Lookahead_radam_seed42.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Lookahead_radam_seed42.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehioQb4Kq5Ny",
        "colab_type": "code",
        "outputId": "93b3e370-42d6-4625-adcf-369fbda51642",
        "colab": {
          "resources": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fb135b1c-a62d-4c84-8b10-322030e1ed76\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-fb135b1c-a62d-4c84-8b10-322030e1ed76\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving SGDW.py to SGDW.py\n",
            "Saving RAdam.py to RAdam.py\n",
            "Saving OneCycle.py to OneCycle.py\n",
            "Saving Lookahead.py to Lookahead.py\n",
            "Saving CLR.py to CLR.py\n",
            "Saving AdamW.py to AdamW.py\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'AdamW.py': b'import math\\nimport torch\\nfrom torch.optim.optimizer import Optimizer\\n\\nclass AdamW(Optimizer):\\n    r\"\"\"Implements Adam algorithm with decoupled weight decay.\\n\\n    It has been proposed in `DECOUPLED WEIGHT DECAY REGULARIZATION`_.\\n    Below is modification of adam code from pytorch repository(https://github.com/pytorch/pytorch)\\n\\n    Arguments:\\n        params (iterable): iterable of parameters to optimize or dicts defining\\n            parameter groups\\n        lr (float, optional): learning rate (default: 1e-3)\\n        betas (Tuple[float, float], optional): coefficients used for computing\\n            running averages of gradient and its square (default: (0.9, 0.999))\\n        eps (float, optional): term added to the denominator to improve\\n            numerical stability (default: 1e-8)\\n        weight_decay (float, optional): weight decay (default: 0)\\n        amsgrad (boolean, optional): whether to use the AMSGrad variant of this\\n            algorithm from the paper `On the Convergence of Adam and Beyond`_\\n            (default: False)\\n           \\n    .. _DECOUPLED WEIGHT DECAY REGULARIZATION:\\n        https://arxiv.org/pdf/1711.05101.pdf\\n    \"\"\"\\n\\n    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,\\n                 weight_decay=0, amsgrad=False):\\n        if not 0.0 <= lr:\\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\\n        if not 0.0 <= eps:\\n            raise ValueError(\"Invalid epsilon value: {}\".format(eps))\\n        if not 0.0 <= betas[0] < 1.0:\\n            raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\\n        if not 0.0 <= betas[1] < 1.0:\\n            raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\\n        defaults = dict(lr=lr, betas=betas, eps=eps,\\n                        weight_decay=weight_decay, amsgrad=amsgrad)\\n        super(AdamW, self).__init__(params, defaults)\\n\\n    def __setstate__(self, state):\\n        super(AdamW, self).__setstate__(state)\\n        for group in self.param_groups:\\n            group.setdefault(\\'amsgrad\\', False)\\n\\n    def step(self, closure=None):\\n        \"\"\"Performs a single optimization step.\\n\\n        Arguments:\\n            closure (callable, optional): A closure that reevaluates the model\\n                and returns the loss.\\n        \"\"\"\\n        loss = None\\n        if closure is not None:\\n            loss = closure()\\n\\n        for group in self.param_groups:\\n            for p in group[\\'params\\']:\\n                if p.grad is None:\\n                    continue\\n                grad = p.grad.data\\n                if grad.is_sparse:\\n                    raise RuntimeError(\\'Adam does not support sparse gradients, please consider SparseAdam instead\\')\\n                amsgrad = group[\\'amsgrad\\']\\n\\n                state = self.state[p]\\n\\n                # State initialization\\n                if len(state) == 0:\\n                    state[\\'step\\'] = 0\\n                    # Exponential moving average of gradient values\\n                    state[\\'exp_avg\\'] = torch.zeros_like(p.data)\\n                    # Exponential moving average of squared gradient values\\n                    state[\\'exp_avg_sq\\'] = torch.zeros_like(p.data)\\n                    if amsgrad:\\n                        # Maintains max of all exp. moving avg. of sq. grad. values\\n                        state[\\'max_exp_avg_sq\\'] = torch.zeros_like(p.data)\\n\\n                exp_avg, exp_avg_sq = state[\\'exp_avg\\'], state[\\'exp_avg_sq\\']\\n                if amsgrad:\\n                    max_exp_avg_sq = state[\\'max_exp_avg_sq\\']\\n                beta1, beta2 = group[\\'betas\\']\\n\\n                state[\\'step\\'] += 1\\n\\n                old = p.data.float()\\n\\n                # Decay the first and second moment running average coefficient\\n                exp_avg.mul_(beta1).add_(1 - beta1, grad)\\n                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\\n                if amsgrad:\\n                    # Maintains the maximum of all 2nd moment running avg. till now\\n                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)\\n                    # Use the max. for normalizing running avg. of gradient\\n                    denom = max_exp_avg_sq.sqrt().add_(group[\\'eps\\'])\\n                else:\\n                    denom = exp_avg_sq.sqrt().add_(group[\\'eps\\'])\\n\\n                bias_correction1 = 1 - beta1 ** state[\\'step\\']\\n                bias_correction2 = 1 - beta2 ** state[\\'step\\']\\n                \\n                step_size = group[\\'lr\\'] * math.sqrt(bias_correction2) / bias_correction1\\n                    \\n                p.data.addcdiv_(-step_size, exp_avg, denom)\\n                \\n                if group[\\'weight_decay\\'] != 0:\\n                    p.data.add_(-group[\\'weight_decay\\'] * group[\\'lr\\'], old)\\n                \\n        return loss',\n",
              " 'CLR.py': b'import math\\nimport matplotlib.pyplot as plt\\n\\n\\nclass CLR(object):\\n    \"\"\"\\n    The method is described in paper : https://arxiv.org/abs/1506.01186 to find out optimum \\n    learning rate. The learning rate is increased from lower value to higher per iteration \\n    for some iterations till loss starts exploding.The learning rate one power lower than \\n    the one where loss is minimum is chosen as optimum learning rate for training.\\n\\n    Args:\\n        optim   Optimizer used in training.\\n\\n        bn      Total number of iterations used for this test run.\\n                The learning rate increasing factor is calculated based on this \\n                iteration number.\\n\\n        base_lr The lower boundary for learning rate which will be used as\\n                initial learning rate during test run. It is adviced to start from\\n                small learning rate value like 1e-4.\\n                Default value is 1e-5\\n\\n        max_lr  The upper boundary for learning rate. This value defines amplitude\\n                for learning rate increase(max_lr-base_lr). max_lr value may not be \\n                reached in test run as loss may explode before reaching max_lr.\\n                It is adviced to use higher value like 10, 100.\\n                Default value is 100.\\n\\n    \"\"\"\\n    def __init__(self, optim, bn, base_lr=1e-7, max_lr=100):\\n        self.base_lr = base_lr\\n        self.max_lr = max_lr\\n        self.optim = optim\\n        self.bn = bn - 1\\n        ratio = self.max_lr/self.base_lr\\n        self.mult = ratio ** (1/self.bn)\\n        self.best_loss = 1e9\\n        self.iteration = 0\\n        self.lrs = []\\n        self.losses = []\\n        \\n    def calc_lr(self, loss):\\n        self.iteration +=1\\n        if math.isnan(loss) or loss > 4 * self.best_loss:\\n            return -1\\n        if loss < self.best_loss and self.iteration > 1:\\n            self.best_loss = loss\\n            \\n        mult = self.mult ** self.iteration\\n        lr = self.base_lr * mult\\n        \\n        self.lrs.append(lr)\\n        self.losses.append(loss)\\n        \\n        return lr\\n        \\n    def plot(self, start=10, end=-5):\\n        plt.xlabel(\"Learning Rate\")\\n        plt.ylabel(\"Losses\")\\n        plt.plot(self.lrs[start:end], self.losses[start:end])\\n        plt.xscale(\\'log\\')\\n        \\n        \\n    def plot_lr(self):\\n        plt.xlabel(\"Iterations\")\\n        plt.ylabel(\"Learning Rate\")\\n        plt.plot(self.lrs)\\n        plt.yscale(\\'log\\')',\n",
              " 'Lookahead.py': b\"import torch\\nfrom torch.optim import Optimizer\\nfrom collections import defaultdict\\n\\nclass Lookahead(Optimizer):\\n    r'''Implements Lookahead optimizer.\\n\\n    It's been proposed in paper: Lookahead Optimizer: k steps forward, 1 step back\\n    (https://arxiv.org/pdf/1907.08610.pdf)\\n\\n    Args:\\n        optimizer: The optimizer object used in inner loop for fast weight updates.\\n        alpha:     The learning rate for slow weight update.\\n                   Default: 0.5\\n        k:         Number of iterations of fast weights updates before updating slow\\n                   weights.\\n                   Default: 5\\n\\n    Example:\\n        > optim = Lookahead(optimizer)\\n        > optim = Lookahead(optimizer, alpha=0.6, k=10)\\n    '''\\n    def __init__(self, optimizer, alpha=0.5, k=5):\\n        assert(0.0 <= alpha <= 1.0)\\n        assert(k >= 1)\\n        self.optimizer = optimizer\\n        self.alpha = alpha\\n        self.k = k\\n        self.k_counter = 0\\n        self.param_groups = self.optimizer.param_groups\\n        self.state = defaultdict(dict)\\n        self.slow_weights = [[param.clone().detach() for param in group['params']] for group in self.param_groups]\\n    \\n    def step(self, closure=None):\\n        loss = self.optimizer.step(closure)\\n        self.k_counter += 1\\n        if self.k_counter >= self.k:\\n            for group, slow_weight in zip(self.param_groups, self.slow_weights):\\n                for param, weight in zip(group['params'], slow_weight):\\n                    weight.data.add_(self.alpha, (param.data - weight.data))\\n                    param.data.copy_(weight.data)\\n            self.k_counter = 0\\n        return loss\\n\\n    def __getstate__(self):\\n        return {\\n            'state': self.state,\\n            'optimizer': self.optimizer,\\n            'alpha': self.alpha,\\n            'k': self.k,\\n            'k_counter': self.k_counter\\n        }\\n\\n    def state_dict(self):\\n        return self.optimizer.state_dict()\\n\\n    def load_state_dict(self, state_dict):\\n        self.optimizer.load_state_dict(state_dict)\",\n",
              " 'OneCycle.py': b'import math\\n\\nclass OneCycle(object):\\n    \"\"\"\\n    In paper (https://arxiv.org/pdf/1803.09820.pdf), author suggests to do one cycle during \\n    whole run with 2 steps of equal length. During first step, increase the learning rate \\n    from lower learning rate to higher learning rate. And in second step, decrease it from \\n    higher to lower learning rate. This is Cyclic learning rate policy. Author suggests one \\n    addition to this. - During last few hundred/thousand iterations of cycle reduce the \\n    learning rate to 1/100th or 1/1000th of the lower learning rate.\\n\\n    Also, Author suggests that reducing momentum when learning rate is increasing. So, we make \\n    one cycle of momentum also with learning rate - Decrease momentum when learning rate is \\n    increasing and increase momentum when learning rate is decreasing.\\n\\n    Args:\\n        nb              Total number of iterations including all epochs\\n\\n        max_lr          The optimum learning rate. This learning rate will be used as highest \\n                        learning rate. The learning rate will fluctuate between max_lr to\\n                        max_lr/div and then (max_lr/div)/div.\\n\\n        momentum_vals   The maximum and minimum momentum values between which momentum will\\n                        fluctuate during cycle.\\n                        Default values are (0.95, 0.85)\\n\\n        prcnt           The percentage of cycle length for which we annihilate learning rate\\n                        way below the lower learnig rate.\\n                        The default value is 10\\n\\n        div             The division factor used to get lower boundary of learning rate. This\\n                        will be used with max_lr value to decide lower learning rate boundary.\\n                        This value is also used to decide how much we annihilate the learning \\n                        rate below lower learning rate.\\n                        The default value is 10.\\n\\n        use_cosine      Use cosine annealation instead of linear to change learning rate and \\n                        momentum. \\n                        The default value is False\\n    \"\"\"\\n    def __init__(self, nb, max_lr, momentum_vals=(0.95, 0.85), prcnt= 10, div=10, use_cosine=False):\\n        self.nb = nb\\n        self.div = div\\n        self.high_lr = max_lr\\n        self.low_mom = momentum_vals[1]\\n        self.high_mom = momentum_vals[0]\\n        self.use_cosine = use_cosine\\n        if self.use_cosine:\\n            self.prcnt = 0\\n        else:\\n            self.prcnt = prcnt\\n        self.iteration = 0\\n        self.lrs = []\\n        self.moms = []\\n        if self.use_cosine:\\n            self.step_len =  int(self.nb / 4)\\n        else:\\n            self.step_len =  int(self.nb * (1- prcnt/100)/2)\\n        \\n    def calc(self):\\n        if self.use_cosine:\\n            lr = self.calc_lr_cosine()\\n            mom = self.calc_mom_cosine()\\n        else:\\n            lr = self.calc_lr()\\n            mom = self.calc_mom()\\n        self.iteration += 1\\n        return (lr, mom)\\n        \\n    def calc_lr(self):\\n        if self.iteration ==  0:\\n            self.lrs.append(self.high_lr/self.div)\\n            return self.high_lr/self.div\\n        elif self.iteration == self.nb:\\n            self.iteration = 0\\n            self.lrs.append(self.high_lr/self.div)\\n            return self.high_lr/self.div\\n        elif self.iteration > 2 * self.step_len:\\n            ratio = (self.iteration - 2 * self.step_len) / (self.nb - 2 * self.step_len)\\n            #lr = self.high_lr * ( 1 - 0.99 * ratio)/self.div\\n            lr = (self.high_lr / self.div) * (1- ratio * (1 - 1/self.div))\\n        elif self.iteration > self.step_len:\\n            ratio = 1- (self.iteration -self.step_len)/self.step_len\\n            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div\\n        else :\\n            ratio = self.iteration/self.step_len\\n            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div\\n        self.lrs.append(lr)\\n        return lr\\n\\n    def calc_mom(self):\\n        if self.iteration == 0:\\n            self.moms.append(self.high_mom)\\n            return self.high_mom\\n        elif self.iteration == self.nb:\\n            self.iteration = 0\\n            self.moms.append(self.high_mom)\\n            return self.high_mom\\n        elif self.iteration > 2 * self.step_len:\\n            mom = self.high_mom\\n        elif self.iteration > self.step_len:\\n            ratio = (self.iteration -self.step_len)/self.step_len\\n            mom = self.low_mom + ratio * (self.high_mom - self.low_mom)\\n        else :\\n            ratio = self.iteration/self.step_len\\n            mom = self.high_mom - ratio * (self.high_mom - self.low_mom)\\n        self.moms.append(mom)\\n        return mom\\n\\n    def calc_lr_cosine(self):\\n        if self.iteration ==  0:\\n            self.lrs.append(self.high_lr/self.div)\\n            return self.high_lr/self.div\\n        elif self.iteration == self.nb:\\n            self.iteration = 0\\n            self.lrs.append(self.high_lr/self.div)\\n            return self.high_lr/self.div\\n        elif self.iteration > self.step_len:\\n            ratio = (self.iteration -self.step_len)/(self.nb - self.step_len)\\n            lr = (self.high_lr/self.div) + 0.5 * (self.high_lr - self.high_lr/self.div) * (1 + math.cos(math.pi * ratio))\\n        else :\\n            ratio = self.iteration/self.step_len\\n            lr = self.high_lr - 0.5 * (self.high_lr - self.high_lr/self.div) * (1 + math.cos(math.pi * ratio))\\n        self.lrs.append(lr)\\n        return lr\\n\\n    def calc_mom_cosine(self):\\n        if self.iteration == 0:\\n            self.moms.append(self.high_mom)\\n            return self.high_mom\\n        elif self.iteration == self.nb:\\n            self.iteration = 0\\n            self.moms.append(self.high_mom)\\n            return self.high_mom\\n        elif self.iteration > self.step_len:\\n            ratio = (self.iteration -self.step_len)/(self.nb - self.step_len)\\n            mom = self.high_mom - 0.5 * (self.high_mom - self.low_mom) * (1 + math.cos(math.pi * ratio))\\n        else :\\n            ratio = self.iteration/self.step_len\\n            mom = self.low_mom + 0.5 * (self.high_mom - self.low_mom) * (1 + math.cos(math.pi * ratio))\\n        self.moms.append(mom)\\n        return mom\\n',\n",
              " 'RAdam.py': b'import math\\nimport torch\\nfrom torch.optim import Optimizer\\n\\n\\nclass RAdam(Optimizer):\\n    r\"\"\"Implements RAdam algorithm.\\n    It has been proposed in `ON THE VARIANCE OF THE ADAPTIVE LEARNING\\n    RATE AND BEYOND(https://arxiv.org/pdf/1908.03265.pdf)`_.\\n    \\n    Arguments:\\n        params (iterable):      iterable of parameters to optimize or dicts defining\\n                                parameter groups\\n        lr (float, optional):   learning rate (default: 1e-3)\\n        betas (Tuple[float, float], optional):  coefficients used for computing\\n                                                running averages of gradient and \\n                                                its square (default: (0.9, 0.999))\\n        eps (float, optional):  term added to the denominator to improve\\n                                numerical stability (default: 1e-8)\\n        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\\n        amsgrad (boolean, optional):    whether to use the AMSGrad variant of this\\n                                        algorithm from the paper `On the Convergence \\n                                        of Adam and Beyond`_(default: False)\\n        \\n        sma_thresh:             simple moving average threshold.\\n                                Length till where the variance of adaptive lr is intracable.\\n                                Default: 4 (as per paper)\\n    \"\"\"\\n    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,\\n                 weight_decay=0, amsgrad=False, sma_thresh=4):\\n        if not 0.0 <= lr:\\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\\n        if not 0.0 <= eps:\\n            raise ValueError(\"Invalid epsilon value: {}\".format(eps))\\n        if not 0.0 <= betas[0] < 1.0:\\n            raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\\n        if not 0.0 <= betas[1] < 1.0:\\n            raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\\n        defaults = dict(lr=lr, betas=betas, eps=eps,\\n                        weight_decay=weight_decay, amsgrad=amsgrad)\\n        super(RAdam, self).__init__(params, defaults)\\n\\n        self.radam_buffer = [[None, None, None] for ind in range(10)]\\n        self.sma_thresh = sma_thresh\\n\\n    def __setstate__(self, state):\\n        super(RAdam, self).__setstate__(state)\\n        for group in self.param_groups:\\n            group.setdefault(\\'amsgrad\\', False)\\n\\n    def step(self, closure=None):\\n        loss = None\\n        if closure is not None:\\n            loss = closure()\\n\\n        for group in self.param_groups:\\n            for p in group[\\'params\\']:\\n                if p.grad is None:\\n                    continue\\n\\n                # Perform optimization step\\n                grad = p.grad.data\\n                if grad.is_sparse:\\n                    raise RuntimeError(\\'Adam does not support sparse gradients, please consider SparseAdam instead\\')\\n                amsgrad = group[\\'amsgrad\\']\\n\\n                state = self.state[p]\\n\\n                # State initialization\\n                if len(state) == 0:\\n                    state[\\'step\\'] = 0\\n                    # Exponential moving average of gradient values\\n                    state[\\'exp_avg\\'] = torch.zeros_like(p.data)\\n                    # Exponential moving average of squared gradient values\\n                    state[\\'exp_avg_sq\\'] = torch.zeros_like(p.data)\\n                    if amsgrad:\\n                        # Maintains max of all exp. moving avg. of sq. grad. values\\n                        state[\\'max_exp_avg_sq\\'] = torch.zeros_like(p.data)\\n\\n                exp_avg, exp_avg_sq = state[\\'exp_avg\\'], state[\\'exp_avg_sq\\']\\n                if amsgrad:\\n                    max_exp_avg_sq = state[\\'max_exp_avg_sq\\']\\n                beta1, beta2 = group[\\'betas\\']\\n\\n                state[\\'step\\'] += 1\\n                old = p.data.float()\\n\\n                # Decay the first and second moment running average coefficient\\n                exp_avg.mul_(beta1).add_(1 - beta1, grad)\\n                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\\n\\n                buffer = self.radam_buffer[int(state[\\'step\\']%10)]\\n\\n                if buffer[0] == state[\\'step\\']:\\n                    sma_t, step_size = buffer[1], buffer[2]\\n                else:                 \\n                    sma_max_len = 2/(1-beta2) - 1  \\n                    beta2_t = beta2 ** state[\\'step\\']\\n                    sma_t = sma_max_len - 2 * state[\\'step\\'] * beta2_t /(1 - beta2_t)\\n                    buffer[0] = state[\\'step\\']\\n                    buffer[1] = sma_t\\n\\n                    if sma_t > self.sma_thresh :\\n                        rt = math.sqrt(((sma_t - 4) * (sma_t - 2) * sma_max_len)/((sma_max_len -4) * (sma_max_len - 2) * sma_t))\\n                        step_size = group[\\'lr\\'] * rt * math.sqrt((1 - beta2_t)) / (1 - beta1 ** state[\\'step\\'])                      \\n                    else:\\n                        step_size = group[\\'lr\\'] / (1 - beta1 ** state[\\'step\\'])                        \\n                    buffer[2] = step_size\\n\\n                if group[\\'weight_decay\\'] != 0:\\n                    p.data.add_(-group[\\'weight_decay\\'] * group[\\'lr\\'], old)\\n\\n                if sma_t > self.sma_thresh :\\n                    denom = exp_avg_sq.sqrt().add_(group[\\'eps\\'])\\n                    p.data.addcdiv_(-step_size, exp_avg, denom)\\n                else:\\n                    p.data.add_(-step_size, exp_avg)\\n\\n        return loss\\n',\n",
              " 'SGDW.py': b'import torch\\nfrom torch.optim.optimizer import Optimizer, required\\n\\n\\nclass SGDW(Optimizer):\\n    r\"\"\"Implements stochastic gradient descent with decoupled weight decay(optionally with momentum).\\n\\n    It has been proposed in `DECOUPLED WEIGHT DECAY REGULARIZATION`_.\\n    Below is modification of adam code from pytorch repository(https://github.com/pytorch/pytorch).\\n\\n    Args:\\n        params (iterable): iterable of parameters to optimize or dicts defining\\n            parameter groups\\n        lr (float): learning rate\\n        momentum (float, optional): momentum factor (default: 0)\\n        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\\n        dampening (float, optional): dampening for momentum (default: 0)\\n        nesterov (bool, optional): enables Nesterov momentum (default: False)\\n\\n    Example:\\n        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)\\n        >>> optimizer.zero_grad()\\n        >>> loss_fn(model(input), target).backward()\\n        >>> optimizer.step()\\n\\n    __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf\\n\\n    .. note::\\n        The implementation of SGD with Momentum/Nesterov subtly differs from\\n        Sutskever et. al. and implementations in some other frameworks.\\n\\n        Considering the specific case of Momentum, the update can be written as\\n\\n        .. math::\\n                  v = \\\\rho * v + g \\\\\\\\\\n                  p = p - lr * v\\n\\n        where p, g, v and :math:`\\\\rho` denote the parameters, gradient,\\n        velocity, and momentum respectively.\\n\\n        This is in contrast to Sutskever et. al. and\\n        other frameworks which employ an update of the form\\n\\n        .. math::\\n             v = \\\\rho * v + lr * g \\\\\\\\\\n             p = p - v\\n\\n        .. _DECOUPLED WEIGHT DECAY REGULARIZATION:\\n        https://arxiv.org/pdf/1711.05101.pdf\\n    \"\"\"\\n\\n    def __init__(self, params, lr=required, momentum=0, dampening=0,\\n                 weight_decay=0, nesterov=False):\\n        if lr is not required and lr < 0.0:\\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\\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(lr=lr, 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        super(SGDW, self).__init__(params, defaults)\\n\\n    def __setstate__(self, state):\\n        super(SGDW, self).__setstate__(state)\\n        for group in self.param_groups:\\n            group.setdefault(\\'nesterov\\', False)\\n\\n    def step(self, closure=None):\\n        \"\"\"Performs a single optimization step.\\n\\n        Arguments:\\n            closure (callable, optional): A closure that reevaluates the model\\n                and returns the loss.\\n        \"\"\"\\n        loss = None\\n        if closure is not None:\\n            loss = closure()\\n\\n        for group in self.param_groups:\\n            weight_decay = group[\\'weight_decay\\']\\n            momentum = group[\\'momentum\\']\\n            dampening = group[\\'dampening\\']\\n            nesterov = group[\\'nesterov\\']\\n\\n            for p in group[\\'params\\']:\\n                if p.grad is None:\\n                    continue\\n                d_p = p.grad.data\\n                \\n                old = torch.clone(p.data).detach()\\n                #if weight_decay != 0:\\n                #    d_p.add_(weight_decay, p.data)\\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\\n                p.data.add_(-group[\\'lr\\'], d_p)\\n                \\n                if weight_decay != 0:\\n                    p.data.add_(-weight_decay, old)\\n\\n        return loss\\n'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7Dp2pUUqgDN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%config InlineBackend.figure_format = 'svg'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7aNg3F9q3zR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "from torch.utils.data.dataset import Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import transforms\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.autograd import Variable\n",
        "from torchvision.datasets import CIFAR10\n",
        "import torchvision.models as models\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import math\n",
        "import tqdm as tqdm\n",
        "from Lookahead import Lookahead\n",
        "from AdamW import AdamW\n",
        "from RAdam import RAdam"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLSYwdcSrXvb",
        "colab_type": "code",
        "outputId": "e28866b3-2bfb-4f3a-fdcd-700135c5a2eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "device"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anHwr0nYuVC0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzJWgAPIrhwO",
        "colab_type": "text"
      },
      "source": [
        "# Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYVOcHGXrf6s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mean = [0.4914, 0.4822, 0.4465]\n",
        "std = [0.2023, 0.1994, 0.2010]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhPxnE1vrtHT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "normalize = transforms.Normalize(mean=mean, std=std)\n",
        "train_transforms = transforms.Compose([\n",
        "                    transforms.RandomCrop(32, padding=4, padding_mode='reflect'),\n",
        "                    transforms.RandomHorizontalFlip(),\n",
        "                    transforms.ToTensor(),\n",
        "                    normalize\n",
        "                ])\n",
        "valid_transforms = transforms.Compose([\n",
        "                    transforms.ToTensor(),\n",
        "                    normalize\n",
        "                ])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6rlE2XJrwRV",
        "colab_type": "code",
        "outputId": "b7267dcc-381e-47f1-f3a8-46a07fa981ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "trainset = CIFAR10('./data', train=True, download=True, transform=train_transforms)\n",
        "testset = CIFAR10('./data', train=False, download=True, transform=valid_transforms)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "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": [
            "170500096it [00:06, 27278046.59it/s]                               \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTnDa9Pqr0Cn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bs=128\n",
        "epochs=90\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5btvC80or6bE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loader = DataLoader(trainset, batch_size=bs, shuffle=True, num_workers=4)\n",
        "test_loader = DataLoader(testset, batch_size=bs, shuffle=False, num_workers=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiRuMuZEsInt",
        "colab_type": "text"
      },
      "source": [
        "# Plot Images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5FF-b2YsHTA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Plot few of the images\n",
        "# This can be used to print predictions too\n",
        "def plot_img(preds=None, is_pred=False):        \n",
        "    fig = plt.figure(figsize=(8,8))\n",
        "    columns = 4\n",
        "    rows = 5\n",
        "\n",
        "    for i in range(1, columns*rows +1):\n",
        "        fig.add_subplot(rows, columns, i)\n",
        "        \n",
        "        if is_pred:\n",
        "            img_xy = np.random.randint(len(testset));\n",
        "            img = testset[img_xy][0].numpy()\n",
        "            #img = img * std + mean\n",
        "        else:\n",
        "            img_xy = np.random.randint(len(trainset));\n",
        "            img = trainset[img_xy][0].numpy()\n",
        "            #img = img * std + mean\n",
        "        \n",
        "        if is_pred:\n",
        "            plt.title(classes[int(preds[img_xy])] + \"/\" + classes[testset[img_xy][1]])\n",
        "        else:\n",
        "            plt.title(classes[trainset[img_xy][1]])\n",
        "        plt.axis('off')\n",
        "        img = np.clip(img, 0, 1)\n",
        "        plt.imshow(np.transpose(img , (1,2,0)))\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7VXJ9KCzsTX5",
        "colab_type": "code",
        "outputId": "f98386c7-186f-4378-a1a7-456a4331d9ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        }
      },
      "source": [
        "plot_img()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 20 Axes>"
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    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "qlQ0W-q7snrR",
        "colab_type": "text"
      },
      "source": [
        "# save and load checkpoint"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGyG96ysskV1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def save_checkpoint(model, is_best, filename='data/checkpoint.pth.tar'):\n",
        "    \"\"\"Save checkpoint if a new best is achieved\"\"\"\n",
        "    if is_best:\n",
        "        torch.save(model.state_dict(), filename)  # save checkpoint\n",
        "    else:\n",
        "        print (\"=> Validation Accuracy did not improve\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODkWdaKUsw8Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_checkpoint(model, filename = 'data/checkpoint.pth.tar'):\n",
        "    sd = torch.load(filename, map_location=lambda storage, loc: storage)\n",
        "    names = set(model.state_dict().keys())\n",
        "    for n in list(sd.keys()):\n",
        "        if n not in names and n+'_raw' in names:\n",
        "            if n+'_raw' not in sd: sd[n+'_raw'] = sd[n]\n",
        "            del sd[n]\n",
        "    model.load_state_dict(sd)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BMqnHHlLs1nL",
        "colab_type": "text"
      },
      "source": [
        "# Global variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oEDCaDYs0qk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loss = 0.0\n",
        "test_loss = 0.0\n",
        "best_acc = 0.0\n",
        "trn_losses = []\n",
        "trn_accs = []\n",
        "val_losses = []\n",
        "val_accs = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "po5STdeZs9ai",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total = 0\n",
        "correct = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogaK2kjKtB1O",
        "colab_type": "text"
      },
      "source": [
        "# Helper functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNVROs82tBDb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def accuracy(output, target, is_test=False):\n",
        "    global total\n",
        "    global correct\n",
        "    batch_size = target.size(0)\n",
        "    total += batch_size    \n",
        "    _, pred = output.max(dim=1)\n",
        "    if is_test:\n",
        "        preds.extend(pred)\n",
        "    correct += torch.sum(pred == target.data)\n",
        "    return (correct.float() / total) * 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OdwknXrXtPWK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def reset():\n",
        "    global total, correct\n",
        "    global train_loss, test_loss, best_acc\n",
        "    global trn_accs, trn_losses, val_accs, val_losses\n",
        "    total, correct = 0, 0\n",
        "    train_loss, test_loss, best_acc = 0.0, 0.0, 0.0\n",
        "    trn_losses, trn_accs, val_accs, val_losses = [], [], [], []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_QEGWOBtSoR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AvgStats(object):\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "        \n",
        "    def reset(self):\n",
        "        self.losses =[]\n",
        "        self.precs =[]\n",
        "        self.its = []\n",
        "        \n",
        "    def append(self, loss, prec, it):\n",
        "        self.losses.append(loss)\n",
        "        self.precs.append(prec)\n",
        "        self.its.append(it)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TiptBnxLtaHx",
        "colab_type": "text"
      },
      "source": [
        "# Train and Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrJGCUsTtZHN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(epoch=0, model=None, optimizer=None):\n",
        "    model.train()\n",
        "    global best_acc\n",
        "    global trn_accs, trn_losses\n",
        "    is_improving = True\n",
        "    counter = 0\n",
        "    running_loss = 0.\n",
        "    avg_beta = 0.98\n",
        "\n",
        "    for i, (input, target) in enumerate(train_loader):\n",
        "        bt_start = time.time()\n",
        "        input, target = input.to(device), target.to(device)\n",
        "        var_ip, var_tg = Variable(input), Variable(target)\n",
        "                                    \n",
        "        output = model(var_ip)\n",
        "        loss = criterion(output, var_tg)\n",
        "            \n",
        "        running_loss = avg_beta * running_loss + (1-avg_beta) *loss.item()\n",
        "        smoothed_loss = running_loss / (1 - avg_beta**(i+1))\n",
        "        \n",
        "        trn_losses.append(smoothed_loss)\n",
        "            \n",
        "        # measure accuracy and record loss\n",
        "        prec = accuracy(output.data, target)\n",
        "        trn_accs.append(prec)\n",
        "\n",
        "        train_stats.append(smoothed_loss, prec, time.time()-bt_start)\n",
        "        if prec > best_acc :\n",
        "            best_acc = prec\n",
        "            save_checkpoint(model, True)\n",
        "\n",
        "        # compute gradient and do SGD step\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOZQwwhXtiUv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test(model=None):\n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "        global val_accs, val_losses\n",
        "        running_loss = 0.\n",
        "        avg_beta = 0.98\n",
        "        for i, (input, target) in enumerate(test_loader):\n",
        "            bt_start = time.time()\n",
        "            input, target = input.to(device), target.to(device)\n",
        "            var_ip, var_tg = Variable(input), Variable(target)\n",
        "            output = model(var_ip)\n",
        "            loss = criterion(output, var_tg)\n",
        "        \n",
        "            running_loss = avg_beta * running_loss + (1-avg_beta) *loss.item()\n",
        "            smoothed_loss = running_loss / (1 - avg_beta**(i+1))\n",
        "\n",
        "            # measure accuracy and record loss\n",
        "            prec = accuracy(output.data, target, is_test=True)\n",
        "            test_stats.append(loss.item(), prec, time.time()-bt_start)\n",
        "        \n",
        "            val_losses.append(smoothed_loss)\n",
        "            val_accs.append(prec)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "code",
      "metadata": {
        "id": "6tp2g4a3tw8p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fit(model=None, optimizer=None, sched=None):\n",
        "    print(\"Epoch\\tTrn_loss\\tVal_loss\\tTrn_acc\\t\\tVal_acc\")\n",
        "    for j in range(epochs):\n",
        "        train(j, model, optimizer)\n",
        "        test(model)\n",
        "        if sched:\n",
        "          sched.step(j)\n",
        "        print(\"{}\\t{:06.8f}\\t{:06.8f}\\t{:06.8f}\\t{:06.8f}\"\n",
        "              .format(j+1, trn_losses[-1], val_losses[-1], trn_accs[-1], val_accs[-1]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vBIKBdbt4f2",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGMxBRimt2xJ",
        "colab_type": "code",
        "outputId": "1c130ea9-0558-4069-ec82-731f796bdc08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "model = models.resnet18(pretrained=True)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/checkpoints/resnet18-5c106cde.pth\n",
            "100%|██████████| 44.7M/44.7M [00:01<00:00, 37.5MB/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pX5ab6Hkh_l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.fc = nn.Linear(in_features=model.fc.in_features, out_features=10, bias=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xSY_R_Wxt986",
        "colab_type": "code",
        "outputId": "561f84ce-861a-4dac-9830-161cca626139",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = model.to(device)\n",
        "model"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=512, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-2R9xnsuCIr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for param in model.parameters():\n",
        "  param.requires_grad = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wFs4jrvuGAM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nxvk1OPTuJhG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = optim.SGD(model.parameters(), lr=1e-2, weight_decay=1e-4, momentum=0.9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkBtb_RGiKrt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sched = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vY-VnxO5ckNS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_checkpoint(model, True, filename='before_sgd_resnet18.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q82CU_THuNY2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = []\n",
        "train_stats = AvgStats()\n",
        "test_stats = AvgStats()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-kJTqs_k_4y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reset()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bH-IxGTelOg5",
        "colab_type": "code",
        "outputId": "7508219f-0871-45a3-ddea-44d43b922ed6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fit(model, optimizer, sched)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch\tTrn_loss\tVal_loss\tTrn_acc\t\tVal_acc\n",
            "1\t0.81690471\t0.70371581\t65.76999664\t67.40999603\n",
            "2\t0.64692206\t0.60518452\t71.73363495\t72.41083527\n",
            "3\t0.57989867\t0.54954065\t74.48117828\t74.88611603\n",
            "4\t0.51921393\t0.52132156\t76.31913757\t76.57333374\n",
            "5\t0.47950729\t0.52758675\t77.64620972\t77.79267120\n",
            "6\t0.47421528\t0.52534188\t78.64743042\t78.74694824\n",
            "7\t0.43572727\t0.50823654\t79.49121857\t79.58833313\n",
            "8\t0.41682540\t0.47581992\t80.19744873\t80.27375031\n",
            "9\t0.40430805\t0.49821593\t80.83415222\t80.88944244\n",
            "10\t0.38623603\t0.47633889\t81.39305115\t81.44149780\n",
            "11\t0.37161840\t0.47630971\t81.88215637\t81.92318726\n",
            "12\t0.36791777\t0.47682066\t82.31619263\t82.33805847\n",
            "13\t0.35056007\t0.46529305\t82.72531891\t82.75153351\n",
            "14\t0.33078309\t0.44768207\t83.09409332\t83.12249756\n",
            "15\t0.31880053\t0.45511365\t83.45381927\t83.47522736\n",
            "16\t0.32347278\t0.44313050\t83.77073669\t83.78531647\n",
            "17\t0.29370204\t0.46006617\t84.08227539\t84.09422302\n",
            "18\t0.29418164\t0.46254597\t84.36411285\t84.37139130\n",
            "19\t0.28816694\t0.48537516\t84.63220978\t84.63859558\n",
            "20\t0.27253683\t0.53425918\t84.88790131\t84.87741089\n",
            "21\t0.26582097\t0.47951462\t85.11816406\t85.11864471\n",
            "22\t0.26249135\t0.46090808\t85.34801483\t85.34644318\n",
            "23\t0.25464311\t0.46596589\t85.57029724\t85.57268524\n",
            "24\t0.24855364\t0.47468792\t85.78258514\t85.77931213\n",
            "25\t0.23435526\t0.50428435\t85.97731781\t85.97106934\n",
            "26\t0.23260380\t0.49336879\t86.16631317\t86.16069794\n",
            "27\t0.22236389\t0.48310512\t86.35453796\t86.34888458\n",
            "28\t0.22624365\t0.49292680\t86.53328705\t86.52458191\n",
            "29\t0.22346105\t0.45584963\t86.70133209\t86.69885254\n",
            "30\t0.19593199\t0.47101885\t86.87737274\t86.87211609\n",
            "31\t0.20750251\t0.46228029\t87.03633118\t87.02989960\n",
            "32\t0.12370203\t0.44134274\t87.24670410\t87.24833679\n",
            "33\t0.10929299\t0.44842977\t87.47390747\t87.47388458\n",
            "34\t0.09630206\t0.46416200\t87.69895935\t87.69828796\n",
            "35\t0.09521714\t0.48542997\t87.91550446\t87.91362000\n",
            "36\t0.08538828\t0.48948139\t88.12553406\t88.12254333\n",
            "37\t0.08141956\t0.49078949\t88.32605743\t88.32350922\n",
            "38\t0.07504277\t0.49720382\t88.52039337\t88.51640320\n",
            "39\t0.08079568\t0.50149624\t88.70584106\t88.70217896\n",
            "40\t0.08037769\t0.50614117\t88.88489532\t88.88070679\n",
            "41\t0.06597710\t0.52877745\t89.05624390\t89.04962921\n",
            "42\t0.06909287\t0.53074234\t89.21733093\t89.21059418\n",
            "43\t0.07089138\t0.52386580\t89.37455750\t89.36829376\n",
            "44\t0.07153317\t0.53470713\t89.52668762\t89.51882935\n",
            "45\t0.06693142\t0.53972651\t89.67301178\t89.66537476\n",
            "46\t0.06195898\t0.53793875\t89.81585693\t89.80858612\n",
            "47\t0.06534900\t0.54633147\t89.95053864\t89.94213104\n",
            "48\t0.06072587\t0.54490396\t90.07992554\t90.07212067\n",
            "49\t0.05652396\t0.54604142\t90.20781708\t90.19941711\n",
            "50\t0.05983682\t0.55209517\t90.33123779\t90.32319641\n",
            "51\t0.05403379\t0.56932280\t90.45252991\t90.44372559\n",
            "52\t0.05395372\t0.57564796\t90.56929016\t90.55903625\n",
            "53\t0.05145460\t0.55971456\t90.68028259\t90.67069244\n",
            "54\t0.04805784\t0.57527379\t90.78770447\t90.77817535\n",
            "55\t0.05735158\t0.57697037\t90.89054108\t90.88160706\n",
            "56\t0.04385659\t0.57286711\t90.99337769\t90.98359680\n",
            "57\t0.04612599\t0.59275950\t91.09158325\t91.08184052\n",
            "58\t0.05239712\t0.56988786\t91.18615723\t91.17623901\n",
            "59\t0.04328917\t0.60455544\t91.27912140\t91.26954651\n",
            "60\t0.04826378\t0.60000612\t91.36914062\t91.35923004\n",
            "61\t0.04532851\t0.58226559\t91.45701599\t91.44611359\n",
            "62\t0.04015693\t0.58080284\t91.54225922\t91.53263855\n",
            "63\t0.02841487\t0.58437660\t91.62976074\t91.61999512\n",
            "64\t0.03187219\t0.59103268\t91.71422577\t91.70437622\n",
            "65\t0.03505033\t0.59791901\t91.79617310\t91.78672028\n",
            "66\t0.03064779\t0.59140149\t91.87653351\t91.86706543\n",
            "67\t0.03430236\t0.59491404\t91.95539093\t91.94519806\n",
            "68\t0.02785115\t0.60365585\t92.03282928\t92.02269745\n",
            "69\t0.03019898\t0.61281302\t92.10718536\t92.09764099\n",
            "70\t0.03147192\t0.59931747\t92.18014526\t92.17059326\n",
            "71\t0.02709779\t0.60805534\t92.25096893\t92.24136353\n",
            "72\t0.02767556\t0.61346882\t92.32013702\t92.31048584\n",
            "73\t0.02883065\t0.62010868\t92.38709259\t92.37721252\n",
            "74\t0.02595686\t0.62076220\t92.45333862\t92.44364166\n",
            "75\t0.03019326\t0.61047759\t92.51666260\t92.50693512\n",
            "76\t0.02899336\t0.62017247\t92.57918549\t92.56962585\n",
            "77\t0.03145250\t0.62868091\t92.64002228\t92.63002014\n",
            "78\t0.02309628\t0.62615985\t92.70011139\t92.68999481\n",
            "79\t0.02286692\t0.63175840\t92.75828552\t92.74814606\n",
            "80\t0.02767830\t0.62819820\t92.81404877\t92.80422974\n",
            "81\t0.02853966\t0.63320126\t92.86868286\t92.85888672\n",
            "82\t0.03030120\t0.63608805\t92.92289734\t92.91287994\n",
            "83\t0.02700243\t0.64609415\t92.97576904\t92.96592712\n",
            "84\t0.02567354\t0.63947013\t93.02843475\t93.01837158\n",
            "85\t0.02690110\t0.64937326\t93.07911682\t93.06919861\n",
            "86\t0.02906105\t0.64901992\t93.12832642\t93.11825562\n",
            "87\t0.02146115\t0.66178548\t93.17745209\t93.16766357\n",
            "88\t0.02324749\t0.65607674\t93.22489166\t93.21514130\n",
            "89\t0.02461159\t0.65036607\t93.27072906\t93.26091766\n",
            "90\t0.02437723\t0.66149793\t93.31575775\t93.30572510\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0OipEoMoaHt-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_checkpoint(model, True, 'sgd_resnet18_9331.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upexYQmyabrD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_sgd_losses = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_sgd_losses.append(train_stats.losses[i])\n",
        "        \n",
        "ep_sgd_lossesv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_sgd_lossesv.append(test_stats.losses[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojQ4G1KWb0zR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_sgd_accs = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_sgd_accs.append(train_stats.precs[i])\n",
        "        \n",
        "ep_sgd_accsv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_sgd_accsv.append(test_stats.precs[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QTgQbtgb1Hi",
        "colab_type": "code",
        "outputId": "926237cb-b826-4fc5-ceee-00fbdda80324",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.plot(ep_sgd_accs, 'r', label='Train')\n",
        "plt.plot(ep_sgd_accsv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('sgd_resnet18_9331_acc.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MffrvIMVb1sB",
        "colab_type": "code",
        "outputId": "238988ee-64f9-4eb9-aea9-0e1fd17e5672",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Losses\")\n",
        "plt.plot(ep_sgd_losses, 'r', label='Train')\n",
        "plt.plot(ep_sgd_lossesv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('sgd_resnet18_9331_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqWhvdXob2XE",
        "colab_type": "code",
        "outputId": "4679902a-d4c8-40a2-ce23-429d83433af4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.download('./sgd_resnet18_9331.pth.tar')\n",
        "files.download('./sgd_resnet18_9331_acc.jpg')\n",
        "files.download('./sgd_resnet18_9331_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Exception happened during processing of request from ('::ffff:127.0.0.1', 49472, 0, 0)\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 320, in _handle_request_noblock\n",
            "    self.process_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 351, in process_request\n",
            "    self.finish_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 364, in finish_request\n",
            "    self.RequestHandlerClass(request, client_address, self)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 724, in __init__\n",
            "    self.handle()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 418, in handle\n",
            "    self.handle_one_request()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 406, in handle_one_request\n",
            "    method()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 639, in do_GET\n",
            "    self.copyfile(f, self.wfile)\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 800, in copyfile\n",
            "    shutil.copyfileobj(source, outputfile)\n",
            "  File \"/usr/lib/python3.6/shutil.py\", line 82, in copyfileobj\n",
            "    fdst.write(buf)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 803, in write\n",
            "    self._sock.sendall(b)\n",
            "ConnectionResetError: [Errno 104] Connection reset by peer\n",
            "----------------------------------------\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCf3vbmn6kbX",
        "colab_type": "text"
      },
      "source": [
        "# ADAMW"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDKSoqqr6i20",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "load_checkpoint(model, filename='before_sgd_resnet18.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LpAxh9gAARoM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = []\n",
        "train_stats = AvgStats()\n",
        "test_stats = AvgStats()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pf0p8aYCAWCZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reset()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctd5bSF0AW8x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = AdamW(model.parameters(), lr=1e-4, weight_decay=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09lIUkbeAcTr",
        "colab_type": "code",
        "outputId": "8843bac5-70f4-4753-abdf-c178d2a2fbf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fit(model, optimizer, sched)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch\tTrn_loss\tVal_loss\tTrn_acc\t\tVal_acc\n",
            "1\t0.85931547\t0.74565890\t61.99799728\t64.05332947\n",
            "2\t0.67852404\t0.62613348\t69.09091187\t69.87916565\n",
            "3\t0.57934476\t0.56550580\t72.49823761\t72.95889282\n",
            "4\t0.54214736\t0.55001908\t74.68304443\t74.97708130\n",
            "5\t0.48800774\t0.51750468\t76.35551453\t76.57033539\n",
            "6\t0.45020250\t0.50406531\t77.65742493\t77.81666565\n",
            "7\t0.42087057\t0.49898211\t78.70146942\t78.81309509\n",
            "8\t0.39916292\t0.49342473\t79.60149384\t79.69166565\n",
            "9\t0.36777102\t0.47970856\t80.37698364\t80.45500183\n",
            "10\t0.36944244\t0.48241919\t81.07847595\t81.12416840\n",
            "11\t0.32664748\t0.46933987\t81.69215393\t81.73636627\n",
            "12\t0.31669978\t0.46578803\t82.25309753\t82.28903198\n",
            "13\t0.29019814\t0.47259745\t82.77818298\t82.80781555\n",
            "14\t0.28558570\t0.47556769\t83.25878906\t83.27761841\n",
            "15\t0.27061262\t0.48767383\t83.70259094\t83.71256256\n",
            "16\t0.25694627\t0.48473809\t84.11262512\t84.12427521\n",
            "17\t0.23527824\t0.49744417\t84.49365997\t84.50196075\n",
            "18\t0.23278298\t0.49140304\t84.84700775\t84.85120392\n",
            "19\t0.22274712\t0.48830497\t85.18628693\t85.18920898\n",
            "20\t0.21774973\t0.50135920\t85.51193237\t85.50624847\n",
            "21\t0.20036335\t0.52084503\t85.80895996\t85.80388641\n",
            "22\t0.20484340\t0.51925273\t86.09175873\t86.08242798\n",
            "23\t0.19083534\t0.50488471\t86.35758972\t86.35362244\n",
            "24\t0.18286532\t0.51811138\t86.61692047\t86.61062622\n",
            "25\t0.16965511\t0.50015329\t86.86369324\t86.85873413\n",
            "26\t0.16259925\t0.52240222\t87.10251617\t87.09371948\n",
            "27\t0.17042498\t0.52252229\t87.32298279\t87.31185150\n",
            "28\t0.15876842\t0.52140279\t87.53125763\t87.51916504\n",
            "29\t0.15363440\t0.52452187\t87.73063660\t87.71764374\n",
            "30\t0.14190036\t0.52617047\t87.91893768\t87.90505981\n",
            "31\t0.14747960\t0.51916487\t88.10340881\t88.09188843\n",
            "32\t0.13922101\t0.53428458\t88.27864838\t88.26406860\n",
            "33\t0.13431631\t0.53893910\t88.44878387\t88.43423462\n",
            "34\t0.12910314\t0.53641237\t88.61339569\t88.59931946\n",
            "35\t0.12631569\t0.55664281\t88.77311707\t88.75823975\n",
            "36\t0.12560540\t0.57253608\t88.92507172\t88.90828705\n",
            "37\t0.12884546\t0.56301366\t89.06678772\t89.04878235\n",
            "38\t0.12765125\t0.56115908\t89.20158386\t89.18412018\n",
            "39\t0.13034888\t0.56855822\t89.32991791\t89.31354523\n",
            "40\t0.11834714\t0.56836808\t89.45489502\t89.43937683\n",
            "41\t0.10994274\t0.58621035\t89.57934570\t89.56215668\n",
            "42\t0.10741490\t0.55570946\t89.70075989\t89.68460083\n",
            "43\t0.11965530\t0.55507830\t89.81594849\t89.80155945\n",
            "44\t0.10536789\t0.57189724\t89.92752075\t89.91015625\n",
            "45\t0.11024964\t0.58469576\t90.03446198\t90.01844788\n",
            "46\t0.10255454\t0.56330458\t90.13672638\t90.12104797\n",
            "47\t0.09726714\t0.57788087\t90.23801422\t90.22064209\n",
            "48\t0.09772969\t0.58331241\t90.33216095\t90.31690979\n",
            "49\t0.10265665\t0.57042640\t90.42720032\t90.41193390\n",
            "50\t0.08874708\t0.58167481\t90.51856232\t90.50173187\n",
            "51\t0.09153503\t0.59007399\t90.60803986\t90.59212494\n",
            "52\t0.09104527\t0.60923633\t90.69492340\t90.67858124\n",
            "53\t0.08780247\t0.58337093\t90.77835846\t90.76358795\n",
            "54\t0.09731169\t0.58011902\t90.85897064\t90.84296417\n",
            "55\t0.09248973\t0.60939242\t90.93769073\t90.92248535\n",
            "56\t0.08767629\t0.60151447\t91.01513672\t90.99945831\n",
            "57\t0.08579422\t0.58253624\t91.09111786\t91.07563782\n",
            "58\t0.08006489\t0.60184014\t91.16558075\t91.14948273\n",
            "59\t0.08692381\t0.60341713\t91.23580170\t91.22007751\n",
            "60\t0.07907666\t0.61047070\t91.30423737\t91.28858185\n",
            "61\t0.08069379\t0.60467800\t91.37049103\t91.35523987\n",
            "62\t0.07566587\t0.59775731\t91.43555450\t91.42021942\n",
            "63\t0.07319628\t0.59446906\t91.49950409\t91.48529053\n",
            "64\t0.08160431\t0.61117286\t91.56312561\t91.54825592\n",
            "65\t0.08502103\t0.62431925\t91.62326813\t91.60754395\n",
            "66\t0.07635524\t0.58994305\t91.68051147\t91.66555023\n",
            "67\t0.08563894\t0.62360708\t91.73763275\t91.72315979\n",
            "68\t0.06522310\t0.62303037\t91.79712677\t91.78287506\n",
            "69\t0.07196117\t0.62603579\t91.85236359\t91.83792114\n",
            "70\t0.07460732\t0.62545455\t91.90623474\t91.89131165\n",
            "71\t0.08028477\t0.60104656\t91.95689392\t91.94288635\n",
            "72\t0.06722399\t0.59899242\t92.01090240\t91.99723816\n",
            "73\t0.07807299\t0.60066407\t92.06345367\t92.04897308\n",
            "74\t0.07175374\t0.59805516\t92.11288452\t92.09839630\n",
            "75\t0.06882259\t0.60104190\t92.15993500\t92.14593506\n",
            "76\t0.06956365\t0.60605492\t92.20678711\t92.19342041\n",
            "77\t0.07666442\t0.59728716\t92.25050354\t92.23712158\n",
            "78\t0.06432569\t0.59717098\t92.29689789\t92.28360748\n",
            "79\t0.06214947\t0.60577342\t92.34339905\t92.33000183\n",
            "80\t0.07039118\t0.60694294\t92.38645172\t92.37322998\n",
            "81\t0.07139732\t0.60259902\t92.43059540\t92.41728210\n",
            "82\t0.06293311\t0.63597544\t92.47346497\t92.46067047\n",
            "83\t0.07265560\t0.61575345\t92.51603699\t92.50327301\n",
            "84\t0.06474998\t0.60857403\t92.55681610\t92.54404449\n",
            "85\t0.06004647\t0.64490427\t92.59712982\t92.58372498\n",
            "86\t0.06465678\t0.62670579\t92.63499451\t92.62162781\n",
            "87\t0.07284803\t0.60984908\t92.67227936\t92.65930939\n",
            "88\t0.06382349\t0.62368553\t92.70937347\t92.69590759\n",
            "89\t0.06909010\t0.62178712\t92.74510193\t92.73203278\n",
            "90\t0.06876515\t0.61862839\t92.78069305\t92.76748657\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "soIYin9sAfXw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_checkpoint(model, True, 'adamw_resnet18_9278.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vF5f3PqoBAKn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_adamw_losses = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_adamw_losses.append(train_stats.losses[i])\n",
        "        \n",
        "ep_adamw_lossesv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_adamw_lossesv.append(test_stats.losses[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VbJIT8mj0Tu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_adamw_accs = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_adamw_accs.append(train_stats.precs[i])\n",
        "        \n",
        "ep_adamw_accsv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_adamw_accsv.append(test_stats.precs[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93Ax_UHsj0ep",
        "colab_type": "code",
        "outputId": "1c909ab8-3c8a-4b25-aa96-4b99c6767edf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.plot(ep_adamw_accs, 'r', label='Train')\n",
        "plt.plot(ep_adamw_accsv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('adamw_resnet18_9278_acc.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxoQon-Yj0po",
        "colab_type": "code",
        "outputId": "6a49707f-768d-44ff-e52e-a6ec190b9bfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Losses\")\n",
        "plt.plot(ep_adamw_losses, 'r', label='Train')\n",
        "plt.plot(ep_adamw_lossesv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('adamw_resnet18_9278_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bdYQCyJEj00H",
        "colab_type": "code",
        "outputId": "340da263-0965-43a6-aab0-7d04b5339f51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.download('./adamw_resnet18_9278.pth.tar')\n",
        "files.download('./adamw_resnet18_9278_acc.jpg')\n",
        "files.download('./adamw_resnet18_9278_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Exception happened during processing of request from ('::ffff:127.0.0.1', 46332, 0, 0)\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 320, in _handle_request_noblock\n",
            "    self.process_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 351, in process_request\n",
            "    self.finish_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 364, in finish_request\n",
            "    self.RequestHandlerClass(request, client_address, self)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 724, in __init__\n",
            "    self.handle()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 418, in handle\n",
            "    self.handle_one_request()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 406, in handle_one_request\n",
            "    method()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 639, in do_GET\n",
            "    self.copyfile(f, self.wfile)\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 800, in copyfile\n",
            "    shutil.copyfileobj(source, outputfile)\n",
            "  File \"/usr/lib/python3.6/shutil.py\", line 82, in copyfileobj\n",
            "    fdst.write(buf)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 803, in write\n",
            "    self._sock.sendall(b)\n",
            "ConnectionResetError: [Errno 104] Connection reset by peer\n",
            "----------------------------------------\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0Z4WML9lYr1",
        "colab_type": "text"
      },
      "source": [
        "# Lookahead with SGD"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWY9RM02ljjd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "load_checkpoint(model, filename='before_sgd_resnet18.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0Hm9c06Bazq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reset()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeygLVC1Bcs3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = []\n",
        "train_stats = AvgStats()\n",
        "test_stats = AvgStats()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QHsZryCBfLK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optims = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9, weight_decay=1e-4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqPU5Im6yHO1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sched_la = torch.optim.lr_scheduler.StepLR(optims, step_size=30, gamma=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVTRIpgiF3Wq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "la_optim = Lookahead(optims, alpha=0.5, k=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1nY9hntBkqz",
        "colab_type": "code",
        "outputId": "f726e774-396a-4745-a974-8e4c6dfc6869",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fit(model, la_optim, sched_la)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch\tTrn_loss\tVal_loss\tTrn_acc\t\tVal_acc\n",
            "1\t0.78376562\t0.67640162\t65.41400146\t67.35832977\n",
            "2\t0.63638799\t0.56469261\t71.63272858\t72.40666962\n",
            "3\t0.56728234\t0.55022529\t74.54941559\t74.90222168\n",
            "4\t0.52422578\t0.51236753\t76.31652069\t76.56083679\n",
            "5\t0.48728402\t0.50122342\t77.66379547\t77.83066559\n",
            "6\t0.46345733\t0.49776740\t78.69713593\t78.81610870\n",
            "7\t0.44508148\t0.53268120\t79.60024261\t79.65690613\n",
            "8\t0.39734845\t0.55754017\t80.32170868\t80.34729004\n",
            "9\t0.38279466\t0.53214014\t80.94773865\t80.97981262\n",
            "10\t0.37531324\t0.44296175\t81.50372314\t81.56616974\n",
            "11\t0.35894964\t0.45049450\t82.03430939\t82.08181763\n",
            "12\t0.32494530\t0.45984588\t82.51816559\t82.55791473\n",
            "13\t0.32157368\t0.44317434\t82.94908905\t82.98513031\n",
            "14\t0.31340349\t0.46740773\t83.33915710\t83.35725403\n",
            "15\t0.31834575\t0.48375036\t83.70584869\t83.71533966\n",
            "16\t0.28913719\t0.49326659\t84.05231476\t84.06250000\n",
            "17\t0.26896076\t0.47256352\t84.37772369\t84.38343048\n",
            "18\t0.27121469\t0.48869965\t84.68000031\t84.68852234\n",
            "19\t0.26140925\t0.54489059\t84.96813965\t84.95587921\n",
            "20\t0.25490629\t0.45584670\t85.22000122\t85.22850037\n",
            "21\t0.24966632\t0.45836483\t85.47776031\t85.48103333\n",
            "22\t0.24212007\t0.47415661\t85.72885895\t85.72825623\n",
            "23\t0.23119200\t0.51735746\t85.95423889\t85.94609070\n",
            "24\t0.21608878\t0.47457321\t86.16936493\t86.16562653\n",
            "25\t0.22075978\t0.53194078\t86.38690948\t86.37239838\n",
            "26\t0.21581033\t0.55971853\t86.57980347\t86.56397247\n",
            "27\t0.20618659\t0.54718188\t86.76068878\t86.74906921\n",
            "28\t0.20855029\t0.52823529\t86.94305420\t86.93160248\n",
            "29\t0.19966028\t0.56401599\t87.11873627\t87.10241699\n",
            "30\t0.19192590\t0.50832584\t87.28983307\t87.28011322\n",
            "31\t0.19843171\t0.51165530\t87.45378876\t87.44462585\n",
            "32\t0.12091765\t0.48641581\t87.66026306\t87.65682220\n",
            "33\t0.09780346\t0.48583683\t87.87903595\t87.87529755\n",
            "34\t0.09211800\t0.50074053\t88.09132385\t88.08657837\n",
            "35\t0.09751329\t0.51614453\t88.29579163\t88.29042816\n",
            "36\t0.08484857\t0.51478610\t88.49181366\t88.48545837\n",
            "37\t0.08412580\t0.52945725\t88.68071747\t88.67395782\n",
            "38\t0.07278831\t0.53621815\t88.86303711\t88.85477448\n",
            "39\t0.07351300\t0.54327395\t89.03738403\t89.02957153\n",
            "40\t0.07266360\t0.54013690\t89.20719910\t89.19866943\n",
            "41\t0.07912832\t0.55473182\t89.36816406\t89.35926819\n",
            "42\t0.07052858\t0.55019039\t89.52438354\t89.51384735\n",
            "43\t0.06914332\t0.57996746\t89.67446899\t89.66507721\n",
            "44\t0.06158848\t0.57130678\t89.82060242\t89.81091309\n",
            "45\t0.06549583\t0.56839742\t89.95680237\t89.94644165\n",
            "46\t0.05528998\t0.58240695\t90.09185791\t90.08152771\n",
            "47\t0.05581712\t0.58606474\t90.22232056\t90.21170044\n",
            "48\t0.05397053\t0.59641014\t90.34822083\t90.33757019\n",
            "49\t0.05914368\t0.62082711\t90.46798706\t90.45567322\n",
            "50\t0.05852708\t0.61361995\t90.58335114\t90.57153320\n",
            "51\t0.05765377\t0.61648541\t90.69528198\t90.68424988\n",
            "52\t0.05572324\t0.61843433\t90.80511475\t90.79288483\n",
            "53\t0.05585580\t0.60681489\t90.90811157\t90.89616394\n",
            "54\t0.04891676\t0.63701715\t91.01119995\t90.99811554\n",
            "55\t0.04818836\t0.62329233\t91.10912323\t91.09667206\n",
            "56\t0.04700981\t0.62113859\t91.20358276\t91.19130707\n",
            "57\t0.05235068\t0.62426136\t91.29484558\t91.28128052\n",
            "58\t0.05243059\t0.62989960\t91.38241577\t91.36985779\n",
            "59\t0.04532187\t0.61795208\t91.46773529\t91.45550537\n",
            "60\t0.04788982\t0.62228630\t91.55206299\t91.54003143\n",
            "61\t0.04655914\t0.62684366\t91.63487244\t91.62236786\n",
            "62\t0.04179200\t0.61889524\t91.71627808\t91.70413971\n",
            "63\t0.03946204\t0.62355883\t91.79676819\t91.78417969\n",
            "64\t0.04047902\t0.62966925\t91.87446594\t91.86224365\n",
            "65\t0.03979536\t0.62698041\t91.95072174\t91.93861389\n",
            "66\t0.03234533\t0.63155503\t92.02539062\t92.01313019\n",
            "67\t0.03417293\t0.62897222\t92.09731293\t92.08541870\n",
            "68\t0.03126113\t0.63288099\t92.16825867\t92.15623474\n",
            "69\t0.03335057\t0.63055318\t92.23818207\t92.22609711\n",
            "70\t0.03514808\t0.63830259\t92.30513763\t92.29321289\n",
            "71\t0.03779141\t0.64197076\t92.36997986\t92.35772705\n",
            "72\t0.03504299\t0.64021488\t92.43264771\t92.42076111\n",
            "73\t0.03365797\t0.63726202\t92.49459839\t92.48246765\n",
            "74\t0.03106892\t0.64251783\t92.55480957\t92.54273987\n",
            "75\t0.03816792\t0.64055634\t92.61293793\t92.60078430\n",
            "76\t0.03205786\t0.64282890\t92.67122650\t92.65901184\n",
            "77\t0.03434141\t0.63960130\t92.72553253\t92.71331787\n",
            "78\t0.03733809\t0.65746535\t92.77955627\t92.76762390\n",
            "79\t0.03102516\t0.64970761\t92.83274841\t92.82035828\n",
            "80\t0.02995183\t0.65222212\t92.88434601\t92.87247467\n",
            "81\t0.02981706\t0.64805243\t92.93478394\t92.92288208\n",
            "82\t0.02863926\t0.65435799\t92.98460388\t92.97284698\n",
            "83\t0.03061148\t0.65901908\t93.03398132\t93.02235413\n",
            "84\t0.03404119\t0.65250661\t93.08099365\t93.06912231\n",
            "85\t0.02648231\t0.66293343\t93.12774658\t93.11592102\n",
            "86\t0.03373742\t0.66438628\t93.17248535\t93.16082001\n",
            "87\t0.03042115\t0.66092969\t93.21660614\t93.20503235\n",
            "88\t0.02896786\t0.66080036\t93.26001740\t93.24824524\n",
            "89\t0.03308336\t0.65755022\t93.30178833\t93.29039001\n",
            "90\t0.03196123\t0.66754225\t93.34350586\t93.33183289\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFEcAgEhPzGv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_checkpoint(model, True, 'lookahead_sgd_resnet18_9334.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4fah7QvhBmOG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_lookahead_losses = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_lookahead_losses.append(train_stats.losses[i])\n",
        "        \n",
        "ep_lookahead_lossesv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_lookahead_lossesv.append(test_stats.losses[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsZWOzOv51tO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_lookahead_accs = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_lookahead_accs.append(train_stats.precs[i])\n",
        "        \n",
        "ep_lookahead_accsv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_lookahead_accsv.append(test_stats.precs[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llTa7tns6uz_",
        "colab_type": "code",
        "outputId": "7689eb83-c1f7-4827-aa55-509df694f240",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.plot(ep_lookahead_accs, 'r', label='Train')\n",
        "plt.plot(ep_lookahead_accsv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('lookahead_sgd_resnet18_9334_acc.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDDR6rDi61eB",
        "colab_type": "code",
        "outputId": "d07d997b-5374-49b8-98cf-cbd28b4c749d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Losses\")\n",
        "plt.plot(ep_lookahead_losses, 'r', label='Train')\n",
        "plt.plot(ep_lookahead_lossesv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('lookahead_sgd_resnet18_9334_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unBgkhOEQUzH",
        "colab_type": "code",
        "outputId": "bc351b39-67db-470f-a8ba-6f4f43cd19fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.download('./lookahead_sgd_resnet18_9334.pth.tar')\n",
        "files.download('./lookahead_sgd_resnet18_9334_acc.jpg')\n",
        "files.download('./lookahead_sgd_resnet18_9334_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Exception happened during processing of request from ('::ffff:127.0.0.1', 47506, 0, 0)\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 320, in _handle_request_noblock\n",
            "    self.process_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 351, in process_request\n",
            "    self.finish_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 364, in finish_request\n",
            "    self.RequestHandlerClass(request, client_address, self)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 724, in __init__\n",
            "    self.handle()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 418, in handle\n",
            "    self.handle_one_request()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 406, in handle_one_request\n",
            "    method()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 639, in do_GET\n",
            "    self.copyfile(f, self.wfile)\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 800, in copyfile\n",
            "    shutil.copyfileobj(source, outputfile)\n",
            "  File \"/usr/lib/python3.6/shutil.py\", line 82, in copyfileobj\n",
            "    fdst.write(buf)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 803, in write\n",
            "    self._sock.sendall(b)\n",
            "ConnectionResetError: [Errno 104] Connection reset by peer\n",
            "----------------------------------------\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUY6Jar0-pHs",
        "colab_type": "text"
      },
      "source": [
        "# RAdam"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhuMIoYH-r2m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "load_checkpoint(model, filename='before_sgd_resnet18.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHH8il-C-uqQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reset()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fv4k7LHH-yVj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = []\n",
        "train_stats = AvgStats()\n",
        "test_stats = AvgStats()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUaNclvt-1kM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = RAdam( model.parameters(), lr=1e-4, weight_decay=1e-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsWliJjKRSzP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sched = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_jUbtha-9V6",
        "colab_type": "code",
        "outputId": "846621a1-6535-448f-a620-c4c25fddbcfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fit(model, optimizer, sched)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch\tTrn_loss\tVal_loss\tTrn_acc\t\tVal_acc\n",
            "1\t1.17956812\t1.03350742\t41.84599686\t45.50333405\n",
            "2\t0.85558891\t0.78601485\t55.61635971\t57.07500076\n",
            "3\t0.71838177\t0.66642632\t62.00117493\t62.84388733\n",
            "4\t0.63973970\t0.60533132\t65.97217560\t66.52625275\n",
            "5\t0.56805268\t0.56476555\t68.85276031\t69.24832916\n",
            "6\t0.54195648\t0.53587582\t71.02085114\t71.31999969\n",
            "7\t0.48986751\t0.52390686\t72.73244476\t72.96714020\n",
            "8\t0.46415678\t0.50217543\t74.17255402\t74.34333801\n",
            "9\t0.42862184\t0.49727216\t75.36245728\t75.50629425\n",
            "10\t0.40800423\t0.47950202\t76.39796448\t76.52099609\n",
            "11\t0.38016815\t0.49098657\t77.33753967\t77.43302917\n",
            "12\t0.36039637\t0.48207030\t78.16928864\t78.25582886\n",
            "13\t0.34154434\t0.49398275\t78.91271973\t78.97999573\n",
            "14\t0.30856392\t0.48183833\t79.59565735\t79.65631104\n",
            "15\t0.31408957\t0.48541321\t80.21157074\t80.25866699\n",
            "16\t0.27965618\t0.49433110\t80.78620911\t80.83020782\n",
            "17\t0.26899641\t0.48141070\t81.32435608\t81.36186218\n",
            "18\t0.26476553\t0.46516748\t81.81813049\t81.85221863\n",
            "19\t0.25845433\t0.48241043\t82.28663635\t82.31289673\n",
            "20\t0.23241759\t0.47445726\t82.72588348\t82.74766541\n",
            "21\t0.22472379\t0.48278155\t83.12768555\t83.14476013\n",
            "22\t0.22763925\t0.50379082\t83.50504303\t83.51697540\n",
            "23\t0.21109898\t0.50119032\t83.87153625\t83.87956238\n",
            "24\t0.19115678\t0.51360223\t84.20937347\t84.21604156\n",
            "25\t0.18634326\t0.51886967\t84.53153992\t84.53379822\n",
            "26\t0.18745176\t0.52133982\t84.83296967\t84.83422852\n",
            "27\t0.17922422\t0.53553119\t85.12031555\t85.11716461\n",
            "28\t0.17189162\t0.51509788\t85.39024353\t85.39047241\n",
            "29\t0.16876861\t0.54153186\t85.65653229\t85.65304565\n",
            "30\t0.15567780\t0.53623441\t85.90776825\t85.90322876\n",
            "31\t0.17007158\t0.54370707\t86.13913727\t86.13220978\n",
            "32\t0.09901191\t0.49773325\t86.39649963\t86.39541626\n",
            "33\t0.08821124\t0.50451842\t86.66182709\t86.66024780\n",
            "34\t0.09044159\t0.50742425\t86.91876221\t86.91647339\n",
            "35\t0.07882222\t0.50763091\t87.16622162\t87.16342926\n",
            "36\t0.07295367\t0.51539320\t87.40241241\t87.39759064\n",
            "37\t0.07436634\t0.51903236\t87.62763977\t87.62423706\n",
            "38\t0.07202277\t0.52390342\t87.84461975\t87.83938599\n",
            "39\t0.06493068\t0.52404569\t88.05330658\t88.04769135\n",
            "40\t0.06176319\t0.54662557\t88.25251770\t88.24507904\n",
            "41\t0.05947980\t0.55169260\t88.44391632\t88.43597412\n",
            "42\t0.05675126\t0.55280887\t88.62856293\t88.61972046\n",
            "43\t0.05947704\t0.55442780\t88.80229950\t88.79364777\n",
            "44\t0.05441109\t0.56565971\t88.97315216\t88.96436310\n",
            "45\t0.05415442\t0.56060069\t89.13627625\t89.12659454\n",
            "46\t0.05290849\t0.56570923\t89.29373932\t89.28376770\n",
            "47\t0.05232730\t0.56477352\t89.44406128\t89.43436432\n",
            "48\t0.04372149\t0.57592301\t89.59125519\t89.58020782\n",
            "49\t0.04390353\t0.58647756\t89.73249054\t89.72149658\n",
            "50\t0.04785531\t0.58468034\t89.86736298\t89.85580444\n",
            "51\t0.04379367\t0.58262936\t89.99734497\t89.98693085\n",
            "52\t0.04584044\t0.57891792\t90.12479401\t90.11413574\n",
            "53\t0.04158446\t0.57633430\t90.24700165\t90.23632050\n",
            "54\t0.04282106\t0.57579006\t90.36615753\t90.35493469\n",
            "55\t0.04500694\t0.59113952\t90.48000336\t90.46918488\n",
            "56\t0.04715647\t0.59105684\t90.59054565\t90.57925415\n",
            "57\t0.04299306\t0.60885249\t90.69789124\t90.68611145\n",
            "58\t0.04103598\t0.60041333\t90.80088806\t90.78948212\n",
            "59\t0.03465919\t0.61314138\t90.90234375\t90.89025116\n",
            "60\t0.03796437\t0.61319354\t90.99905396\t90.98608398\n",
            "61\t0.03744778\t0.60642962\t91.09216309\t91.07950592\n",
            "62\t0.03569659\t0.60791448\t91.18323517\t91.17118835\n",
            "63\t0.03070219\t0.60445196\t91.27435303\t91.26161194\n",
            "64\t0.02611108\t0.60461901\t91.36219025\t91.34994507\n",
            "65\t0.03179570\t0.60832560\t91.44871521\t91.43599701\n",
            "66\t0.02886291\t0.61020812\t91.53182220\t91.51934052\n",
            "67\t0.03102842\t0.60421650\t91.61277771\t91.60037231\n",
            "68\t0.02971137\t0.59933071\t91.69155121\t91.67936707\n",
            "69\t0.03092088\t0.60557484\t91.76895905\t91.75691223\n",
            "70\t0.02720169\t0.60844097\t91.84481812\t91.83257294\n",
            "71\t0.03025340\t0.60338270\t91.91823578\t91.90620422\n",
            "72\t0.02582792\t0.60526305\t91.99037170\t91.97795868\n",
            "73\t0.02690727\t0.60748673\t92.05999756\t92.04792023\n",
            "74\t0.03033297\t0.60857739\t92.12789917\t92.11592102\n",
            "75\t0.02672191\t0.61403674\t92.19443512\t92.18206787\n",
            "76\t0.02767989\t0.60698128\t92.25841522\t92.24666595\n",
            "77\t0.02595288\t0.61535094\t92.32130432\t92.30917358\n",
            "78\t0.02358904\t0.61770745\t92.38331604\t92.37100220\n",
            "79\t0.02633754\t0.61816103\t92.44226074\t92.43040466\n",
            "80\t0.02378905\t0.61871268\t92.50087738\t92.48868561\n",
            "81\t0.02450279\t0.61731110\t92.55805969\t92.54617310\n",
            "82\t0.02548756\t0.62480298\t92.61403656\t92.60231018\n",
            "83\t0.02534246\t0.61766316\t92.66869354\t92.65696716\n",
            "84\t0.02052000\t0.61319118\t92.72238922\t92.71064758\n",
            "85\t0.02244462\t0.61971161\t92.77424622\t92.76251221\n",
            "86\t0.02370342\t0.61253880\t92.82491302\t92.81287384\n",
            "87\t0.02642670\t0.61592688\t92.87382507\t92.86225891\n",
            "88\t0.02493823\t0.61779735\t92.92298126\t92.91138458\n",
            "89\t0.02400948\t0.61993993\t92.97138977\t92.95999146\n",
            "90\t0.02132687\t0.62353463\t93.01802063\t93.00679779\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFGTZvRw_Bl8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_checkpoint(model, True, 'radam_resnet18_9301.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCJQ2vCwHWNp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_lookahead_losses = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_lookahead_losses.append(train_stats.losses[i])\n",
        "        \n",
        "ep_lookahead_lossesv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_lookahead_lossesv.append(test_stats.losses[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVM5wkzAHbhi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_lookahead_accs = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_lookahead_accs.append(train_stats.precs[i])\n",
        "        \n",
        "ep_lookahead_accsv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_lookahead_accsv.append(test_stats.precs[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ss4EKzWCHfXl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "bbde4e31-8709-4581-a10a-fc1e2b36b46b"
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.plot(ep_lookahead_accs, 'r', label='Train')\n",
        "plt.plot(ep_lookahead_accsv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('radam_resnet18_9301_acc.jpg')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDO1w_zSIX08",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "fb26a931-f52f-4e61-b3b4-8bffe4d512c5"
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Losses\")\n",
        "plt.plot(ep_lookahead_losses, 'r', label='Train')\n",
        "plt.plot(ep_lookahead_lossesv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('radam_resnet18_9301_loss.jpg')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qf2xc-E0Jj4g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download('./radam_resnet18_9301.pth.tar')\n",
        "files.download('./radam_resnet18_9301_acc.jpg')\n",
        "files.download('./radam_resnet18_9301_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIXHO2jpKxte",
        "colab_type": "text"
      },
      "source": [
        "# Lookahead with radam"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwfdntdgJ5AD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "load_checkpoint(model, filename='before_sgd_resnet18.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PUungkDK84g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reset()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPPSktmSLA0d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = []\n",
        "train_stats = AvgStats()\n",
        "test_stats = AvgStats()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_9RymEyLD6k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optim = RAdam( model.parameters(), lr=1e-3, weight_decay=1e-4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhPiSEpQWvog",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sched = torch.optim.lr_scheduler.StepLR(optim, step_size=30, gamma=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AwTxvz0L4qa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = Lookahead(optim, alpha=0.5, k=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYXjaVT7MPRK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e29949cd-f78a-4733-dc91-8d960e234845"
      },
      "source": [
        "fit(model, optimizer, sched)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch\tTrn_loss\tVal_loss\tTrn_acc\t\tVal_acc\n",
            "1\t0.84039709\t0.73238619\t58.65799713\t61.38499832\n",
            "2\t0.66926891\t0.61152111\t67.62818146\t68.59583282\n",
            "3\t0.62486414\t0.57794141\t71.53353119\t72.03222656\n",
            "4\t0.55522068\t0.57696064\t73.90174103\t74.17916870\n",
            "5\t0.52561849\t0.59002180\t75.52620697\t75.68133545\n",
            "6\t0.49826776\t0.56802273\t76.68943024\t76.81278229\n",
            "7\t0.46899372\t0.53620532\t77.66439056\t77.76332855\n",
            "8\t0.45046685\t0.54581055\t78.47000122\t78.53542328\n",
            "9\t0.44863516\t0.59643967\t79.17263794\t79.18926239\n",
            "10\t0.41838204\t0.43705980\t79.74796295\t79.83950043\n",
            "11\t0.39751629\t0.43282110\t80.35430908\t80.43394470\n",
            "12\t0.39888706\t0.44356672\t80.88168335\t80.94152832\n",
            "13\t0.37322336\t0.44731551\t81.35636139\t81.40499878\n",
            "14\t0.35722745\t0.48546841\t81.79071808\t81.82142639\n",
            "15\t0.34940608\t0.45884905\t82.19045258\t82.22200012\n",
            "16\t0.35149808\t0.52451462\t82.55905151\t82.56771088\n",
            "17\t0.32982025\t0.48660794\t82.88891602\t82.90304565\n",
            "18\t0.31618896\t0.49432394\t83.20410919\t83.21592712\n",
            "19\t0.30027402\t0.50022415\t83.50017548\t83.50605011\n",
            "20\t0.29747252\t0.44017676\t83.78185272\t83.80324554\n",
            "21\t0.27890461\t0.44272641\t84.06991577\t84.08864594\n",
            "22\t0.27071345\t0.45369489\t84.34633636\t84.35955048\n",
            "23\t0.27119609\t0.45905216\t84.60643005\t84.61478424\n",
            "24\t0.25462930\t0.44320851\t84.84909058\t84.85583496\n",
            "25\t0.24883042\t0.48697005\t85.08261108\t85.08346558\n",
            "26\t0.23141073\t0.48040289\t85.30483246\t85.30461121\n",
            "27\t0.22765949\t0.49486541\t85.51770782\t85.51580048\n",
            "28\t0.22378179\t0.48548205\t85.72209167\t85.71862793\n",
            "29\t0.21599696\t0.48411146\t85.91994476\t85.91580200\n",
            "30\t0.21238618\t0.43644951\t86.10776520\t86.11189270\n",
            "31\t0.19644633\t0.45074566\t86.30178833\t86.30511475\n",
            "32\t0.12032370\t0.42609238\t86.54497528\t86.55146027\n",
            "33\t0.10809801\t0.43306816\t86.79421234\t86.80055237\n",
            "34\t0.10089784\t0.45750760\t87.04284668\t87.04775238\n",
            "35\t0.08969133\t0.45255405\t87.28004456\t87.28376007\n",
            "36\t0.08081118\t0.47100243\t87.51190186\t87.51490784\n",
            "37\t0.08136638\t0.46658158\t87.73533630\t87.73802185\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2z8fDSpsMT9U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_checkpoint(model, True, 'radam_lookahead_resnet18_9321.pth.tar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wbix-lBWov4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_lookahead_losses = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_lookahead_losses.append(train_stats.losses[i])\n",
        "        \n",
        "ep_lookahead_lossesv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_lookahead_lossesv.append(test_stats.losses[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ek4v7sy_WvKr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ep_lookahead_accs = []\n",
        "for i in range(0, len(train_stats.losses), len(train_loader)):\n",
        "    if i != 0 :\n",
        "        ep_lookahead_accs.append(train_stats.precs[i])\n",
        "        \n",
        "ep_lookahead_accsv = []\n",
        "for i in range(0, len(test_stats.losses), len(test_loader)):\n",
        "    if(i != 0):\n",
        "        ep_lookahead_accsv.append(test_stats.precs[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ltn4TITVWzpW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "3ee86ebb-5b56-4670-b46e-e383f1f3d8ef"
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.plot(ep_lookahead_accs, 'r', label='Train')\n",
        "plt.plot(ep_lookahead_accsv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('radam_lookahead_resnet18_9321_acc.jpg')"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87N8H050W7yp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "64487597-787b-4faa-8a07-4d6d75560e76"
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Losses\")\n",
        "plt.plot(ep_lookahead_losses, 'r', label='Train')\n",
        "plt.plot(ep_lookahead_lossesv, 'b', label='Val')\n",
        "plt.legend()\n",
        "plt.savefig('radam_lookahead_resnet18_9321_loss.jpg')"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SV5OcSRgXEBs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download('./radam_lookahead_resnet18_9321.pth.tar')\n",
        "files.download('./radam_lookahead_resnet18_9321_acc.jpg')\n",
        "files.download('./radam_lookahead_resnet18_9321_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}