SUG, self).__init__(params, defaults)\n",
        "\n",
        "    def __setstate__(self, state):\n",
        "        super(SUG, self).__setstate__(state)\n",
        "        for group in self.param_groups:\n",
        "            group.setdefault('nesterov', False)\n",
        "\n",
        "    def comp_batch_size(self):\n",
        "        \"\"\"Returns optimal batch size for given d_0, eps and l_0;\n",
        "\n",
        "        \"\"\"\n",
        "        return math.ceil(2 * self.D_0 * self.eps / self.prev_Lips)\n",
        "\n",
        "    def step(self, loss, closure):\n",
        "        \"\"\"Performs a single optimization step.\n",
        "\n",
        "        Arguments:\n",
        "            loss : current loss\n",
        "\n",
        "            closure (callable, optional): A closure that reevaluates the model\n",
        "                and returns the loss.\n",
        "        \"\"\"\n",
        "        self.start_params = []\n",
        "        self.loss = loss\n",
        "        self.sq_grad_norm = 0\n",
        "        self.cur_loss = loss\n",
        "        self.closure = closure\n",
        "        for gr_idx, group in enumerate(self.param_groups):\n",
        "            weight_decay = group['weight_decay']\n",
        "            momentum = group['momentum']\n",
        "            dampening = group['dampening']\n",
        "            nesterov = group['nesterov']\n",
        "            self.start_params.append([])\n",
        "            for p_idx, p in enumerate(group['params']):\n",
        "                self.start_params[gr_idx].append([p.data.clone()])\n",
        "                if p.grad is None:\n",
        "                    continue\n",
        "                self.start_params[gr_idx][p_idx].append(p.grad.data.clone())\n",
        "                d_p = self.start_params[gr_idx][p_idx][1]\n",
        "                p_ = self.start_params[gr_idx][p_idx][0]\n",
        "                \n",
        "                \n",
        "                if weight_decay != 0:\n",
        "                    d_p.add_(weight_decay, p.data)\n",
        "                    self.cur_loss += weight_decay * torch.sum(p * p).item()\n",
        "                   \n",
        "                \n",
        "                self.sq_grad_norm += torch.sum(d_p * d_p).item()\n",
        "                \n",
        "                if momentum != 0:\n",
        "                    param_state = self.state[p]\n",
        "                    if 'momentum_buffer' not in param_state:\n",
        "                        buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)\n",
        "                        buf.mul_(momentum).add_(d_p)\n",
        "                    else:\n",
        "                        buf = param_state['momentum_buffer']\n",
        "                        buf.mul_(momentum).add_(1 - dampening, d_p)\n",
        "                    if nesterov:\n",
        "                        d_p = d_p.add(momentum, buf)\n",
        "                    else:\n",
        "                        d_p = buf\n",
        "                self.start_params[gr_idx][p_idx][1] = d_p\n",
        "                \n",
        "        i = 0\n",
        "        self.Lips = max(self.prev_Lips / 2, 0.1)\n",
        "        difference = -1\n",
        "        while difference < 0 or i == 0:\n",
        "            if (i > 0): \n",
        "                self.Lips = max(self.Lips * 2, 0.1)\n",
        "            for gr_idx, group in enumerate(self.param_groups):\n",
        "                for p_idx, p in enumerate(group['params']):\n",
        "                    if p.grad is None:\n",
        "                        continue\n",
        "                    start_param_val = self.start_params[gr_idx][p_idx][0]\n",
        "                    start_param_grad = self.start_params[gr_idx][p_idx][1]\n",
        "                    p.data = start_param_val - 1/(2*self.Lips) * start_param_grad\n",
        "            difference, upd_loss = self.stop_criteria()\n",
        "            i += 1\n",
        "        self.prev_Lips = self.Lips\n",
        "\n",
        "        return self.Lips, i\n",
        "\n",
        "    def stop_criteria(self):\n",
        "        \"\"\"Checks if the Lipsitz constant of gradient is appropriate\n",
        "        \n",
        "           <g(x_k), w_k - x_k> + 2L_k / 2 ||x_k - w_k||^2 = - 1 / (2L_k)||g(x_k)||^2 + 1 / (4L_k)||g(x_k)||^2 = -1 / (4L_k)||g(x_k)||^2                \n",
        "        \"\"\"\n",
        "        upd_loss = self.closure()\n",
        "        major =  self.cur_loss - 1 / (4 * self.Lips) * self.sq_grad_norm\n",
        "        return major - upd_loss - self.l2_reg() + self.eps / 10, upd_loss\n",
        "\n",
        "    def get_lipsitz_const(self):\n",
        "        \"\"\"Returns current Lipsitz constant of the gradient of the loss function\n",
        "        \"\"\"\n",
        "        return self.Lips\n",
        "    \n",
        "    def get_sq_grad(self):\n",
        "        \"\"\"Returns the current second norm of the gradient of the loss function \n",
        "           calculated by the formula\n",
        "           \n",
        "           ||f'(p_1,...,p_n)||_2^2 ~ \\sum\\limits_{i=1}^n ((df/dp_i) * (df/dp_i))(p1,...,p_n))\n",
        "           \n",
        "        \"\"\"\n",
        "        self.upd_sq_grad_norm = 0\n",
        "        for gr_idx, group in enumerate(self.param_groups):\n",
        "            for p_idx, p in enumerate(group['params']):\n",
        "                if p.grad is None:\n",
        "                    continue\n",
        "                self.upd_sq_grad_norm += torch.sum(p.grad.data * p.grad.data).item()\n",
        "        \n",
        "        return self.upd_sq_grad_norm\n",
        "    \n",
        "    def l2_reg(self):\n",
        "        \"\"\"Returns the current l2 regularization addiction\n",
        "           \n",
        "        \"\"\"\n",
        "        self.upd_l2_reg = 0\n",
        "        for gr_idx, group in enumerate(self.param_groups):\n",
        "            weight_decay = group['weight_decay']\n",
        "            if weight_decay != 0:\n",
        "                for p_idx, p in enumerate(group['params']):\n",
        "                    self.upd_l2_reg += weight_decay * torch.sum(p * p).item()\n",
        "        \n",
        "        return self.upd_l2_reg"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7txImi-VYhOM",
        "colab_type": "code",
        "outputId": "7c4d3e92-dc4c-40ee-de63-cc17392a57e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "device"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBA2Gv8ZqT1T",
        "colab_type": "text"
      },
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sS_wSA0-qV0w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-LK93IAGkTlD",
        "colab_type": "code",
        "outputId": "966c35b1-be0f-4f9a-a1f2-1629a702f9cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "\n",
        "valid_dataset = torchvision.datasets.CIFAR10(root='/data', train=True, \n",
        "                                        download=True, transform=transform)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                        download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
        "                                         shuffle=False, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|█████████▉| 169795584/170498071 [00:21<00:00, 7490036.30it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEl5iQJ9ZZ4n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "valid_size=0.15\n",
        "num_train = len(trainset)\n",
        "indices = list(range(num_train))\n",
        "split = int(np.floor(valid_size * num_train))\n",
        "\n",
        "np.random.seed(42)\n",
        "np.random.shuffle(indices)\n",
        "\n",
        "train_idx, valid_idx = indices[split:], indices[:split]\n",
        "\n",
        "train_sampler = SubsetRandomSampler(train_idx)\n",
        "valid_sampler = SubsetRandomSampler(valid_idx)\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(trainset, \n",
        "               batch_size=batch_size, sampler=train_sampler,\n",
        "               num_workers=2)\n",
        "\n",
        "validloader = torch.utils.data.DataLoader(valid_dataset, \n",
        "               batch_size=batch_size, sampler=valid_sampler,\n",
        "               num_workers=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yhxBsWJGdsQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batchs_per_epoch = len(trainloader) / batch_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCF6wqjvqQf2",
        "colab_type": "code",
        "outputId": "320eba9e-9beb-4442-de0a-3993c3b456ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        }
      },
      "source": [
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(validloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print('\\t'.join('%5s' % classes[labels[j]] for j in range(batch_size)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mtiKIUowQWPGgor948RyTkbugv/3SH/Paq99jc3eTtY11Ll50e1HmmuXlDdI0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c+TwjkS4FkFxoEUQhUlTM5kui8BG0BT86L75C0oh3FVjPFlrd/30uu9T883/5ZV5/+y6X/cL/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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "horse\t deer\tplane\tplane\t ship\t  dog\t  cat\t frog\t  car\t bird\t ship\t deer\t  car\t ship\t  car\ttruck\t bird\t  cat\tplane\tplane\t ship\t frog\t frog\ttruck\t  dog\t bird\t frog\t ship\thorse\t  dog\t  cat\thorse\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VAUL8ZkoKPw",
        "colab_type": "text"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-Su2BI5Zw_c",
        "colab_type": "text"
      },
      "source": [
        "### simple CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNiBcg-QkYzu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 5 * 5)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmQgQwlwIGyW",
        "colab_type": "text"
      },
      "source": [
        "### AlexNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMRMEdc6IJN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AlexNet(nn.Module):\n",
        "    def __init__(self, num_classes):\n",
        "        super(AlexNet, self).__init__()\n",
        "        self.features = nn.Sequential(\n",
        "            nn.Conv2d(3, 24, 3, padding=1),  # Conv1\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(2, 2),  # Pool1\n",
        "            nn.Conv2d(24, 96, 3, padding=1),  # Conv2\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(2, 2),  # Pool2\n",
        "            nn.Conv2d(96, 192, 3, padding=1),  # Conv3\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(192, 192, 3, padding=1),  # Conv4\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(192, 96, 3, padding=1),  # Conv5\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(2, 2),  # Pool3\n",
        "        )\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(96 * 4 * 4, 1024),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(1024, 1024),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Linear(1024, num_classes),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.features(x)\n",
        "        x = x.view(x.size(0), 96 * 4 * 4) \n",
        "        x = self.classifier(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFUdCfE9oOwh",
        "colab_type": "text"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P31TyRm2tcgN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "\n",
        "def time_since(since):\n",
        "    s = time.time() - since\n",
        "    m = math.floor(s / 60)\n",
        "    s -= m * 60\n",
        "    return '%dm %ds' % (m, s)\n",
        "\n",
        "\n",
        "def model_step(model, optimizer, criterion, inputs, labels):\n",
        "    outputs = model(inputs)\n",
        "    loss = criterion(outputs, labels)\n",
        "\n",
        "    if model.training:\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward(retain_graph=True)\n",
        "    if optimizer.__class__.__name__ != 'SUG':\n",
        "        optimizer.step()\n",
        "    else:\n",
        "        def closure():\n",
        "            optimizer.zero_grad()\n",
        "            upd_outputs = model(inputs)\n",
        "            upd_loss = criterion(upd_outputs, labels).item()\n",
        "\n",
        "            return upd_loss\n",
        "\n",
        "        optimizer.step(loss.item(), closure)\n",
        "\n",
        "    return loss.item()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECO6k5-xkzl1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(model, trainloader, criterion, optimizer, n_epochs=2, validloader=None, eps=1e-5, print_every=1):\n",
        "    tr_loss, val_loss, lips, times, grad, acc = ([] for i in range(6))\n",
        "    start_time = time.time()\n",
        "    model.to(device=device)\n",
        "    for ep in range(n_epochs):\n",
        "        model.train()\n",
        "        i = 0\n",
        "        for i, data in enumerate(trainloader):\n",
        "            inputs, labels = data\n",
        "            inputs, labels = Variable(inputs).to(device=device), Variable(labels).to(device=device)\n",
        "\n",
        "            tr_loss.append(model_step(model, optimizer, criterion, inputs, labels))\n",
        "            if optimizer.__class__.__name__ == 'SUG':\n",
        "                lips.append(optimizer.get_lipsitz_const())\n",
        "                grad.append(optimizer.get_sq_grad)\n",
        "        times.append(time_since(start_time))\n",
        "        if ep % print_every == 0:\n",
        "            print(\"Epoch {}, training loss {}, time passed {}\".format(ep, sum(tr_loss[-i:]) / i, time_since(start_time)))\n",
        "\n",
        "        if validloader is None:\n",
        "            continue\n",
        "        model.zero_grad()\n",
        "        model.eval()\n",
        "        j = 0\n",
        "        count = 0\n",
        "        n_ex = 0\n",
        "        for j, data in enumerate(validloader):\n",
        "            inputs, labels = data\n",
        "            inputs, labels = inputs.to(device=device), labels.to(device=device)\n",
        "            outputs = model(inputs)\n",
        "            #outputs_lab = torch.argmax(outputs, 1)\n",
        "            count += (torch.argmax(outputs, 1) == labels).float().sum().item()\n",
        "            n_ex += outputs.size(0) \n",
        "            val_loss.append(criterion(outputs, labels).item())\n",
        "        acc.append(count / n_ex)\n",
        "        if ep % print_every == 0:\n",
        "            print(\"Validation loss {}, validation accuracy {}\".format(sum(val_loss[-j:]) / j, acc[-1]))\n",
        "        \n",
        "    return tr_loss, times, val_loss, lips, grad, acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wX-aY0Qmi3NP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def concat_states(state1, state2):\n",
        "    states = {\n",
        "            'epoch': state1['epoch'] + state2['epoch'],\n",
        "            'state_dict': state2['state_dict'],\n",
        "            'optimizer': state2['optimizer'],\n",
        "            'tr_loss' : state1['tr_loss'] + state2['tr_loss'],\n",
        "            'val_loss' : state1['val_loss'] + state2['val_loss'],\n",
        "            'lips' : state1['lips'] + state2['lips'],\n",
        "            'grad' : state1['grad'] + state2['grad'],\n",
        "            'times' : state1['times'] + list(map(lambda x: x + state1['times'][-1],state2['times']))\n",
        "             }\n",
        "    return states"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZrx1G-ykasC",
        "colab_type": "code",
        "outputId": "2cf13d01-08d3-422c-d93b-47ad5631fc49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print_every = 4\n",
        "n_epochs = 20\n",
        "tr_loss = {}\n",
        "tr_loss['sgd'] = {}\n",
        "val_loss = {}\n",
        "val_loss['sgd'] = {}\n",
        "lrs = [0.001, 0.005]\n",
        "criterion = nn.CrossEntropyLoss(reduction=\"mean\")\n",
        "torch.manual_seed(999)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f70559a3830>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgkvXkyyWR5b",
        "colab_type": "code",
        "outputId": "418527a6-d00d-4c4d-9414-1728e5e7759b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "for lr in lrs:\n",
        "  #model = AlexNet(10)\n",
        "  model = CNN()\n",
        "  print(\"SGD  lr={}, momentum=0. :\".format(lr))\n",
        "  #model = CNN()\n",
        "  optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0., weight_decay=1e-3)\n",
        "  tr_loss['sgd'][lr], times, val_loss['sgd'][lr], lips, grad, acc = train(model, trainloader, criterion, optimizer, n_epochs=n_epochs, print_every=print_every, validloader=validloader)\n",
        "  states = {\n",
        "            'epoch': n_epochs,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'tr_loss' : tr_loss['sgd'][lr],\n",
        "            'val_loss' : val_loss['sgd'][lr],\n",
        "            'lips' : lips,\n",
        "            'grad' : grad,\n",
        "            'times' : times,\n",
        "            'acc' : acc\n",
        "             }\n",
        "  torch.save(states, './CIFAR10/cnn_' + str(lr))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SGD  lr=0.001, momentum=0. :\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r170500096it [00:50, 6067495.74it/s]                               "
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 0, training loss 2.3046078457530723, time passed 0m 15s\n",
            "Validation loss 2.303295273047227, validation accuracy 0.09986666666666667\n",
            "Epoch 4, training loss 2.301139187740992, time passed 1m 0s\n",
            "Validation loss 2.300287951771011, validation accuracy 0.10066666666666667\n",
            "Epoch 8, training loss 2.294662985816059, time passed 1m 43s\n",
            "Validation loss 2.2926147259198704, validation accuracy 0.18186666666666668\n",
            "Epoch 12, training loss 2.198111739952162, time passed 2m 27s\n",
            "Validation loss 2.156626744148059, validation accuracy 0.23386666666666667\n",
            "Epoch 16, training loss 1.946737550558096, time passed 3m 10s\n",
            "Validation loss 1.926912954220405, validation accuracy 0.29786666666666667\n",
            "SGD  lr=0.005, momentum=0. :\n",
            "Epoch 0, training loss 2.30141891467284, time passed 0m 8s\n",
            "Validation loss 2.298534059116983, validation accuracy 0.14586666666666667\n",
            "Epoch 4, training loss 1.7342122878475361, time passed 0m 52s\n",
            "Validation loss 2.03933955104942, validation accuracy 0.30773333333333336\n",
            "Epoch 8, training loss 1.4671413745865765, time passed 1m 33s\n",
            "Validation loss 1.5293442882024324, validation accuracy 0.44293333333333335\n",
            "Epoch 12, training loss 1.3092263472457248, time passed 2m 18s\n",
            "Validation loss 1.4698136387727199, validation accuracy 0.47053333333333336\n",
            "Epoch 16, training loss 1.1882751475436142, time passed 2m 59s\n",
            "Validation loss 1.2870378331241445, validation accuracy 0.532\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Q0QcJEGWgVO",
        "colab_type": "code",
        "outputId": "9bfa8709-68d7-4925-93e4-82bcce8587e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "for lr in lrs:\n",
        "  #model = AlexNet(10)\n",
        "  model = CNN()\n",
        "  print(\"SGD  lr={}, momentum=0. :\".format(lr))\n",
        "  #model = CNN()\n",
        "  optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0., weight_decay=1e-3)\n",
        "  tr_loss['sgd'][lr], times, val_loss['sgd'][lr], lips, grad, acc = train(model, trainloader, criterion, optimizer, n_epochs=n_epochs, print_every=print_every, validloader=validloader)\n",
        "  states = {\n",
        "            'epoch': n_epochs,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'tr_loss' : tr_loss['sgd'][lr],\n",
        "            'val_loss' : val_loss['sgd'][lr],\n",
        "            'lips' : lips,\n",
        "            'grad' : grad,\n",
        "            'times' : times,\n",
        "            'acc' : acc\n",
        "             }\n",
        "  torch.save(states, './CIFAR10/cnn_' + str(lr))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SGD  lr=0.001, momentum=0. :\n",
            "Epoch 0, training loss 2.3033256399703315, time passed 0m 8s\n",
            "Validation loss 2.3029774947044177, validation accuracy 0.09786666666666667\n",
            "Epoch 4, training loss 2.2995248004255524, time passed 0m 49s\n",
            "Validation loss 2.2990700179695063, validation accuracy 0.10426666666666666\n",
            "Epoch 8, training loss 2.2841472835784935, time passed 1m 33s\n",
            "Validation loss 2.277648075014098, validation accuracy 0.15453333333333333\n",
            "Epoch 12, training loss 2.108142703232995, time passed 2m 14s\n",
            "Validation loss 2.0859479858325076, validation accuracy 0.2614666666666667\n",
            "Epoch 16, training loss 1.9108273721782558, time passed 2m 58s\n",
            "Validation loss 1.9045408444526868, validation accuracy 0.31706666666666666\n",
            "SGD  lr=0.005, momentum=0. :\n",
            "Epoch 0, training loss 2.302613575056375, time passed 0m 9s\n",
            "Validation loss 2.3004075064618363, validation accuracy 0.10266666666666667\n",
            "Epoch 4, training loss 1.7911202294460262, time passed 0m 52s\n",
            "Validation loss 1.7926278577910528, validation accuracy 0.34626666666666667\n",
            "Epoch 8, training loss 1.4683636813817253, time passed 1m 38s\n",
            "Validation loss 1.4796606541698814, validation accuracy 0.45413333333333333\n",
            "Epoch 12, training loss 1.3114965439112072, time passed 2m 19s\n",
            "Validation loss 1.3331867509927504, validation accuracy 0.5145333333333333\n",
            "Epoch 16, training loss 1.198975688362696, time passed 3m 3s\n",
            "Validation loss 1.2961173296993613, validation accuracy 0.5386666666666666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAZ4zdE2WU9k",
        "colab_type": "code",
        "outputId": "2f95379a-747c-4f02-cd6a-d4107bd6b88f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "model = CNN()\n",
        "#n_epochs = 4\n",
        "l_0 = 10\n",
        "optimizer = SUG(model.parameters(), l_0=l_0, momentum=0., weight_decay=1e-3)\n",
        "tr_loss['sug'], times, val_loss['sug'], lips, grad, acc = train(model, trainloader, criterion, optimizer, n_epochs=n_epochs, print_every=print_every, validloader=validloader)\n",
        "states = {\n",
        "            'epoch': n_epochs,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'tr_loss' : tr_loss['sug'],\n",
        "            'val_loss' : val_loss['sug'],\n",
        "            'lips' : lips,\n",
        "            'grad' : grad,\n",
        "            'times' : times,\n",
        "            'acc' : acc\n",
        "        }\n",
        "torch.save(states, './CIFAR10/cnn_sug')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r170500096it [00:40, 7490036.30it/s]                               "
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 0, training loss 1.7287127760698997, time passed 0m 18s\n",
            "Validation loss 1.5798041545427763, validation accuracy 0.4128\n",
            "Epoch 4, training loss 1.1760990708078964, time passed 1m 25s\n",
            "Validation loss 1.2566284550051403, validation accuracy 0.5497333333333333\n",
            "Epoch 8, training loss 0.9532181326614086, time passed 2m 31s\n",
            "Validation loss 1.1652924087312486, validation accuracy 0.5942666666666667\n",
            "Epoch 12, training loss 0.8039256114602448, time passed 3m 39s\n",
            "Validation loss 1.0796798959247067, validation accuracy 0.6341333333333333\n",
            "Epoch 16, training loss 0.6847569455044815, time passed 4m 48s\n",
            "Validation loss 1.066927704291466, validation accuracy 0.6490666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ixg3JYwdLm_2",
        "colab_type": "code",
        "outputId": "a8e3d423-2b9e-4a5b-ce95-0ec5014157ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "for lr in lrs:\n",
        "  #model = AlexNet(10)\n",
        "  model = CNN()\n",
        "  print(\"SGD  lr={}, momentum=0.9 :\".format(lr))\n",
        "  #model = CNN()\n",
        "  optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=1e-3)\n",
        "  tr_loss['sgd'][lr], times, val_loss['sgd'][lr], lips, grad, acc = train(model, trainloader, criterion, optimizer, n_epochs=n_epochs, print_every=print_every, validloader=validloader)\n",
        "  states = {\n",
        "            'epoch': n_epochs,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'tr_loss' : tr_loss['sgd'][lr],\n",
        "            'val_loss' : val_loss['sgd'][lr],\n",
        "            'lips' : lips,\n",
        "            'grad' : grad,\n",
        "            'times' : times,\n",
        "            'acc' : acc\n",
        "             }\n",
        "  torch.save(states, './CIFAR10/cnn_' + str(lr)+'_0.9')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SGD  lr=0.001, momentum=0.9 :\n",
            "Epoch 0, training loss 2.2413963797580765, time passed 0m 9s\n",
            "Validation loss 2.047213161093557, validation accuracy 0.2636\n",
            "Epoch 4, training loss 1.4543043402842728, time passed 0m 56s\n",
            "Validation loss 1.4251962795726254, validation accuracy 0.4765333333333333\n",
            "Epoch 8, training loss 1.2309847575084991, time passed 1m 43s\n",
            "Validation loss 1.2635090781582727, validation accuracy 0.5481333333333334\n",
            "Epoch 12, training loss 1.0850616734340248, time passed 2m 30s\n",
            "Validation loss 1.146678028962551, validation accuracy 0.5938666666666667\n",
            "Epoch 16, training loss 0.9695697107004473, time passed 3m 18s\n",
            "Validation loss 1.0880737001569862, validation accuracy 0.6208\n",
            "SGD  lr=0.005, momentum=0.9 :\n",
            "Epoch 0, training loss 1.9004747491464558, time passed 0m 9s\n",
            "Validation loss 1.7008630414294381, validation accuracy 0.3910666666666667\n",
            "Epoch 4, training loss 1.1192579835384007, time passed 0m 54s\n",
            "Validation loss 1.147972180802598, validation accuracy 0.5872\n",
            "Epoch 8, training loss 0.9187869095883097, time passed 1m 41s\n",
            "Validation loss 1.2576614694717603, validation accuracy 0.5797333333333333\n",
            "Epoch 12, training loss 0.7934493863708284, time passed 2m 28s\n",
            "Validation loss 1.0737906472805219, validation accuracy 0.6325333333333333\n",
            "Epoch 16, training loss 0.7099852131634771, time passed 3m 14s\n",
            "Validation loss 1.1335014709804812, validation accuracy 0.6324\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOIt6e2RJHdp",
        "colab_type": "code",
        "outputId": "003b9ba9-49a0-4583-8f91-2e98f453c772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "model = CNN()\n",
        "#n_epochs = 4\n",
        "l_0 = 10\n",
        "optimizer = SUG(model.parameters(), l_0=l_0, momentum=0.9, weight_decay=1e-3)\n",
        "tr_loss['sug'], times, val_loss['sug'], lips, grad, acc = train(model, trainloader, criterion, optimizer, n_epochs=n_epochs, print_every=print_every, validloader=validloader)\n",
        "states = {\n",
        "            'epoch': n_epochs,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'tr_loss' : tr_loss['sug'],\n",
        "            'val_loss' : val_loss['sug'],\n",
        "            'lips' : lips,\n",
        "            'grad' : grad,\n",
        "            'times' : times,\n",
        "            'acc' : acc\n",
        "        }\n",
        "torch.save(states, './CIFAR10/cnn_sug_0.9')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0, training loss 2.0249016348676507, time passed 0m 15s\n",
            "Validation loss 1.9214284949832492, validation accuracy 0.2730666666666667\n",
            "Epoch 4, training loss 1.7294441840375763, time passed 1m 24s\n",
            "Validation loss 1.7247315156154144, validation accuracy 0.3506666666666667\n",
            "Epoch 8, training loss 1.6675567831619675, time passed 2m 33s\n",
            "Validation loss 1.660256328745785, validation accuracy 0.3816\n",
            "Epoch 12, training loss 1.6189166610858527, time passed 3m 47s\n",
            "Validation loss 1.6111215139046693, validation accuracy 0.39266666666666666\n",
            "Epoch 16, training loss 1.562051878636142, time passed 4m 57s\n",
            "Validation loss 1.56002251816611, validation accuracy 0.4196\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ynPpz-CFbk3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_epochs = 30"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMkBPTv7-39y",
        "colab_type": "code",
        "outputId": "cfc62532-f978-491a-8d36-d4eea886e80f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "n_epochs = 30\n",
        "\n",
        "for lr in lrs:\n",
        "  model = AlexNet(10)\n",
        "  #model = CNN()\n",
        "  print(\"SGD  lr={}, momentum=0.9 :\".format(lr))\n",
        "  #model = CNN()\n",
        "  optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=1e-3)\n",
        "  tr_loss['sgd'][lr], times, val_loss['sgd'][lr], lips, grad, acc = train(model, trainloader, criterion, optimizer, n_epochs=n_epochs, print_every=print_every, validloader=validloader)\n",
        "  states = {\n",
        "            'epoch': n_epochs,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'tr_loss' : tr_loss['sgd'][lr],\n",
        "            'val_loss' : val_loss['sgd'][lr],\n",
        "            'lips' : lips,\n",
        "            'grad' : grad,\n",
        "            'times' : times,\n",
        "            'acc' : acc\n",
        "             }\n",
        "  torch.save(states, './CIFAR10/alexnet_' + str(lr)+'_0.9')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SGD  lr=0.001, momentum=0.9 :\n",
            "Epoch 0, training loss 2.30262310928609, time passed 0m 12s\n",
            "Validation loss 2.302406350771586, validation accuracy 0.09613333333333333\n",
            "Epoch 4, training loss 1.9233037017914185, time passed 1m 7s\n",
            "Validation loss 1.795741380789341, validation accuracy 0.32466666666666666\n",
            "Epoch 8, training loss 1.5064488498561353, time passed 2m 2s\n",
            "Validation loss 1.435322539673911, validation accuracy 0.4629333333333333\n",
            "Epoch 12, training loss 1.2342443373727512, time passed 2m 58s\n",
            "Validation loss 1.1426196865036957, validation accuracy 0.5796\n",
            "Epoch 16, training loss 1.0220994622368051, time passed 3m 51s\n",
            "Validation loss 1.0097333478112507, validation accuracy 0.6316\n",
            "Epoch 20, training loss 0.8725613497018095, time passed 4m 46s\n",
            "Validation loss 0.8407612826324936, validation accuracy 0.6990666666666666\n",
            "Epoch 24, training loss 0.757881903953581, time passed 5m 43s\n",
            "Validation loss 0.770922051408352, validation accuracy 0.7288\n",
            "Epoch 28, training loss 0.6628517944769687, time passed 6m 36s\n",
            "Validation loss 0.713519621353883, validation accuracy 0.75\n",
            "SGD  lr=0.005, momentum=0.9 :\n",
            "Epoch 0, training loss 2.3013343358614358, time passed 0m 11s\n",
            "Validation loss 2.272929388233739, validation accuracy 0.1752\n",
            "Epoch 4, training loss 1.2012391400534705, time passed 1m 5s\n",
            "Validation loss 1.066346265311934, validation accuracy 0.6158666666666667\n",
            "Epoch 8, training loss 0.8006648439240743, time passed 1m 58s\n",
            "Validation loss 0.7861595646693156, validation accuracy 0.7256\n",
            "Epoch 12, training loss 0.5901236254048635, time passed 2m 54s\n",
            "Validation loss 0.720253175840928, validation accuracy 0.7509333333333333\n",
            "Epoch 16, training loss 0.4623109918390681, time passed 3m 49s\n",
            "Validation loss 0.601919883559657, validation accuracy 0.7950666666666667\n",
            "Epoch 20, training loss 0.35417105389916037, time passed 4m 42s\n",
            "Validation loss 0.7328792033223515, validation accuracy 0.7626666666666667\n",
            "Epoch 24, training loss 0.26902509217588116, time passed 5m 36s\n",
            "Validation loss 0.6470244179487737, validation accuracy 0.802\n",
            "Epoch 28, training loss 0.22479642242058573, time passed 6m 30s\n",
            "Validation loss 0.6493660968083602, validation accuracy 0.8110666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwecxABrKwrb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = AlexNet(10)\n",
        "#n_epochs = 4\n",
        "l_0 = 10\n",
        "optimizer = SUG(model.parameters(), l_0=l_0, momentum=0.9, weight_decay=1e-3)\n",
        "tr_loss['sug'], times, val_loss['sug'], lips, grad, acc = train(model, trainloader, criterion, optimizer, n_epochs=n_epochs, print_every=print_every, validloader=validloader)\n",
        "states = {\n",
        "            'epoch': n_epochs,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'tr_loss' : tr_loss['sug'],\n",
        "            'val_loss' : val_loss['sug'],\n",
        "            'lips' : lips,\n",
        "            'grad' : grad,\n",
        "            'times' : times,\n",
        "            'acc' : acc\n",
        "        }\n",
        "torch.save(states, './CIFAR10/alexnet_sug_0.9')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-VvI5QWyVuD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = AlexNet(10)\n",
        "#n_epochs = 4\n",
        "l_0 = 10\n",
        "optimizer = SUG(model.parameters(), l_0=l_0, momentum=0., weight_decay=1e-3)\n",
        "tr_loss['sug'], times, val_loss['sug'], lips, grad, acc = train(model, trainloader, criterion, optimizer, n_epochs=n_epochs, print_every=print_every, validloader=validloader)\n",
        "states = {\n",
        "            'epoch': n_epochs,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'tr_loss' : tr_loss['sug'],\n",
        "            'val_loss' : val_loss['sug'],\n",
        "            'lips' : lips,\n",
        "            'grad' : grad,\n",
        "            'times' : times,\n",
        "            'acc' : acc\n",
        "        }\n",
        "torch.save(states, './CIFAR10/alexnet_sug')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMpe2-J1HT8e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_epochs = 30\n",
        "\n",
        "for lr in lrs:\n",
        "  model = AlexNet(10)\n",
        "  #model = CNN()\n",
        "  print(\"SGD  lr={}, momentum=0. :\".format(lr))\n",
        "  #model = CNN()\n",
        "  optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0., weight_decay=1e-3)\n",
        "  tr_loss['sgd'][lr], times, val_loss['sgd'][lr], lips, grad, acc = train(model, trainloader, criterion, optimizer, n_epochs=n_epochs, print_every=print_every, validloader=validloader)\n",
        "  states = {\n",
        "            'epoch': n_epochs,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'tr_loss' : tr_loss['sgd'][lr],\n",
        "            'val_loss' : val_loss['sgd'][lr],\n",
        "            'lips' : lips,\n",
        "            'grad' : grad,\n",
        "            'times' : times,\n",
        "            'acc' : acc\n",
        "             }\n",
        "  torch.save(states, './CIFAR10/alexnet_' + str(lr)+'_')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PP_mXBJLcet",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}