Lookahead_radam_seed11.ipynb

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  "metadata": {
    "colab": {
      "name": "Lookahead_radam_seed11.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehioQb4Kq5Ny",
        "colab_type": "code",
        "outputId": "d37a6a1c-472e-4e78-fe27-42382ee1caa7",
        "colab": {
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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-ac26bb95-fcfa-4144-bfd2-515c98201d1b\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-ac26bb95-fcfa-4144-bfd2-515c98201d1b\">\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": "d2957bb1-8016-4f7b-f4cf-d44c5a36bdc7",
        "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(11)"
      ],
      "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": "da06e422-dcac-467b-c9f5-f40bfcf513bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "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": [
            "100%|█████████▉| 169959424/170498071 [00:11<00:00, 35218660.80it/s]"
          ],
          "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": "1c193112-d16c-4e21-b05c-e8a340ede34d",
        "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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          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": []
    },
    {
      "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": "9394ecf9-70de-4570-ad33-de915d7d27e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "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",
            "\n",
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          ],
          "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": "97c8c8c5-c51e-49d1-a54a-7f10ee8b06ac",
        "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": "99fdc1b8-7b05-4878-d876-cf05bf688a31",
        "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\t0.78144722\t0.69162591\t65.81599426\t67.58833313\n",
            "2\t0.65414955\t0.71061546\t71.79454803\t72.21582794\n",
            "3\t0.57582089\t0.56978001\t74.41706085\t74.75389099\n",
            "4\t0.52894990\t0.55394383\t76.24652100\t76.46125031\n",
            "5\t0.49460261\t0.54243252\t77.55241394\t77.68333435\n",
            "6\t0.46320435\t0.49345893\t78.56885529\t78.70999908\n",
            "7\t0.43627403\t0.50981798\t79.44195557\t79.53546906\n",
            "8\t0.41583075\t0.50034805\t80.20213318\t80.27354431\n",
            "9\t0.41100989\t0.49854687\t80.81623077\t80.86592865\n",
            "10\t0.37449969\t0.48470622\t81.36474609\t81.41683197\n",
            "11\t0.37572437\t0.50206496\t81.88569641\t81.90985107\n",
            "12\t0.37215963\t0.46065421\t82.31535339\t82.34542084\n",
            "13\t0.35110608\t0.46568915\t82.71428680\t82.73769379\n",
            "14\t0.35841818\t0.48984524\t83.07758331\t83.09571075\n",
            "15\t0.32141879\t0.45238774\t83.43809509\t83.46067047\n",
            "16\t0.30553973\t0.48818813\t83.76936340\t83.77687073\n",
            "17\t0.29430746\t0.45426958\t84.07563782\t84.09353638\n",
            "18\t0.28268132\t0.46222674\t84.37775421\t84.38907623\n",
            "19\t0.28569646\t0.47280294\t84.65256500\t84.66008759\n",
            "20\t0.28531800\t0.44965189\t84.91025543\t84.91641998\n",
            "21\t0.26935034\t0.47002669\t85.15056610\t85.15222168\n",
            "22\t0.26240291\t0.44387513\t85.37977600\t85.38204956\n",
            "23\t0.26239789\t0.45728422\t85.59912109\t85.60014343\n",
            "24\t0.25964741\t0.47176672\t85.80853271\t85.80958557\n",
            "25\t0.23906982\t0.52712609\t86.00878906\t86.00073242\n",
            "26\t0.22679538\t0.46380702\t86.19457245\t86.19563293\n",
            "27\t0.22266554\t0.47822520\t86.38317108\t86.38130188\n",
            "28\t0.21552303\t0.47845361\t86.56371307\t86.55916595\n",
            "29\t0.22109193\t0.49941726\t86.73104095\t86.72425842\n",
            "30\t0.21254848\t0.49294827\t86.89726257\t86.89033508\n",
            "31\t0.20524431\t0.49803340\t87.05368042\t87.04666901\n",
            "32\t0.12223388\t0.44481805\t87.26508331\t87.26666260\n",
            "33\t0.11483878\t0.45165015\t87.48979950\t87.49069977\n",
            "34\t0.10263083\t0.45411395\t87.70970154\t87.71020508\n",
            "35\t0.08485959\t0.47894930\t87.92780304\t87.92832947\n",
            "36\t0.08564794\t0.47334677\t88.13716125\t88.13694000\n",
            "37\t0.08475136\t0.49398636\t88.33728790\t88.33414459\n",
            "38\t0.08418121\t0.49219455\t88.52955627\t88.52701569\n",
            "39\t0.08280338\t0.49692313\t88.71524048\t88.71247864\n",
            "40\t0.07249240\t0.51146392\t88.89556885\t88.89175415\n",
            "41\t0.07419175\t0.52430977\t89.06685638\t89.06240082\n",
            "42\t0.06878459\t0.52904250\t89.23278809\t89.22714233\n",
            "43\t0.06290421\t0.53475052\t89.39155579\t89.38535309\n",
            "44\t0.06294168\t0.52847806\t89.54341888\t89.53640747\n",
            "45\t0.06368887\t0.54279973\t89.68992615\t89.68296814\n",
            "46\t0.06367407\t0.54244225\t89.83134460\t89.82456207\n",
            "47\t0.06064554\t0.55301326\t89.97149658\t89.96446991\n",
            "48\t0.06047247\t0.56058936\t90.10320282\t90.09638977\n",
            "49\t0.05468784\t0.56852159\t90.23217773\t90.22448730\n",
            "50\t0.05125382\t0.55770848\t90.35535431\t90.34746552\n",
            "51\t0.05895822\t0.56085113\t90.47380829\t90.46558380\n",
            "52\t0.05690983\t0.57322278\t90.58964539\t90.58150482\n",
            "53\t0.05754229\t0.56996704\t90.70091248\t90.69255066\n",
            "54\t0.05157163\t0.56895293\t90.80968475\t90.80095673\n",
            "55\t0.05199715\t0.57883621\t90.91400909\t90.90482330\n",
            "56\t0.04900707\t0.58228877\t91.01633453\t91.00687408\n",
            "57\t0.04896620\t0.58603122\t91.11516571\t91.10520172\n",
            "58\t0.04941753\t0.58573153\t91.20982361\t91.20086670\n",
            "59\t0.04786176\t0.58134269\t91.30328369\t91.29378510\n",
            "60\t0.04861867\t0.58566259\t91.39295197\t91.38325500\n",
            "61\t0.04482435\t0.58621659\t91.48011017\t91.47092438\n",
            "62\t0.03308012\t0.59061824\t91.56916046\t91.56040192\n",
            "63\t0.03861737\t0.57706479\t91.65531158\t91.64669800\n",
            "64\t0.03420837\t0.59411220\t91.74198151\t91.73307800\n",
            "65\t0.03718401\t0.58584159\t91.82583618\t91.81684875\n",
            "66\t0.02839747\t0.60103405\t91.90749359\t91.89848328\n",
            "67\t0.03519831\t0.60170821\t91.98548889\t91.97653961\n",
            "68\t0.03086461\t0.59850751\t92.06270599\t92.05343628\n",
            "69\t0.02995454\t0.61951537\t92.13680267\t92.12731934\n",
            "70\t0.02962373\t0.61467269\t92.20899963\t92.19947815\n",
            "71\t0.02953486\t0.61428838\t92.27993011\t92.27040100\n",
            "72\t0.03396872\t0.61745183\t92.34838104\t92.33872986\n",
            "73\t0.03021580\t0.61958406\t92.41548920\t92.40554810\n",
            "74\t0.02974527\t0.63846809\t92.48011017\t92.47064972\n",
            "75\t0.02666571\t0.63260618\t92.54344940\t92.53382874\n",
            "76\t0.02712954\t0.63659564\t92.60566711\t92.59616089\n",
            "77\t0.03105857\t0.63997878\t92.66657257\t92.65673065\n",
            "78\t0.02637407\t0.63894693\t92.72623444\t92.71696472\n",
            "79\t0.02386208\t0.63523418\t92.78497314\t92.77540588\n",
            "80\t0.02386936\t0.63527626\t92.84188080\t92.83235931\n",
            "81\t0.02406580\t0.64828197\t92.89692688\t92.88762665\n",
            "82\t0.03167466\t0.63702993\t92.95046997\t92.94017792\n",
            "83\t0.02295887\t0.64080570\t93.00249481\t92.99279785\n",
            "84\t0.02494354\t0.64424266\t93.05413055\t93.04428101\n",
            "85\t0.02244920\t0.64059627\t93.10459900\t93.09470367\n",
            "86\t0.02765279\t0.66538683\t93.15339661\t93.14376068\n",
            "87\t0.02559112\t0.64461394\t93.20191956\t93.19231415\n",
            "88\t0.02347376\t0.64499406\t93.24899292\t93.23933411\n",
            "89\t0.02663594\t0.66287034\t93.29437256\t93.28482819\n",
            "90\t0.02435136\t0.64781841\t93.33972168\t93.33050537\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0OipEoMoaHt-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_checkpoint(model, True, 'sgd_resnet18_9333.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": "ab615855-4934-46b2-d01e-733ccb05df98",
        "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_9333_acc.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MffrvIMVb1sB",
        "colab_type": "code",
        "outputId": "44f616f2-7a90-4efb-cc8c-09b5b73846d4",
        "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_9333_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqWhvdXob2XE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download('./sgd_resnet18_9333.pth.tar')\n",
        "files.download('./sgd_resnet18_9333_acc.jpg')\n",
        "files.download('./sgd_resnet18_9333_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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": "73cf31df-1afd-46ae-d34c-b61c9a981368",
        "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\t0.86885929\t0.74737408\t60.95799637\t63.12666702\n",
            "2\t0.71511656\t0.62655072\t68.41181183\t69.23583221\n",
            "3\t0.60800268\t0.55988485\t72.01058960\t72.50555420\n",
            "4\t0.53849239\t0.53487780\t74.34696198\t74.66541290\n",
            "5\t0.50491721\t0.50635128\t76.07897186\t76.29433441\n",
            "6\t0.46538620\t0.49752907\t77.41943359\t77.57805634\n",
            "7\t0.42562772\t0.48834966\t78.50219727\t78.62023926\n",
            "8\t0.39749796\t0.50960226\t79.41277313\t79.49604034\n",
            "9\t0.37638987\t0.48983808\t80.20792389\t80.28277588\n",
            "10\t0.35278105\t0.48467824\t80.92592621\t80.97983551\n",
            "11\t0.34237109\t0.47242582\t81.56430817\t81.61075592\n",
            "12\t0.32274716\t0.46722133\t82.13605499\t82.17694855\n",
            "13\t0.29914275\t0.47568831\t82.68207550\t82.71371460\n",
            "14\t0.27742496\t0.49464957\t83.17023468\t83.19213867\n",
            "15\t0.26727694\t0.47494474\t83.61775970\t83.63311005\n",
            "16\t0.26196750\t0.49721024\t84.03568268\t84.04364777\n",
            "17\t0.25785484\t0.47724417\t84.41980743\t84.42706299\n",
            "18\t0.23974537\t0.49774931\t84.78849792\t84.78731537\n",
            "19\t0.21883746\t0.48102030\t85.12876129\t85.13342285\n",
            "20\t0.21856885\t0.50451101\t85.46100616\t85.45967102\n",
            "21\t0.20977119\t0.50405136\t85.76448059\t85.75928497\n",
            "22\t0.19639645\t0.52087879\t86.04358673\t86.03780365\n",
            "23\t0.18116844\t0.52060448\t86.32569122\t86.31753540\n",
            "24\t0.18732024\t0.50350373\t86.58391571\t86.57729340\n",
            "25\t0.15771022\t0.52362258\t86.83523560\t86.82493591\n",
            "26\t0.16633219\t0.52266230\t87.06709290\t87.05615234\n",
            "27\t0.16246299\t0.53564277\t87.28459930\t87.27382660\n",
            "28\t0.16064102\t0.54393020\t87.49670410\t87.48279572\n",
            "29\t0.15934023\t0.53175594\t87.69329834\t87.67902374\n",
            "30\t0.14530354\t0.56332928\t87.88994598\t87.87184143\n",
            "31\t0.14020827\t0.54007838\t88.07059479\t88.05543518\n",
            "32\t0.13677383\t0.53587875\t88.24733734\t88.23162079\n",
            "33\t0.13331429\t0.53813368\t88.41584015\t88.40211487\n",
            "34\t0.13055344\t0.53478249\t88.57832336\t88.56289673\n",
            "35\t0.11910698\t0.55431086\t88.73641205\t88.72299957\n",
            "36\t0.11847742\t0.55223649\t88.88995361\t88.87541199\n",
            "37\t0.12628543\t0.55532533\t89.03294373\t89.01634979\n",
            "38\t0.11991744\t0.54868266\t89.17207336\t89.15727997\n",
            "39\t0.11019968\t0.53892465\t89.30764008\t89.29332733\n",
            "40\t0.11368145\t0.55632116\t89.43795013\t89.42179108\n",
            "41\t0.10223443\t0.55794273\t89.56277466\t89.54768372\n",
            "42\t0.12333903\t0.57570558\t89.68167114\t89.66606903\n",
            "43\t0.10693808\t0.57334619\t89.79965210\t89.78422546\n",
            "44\t0.11304180\t0.58082106\t89.91406250\t89.89836884\n",
            "45\t0.10660841\t0.58676646\t90.02382660\t90.00748444\n",
            "46\t0.10605339\t0.56776431\t90.12799835\t90.11260986\n",
            "47\t0.09565327\t0.59423547\t90.23099518\t90.21457672\n",
            "48\t0.10210508\t0.57617647\t90.32745361\t90.31166840\n",
            "49\t0.10018528\t0.58370494\t90.42269897\t90.40632629\n",
            "50\t0.09739981\t0.58227730\t90.51391602\t90.49639893\n",
            "51\t0.08977212\t0.56857956\t90.60259247\t90.58752441\n",
            "52\t0.09245112\t0.58131473\t90.69106293\t90.67390442\n",
            "53\t0.08863737\t0.56325194\t90.77299500\t90.75708008\n",
            "54\t0.08821565\t0.57610760\t90.85185242\t90.83564758\n",
            "55\t0.09195512\t0.59847131\t90.92969513\t90.91400146\n",
            "56\t0.08144768\t0.57961674\t91.00926208\t90.99511719\n",
            "57\t0.08615041\t0.59407162\t91.08663177\t91.07128143\n",
            "58\t0.10075398\t0.59050067\t91.15915680\t91.14396667\n",
            "59\t0.08534357\t0.59125555\t91.23045349\t91.21386719\n",
            "60\t0.07793285\t0.59171913\t91.29978180\t91.28514099\n",
            "61\t0.08698754\t0.59697034\t91.36585999\t91.34953308\n",
            "62\t0.08326742\t0.60926667\t91.43026733\t91.41551208\n",
            "63\t0.07884124\t0.61590368\t91.49557495\t91.47999573\n",
            "64\t0.07409739\t0.60698526\t91.55760193\t91.54293823\n",
            "65\t0.07538815\t0.60389001\t91.62020874\t91.60563660\n",
            "66\t0.07295164\t0.60873766\t91.67934418\t91.66494751\n",
            "67\t0.07750176\t0.60424264\t91.73738098\t91.72288513\n",
            "68\t0.07602248\t0.62481495\t91.79373169\t91.77857971\n",
            "69\t0.06748609\t0.58288625\t91.85024261\t91.83650208\n",
            "70\t0.07881846\t0.60861969\t91.90566254\t91.89197540\n",
            "71\t0.07035099\t0.59868685\t91.95943451\t91.94556427\n",
            "72\t0.06779363\t0.60984974\t92.01199341\t91.99733734\n",
            "73\t0.07705612\t0.60890431\t92.06130219\t92.04650879\n",
            "74\t0.07096647\t0.60641794\t92.11026764\t92.09636688\n",
            "75\t0.06681748\t0.63988388\t92.15743256\t92.14286804\n",
            "76\t0.06997087\t0.58684976\t92.20371246\t92.18980408\n",
            "77\t0.06980174\t0.61379246\t92.25006866\t92.23673248\n",
            "78\t0.06523079\t0.59591913\t92.29672241\t92.28313446\n",
            "79\t0.06641387\t0.63983283\t92.34293365\t92.32933044\n",
            "80\t0.06717019\t0.60640778\t92.38620758\t92.37297821\n",
            "81\t0.06520993\t0.60531530\t92.42956543\t92.41615295\n",
            "82\t0.07271290\t0.61563780\t92.47118378\t92.45710754\n",
            "83\t0.05861622\t0.62599737\t92.51187134\t92.49829865\n",
            "84\t0.06489568\t0.63374087\t92.55184937\t92.53819275\n",
            "85\t0.07016231\t0.61839045\t92.59047699\t92.57717896\n",
            "86\t0.06695395\t0.59576607\t92.62882233\t92.61614990\n",
            "87\t0.06584864\t0.62510194\t92.66713715\t92.65454102\n",
            "88\t0.06217344\t0.58309269\t92.70525360\t92.69297791\n",
            "89\t0.06342361\t0.61500648\t92.74295807\t92.73039246\n",
            "90\t0.06365026\t0.60535244\t92.77935028\t92.76688385\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": "74692f38-dd20-4301-d7fc-3cd7345758bf",
        "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": {
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxoQon-Yj0po",
        "colab_type": "code",
        "outputId": "64cc1437-0174-4800-8af6-5ce54ea42527",
        "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": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bdYQCyJEj00H",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "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": "e000b958-6b09-465d-ab90-f972f8bff0f7",
        "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\t0.78479352\t0.67165694\t65.26199341\t67.26000214\n",
            "2\t0.65817790\t0.57374612\t71.45182037\t72.19833374\n",
            "3\t0.58128622\t0.55141889\t74.37470245\t74.75722504\n",
            "4\t0.52962197\t0.51966717\t76.22391510\t76.48666382\n",
            "5\t0.47890591\t0.52298214\t77.58689880\t77.75066376\n",
            "6\t0.46181699\t0.53919718\t78.61714172\t78.70610809\n",
            "7\t0.42780571\t0.53746683\t79.46121979\t79.52833557\n",
            "8\t0.41239739\t0.51101025\t80.19277191\t80.26103973\n",
            "9\t0.37363482\t0.53151295\t80.84622955\t80.88333130\n",
            "10\t0.36207809\t0.44956800\t81.41728973\t81.47850037\n",
            "11\t0.35154171\t0.45184920\t81.96661377\t82.02075958\n",
            "12\t0.34081617\t0.46462456\t82.44577026\t82.48000336\n",
            "13\t0.34036227\t0.44493051\t82.88973236\t82.91999817\n",
            "14\t0.32164129\t0.45059167\t83.30180359\t83.32344818\n",
            "15\t0.31193304\t0.46006976\t83.67045593\t83.68900299\n",
            "16\t0.29992676\t0.51223011\t84.02136230\t84.02281189\n",
            "17\t0.28413972\t0.47487951\t84.33504486\t84.33735657\n",
            "18\t0.26384980\t0.48605247\t84.63252258\t84.63741302\n",
            "19\t0.25498864\t0.52070410\t84.91609955\t84.90903473\n",
            "20\t0.26259867\t0.45545643\t85.17688751\t85.18450165\n",
            "21\t0.23760753\t0.46622807\t85.44519806\t85.44618988\n",
            "22\t0.24255486\t0.46946366\t85.69656372\t85.69879150\n",
            "23\t0.22672451\t0.46840918\t85.93715668\t85.93702698\n",
            "24\t0.21695489\t0.48691098\t86.16217041\t86.16041565\n",
            "25\t0.20339894\t0.48078232\t86.37805176\t86.37593079\n",
            "26\t0.20584520\t0.52676247\t86.58902740\t86.58108521\n",
            "27\t0.20325725\t0.51004472\t86.78167725\t86.77364349\n",
            "28\t0.20174108\t0.56362325\t86.96460724\t86.94892883\n",
            "29\t0.17641803\t0.54775229\t87.14249420\t87.12988281\n",
            "30\t0.18517546\t0.48547372\t87.31072235\t87.30655670\n",
            "31\t0.18311003\t0.49747002\t87.48091888\t87.47312164\n",
            "32\t0.11770682\t0.47469718\t87.69047546\t87.68786621\n",
            "33\t0.09852085\t0.49028656\t87.91304779\t87.90979004\n",
            "34\t0.09353052\t0.50164676\t88.12792206\t88.12250519\n",
            "35\t0.08258645\t0.50918324\t88.33698273\t88.33148193\n",
            "36\t0.08270813\t0.51370429\t88.53409576\t88.52745056\n",
            "37\t0.08202273\t0.52720806\t88.72483826\t88.71720123\n",
            "38\t0.07250305\t0.52979381\t88.91039276\t88.90232086\n",
            "39\t0.07788631\t0.53769265\t89.08399200\t89.07555389\n",
            "40\t0.07344846\t0.53455959\t89.25167084\t89.24320984\n",
            "41\t0.07574758\t0.55111005\t89.41204071\t89.40232086\n",
            "42\t0.06631801\t0.55573800\t89.56621552\t89.55599213\n",
            "43\t0.06034936\t0.56250256\t89.71681213\t89.70706177\n",
            "44\t0.05874341\t0.56670799\t89.86308289\t89.85325623\n",
            "45\t0.06651991\t0.56670099\t90.00093079\t89.98992157\n",
            "46\t0.05964406\t0.57724771\t90.13385010\t90.12402344\n",
            "47\t0.06375027\t0.57393077\t90.26477051\t90.25365448\n",
            "48\t0.06160641\t0.58719176\t90.38867188\t90.37687683\n",
            "49\t0.05847202\t0.59080107\t90.50811768\t90.49550629\n",
            "50\t0.05390333\t0.57871168\t90.62171173\t90.61003113\n",
            "51\t0.05187226\t0.59847106\t90.73498535\t90.72349548\n",
            "52\t0.05067725\t0.60393530\t90.84476471\t90.83364868\n",
            "53\t0.05032047\t0.59459309\t90.94924164\t90.93776703\n",
            "54\t0.04999526\t0.60690585\t91.05210114\t91.04015350\n",
            "55\t0.04876929\t0.60497789\t91.15112305\t91.13830566\n",
            "56\t0.04702803\t0.61233238\t91.24507904\t91.23258972\n",
            "57\t0.04529182\t0.63122132\t91.33774567\t91.32514954\n",
            "58\t0.04928536\t0.62479572\t91.42538452\t91.41362000\n",
            "59\t0.05130819\t0.62790722\t91.51277161\t91.49971771\n",
            "60\t0.04737572\t0.62735807\t91.59729767\t91.58511353\n",
            "61\t0.04681145\t0.64062749\t91.68005371\t91.66756439\n",
            "62\t0.03717868\t0.62661039\t91.76210022\t91.75051117\n",
            "63\t0.04093176\t0.62192550\t91.84297180\t91.83094788\n",
            "64\t0.03740751\t0.62778422\t91.92111969\t91.90934753\n",
            "65\t0.03475681\t0.62757940\t91.99663544\t91.98481750\n",
            "66\t0.03433142\t0.62113800\t92.07197571\t92.06011963\n",
            "67\t0.03221090\t0.62738332\t92.14533997\t92.13382721\n",
            "68\t0.03214618\t0.62987306\t92.21690369\t92.20561981\n",
            "69\t0.03363933\t0.62785299\t92.28571320\t92.27406311\n",
            "70\t0.03314294\t0.62456131\t92.35267639\t92.34119415\n",
            "71\t0.03149585\t0.62594646\t92.41838074\t92.40718842\n",
            "72\t0.03638247\t0.63611133\t92.48211670\t92.47039795\n",
            "73\t0.03179798\t0.63275786\t92.54422760\t92.53244019\n",
            "74\t0.03339371\t0.64121875\t92.60473633\t92.59299469\n",
            "75\t0.03191241\t0.64356601\t92.66420746\t92.65224457\n",
            "76\t0.03231497\t0.63882180\t92.72176361\t92.71009064\n",
            "77\t0.02790899\t0.63737491\t92.77805328\t92.76675415\n",
            "78\t0.02823592\t0.63953328\t92.83351135\t92.82230377\n",
            "79\t0.03326251\t0.64097619\t92.88841248\t92.87715912\n",
            "80\t0.03119475\t0.64380172\t92.94077301\t92.92943573\n",
            "81\t0.03147747\t0.64552225\t92.99172974\t92.98023987\n",
            "82\t0.02918501\t0.64995064\t93.04177094\t93.03067017\n",
            "83\t0.02901937\t0.64979476\t93.09052277\t93.07925415\n",
            "84\t0.02677197\t0.64367766\t93.13864899\t93.12737274\n",
            "85\t0.02616902\t0.64887991\t93.18527222\t93.17394257\n",
            "86\t0.02945937\t0.65496573\t93.23079681\t93.21977234\n",
            "87\t0.02675074\t0.65628454\t93.27606201\t93.26506042\n",
            "88\t0.02595707\t0.64726500\t93.32015228\t93.30898285\n",
            "89\t0.02605304\t0.65327877\t93.36235809\t93.35136414\n",
            "90\t0.02642076\t0.65538199\t93.40431976\t93.39311218\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFEcAgEhPzGv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_checkpoint(model, True, 'lookahead_sgd_resnet18_9340.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": "0aacb895-384a-4f71-eecf-77096b454a12",
        "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_9340_acc.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDDR6rDi61eB",
        "colab_type": "code",
        "outputId": "5b9311ae-1f1b-4036-d185-248ab886a04b",
        "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_9340_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": "cdde2fcf-42f8-46ed-97f9-3b961a37c375",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.download('./lookahead_sgd_resnet18_9340.pth.tar')\n",
        "files.download('./lookahead_sgd_resnet18_9340_acc.jpg')\n",
        "files.download('./lookahead_sgd_resnet18_9340_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Exception happened during processing of request from ('::ffff:127.0.0.1', 43182, 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": "1a272d7f-d57f-4001-bfda-587c47db4e25",
        "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\t1.18530725\t1.05326346\t41.71199799\t45.27833557\n",
            "2\t0.84762171\t0.78671370\t55.37272644\t56.84083176\n",
            "3\t0.73144797\t0.65561085\t61.93588257\t62.82055283\n",
            "4\t0.64784921\t0.60311341\t65.96218109\t66.51333618\n",
            "5\t0.57844064\t0.57227305\t68.77137756\t69.16300201\n",
            "6\t0.53403480\t0.53500383\t70.91000366\t71.20666504\n",
            "7\t0.48211888\t0.50810422\t72.64511871\t72.87571716\n",
            "8\t0.44835984\t0.50016035\t74.09233856\t74.28416443\n",
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            "10\t0.40778929\t0.49177240\t76.39678192\t76.51966858\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFGTZvRw_Bl8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_checkpoint(model, True, 'radam_resnet18_9303.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",
        "outputId": "4ee845ed-d694-4e14-90cc-0fa234030cce",
        "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('radam_resnet18_9303_acc.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDO1w_zSIX08",
        "colab_type": "code",
        "outputId": "615666e9-8d07-48c0-c660-f497cb740a3a",
        "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('radam_resnet18_9303_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qf2xc-E0Jj4g",
        "colab_type": "code",
        "outputId": "da8c2154-f660-4c67-e10a-3f21cefce3bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.download('./radam_resnet18_9303.pth.tar')\n",
        "files.download('./radam_resnet18_9303_acc.jpg')\n",
        "files.download('./radam_resnet18_9303_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Exception happened during processing of request from ('::ffff:127.0.0.1', 46536, 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": "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",
        "outputId": "1102d442-13b1-4a5e-c211-86b52d677bd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fit(model, optimizer, sched)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch\tTrn_loss\tVal_loss\tTrn_acc\t\tVal_acc\n",
            "1\t0.84583154\t0.73178216\t58.65999985\t61.38833237\n",
            "2\t0.69071751\t0.61502318\t67.61090851\t68.56082916\n",
            "3\t0.61432442\t0.58344361\t71.45764923\t71.96111298\n",
            "4\t0.55044891\t0.56880743\t73.80130768\t74.10124969\n",
            "5\t0.52851939\t0.58787560\t75.38758850\t75.55400085\n",
            "6\t0.51058717\t0.63163685\t76.59285736\t76.66361237\n",
            "7\t0.47908891\t0.55923976\t77.52000427\t77.63571167\n",
            "8\t0.46200907\t0.55604542\t78.35383606\t78.41625214\n",
            "9\t0.43321949\t0.56300320\t79.05509186\t79.11425781\n",
            "10\t0.41723501\t0.42355409\t79.69135284\t79.79199982\n",
            "11\t0.41177784\t0.43361225\t80.30723572\t80.38197327\n",
            "12\t0.38238970\t0.47381668\t80.84745789\t80.89416504\n",
            "13\t0.38201587\t0.45383021\t81.30129242\t81.34756470\n",
            "14\t0.35697211\t0.47005762\t81.73987579\t81.77285004\n",
            "15\t0.35281760\t0.48937094\t82.13303375\t82.15711212\n",
            "16\t0.33853201\t0.57281240\t82.49789429\t82.49343872\n",
            "17\t0.32870081\t0.49624227\t82.82604218\t82.84352875\n",
            "18\t0.31817437\t0.50240642\t83.14177704\t83.15074158\n",
            "19\t0.31168358\t0.54314920\t83.44114685\t83.43403625\n",
            "20\t0.29778440\t0.42684041\t83.71403503\t83.73725128\n",
            "21\t0.28950245\t0.43814740\t84.00888062\t84.02912903\n",
            "22\t0.26912155\t0.44024860\t84.28878021\t84.30477142\n",
            "23\t0.24559280\t0.48770444\t84.55335999\t84.55725098\n",
            "24\t0.25213538\t0.49284696\t84.79972076\t84.79721832\n",
            "25\t0.24291112\t0.48905003\t85.02469635\t85.02793121\n",
            "26\t0.23536410\t0.49165265\t85.24393463\t85.24326324\n",
            "27\t0.23561492\t0.51303609\t85.44981384\t85.44531250\n",
            "28\t0.22331858\t0.52899345\t85.65431213\t85.64749908\n",
            "29\t0.21541679\t0.52224553\t85.85138702\t85.84620667\n",
            "30\t0.21669339\t0.46055796\t86.04351807\t86.04461670\n",
            "31\t0.20825434\t0.45582019\t86.23400116\t86.23500824\n",
            "32\t0.12120238\t0.44989544\t86.47309875\t86.47921753\n",
            "33\t0.10029266\t0.46840322\t86.72679901\t86.73120880\n",
            "34\t0.09548170\t0.47698453\t86.97580719\t86.98020172\n",
            "35\t0.08822035\t0.49539950\t87.21784973\t87.21995544\n",
            "36\t0.08178912\t0.49046533\t87.44799805\t87.44889069\n",
            "37\t0.07418045\t0.50228717\t87.66877747\t87.66810608\n",
            "38\t0.07333287\t0.49683119\t87.88202667\t87.88069916\n",
            "39\t0.07588473\t0.50824206\t88.08669281\t88.08585358\n",
            "40\t0.06864681\t0.50225980\t88.28389740\t88.28246307\n",
            "41\t0.06583559\t0.50954517\t88.47436523\t88.47162628\n",
            "42\t0.06365624\t0.51905951\t88.65657806\t88.65316772\n",
            "43\t0.06338047\t0.52363742\t88.83148193\t88.82733154\n",
            "44\t0.06554511\t0.52364057\t89.00022888\t88.99523163\n",
            "45\t0.05613548\t0.53871774\t89.16371155\t89.15814972\n",
            "46\t0.05939867\t0.53777986\t89.32032776\t89.31478119\n",
            "47\t0.05566475\t0.53031196\t89.46875000\t89.46276855\n",
            "48\t0.05169750\t0.55449461\t89.61428070\t89.60801697\n",
            "49\t0.04915052\t0.55171741\t89.75614166\t89.74942017\n",
            "50\t0.04991113\t0.55211403\t89.89241028\t89.88539886\n",
            "51\t0.04619110\t0.57629159\t90.02455902\t90.01624298\n",
            "52\t0.05279033\t0.57596167\t90.15016174\t90.14188385\n",
            "53\t0.04591326\t0.57857792\t90.27252197\t90.26443481\n",
            "54\t0.04275813\t0.58670509\t90.39163208\t90.38302612\n",
            "55\t0.04372218\t0.59639015\t90.50692749\t90.49866486\n",
            "56\t0.04147011\t0.57673445\t90.61880493\t90.61056519\n",
            "57\t0.04082545\t0.58746819\t90.72721863\t90.71743011\n",
            "58\t0.03822577\t0.62693787\t90.83215332\t90.82287598\n",
            "59\t0.03952353\t0.60966596\t90.93357086\t90.92417908\n",
            "60\t0.04085338\t0.60720310\t91.03198242\t91.02225494\n",
            "61\t0.03871442\t0.58756955\t91.12698364\t91.11726379\n",
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            "63\t0.03022732\t0.57851944\t91.31674194\t91.30777740\n",
            "64\t0.02630382\t0.58138011\t91.40968323\t91.40059662\n",
            "65\t0.02433522\t0.58392457\t91.50031281\t91.49105835\n",
            "66\t0.02211782\t0.58984810\t91.58847809\t91.57956696\n",
            "67\t0.02457309\t0.59865635\t91.67407227\t91.66477203\n",
            "68\t0.02245167\t0.60278696\t91.75724792\t91.74772644\n",
            "69\t0.02309591\t0.60668504\t91.83769226\t91.82838440\n",
            "70\t0.02112753\t0.62456920\t91.91683197\t91.90762329\n",
            "71\t0.02516608\t0.61297931\t91.99393463\t91.98493195\n",
            "72\t0.02210851\t0.62760427\t92.06953430\t92.05997467\n",
            "73\t0.02099504\t0.62951990\t92.14255524\t92.13287354\n",
            "74\t0.02291767\t0.62615180\t92.21419525\t92.20465851\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_9314.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",
        "outputId": "8ff0d91b-e40c-45d6-e8ed-02d73aeccdf1",
        "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('radam_lookahead_resnet18_9314_acc.jpg')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87N8H050W7yp",
        "colab_type": "code",
        "outputId": "613993c0-cbea-4983-c212-83bb2b34a3a6",
        "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('radam_lookahead_resnet18_9314_loss.jpg')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SV5OcSRgXEBs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download('./radam_lookahead_resnet18_9314.pth.tar')\n",
        "files.download('./radam_lookahead_resnet18_9314_acc.jpg')\n",
        "files.download('./radam_lookahead_resnet18_9314_loss.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "um6lklwAFTUo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}