Latent Space Experiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import * #import from vision to use the classification interpreter\n",
    "from pathlib import Path\n",
    "import pdb\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from timeseries import TimeSeriesItem, TimeSeriesList, UCRArchive\n",
    "import hiddenlayer as hl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ucr = UCRArchive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_name = \"InsectWingbeatSound\"\n",
    "vc = ucr.category_distribution(dataset_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = TimeSeriesList.from_csv_list(ucr.get_csv_files(dataset_name),labelCol=0)\n",
    "valIdxs = np.random.choice(len(src.items),int(len(src.items)*0.3),replace=False)\n",
    "data = src.split_by_idx(valIdxs)\n",
    "data = data.label_from_col()\n",
    "idxs = np.random.choice(len(data.x),size=len(data.x)//10,replace=False)\n",
    "bs = min(64,len(data.x)//50)\n",
    "data.x.items = data.train.x.items[idxs]\n",
    "data.y.items = data.train.y.items[idxs]\n",
    "data = data.databunch(bs=bs,num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.train_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1540"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src = TimeSeriesList.from_csv_list(ucr.get_csv_files(dataset_name),labelCol=0)\n",
    "dataAE = src.split_by_idx(valIdxs)\n",
    "dataAE = dataAE.label_from_self()\n",
    "dataAE = dataAE.databunch(bs=bs,num_workers=0)\n",
    "len(dataAE.train_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TSAE(torch.nn.Module):\n",
    "    def __init__(self,seqLen,latentDim=24):\n",
    "        super().__init__()\n",
    "        #latentDim = max(seqLen // 8,8)\n",
    "        self.conv = HeroResnet(3,3,12)\n",
    "        self.mean = torch.nn.Linear(self.conv.nf,latentDim)\n",
    "        self.logvar = torch.nn.Linear(self.conv.nf,latentDim)\n",
    "        \n",
    "\n",
    "        layers = []\n",
    "        for a,b in [(latentDim,100),(100,200),(200,300)]:\n",
    "            layers += bn_drop_lin(a,b,actn=torch.nn.ReLU())\n",
    "        self.lin = torch.nn.Sequential(*layers)\n",
    "        self.out = torch.nn.Linear(300,seqLen)\n",
    "\n",
    "    def forward(self,ts):\n",
    "        seqLen = ts.shape[1]\n",
    "        ts, _ = self.conv(ts.unsqueeze(1))\n",
    "        ts = ts.squeeze(-1)\n",
    "        mean, logvar = self.mean(ts), self.logvar(ts)\n",
    "        #mean = self.mean(ts)\n",
    "        \n",
    "        ls = mean\n",
    "#         if self.training:\n",
    "#             std = torch.exp(0.5 * logvar)\n",
    "#             eps = torch.randn_like(std)\n",
    "#             ls = eps.mul(std).add_(mean)\n",
    "        return self.out(self.lin(ls))          \n",
    "        #return self.out(self.lin(ls)), mean, logvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VAELoss(torch.nn.Module):\n",
    "    def forward(self,p,target):\n",
    "        pred,mean,logvar = p\n",
    "        self.mse = torch.nn.functional.mse_loss(pred,target,reduction=\"sum\")\n",
    "        self.kld = -0.5 * torch.sum(1+logvar-mean.pow(2)-logvar.exp())\n",
    "        return self.mse + self.kld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PrintLosses(LearnerCallback):\n",
    "    _order=-20\n",
    "    def __init__(self,learn):\n",
    "        super().__init__(learn)\n",
    "    def on_train_begin(self, **kwargs):\n",
    "        self.learn.recorder.add_metric_names([\"mse\",\"kld\"])\n",
    "    def on_epoch_end(self, **kwargs):\n",
    "        self.learn.recorder.add_metrics([self.learn.loss_func.mse,self.learn.loss_func.kld])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_run(latentDim):\n",
    "    learnAE = Learner(dataAE,TSAE(len(data.train_ds[0][0].data),latentDim=latentDim),loss_func=F.mse_loss)\n",
    "    learnAE.fit_one_cycle(10,1e-2)\n",
    "    \n",
    "    learn = Learner(data,SidekickResnet(data.train_ds.c,3,3,6,12), loss_func=F.cross_entropy,metrics=[accuracy],callback_fns=BnFreeze,bn_wd=False,train_bn=False)\n",
    "    learn.split(split_model(learn.model,[learn.model.hero,learn.model.layer0,learn.model.avg,learn.model.out]))\n",
    "    learn.model.hero.load_state_dict(learnAE.model.conv.state_dict())\n",
    "    learn.freeze_to(1)\n",
    "    learn.fit_one_cycle(20,1e-3)\n",
    "    learn.fit_one_cycle(10,1e-4)\n",
    "    \n",
    "    return max([m[0].item() for m in learn.recorder.metrics])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 00:40 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.845528</th>\n",
       "    <th>1.629035</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.762400</th>\n",
       "    <th>24.220501</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.717378</th>\n",
       "    <th>1.226475</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.649192</th>\n",
       "    <th>12225.248047</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.605383</th>\n",
       "    <th>0.740381</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.571190</th>\n",
       "    <th>0.778655</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.526305</th>\n",
       "    <th>0.530694</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.482154</th>\n",
       "    <th>0.432106</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.445346</th>\n",
       "    <th>0.417791</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.429175</th>\n",
       "    <th>0.392192</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:15 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.407362</th>\n",
       "    <th>2.392303</th>\n",
       "    <th>0.096970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.136626</th>\n",
       "    <th>2.373777</th>\n",
       "    <th>0.156061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.880439</th>\n",
       "    <th>2.324863</th>\n",
       "    <th>0.166667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.667987</th>\n",
       "    <th>2.232530</th>\n",
       "    <th>0.228788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.487401</th>\n",
       "    <th>2.101969</th>\n",
       "    <th>0.284848</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.326052</th>\n",
       "    <th>1.988065</th>\n",
       "    <th>0.306061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.185700</th>\n",
       "    <th>1.873494</th>\n",
       "    <th>0.362121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.054162</th>\n",
       "    <th>1.755440</th>\n",
       "    <th>0.404545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.940063</th>\n",
       "    <th>1.667247</th>\n",
       "    <th>0.445455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.837415</th>\n",
       "    <th>1.575274</th>\n",
       "    <th>0.413636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.745959</th>\n",
       "    <th>1.514746</th>\n",
       "    <th>0.471212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.665391</th>\n",
       "    <th>1.556191</th>\n",
       "    <th>0.415152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.592295</th>\n",
       "    <th>1.571552</th>\n",
       "    <th>0.434848</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.530066</th>\n",
       "    <th>1.467688</th>\n",
       "    <th>0.498485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.473502</th>\n",
       "    <th>1.485951</th>\n",
       "    <th>0.512121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.425110</th>\n",
       "    <th>1.526967</th>\n",
       "    <th>0.495455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.381797</th>\n",
       "    <th>1.516712</th>\n",
       "    <th>0.496970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.343912</th>\n",
       "    <th>1.502701</th>\n",
       "    <th>0.507576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.310950</th>\n",
       "    <th>1.494458</th>\n",
       "    <th>0.512121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.282148</th>\n",
       "    <th>1.491648</th>\n",
       "    <th>0.519697</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:07 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.057059</th>\n",
       "    <th>1.491826</th>\n",
       "    <th>0.521212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.056866</th>\n",
       "    <th>1.492147</th>\n",
       "    <th>0.521212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.052857</th>\n",
       "    <th>1.494216</th>\n",
       "    <th>0.515152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.051877</th>\n",
       "    <th>1.501389</th>\n",
       "    <th>0.521212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.049599</th>\n",
       "    <th>1.511074</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.047174</th>\n",
       "    <th>1.511365</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.045624</th>\n",
       "    <th>1.506610</th>\n",
       "    <th>0.527273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.043754</th>\n",
       "    <th>1.508126</th>\n",
       "    <th>0.528788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.044055</th>\n",
       "    <th>1.506467</th>\n",
       "    <th>0.525758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.043764</th>\n",
       "    <th>1.507596</th>\n",
       "    <th>0.522727</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:39 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.824300</th>\n",
       "    <th>20.997478</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.719862</th>\n",
       "    <th>7.423917</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.648783</th>\n",
       "    <th>1.823049</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.576846</th>\n",
       "    <th>0.939819</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.518541</th>\n",
       "    <th>0.734148</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.453460</th>\n",
       "    <th>0.627620</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.411263</th>\n",
       "    <th>0.353989</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.370180</th>\n",
       "    <th>0.370092</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.334121</th>\n",
       "    <th>0.331360</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.313349</th>\n",
       "    <th>0.271769</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:15 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.390430</th>\n",
       "    <th>2.398342</th>\n",
       "    <th>0.109091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.155949</th>\n",
       "    <th>2.385123</th>\n",
       "    <th>0.178788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.883141</th>\n",
       "    <th>2.343123</th>\n",
       "    <th>0.204545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.636337</th>\n",
       "    <th>2.255844</th>\n",
       "    <th>0.257576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.452566</th>\n",
       "    <th>2.132629</th>\n",
       "    <th>0.357576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.289121</th>\n",
       "    <th>1.999827</th>\n",
       "    <th>0.469697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.143185</th>\n",
       "    <th>1.889005</th>\n",
       "    <th>0.383333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.023414</th>\n",
       "    <th>1.741421</th>\n",
       "    <th>0.487879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.902442</th>\n",
       "    <th>1.574776</th>\n",
       "    <th>0.575758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.800650</th>\n",
       "    <th>1.478474</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.706859</th>\n",
       "    <th>1.394523</th>\n",
       "    <th>0.542424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.627820</th>\n",
       "    <th>1.333105</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.558249</th>\n",
       "    <th>1.289246</th>\n",
       "    <th>0.565152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.496425</th>\n",
       "    <th>1.288667</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.442462</th>\n",
       "    <th>1.271656</th>\n",
       "    <th>0.550000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.395805</th>\n",
       "    <th>1.249519</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.357191</th>\n",
       "    <th>1.247091</th>\n",
       "    <th>0.571212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.321899</th>\n",
       "    <th>1.253838</th>\n",
       "    <th>0.562121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.291289</th>\n",
       "    <th>1.259179</th>\n",
       "    <th>0.562121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.264259</th>\n",
       "    <th>1.262949</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:07 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.045986</th>\n",
       "    <th>1.269311</th>\n",
       "    <th>0.562121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.043594</th>\n",
       "    <th>1.273223</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.043759</th>\n",
       "    <th>1.279476</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.043266</th>\n",
       "    <th>1.280322</th>\n",
       "    <th>0.556061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.042584</th>\n",
       "    <th>1.277824</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.040406</th>\n",
       "    <th>1.277817</th>\n",
       "    <th>0.562121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.039980</th>\n",
       "    <th>1.276693</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.039311</th>\n",
       "    <th>1.275554</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.037933</th>\n",
       "    <th>1.276958</th>\n",
       "    <th>0.556061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.037666</th>\n",
       "    <th>1.277381</th>\n",
       "    <th>0.556061</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:39 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.852403</th>\n",
       "    <th>2.588051</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.738162</th>\n",
       "    <th>12.976227</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.655021</th>\n",
       "    <th>0.664040</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.629294</th>\n",
       "    <th>1.184052</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.565867</th>\n",
       "    <th>2.355113</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.485894</th>\n",
       "    <th>0.647061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.424265</th>\n",
       "    <th>0.619116</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.366323</th>\n",
       "    <th>0.418279</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.322556</th>\n",
       "    <th>0.278754</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.293079</th>\n",
       "    <th>0.253167</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:15 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.396496</th>\n",
       "    <th>2.394889</th>\n",
       "    <th>0.081818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.124752</th>\n",
       "    <th>2.380469</th>\n",
       "    <th>0.239394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.845636</th>\n",
       "    <th>2.330393</th>\n",
       "    <th>0.272727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.624074</th>\n",
       "    <th>2.223966</th>\n",
       "    <th>0.337879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.442906</th>\n",
       "    <th>2.080392</th>\n",
       "    <th>0.406061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.300780</th>\n",
       "    <th>1.932613</th>\n",
       "    <th>0.483333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.162724</th>\n",
       "    <th>1.789709</th>\n",
       "    <th>0.477273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.033199</th>\n",
       "    <th>1.652174</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.925089</th>\n",
       "    <th>1.572561</th>\n",
       "    <th>0.516667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.826329</th>\n",
       "    <th>1.565549</th>\n",
       "    <th>0.459091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.736435</th>\n",
       "    <th>1.488788</th>\n",
       "    <th>0.442424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.653631</th>\n",
       "    <th>1.291178</th>\n",
       "    <th>0.545455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.582011</th>\n",
       "    <th>1.277887</th>\n",
       "    <th>0.545455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.519784</th>\n",
       "    <th>1.252629</th>\n",
       "    <th>0.571212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.464171</th>\n",
       "    <th>1.272555</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.415479</th>\n",
       "    <th>1.298658</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.372698</th>\n",
       "    <th>1.299261</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.334723</th>\n",
       "    <th>1.293441</th>\n",
       "    <th>0.572727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.302963</th>\n",
       "    <th>1.294502</th>\n",
       "    <th>0.569697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.274328</th>\n",
       "    <th>1.297871</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:07 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.049009</th>\n",
       "    <th>1.300364</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.047560</th>\n",
       "    <th>1.294201</th>\n",
       "    <th>0.562121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.048125</th>\n",
       "    <th>1.290998</th>\n",
       "    <th>0.557576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.046904</th>\n",
       "    <th>1.290027</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.045726</th>\n",
       "    <th>1.293172</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.044706</th>\n",
       "    <th>1.300485</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.043387</th>\n",
       "    <th>1.300178</th>\n",
       "    <th>0.556061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.042354</th>\n",
       "    <th>1.305399</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.042262</th>\n",
       "    <th>1.309109</th>\n",
       "    <th>0.565152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.040351</th>\n",
       "    <th>1.310459</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:40 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.853790</th>\n",
       "    <th>4.568400</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.734616</th>\n",
       "    <th>1.341045</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.681236</th>\n",
       "    <th>0.681843</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.598241</th>\n",
       "    <th>1.794499</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.550185</th>\n",
       "    <th>0.789109</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.487597</th>\n",
       "    <th>1.115804</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.443854</th>\n",
       "    <th>0.547443</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.393943</th>\n",
       "    <th>0.421192</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.355118</th>\n",
       "    <th>0.339463</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.332530</th>\n",
       "    <th>0.299581</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.445923</th>\n",
       "    <th>2.395947</th>\n",
       "    <th>0.177273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.188025</th>\n",
       "    <th>2.380904</th>\n",
       "    <th>0.119697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.928000</th>\n",
       "    <th>2.330513</th>\n",
       "    <th>0.289394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.688583</th>\n",
       "    <th>2.231505</th>\n",
       "    <th>0.366667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.502415</th>\n",
       "    <th>2.098035</th>\n",
       "    <th>0.453030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.352723</th>\n",
       "    <th>1.964249</th>\n",
       "    <th>0.450000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.232778</th>\n",
       "    <th>1.818725</th>\n",
       "    <th>0.504545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.101743</th>\n",
       "    <th>1.709768</th>\n",
       "    <th>0.545455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.993478</th>\n",
       "    <th>1.589784</th>\n",
       "    <th>0.477273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.886767</th>\n",
       "    <th>1.474780</th>\n",
       "    <th>0.545455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.800002</th>\n",
       "    <th>1.431294</th>\n",
       "    <th>0.512121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.714349</th>\n",
       "    <th>1.414497</th>\n",
       "    <th>0.506061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.639630</th>\n",
       "    <th>1.356096</th>\n",
       "    <th>0.545455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.572042</th>\n",
       "    <th>1.362309</th>\n",
       "    <th>0.527273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.511765</th>\n",
       "    <th>1.364852</th>\n",
       "    <th>0.521212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.459427</th>\n",
       "    <th>1.351671</th>\n",
       "    <th>0.525758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.412741</th>\n",
       "    <th>1.345105</th>\n",
       "    <th>0.533333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.372912</th>\n",
       "    <th>1.346963</th>\n",
       "    <th>0.533333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.337714</th>\n",
       "    <th>1.348316</th>\n",
       "    <th>0.533333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.306629</th>\n",
       "    <th>1.355503</th>\n",
       "    <th>0.533333</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.068228</th>\n",
       "    <th>1.358930</th>\n",
       "    <th>0.530303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.061248</th>\n",
       "    <th>1.361188</th>\n",
       "    <th>0.527273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.060487</th>\n",
       "    <th>1.363224</th>\n",
       "    <th>0.521212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.057739</th>\n",
       "    <th>1.371738</th>\n",
       "    <th>0.516667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.055445</th>\n",
       "    <th>1.370076</th>\n",
       "    <th>0.522727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.053303</th>\n",
       "    <th>1.370877</th>\n",
       "    <th>0.525758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.051647</th>\n",
       "    <th>1.372106</th>\n",
       "    <th>0.519697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.049020</th>\n",
       "    <th>1.378382</th>\n",
       "    <th>0.525758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.048360</th>\n",
       "    <th>1.383999</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.047138</th>\n",
       "    <th>1.382501</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "Total time: 00:40 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.817955</th>\n",
       "    <th>2.089393</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.700127</th>\n",
       "    <th>6.438977</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.607899</th>\n",
       "    <th>1.444449</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.551874</th>\n",
       "    <th>1.185427</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.480785</th>\n",
       "    <th>0.448581</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.413446</th>\n",
       "    <th>0.599768</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.371035</th>\n",
       "    <th>0.300200</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.319028</th>\n",
       "    <th>0.294259</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.288889</th>\n",
       "    <th>0.251457</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.264703</th>\n",
       "    <th>0.233550</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.386202</th>\n",
       "    <th>2.398227</th>\n",
       "    <th>0.078788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.132844</th>\n",
       "    <th>2.389349</th>\n",
       "    <th>0.078788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.872743</th>\n",
       "    <th>2.358658</th>\n",
       "    <th>0.130303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.634792</th>\n",
       "    <th>2.291796</th>\n",
       "    <th>0.248485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.456457</th>\n",
       "    <th>2.195989</th>\n",
       "    <th>0.322727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.298056</th>\n",
       "    <th>2.078220</th>\n",
       "    <th>0.413636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.159079</th>\n",
       "    <th>1.962304</th>\n",
       "    <th>0.484849</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.034223</th>\n",
       "    <th>1.826948</th>\n",
       "    <th>0.525758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.916404</th>\n",
       "    <th>1.720272</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.809057</th>\n",
       "    <th>1.586216</th>\n",
       "    <th>0.536364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.714973</th>\n",
       "    <th>1.512629</th>\n",
       "    <th>0.518182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.632361</th>\n",
       "    <th>1.389459</th>\n",
       "    <th>0.543939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.560006</th>\n",
       "    <th>1.319228</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.498174</th>\n",
       "    <th>1.256033</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.443331</th>\n",
       "    <th>1.232014</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.396510</th>\n",
       "    <th>1.225202</th>\n",
       "    <th>0.565152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.355169</th>\n",
       "    <th>1.220616</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.318922</th>\n",
       "    <th>1.219162</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.288884</th>\n",
       "    <th>1.219720</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.262267</th>\n",
       "    <th>1.219959</th>\n",
       "    <th>0.580303</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.038299</th>\n",
       "    <th>1.221648</th>\n",
       "    <th>0.578788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.041415</th>\n",
       "    <th>1.222424</th>\n",
       "    <th>0.578788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.043438</th>\n",
       "    <th>1.224018</th>\n",
       "    <th>0.571212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.045414</th>\n",
       "    <th>1.225826</th>\n",
       "    <th>0.577273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.043666</th>\n",
       "    <th>1.225168</th>\n",
       "    <th>0.581818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.043094</th>\n",
       "    <th>1.225353</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.042382</th>\n",
       "    <th>1.227528</th>\n",
       "    <th>0.577273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.041146</th>\n",
       "    <th>1.229068</th>\n",
       "    <th>0.577273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.039702</th>\n",
       "    <th>1.229699</th>\n",
       "    <th>0.571212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.038649</th>\n",
       "    <th>1.229721</th>\n",
       "    <th>0.569697</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:41 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.820521</th>\n",
       "    <th>2.734882</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.703745</th>\n",
       "    <th>8.028026</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.615423</th>\n",
       "    <th>2.912169</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.535782</th>\n",
       "    <th>1.184908</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.467096</th>\n",
       "    <th>1.095582</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.410005</th>\n",
       "    <th>0.660209</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.360635</th>\n",
       "    <th>0.324528</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.321087</th>\n",
       "    <th>0.255766</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.287795</th>\n",
       "    <th>0.251430</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.265616</th>\n",
       "    <th>0.231219</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.442635</th>\n",
       "    <th>2.394826</th>\n",
       "    <th>0.100000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.151713</th>\n",
       "    <th>2.383733</th>\n",
       "    <th>0.175758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.873665</th>\n",
       "    <th>2.348496</th>\n",
       "    <th>0.262121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.647368</th>\n",
       "    <th>2.274535</th>\n",
       "    <th>0.343939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.453277</th>\n",
       "    <th>2.165566</th>\n",
       "    <th>0.439394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.297154</th>\n",
       "    <th>2.041610</th>\n",
       "    <th>0.437879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.160927</th>\n",
       "    <th>1.905090</th>\n",
       "    <th>0.542424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.031874</th>\n",
       "    <th>1.792889</th>\n",
       "    <th>0.462121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.922814</th>\n",
       "    <th>1.703313</th>\n",
       "    <th>0.486364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.830113</th>\n",
       "    <th>1.536791</th>\n",
       "    <th>0.540909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.746626</th>\n",
       "    <th>1.384846</th>\n",
       "    <th>0.578788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.673258</th>\n",
       "    <th>1.311821</th>\n",
       "    <th>0.589394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.607734</th>\n",
       "    <th>1.251177</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.544621</th>\n",
       "    <th>1.195919</th>\n",
       "    <th>0.598485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.489310</th>\n",
       "    <th>1.191691</th>\n",
       "    <th>0.606061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.442203</th>\n",
       "    <th>1.203760</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.398370</th>\n",
       "    <th>1.208561</th>\n",
       "    <th>0.600000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.359587</th>\n",
       "    <th>1.210015</th>\n",
       "    <th>0.593939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.327074</th>\n",
       "    <th>1.208067</th>\n",
       "    <th>0.592424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.297381</th>\n",
       "    <th>1.203861</th>\n",
       "    <th>0.593939</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.060070</th>\n",
       "    <th>1.202576</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.055823</th>\n",
       "    <th>1.208344</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.054557</th>\n",
       "    <th>1.224044</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.053353</th>\n",
       "    <th>1.224404</th>\n",
       "    <th>0.592424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.052080</th>\n",
       "    <th>1.222434</th>\n",
       "    <th>0.587879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.050126</th>\n",
       "    <th>1.210741</th>\n",
       "    <th>0.596970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.048665</th>\n",
       "    <th>1.207146</th>\n",
       "    <th>0.603030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.047605</th>\n",
       "    <th>1.204268</th>\n",
       "    <th>0.601515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.045779</th>\n",
       "    <th>1.206024</th>\n",
       "    <th>0.600000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.044567</th>\n",
       "    <th>1.207482</th>\n",
       "    <th>0.600000</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
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       "<IPython.core.display.HTML object>"
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     },
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    },
    {
     "data": {
      "text/html": [
       "Total time: 00:41 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.815520</th>\n",
       "    <th>8.041189</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.746807</th>\n",
       "    <th>5.825033</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.689192</th>\n",
       "    <th>1.355944</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.611163</th>\n",
       "    <th>0.562143</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.534818</th>\n",
       "    <th>1.459951</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.467058</th>\n",
       "    <th>0.523160</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.411952</th>\n",
       "    <th>0.368886</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.354173</th>\n",
       "    <th>0.384938</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.322743</th>\n",
       "    <th>0.270909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.307063</th>\n",
       "    <th>0.255128</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.314802</th>\n",
       "    <th>2.394416</th>\n",
       "    <th>0.078788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.031281</th>\n",
       "    <th>2.379420</th>\n",
       "    <th>0.078788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.763133</th>\n",
       "    <th>2.335956</th>\n",
       "    <th>0.175758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.546629</th>\n",
       "    <th>2.256847</th>\n",
       "    <th>0.239394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.392127</th>\n",
       "    <th>2.151397</th>\n",
       "    <th>0.322727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.258547</th>\n",
       "    <th>2.027936</th>\n",
       "    <th>0.377273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.133706</th>\n",
       "    <th>1.892578</th>\n",
       "    <th>0.448485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.037027</th>\n",
       "    <th>1.777619</th>\n",
       "    <th>0.474242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.938884</th>\n",
       "    <th>1.661096</th>\n",
       "    <th>0.481818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.849249</th>\n",
       "    <th>1.479598</th>\n",
       "    <th>0.548485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.767184</th>\n",
       "    <th>1.507436</th>\n",
       "    <th>0.512121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.688218</th>\n",
       "    <th>1.422842</th>\n",
       "    <th>0.510606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.617006</th>\n",
       "    <th>1.233144</th>\n",
       "    <th>0.580303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.548775</th>\n",
       "    <th>1.232230</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.492627</th>\n",
       "    <th>1.223749</th>\n",
       "    <th>0.575758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.442964</th>\n",
       "    <th>1.221088</th>\n",
       "    <th>0.587879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.397322</th>\n",
       "    <th>1.220633</th>\n",
       "    <th>0.595455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.358285</th>\n",
       "    <th>1.226332</th>\n",
       "    <th>0.598485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.325390</th>\n",
       "    <th>1.229540</th>\n",
       "    <th>0.595455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.295656</th>\n",
       "    <th>1.236431</th>\n",
       "    <th>0.589394</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.050386</th>\n",
       "    <th>1.240671</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.051765</th>\n",
       "    <th>1.245381</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.051841</th>\n",
       "    <th>1.256441</th>\n",
       "    <th>0.581818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.050431</th>\n",
       "    <th>1.264518</th>\n",
       "    <th>0.583333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.050993</th>\n",
       "    <th>1.262698</th>\n",
       "    <th>0.583333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.049283</th>\n",
       "    <th>1.256751</th>\n",
       "    <th>0.575758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.048062</th>\n",
       "    <th>1.259479</th>\n",
       "    <th>0.584848</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.048254</th>\n",
       "    <th>1.262700</th>\n",
       "    <th>0.586364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.047116</th>\n",
       "    <th>1.261396</th>\n",
       "    <th>0.586364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.046171</th>\n",
       "    <th>1.261203</th>\n",
       "    <th>0.586364</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:40 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.833073</th>\n",
       "    <th>3.241596</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.723579</th>\n",
       "    <th>73.856239</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.635644</th>\n",
       "    <th>0.709678</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.539419</th>\n",
       "    <th>0.442084</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.475208</th>\n",
       "    <th>0.502777</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.413305</th>\n",
       "    <th>0.401088</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.368249</th>\n",
       "    <th>0.392858</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.322076</th>\n",
       "    <th>0.284782</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.286400</th>\n",
       "    <th>0.237017</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.267126</th>\n",
       "    <th>0.234009</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.487550</th>\n",
       "    <th>2.396713</th>\n",
       "    <th>0.092424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.216394</th>\n",
       "    <th>2.385649</th>\n",
       "    <th>0.233333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.921997</th>\n",
       "    <th>2.346882</th>\n",
       "    <th>0.310606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.681056</th>\n",
       "    <th>2.265676</th>\n",
       "    <th>0.377273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.480219</th>\n",
       "    <th>2.143427</th>\n",
       "    <th>0.403030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.315145</th>\n",
       "    <th>2.006982</th>\n",
       "    <th>0.456061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.168802</th>\n",
       "    <th>1.878462</th>\n",
       "    <th>0.436364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.036277</th>\n",
       "    <th>1.753397</th>\n",
       "    <th>0.442424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.918945</th>\n",
       "    <th>1.594415</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.820991</th>\n",
       "    <th>1.490742</th>\n",
       "    <th>0.536364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.733873</th>\n",
       "    <th>1.432628</th>\n",
       "    <th>0.534849</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.651032</th>\n",
       "    <th>1.362832</th>\n",
       "    <th>0.548485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.579520</th>\n",
       "    <th>1.312215</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.516816</th>\n",
       "    <th>1.256964</th>\n",
       "    <th>0.571212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.461410</th>\n",
       "    <th>1.261978</th>\n",
       "    <th>0.565152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.412888</th>\n",
       "    <th>1.280534</th>\n",
       "    <th>0.578788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.371513</th>\n",
       "    <th>1.275654</th>\n",
       "    <th>0.581818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.334490</th>\n",
       "    <th>1.261244</th>\n",
       "    <th>0.572727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.302381</th>\n",
       "    <th>1.257171</th>\n",
       "    <th>0.569697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.274986</th>\n",
       "    <th>1.254746</th>\n",
       "    <th>0.572727</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.046511</th>\n",
       "    <th>1.254852</th>\n",
       "    <th>0.578788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.047033</th>\n",
       "    <th>1.252968</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.046088</th>\n",
       "    <th>1.248716</th>\n",
       "    <th>0.572727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.044471</th>\n",
       "    <th>1.247488</th>\n",
       "    <th>0.578788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.042571</th>\n",
       "    <th>1.249793</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.041914</th>\n",
       "    <th>1.251388</th>\n",
       "    <th>0.572727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.042110</th>\n",
       "    <th>1.258378</th>\n",
       "    <th>0.586364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.042102</th>\n",
       "    <th>1.264789</th>\n",
       "    <th>0.583333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.041885</th>\n",
       "    <th>1.267109</th>\n",
       "    <th>0.581818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.041245</th>\n",
       "    <th>1.265169</th>\n",
       "    <th>0.583333</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:40 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.825760</th>\n",
       "    <th>19.934975</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.711225</th>\n",
       "    <th>4.934133</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.655039</th>\n",
       "    <th>2.354051</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.591781</th>\n",
       "    <th>1.115814</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.499828</th>\n",
       "    <th>0.760193</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.451936</th>\n",
       "    <th>0.709095</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.399196</th>\n",
       "    <th>0.475660</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.350695</th>\n",
       "    <th>0.288583</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.320946</th>\n",
       "    <th>0.281808</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.298258</th>\n",
       "    <th>0.260007</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:15 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.389429</th>\n",
       "    <th>2.396465</th>\n",
       "    <th>0.119697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.156542</th>\n",
       "    <th>2.383746</th>\n",
       "    <th>0.087879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.879732</th>\n",
       "    <th>2.343878</th>\n",
       "    <th>0.112121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.653836</th>\n",
       "    <th>2.266121</th>\n",
       "    <th>0.201515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.468112</th>\n",
       "    <th>2.165151</th>\n",
       "    <th>0.274242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.322571</th>\n",
       "    <th>2.044613</th>\n",
       "    <th>0.362121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.189621</th>\n",
       "    <th>1.905388</th>\n",
       "    <th>0.398485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.067282</th>\n",
       "    <th>1.753432</th>\n",
       "    <th>0.468182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.955148</th>\n",
       "    <th>1.616445</th>\n",
       "    <th>0.531818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.853963</th>\n",
       "    <th>1.536203</th>\n",
       "    <th>0.506061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.763787</th>\n",
       "    <th>1.458297</th>\n",
       "    <th>0.492424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.683531</th>\n",
       "    <th>1.482911</th>\n",
       "    <th>0.466667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.609236</th>\n",
       "    <th>1.369411</th>\n",
       "    <th>0.540909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.543397</th>\n",
       "    <th>1.304162</th>\n",
       "    <th>0.543939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.487696</th>\n",
       "    <th>1.244376</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.436224</th>\n",
       "    <th>1.229351</th>\n",
       "    <th>0.557576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.392449</th>\n",
       "    <th>1.225636</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.353797</th>\n",
       "    <th>1.229303</th>\n",
       "    <th>0.556061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.321095</th>\n",
       "    <th>1.233506</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.291175</th>\n",
       "    <th>1.231225</th>\n",
       "    <th>0.557576</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:07 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.060002</th>\n",
       "    <th>1.232814</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.055332</th>\n",
       "    <th>1.233604</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.052643</th>\n",
       "    <th>1.239720</th>\n",
       "    <th>0.562121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.052580</th>\n",
       "    <th>1.244586</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.050802</th>\n",
       "    <th>1.247010</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.048951</th>\n",
       "    <th>1.250261</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.048215</th>\n",
       "    <th>1.251344</th>\n",
       "    <th>0.569697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.047621</th>\n",
       "    <th>1.250992</th>\n",
       "    <th>0.569697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.047122</th>\n",
       "    <th>1.252040</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.046967</th>\n",
       "    <th>1.251413</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:40 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.833391</th>\n",
       "    <th>7.918275</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.767774</th>\n",
       "    <th>1.529386</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.754147</th>\n",
       "    <th>1.254720</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.678970</th>\n",
       "    <th>0.929690</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.617526</th>\n",
       "    <th>0.597122</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.561643</th>\n",
       "    <th>0.565293</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.521369</th>\n",
       "    <th>0.539634</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.472154</th>\n",
       "    <th>0.414538</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.434367</th>\n",
       "    <th>0.392394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.415724</th>\n",
       "    <th>0.382350</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.329918</th>\n",
       "    <th>2.393630</th>\n",
       "    <th>0.146970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.084487</th>\n",
       "    <th>2.376599</th>\n",
       "    <th>0.180303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.809015</th>\n",
       "    <th>2.326086</th>\n",
       "    <th>0.237879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.602215</th>\n",
       "    <th>2.229428</th>\n",
       "    <th>0.384848</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.417120</th>\n",
       "    <th>2.102753</th>\n",
       "    <th>0.433333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.267278</th>\n",
       "    <th>1.970192</th>\n",
       "    <th>0.475758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.140383</th>\n",
       "    <th>1.820888</th>\n",
       "    <th>0.537879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.028942</th>\n",
       "    <th>1.713608</th>\n",
       "    <th>0.481818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.919136</th>\n",
       "    <th>1.584703</th>\n",
       "    <th>0.493939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.822708</th>\n",
       "    <th>1.480955</th>\n",
       "    <th>0.512121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.734999</th>\n",
       "    <th>1.433034</th>\n",
       "    <th>0.498485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.655599</th>\n",
       "    <th>1.400611</th>\n",
       "    <th>0.503030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.586119</th>\n",
       "    <th>1.377549</th>\n",
       "    <th>0.507576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.523798</th>\n",
       "    <th>1.390020</th>\n",
       "    <th>0.521212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.468565</th>\n",
       "    <th>1.405949</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.420210</th>\n",
       "    <th>1.410834</th>\n",
       "    <th>0.518182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.376972</th>\n",
       "    <th>1.417929</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.341192</th>\n",
       "    <th>1.413244</th>\n",
       "    <th>0.536364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.310001</th>\n",
       "    <th>1.417201</th>\n",
       "    <th>0.528788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.281253</th>\n",
       "    <th>1.422332</th>\n",
       "    <th>0.525758</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.049720</th>\n",
       "    <th>1.427868</th>\n",
       "    <th>0.527273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.050213</th>\n",
       "    <th>1.425055</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.046713</th>\n",
       "    <th>1.423205</th>\n",
       "    <th>0.522727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.046324</th>\n",
       "    <th>1.426207</th>\n",
       "    <th>0.533333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.043935</th>\n",
       "    <th>1.423781</th>\n",
       "    <th>0.525758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.043085</th>\n",
       "    <th>1.417075</th>\n",
       "    <th>0.528788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.042317</th>\n",
       "    <th>1.413434</th>\n",
       "    <th>0.531818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.042406</th>\n",
       "    <th>1.416999</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.041507</th>\n",
       "    <th>1.419079</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.040740</th>\n",
       "    <th>1.423480</th>\n",
       "    <th>0.524242</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:42 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.823595</th>\n",
       "    <th>11.807104</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.719942</th>\n",
       "    <th>2.955773</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.670936</th>\n",
       "    <th>27.544741</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.609220</th>\n",
       "    <th>1.479204</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.535035</th>\n",
       "    <th>2.218460</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.485487</th>\n",
       "    <th>1.078941</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.428833</th>\n",
       "    <th>0.465084</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.383413</th>\n",
       "    <th>0.412217</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.341403</th>\n",
       "    <th>0.301461</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.319950</th>\n",
       "    <th>0.283099</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.376566</th>\n",
       "    <th>2.394859</th>\n",
       "    <th>0.092424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.132900</th>\n",
       "    <th>2.381525</th>\n",
       "    <th>0.224242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.873598</th>\n",
       "    <th>2.342645</th>\n",
       "    <th>0.298485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.637528</th>\n",
       "    <th>2.264153</th>\n",
       "    <th>0.362121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.447264</th>\n",
       "    <th>2.152470</th>\n",
       "    <th>0.372727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.281263</th>\n",
       "    <th>2.037517</th>\n",
       "    <th>0.371212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.139245</th>\n",
       "    <th>1.929203</th>\n",
       "    <th>0.415152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.004815</th>\n",
       "    <th>1.803326</th>\n",
       "    <th>0.487879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.887770</th>\n",
       "    <th>1.716392</th>\n",
       "    <th>0.442424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.788344</th>\n",
       "    <th>1.548979</th>\n",
       "    <th>0.530303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.703773</th>\n",
       "    <th>1.503407</th>\n",
       "    <th>0.484848</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.625715</th>\n",
       "    <th>1.463795</th>\n",
       "    <th>0.507576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.562509</th>\n",
       "    <th>1.366440</th>\n",
       "    <th>0.527273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.503397</th>\n",
       "    <th>1.328250</th>\n",
       "    <th>0.539394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.450084</th>\n",
       "    <th>1.340150</th>\n",
       "    <th>0.539394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.404053</th>\n",
       "    <th>1.348970</th>\n",
       "    <th>0.525758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.365723</th>\n",
       "    <th>1.341454</th>\n",
       "    <th>0.531818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.329808</th>\n",
       "    <th>1.333125</th>\n",
       "    <th>0.540909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.298796</th>\n",
       "    <th>1.333084</th>\n",
       "    <th>0.539394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.271460</th>\n",
       "    <th>1.336779</th>\n",
       "    <th>0.543939</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.052980</th>\n",
       "    <th>1.336675</th>\n",
       "    <th>0.546970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.046166</th>\n",
       "    <th>1.343286</th>\n",
       "    <th>0.550000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.046342</th>\n",
       "    <th>1.351566</th>\n",
       "    <th>0.546970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.046055</th>\n",
       "    <th>1.358888</th>\n",
       "    <th>0.542424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.047138</th>\n",
       "    <th>1.361818</th>\n",
       "    <th>0.543939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.046602</th>\n",
       "    <th>1.352281</th>\n",
       "    <th>0.546970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.044605</th>\n",
       "    <th>1.354042</th>\n",
       "    <th>0.551515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.043223</th>\n",
       "    <th>1.353593</th>\n",
       "    <th>0.551515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.042577</th>\n",
       "    <th>1.354964</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.041094</th>\n",
       "    <th>1.357498</th>\n",
       "    <th>0.553030</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:51 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.810008</th>\n",
       "    <th>7.332333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.706837</th>\n",
       "    <th>9.964125</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.620138</th>\n",
       "    <th>2.468890</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.544174</th>\n",
       "    <th>2.190509</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.481238</th>\n",
       "    <th>0.613485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.403800</th>\n",
       "    <th>0.400607</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.349787</th>\n",
       "    <th>0.307445</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.302287</th>\n",
       "    <th>0.284720</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.272116</th>\n",
       "    <th>0.224352</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.259348</th>\n",
       "    <th>0.226678</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:23 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.350188</th>\n",
       "    <th>2.396839</th>\n",
       "    <th>0.072727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.068000</th>\n",
       "    <th>2.384846</th>\n",
       "    <th>0.186364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.769054</th>\n",
       "    <th>2.347686</th>\n",
       "    <th>0.310606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.527348</th>\n",
       "    <th>2.273696</th>\n",
       "    <th>0.377273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.333609</th>\n",
       "    <th>2.171682</th>\n",
       "    <th>0.346970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.177531</th>\n",
       "    <th>2.048813</th>\n",
       "    <th>0.413636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.045144</th>\n",
       "    <th>1.953973</th>\n",
       "    <th>0.457576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.928345</th>\n",
       "    <th>1.816098</th>\n",
       "    <th>0.450000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.828331</th>\n",
       "    <th>1.663808</th>\n",
       "    <th>0.527273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.731616</th>\n",
       "    <th>1.615769</th>\n",
       "    <th>0.471212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.649700</th>\n",
       "    <th>1.445094</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.576438</th>\n",
       "    <th>1.417068</th>\n",
       "    <th>0.516667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.512123</th>\n",
       "    <th>1.417623</th>\n",
       "    <th>0.513636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.455233</th>\n",
       "    <th>1.347975</th>\n",
       "    <th>0.536364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.405565</th>\n",
       "    <th>1.255575</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.363168</th>\n",
       "    <th>1.244850</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.326648</th>\n",
       "    <th>1.247053</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.294177</th>\n",
       "    <th>1.256723</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.265836</th>\n",
       "    <th>1.264635</th>\n",
       "    <th>0.569697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.241103</th>\n",
       "    <th>1.270901</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.049809</th>\n",
       "    <th>1.276773</th>\n",
       "    <th>0.571212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.043845</th>\n",
       "    <th>1.273576</th>\n",
       "    <th>0.577273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.043335</th>\n",
       "    <th>1.281390</th>\n",
       "    <th>0.583333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.041088</th>\n",
       "    <th>1.280518</th>\n",
       "    <th>0.581818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.040240</th>\n",
       "    <th>1.280850</th>\n",
       "    <th>0.575758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.039651</th>\n",
       "    <th>1.274319</th>\n",
       "    <th>0.572727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.038042</th>\n",
       "    <th>1.276182</th>\n",
       "    <th>0.572727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.037295</th>\n",
       "    <th>1.278540</th>\n",
       "    <th>0.572727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.036544</th>\n",
       "    <th>1.280832</th>\n",
       "    <th>0.569697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.035985</th>\n",
       "    <th>1.281949</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:40 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.818888</th>\n",
       "    <th>13.209971</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.702422</th>\n",
       "    <th>10.496621</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.638858</th>\n",
       "    <th>4.975473</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.556778</th>\n",
       "    <th>0.628205</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.505058</th>\n",
       "    <th>0.497812</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.447640</th>\n",
       "    <th>0.511273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.410154</th>\n",
       "    <th>0.470049</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.363768</th>\n",
       "    <th>0.356341</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.331103</th>\n",
       "    <th>0.324786</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.310757</th>\n",
       "    <th>0.268936</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:17 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.322874</th>\n",
       "    <th>2.394989</th>\n",
       "    <th>0.087879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.117776</th>\n",
       "    <th>2.381001</th>\n",
       "    <th>0.101515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.861919</th>\n",
       "    <th>2.344332</th>\n",
       "    <th>0.219697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.641730</th>\n",
       "    <th>2.268719</th>\n",
       "    <th>0.300000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.459493</th>\n",
       "    <th>2.163143</th>\n",
       "    <th>0.377273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.299636</th>\n",
       "    <th>2.044084</th>\n",
       "    <th>0.457576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.177791</th>\n",
       "    <th>1.940923</th>\n",
       "    <th>0.468182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.058377</th>\n",
       "    <th>1.819368</th>\n",
       "    <th>0.466667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.947558</th>\n",
       "    <th>1.727118</th>\n",
       "    <th>0.515152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.846036</th>\n",
       "    <th>1.568026</th>\n",
       "    <th>0.516667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.756712</th>\n",
       "    <th>1.443117</th>\n",
       "    <th>0.548485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.674283</th>\n",
       "    <th>1.530163</th>\n",
       "    <th>0.492424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.601342</th>\n",
       "    <th>1.452759</th>\n",
       "    <th>0.510606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.535970</th>\n",
       "    <th>1.296709</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.478727</th>\n",
       "    <th>1.320557</th>\n",
       "    <th>0.548485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.427890</th>\n",
       "    <th>1.281475</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.384082</th>\n",
       "    <th>1.261429</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.345768</th>\n",
       "    <th>1.255497</th>\n",
       "    <th>0.583333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.312024</th>\n",
       "    <th>1.252921</th>\n",
       "    <th>0.578788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.283478</th>\n",
       "    <th>1.252796</th>\n",
       "    <th>0.575758</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.057554</th>\n",
       "    <th>1.252578</th>\n",
       "    <th>0.577273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.056223</th>\n",
       "    <th>1.258368</th>\n",
       "    <th>0.575758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.053397</th>\n",
       "    <th>1.262844</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.049986</th>\n",
       "    <th>1.265195</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.047156</th>\n",
       "    <th>1.270368</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.046381</th>\n",
       "    <th>1.271404</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.044921</th>\n",
       "    <th>1.269009</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.042741</th>\n",
       "    <th>1.268692</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.042337</th>\n",
       "    <th>1.265579</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.042567</th>\n",
       "    <th>1.265936</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:44 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.832724</th>\n",
       "    <th>5.070494</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.722949</th>\n",
       "    <th>0.958255</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.625782</th>\n",
       "    <th>0.860363</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.527219</th>\n",
       "    <th>0.771258</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.437486</th>\n",
       "    <th>0.399974</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.382071</th>\n",
       "    <th>0.783776</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.331514</th>\n",
       "    <th>0.486717</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.290647</th>\n",
       "    <th>0.251109</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.263349</th>\n",
       "    <th>0.219931</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.243161</th>\n",
       "    <th>0.213883</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.459691</th>\n",
       "    <th>2.397284</th>\n",
       "    <th>0.080303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.189296</th>\n",
       "    <th>2.385771</th>\n",
       "    <th>0.181818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.892309</th>\n",
       "    <th>2.347974</th>\n",
       "    <th>0.239394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.650588</th>\n",
       "    <th>2.271123</th>\n",
       "    <th>0.286364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.457796</th>\n",
       "    <th>2.156323</th>\n",
       "    <th>0.321212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.291984</th>\n",
       "    <th>2.030913</th>\n",
       "    <th>0.346970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.139430</th>\n",
       "    <th>1.902215</th>\n",
       "    <th>0.475758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.003745</th>\n",
       "    <th>1.764655</th>\n",
       "    <th>0.503030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.893927</th>\n",
       "    <th>1.633362</th>\n",
       "    <th>0.528788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.791026</th>\n",
       "    <th>1.486168</th>\n",
       "    <th>0.584848</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.700999</th>\n",
       "    <th>1.441215</th>\n",
       "    <th>0.554546</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.621967</th>\n",
       "    <th>1.455626</th>\n",
       "    <th>0.513636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.553496</th>\n",
       "    <th>1.262960</th>\n",
       "    <th>0.612121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.491573</th>\n",
       "    <th>1.274603</th>\n",
       "    <th>0.603030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.438020</th>\n",
       "    <th>1.264939</th>\n",
       "    <th>0.596970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.391172</th>\n",
       "    <th>1.250059</th>\n",
       "    <th>0.600000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.349957</th>\n",
       "    <th>1.243854</th>\n",
       "    <th>0.595455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.315096</th>\n",
       "    <th>1.250875</th>\n",
       "    <th>0.595455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.284044</th>\n",
       "    <th>1.256969</th>\n",
       "    <th>0.592424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.258381</th>\n",
       "    <th>1.260986</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:07 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.037909</th>\n",
       "    <th>1.263027</th>\n",
       "    <th>0.593939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.037496</th>\n",
       "    <th>1.264890</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.036692</th>\n",
       "    <th>1.265272</th>\n",
       "    <th>0.583333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.038795</th>\n",
       "    <th>1.271345</th>\n",
       "    <th>0.587879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.037719</th>\n",
       "    <th>1.271136</th>\n",
       "    <th>0.593939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.037008</th>\n",
       "    <th>1.275810</th>\n",
       "    <th>0.586364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.036468</th>\n",
       "    <th>1.272061</th>\n",
       "    <th>0.592424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.035927</th>\n",
       "    <th>1.270795</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.035823</th>\n",
       "    <th>1.270353</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.035292</th>\n",
       "    <th>1.271054</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:40 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.826457</th>\n",
       "    <th>1.993093</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.718725</th>\n",
       "    <th>34.362350</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.644587</th>\n",
       "    <th>2.786315</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.566208</th>\n",
       "    <th>1.435635</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.506907</th>\n",
       "    <th>0.660412</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.455289</th>\n",
       "    <th>0.520099</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.401933</th>\n",
       "    <th>0.637151</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.358589</th>\n",
       "    <th>0.333446</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.325457</th>\n",
       "    <th>0.272107</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.299186</th>\n",
       "    <th>0.261832</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:17 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.393548</th>\n",
       "    <th>2.393323</th>\n",
       "    <th>0.240909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.117429</th>\n",
       "    <th>2.379714</th>\n",
       "    <th>0.213636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.842467</th>\n",
       "    <th>2.337147</th>\n",
       "    <th>0.231818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.604221</th>\n",
       "    <th>2.250817</th>\n",
       "    <th>0.356061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.421246</th>\n",
       "    <th>2.130521</th>\n",
       "    <th>0.404545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.263343</th>\n",
       "    <th>2.003291</th>\n",
       "    <th>0.396970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.122180</th>\n",
       "    <th>1.868678</th>\n",
       "    <th>0.489394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.993528</th>\n",
       "    <th>1.772210</th>\n",
       "    <th>0.471212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.887220</th>\n",
       "    <th>1.650668</th>\n",
       "    <th>0.481818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.792846</th>\n",
       "    <th>1.576905</th>\n",
       "    <th>0.439394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.706460</th>\n",
       "    <th>1.542550</th>\n",
       "    <th>0.465152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.633402</th>\n",
       "    <th>1.434942</th>\n",
       "    <th>0.512121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.565582</th>\n",
       "    <th>1.363285</th>\n",
       "    <th>0.536364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.504098</th>\n",
       "    <th>1.432261</th>\n",
       "    <th>0.504545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.449992</th>\n",
       "    <th>1.394651</th>\n",
       "    <th>0.515152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.404183</th>\n",
       "    <th>1.318176</th>\n",
       "    <th>0.551515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.364334</th>\n",
       "    <th>1.301410</th>\n",
       "    <th>0.556061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.328647</th>\n",
       "    <th>1.308235</th>\n",
       "    <th>0.565152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.296915</th>\n",
       "    <th>1.314946</th>\n",
       "    <th>0.565152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.270146</th>\n",
       "    <th>1.322285</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.049494</th>\n",
       "    <th>1.327324</th>\n",
       "    <th>0.565152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.045789</th>\n",
       "    <th>1.323922</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.047883</th>\n",
       "    <th>1.329465</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.046985</th>\n",
       "    <th>1.336009</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.047287</th>\n",
       "    <th>1.331708</th>\n",
       "    <th>0.556061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.045188</th>\n",
       "    <th>1.324908</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.044940</th>\n",
       "    <th>1.327407</th>\n",
       "    <th>0.560606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.043863</th>\n",
       "    <th>1.334577</th>\n",
       "    <th>0.568182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.043079</th>\n",
       "    <th>1.338737</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.041938</th>\n",
       "    <th>1.338737</th>\n",
       "    <th>0.565152</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:44 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.810548</th>\n",
       "    <th>1.323835</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.689987</th>\n",
       "    <th>2.098926</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.618656</th>\n",
       "    <th>0.810415</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.545312</th>\n",
       "    <th>0.954596</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.479586</th>\n",
       "    <th>0.812984</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.430622</th>\n",
       "    <th>0.636112</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.376153</th>\n",
       "    <th>0.453575</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.328083</th>\n",
       "    <th>0.341866</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.293862</th>\n",
       "    <th>0.243201</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.282351</th>\n",
       "    <th>0.240016</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.303383</th>\n",
       "    <th>2.394457</th>\n",
       "    <th>0.087879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.066492</th>\n",
       "    <th>2.384255</th>\n",
       "    <th>0.160606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.804582</th>\n",
       "    <th>2.350689</th>\n",
       "    <th>0.224242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.584164</th>\n",
       "    <th>2.280347</th>\n",
       "    <th>0.306061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.401662</th>\n",
       "    <th>2.174712</th>\n",
       "    <th>0.351515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.237256</th>\n",
       "    <th>2.067998</th>\n",
       "    <th>0.345455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.094054</th>\n",
       "    <th>1.949666</th>\n",
       "    <th>0.337879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.968828</th>\n",
       "    <th>1.846666</th>\n",
       "    <th>0.395455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.853970</th>\n",
       "    <th>1.642351</th>\n",
       "    <th>0.518182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.755641</th>\n",
       "    <th>1.496174</th>\n",
       "    <th>0.583333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.667313</th>\n",
       "    <th>1.431183</th>\n",
       "    <th>0.545455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.592011</th>\n",
       "    <th>1.369281</th>\n",
       "    <th>0.551515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.524999</th>\n",
       "    <th>1.292387</th>\n",
       "    <th>0.578788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.466779</th>\n",
       "    <th>1.265479</th>\n",
       "    <th>0.563636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.416691</th>\n",
       "    <th>1.238065</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.372227</th>\n",
       "    <th>1.225100</th>\n",
       "    <th>0.595455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.333880</th>\n",
       "    <th>1.223322</th>\n",
       "    <th>0.609091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.299903</th>\n",
       "    <th>1.226079</th>\n",
       "    <th>0.606061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.271654</th>\n",
       "    <th>1.228136</th>\n",
       "    <th>0.609091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.245620</th>\n",
       "    <th>1.228210</th>\n",
       "    <th>0.615152</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.035503</th>\n",
       "    <th>1.227234</th>\n",
       "    <th>0.610606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.036037</th>\n",
       "    <th>1.229742</th>\n",
       "    <th>0.610606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.036698</th>\n",
       "    <th>1.234930</th>\n",
       "    <th>0.609091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.035147</th>\n",
       "    <th>1.238034</th>\n",
       "    <th>0.612121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.034112</th>\n",
       "    <th>1.236320</th>\n",
       "    <th>0.609091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.033776</th>\n",
       "    <th>1.238639</th>\n",
       "    <th>0.609091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.034988</th>\n",
       "    <th>1.239619</th>\n",
       "    <th>0.603030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.035589</th>\n",
       "    <th>1.239411</th>\n",
       "    <th>0.607576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.034494</th>\n",
       "    <th>1.239635</th>\n",
       "    <th>0.607576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.034691</th>\n",
       "    <th>1.237860</th>\n",
       "    <th>0.604545</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:40 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.821133</th>\n",
       "    <th>2.584166</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.742031</th>\n",
       "    <th>11.404493</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.656286</th>\n",
       "    <th>0.891582</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.586595</th>\n",
       "    <th>0.988058</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.533334</th>\n",
       "    <th>0.611835</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.477163</th>\n",
       "    <th>0.442906</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.432234</th>\n",
       "    <th>0.514804</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.391826</th>\n",
       "    <th>0.343713</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.364127</th>\n",
       "    <th>0.323217</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.337696</th>\n",
       "    <th>0.293707</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.320446</th>\n",
       "    <th>2.395761</th>\n",
       "    <th>0.078788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.046307</th>\n",
       "    <th>2.383243</th>\n",
       "    <th>0.080303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.806911</th>\n",
       "    <th>2.343741</th>\n",
       "    <th>0.233333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.599660</th>\n",
       "    <th>2.267994</th>\n",
       "    <th>0.274242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.432505</th>\n",
       "    <th>2.152300</th>\n",
       "    <th>0.392424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.280953</th>\n",
       "    <th>2.023644</th>\n",
       "    <th>0.477273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.140588</th>\n",
       "    <th>1.903512</th>\n",
       "    <th>0.480303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.022115</th>\n",
       "    <th>1.764283</th>\n",
       "    <th>0.472727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.906236</th>\n",
       "    <th>1.659617</th>\n",
       "    <th>0.510606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.805569</th>\n",
       "    <th>1.534714</th>\n",
       "    <th>0.512121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.713748</th>\n",
       "    <th>1.454150</th>\n",
       "    <th>0.519697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.634233</th>\n",
       "    <th>1.367864</th>\n",
       "    <th>0.533333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.566920</th>\n",
       "    <th>1.314915</th>\n",
       "    <th>0.559091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.507356</th>\n",
       "    <th>1.321951</th>\n",
       "    <th>0.545455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.453512</th>\n",
       "    <th>1.326762</th>\n",
       "    <th>0.550000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.406815</th>\n",
       "    <th>1.327838</th>\n",
       "    <th>0.546970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.365999</th>\n",
       "    <th>1.312308</th>\n",
       "    <th>0.543939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.331322</th>\n",
       "    <th>1.307978</th>\n",
       "    <th>0.546970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.300226</th>\n",
       "    <th>1.313875</th>\n",
       "    <th>0.550000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.273902</th>\n",
       "    <th>1.319707</th>\n",
       "    <th>0.553030</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.050999</th>\n",
       "    <th>1.321943</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.050877</th>\n",
       "    <th>1.322112</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.050093</th>\n",
       "    <th>1.327104</th>\n",
       "    <th>0.550000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.047938</th>\n",
       "    <th>1.329460</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.048140</th>\n",
       "    <th>1.334371</th>\n",
       "    <th>0.540909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.046862</th>\n",
       "    <th>1.337911</th>\n",
       "    <th>0.537879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.045368</th>\n",
       "    <th>1.340761</th>\n",
       "    <th>0.540909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.044167</th>\n",
       "    <th>1.341566</th>\n",
       "    <th>0.543939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.043925</th>\n",
       "    <th>1.342011</th>\n",
       "    <th>0.545455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.044214</th>\n",
       "    <th>1.341189</th>\n",
       "    <th>0.545455</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
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       "<IPython.core.display.HTML object>"
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      "text/html": [
       "Total time: 00:41 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.819428</th>\n",
       "    <th>22.063360</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.688447</th>\n",
       "    <th>4.009801</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.602428</th>\n",
       "    <th>2.116347</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.526045</th>\n",
       "    <th>1.014110</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.453103</th>\n",
       "    <th>0.648385</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.394534</th>\n",
       "    <th>0.440766</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.357161</th>\n",
       "    <th>0.355578</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.323035</th>\n",
       "    <th>0.277670</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.296413</th>\n",
       "    <th>0.239527</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.268004</th>\n",
       "    <th>0.223859</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
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    {
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      "text/html": [
       "Total time: 00:16 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.384904</th>\n",
       "    <th>2.395262</th>\n",
       "    <th>0.118182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.104218</th>\n",
       "    <th>2.383985</th>\n",
       "    <th>0.109091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.822235</th>\n",
       "    <th>2.350399</th>\n",
       "    <th>0.125758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.586094</th>\n",
       "    <th>2.286280</th>\n",
       "    <th>0.225758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.406809</th>\n",
       "    <th>2.196916</th>\n",
       "    <th>0.193939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.251557</th>\n",
       "    <th>2.098246</th>\n",
       "    <th>0.290909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.113823</th>\n",
       "    <th>2.001618</th>\n",
       "    <th>0.353030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.989389</th>\n",
       "    <th>1.847267</th>\n",
       "    <th>0.425758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.873351</th>\n",
       "    <th>1.731185</th>\n",
       "    <th>0.465152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.774805</th>\n",
       "    <th>1.613208</th>\n",
       "    <th>0.472727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.685205</th>\n",
       "    <th>1.468453</th>\n",
       "    <th>0.525758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.606377</th>\n",
       "    <th>1.354900</th>\n",
       "    <th>0.566667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.538548</th>\n",
       "    <th>1.367573</th>\n",
       "    <th>0.546970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.478772</th>\n",
       "    <th>1.347408</th>\n",
       "    <th>0.554545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.428651</th>\n",
       "    <th>1.311271</th>\n",
       "    <th>0.569697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.383325</th>\n",
       "    <th>1.291725</th>\n",
       "    <th>0.575758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.343446</th>\n",
       "    <th>1.278642</th>\n",
       "    <th>0.577273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.308535</th>\n",
       "    <th>1.263599</th>\n",
       "    <th>0.575758</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.278669</th>\n",
       "    <th>1.257841</th>\n",
       "    <th>0.577273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.252847</th>\n",
       "    <th>1.252703</th>\n",
       "    <th>0.577273</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
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      "text/html": [
       "Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.043728</th>\n",
       "    <th>1.245908</th>\n",
       "    <th>0.580303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.040941</th>\n",
       "    <th>1.249479</th>\n",
       "    <th>0.580303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.041797</th>\n",
       "    <th>1.253074</th>\n",
       "    <th>0.578788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.039036</th>\n",
       "    <th>1.259607</th>\n",
       "    <th>0.580303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.038349</th>\n",
       "    <th>1.264090</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.038529</th>\n",
       "    <th>1.268437</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.037984</th>\n",
       "    <th>1.273192</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.037646</th>\n",
       "    <th>1.271178</th>\n",
       "    <th>0.574242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.036624</th>\n",
       "    <th>1.266540</th>\n",
       "    <th>0.569697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.036708</th>\n",
       "    <th>1.269021</th>\n",
       "    <th>0.572727</th>\n",
       "  </tr>\n",
       "</table>\n"
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       "    \n",
       "<table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
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    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 5.63 MiB (GPU 0; 8.00 GiB total capacity; 2.17 GiB already allocated; 0 bytes free; 68.29 MiB cached)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-7a331d2ce94d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m150\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mldim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdo_run\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mldim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mldim\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mls\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-21-7a331d2ce94d>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m150\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mldim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdo_run\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mldim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mldim\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mls\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-13-c13884d0d2a1>\u001b[0m in \u001b[0;36mdo_run\u001b[1;34m(latentDim)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdo_run\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlatentDim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mlearnAE\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLearner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataAE\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTSAE\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_ds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlatentDim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlatentDim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmse_loss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mlearnAE\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1e-2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mlearn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLearner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mSidekickResnet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_ds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss_func\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcross_entropy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcallback_fns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mBnFreeze\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbn_wd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_bn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\fastai\\train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[1;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, wd, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m     19\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor,\n\u001b[0;32m     20\u001b[0m                                         pct_start=pct_start, **kwargs))\n\u001b[1;32m---> 21\u001b[1;33m     \u001b[0mlearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mlr_find\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mLearner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_lr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1e-7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend_lr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_it\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstop_div\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mbool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mAny\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\fastai\\basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[0;32m    164\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallback_fns\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m         fit(epochs, self.model, self.loss_func, opt=self.opt, data=self.data, metrics=self.metrics,\n\u001b[1;32m--> 166\u001b[1;33m             callbacks=self.callbacks+callbacks)\n\u001b[0m\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m->\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\fastai\\basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(epochs, model, loss_func, opt, data, callbacks, metrics)\u001b[0m\n\u001b[0;32m     92\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[0mexception\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 94\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     95\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\fastai\\basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(epochs, model, loss_func, opt, data, callbacks, metrics)\u001b[0m\n\u001b[0;32m     82\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpbar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m                 \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\fastai\\basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[1;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mxb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mxb\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0myb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0myb\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m     \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mxb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m     \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_loss_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 489\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 489\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 489\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\container.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m     90\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m             \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 489\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-14-41d0c93bdaeb>\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[0mx2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshortcut\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 489\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\batchnorm.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m     74\u001b[0m             \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrunning_mean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrunning_var\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraining\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrack_running_stats\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m             exponential_average_factor, self.eps)\n\u001b[0m\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\functional.py\u001b[0m in \u001b[0;36mbatch_norm\u001b[1;34m(input, running_mean, running_var, weight, bias, training, momentum, eps)\u001b[0m\n\u001b[0;32m   1621\u001b[0m     return torch.batch_norm(\n\u001b[0;32m   1622\u001b[0m         \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrunning_mean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrunning_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1623\u001b[1;33m         \u001b[0mtraining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcudnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menabled\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1624\u001b[0m     )\n\u001b[0;32m   1625\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 5.63 MiB (GPU 0; 8.00 GiB total capacity; 2.17 GiB already allocated; 0 bytes free; 68.29 MiB cached)"
     ]
    }
   ],
   "source": [
    "ls = np.linspace(2,150,20,dtype=np.int)\n",
    "results = [(ldim,do_run(ldim)) for ldim in ls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_head_1d(nf:int, nc:int, lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5, bn_final:bool=False):\n",
    "    \"Model head that takes `nf` features, runs through `lin_ftrs`, and about `nc` classes.\"\n",
    "    lin_ftrs = [nf, 512, nc] if lin_ftrs is None else [nf] + lin_ftrs + [nc]\n",
    "    ps = listify(ps)\n",
    "    if len(ps)==1: ps = [ps[0]/2] * (len(lin_ftrs)-2) + ps\n",
    "    actns = [nn.ReLU(inplace=True)] * (len(lin_ftrs)-2) + [None]\n",
    "    layers = []\n",
    "    for ni,no,p,actn in zip(lin_ftrs[:-1],lin_ftrs[1:],ps,actns):\n",
    "        layers += bn_drop_lin(ni,no,True,p,actn)\n",
    "    if bn_final: layers.append(nn.BatchNorm1d(lin_ftrs[-1], momentum=0.01))\n",
    "    return nn.Sequential(*layers)\n",
    "\n",
    "def conv1d(ni:int, nf:int, ks:int=3, stride:int=1, padding:int=None, bias=False, init:LayerFunc=nn.init.kaiming_normal_) -> nn.Conv1d:\n",
    "    \"Create and initialize `nn.Conv1d` layer. `padding` defaults to `ks//2`.\"\n",
    "    if padding is None: padding = ks//2\n",
    "    return init_default(nn.Conv1d(ni, nf, kernel_size=ks, stride=stride, padding=padding, bias=bias), init)\n",
    "\n",
    "def _bn1d(ni, init_zero=False):\n",
    "    \"Batchnorm layer with 0 initialization\"\n",
    "    m = nn.BatchNorm1d(ni)\n",
    "    m.weight.data.fill_(0 if init_zero else 1)\n",
    "    m.bias.data.zero_()\n",
    "    return m\n",
    "\n",
    "def bn_relu_conv1d(ni, nf, ks, stride, init_zero=False):\n",
    "    bn_initzero = _bn1d(ni, init_zero=init_zero)\n",
    "    return nn.Sequential(bn_initzero, nn.ReLU(inplace=True), conv1d(ni, nf, ks, stride))\n",
    "\n",
    "class ResBlock(torch.nn.Module):\n",
    "    def __init__(self, ni, nf, stride, drop_p=0.0):\n",
    "        super().__init__()\n",
    "        self.bn = nn.BatchNorm1d(ni)\n",
    "        self.conv1 = conv1d(ni, nf, 3, stride)\n",
    "        self.conv2 = bn_relu_conv1d(nf, nf, 3, 1)\n",
    "        self.drop = nn.Dropout(drop_p, inplace=True) if drop_p else None\n",
    "        self.shortcut = conv1d(ni, nf, 1, stride) if ni != nf or stride > 1 else noop\n",
    "\n",
    "    def forward(self, x):\n",
    "        x2 = F.relu(self.bn(x), inplace=True)\n",
    "        r = self.shortcut(x2)\n",
    "        x = self.conv1(x2)\n",
    "        if self.drop: x = self.drop(x)\n",
    "        x = self.conv2(x) * 0.2\n",
    "        return x.add_(r)\n",
    "\n",
    "def _make_group(N, ni, nf, block, stride, drop_p):\n",
    "    return [block(ni if i == 0 else nf, nf, stride if i == 0 else 1, drop_p) for i in range(N)]\n",
    "\n",
    "class WideResNet1d(nn.Module):\n",
    "    \"Wide ResNet with `num_groups` and a width of `k`.\"\n",
    "    def __init__(self, num_groups:int, N:int, k:int=1, drop_p:float=0.0, start_nf:int=16,maxY=20.0):\n",
    "        super().__init__()\n",
    "        self.maxY = maxY\n",
    "        n_channels = [start_nf]\n",
    "        for i in range(num_groups): n_channels.append(start_nf*(2**i)*k)\n",
    "        \n",
    "        \n",
    "        layers = [conv1d(1, n_channels[0], 3, 1)]  # conv1\n",
    "        self.split_groups = [layers[-1]] \n",
    "        \n",
    "        for i in range(num_groups):\n",
    "            layers += _make_group(N, n_channels[i], n_channels[i+1], ResBlock, (1 if i==0 else 2), drop_p)\n",
    "            self.split_groups.append(layers[-N])\n",
    "            \n",
    "        layers += [nn.BatchNorm1d(n_channels[-1]), nn.ReLU(inplace=True), nn.AdaptiveAvgPool1d(1),\n",
    "                   Flatten()]\n",
    "        #self.split_groups.append(layers[-1])\n",
    "        self.nf = n_channels[-1]\n",
    "        self.features = nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        #x = x.unsqueeze(1)\n",
    "        return self.features(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HeroResnet(nn.Module):\n",
    "    def __init__(self, num_groups:int, N:int, k:int=1, drop_p:float=0.0, start_nf:int=16):\n",
    "        super().__init__()\n",
    "        n_channels = [start_nf]\n",
    "        for i in range(num_groups): n_channels.append(start_nf*(2**i)*k)\n",
    "\n",
    "        self.layer0 = conv1d(1,n_channels[0],3,1)\n",
    "        self.split_groups = [self.layer0]\n",
    "        \n",
    "        groups = [nn.Sequential(*_make_group(N,n_channels[i],n_channels[i+1],ResBlock,(1 if i==0 else 2),drop_p)) for i in range(num_groups)]\n",
    "        \n",
    "        self.groups = nn.ModuleList(groups)\n",
    "        for g in self.groups: self.split_groups.append(g)\n",
    "        \n",
    "        self.out = nn.Sequential(nn.BatchNorm1d(n_channels[-1]),\n",
    "                                 nn.ReLU(inplace=True),\n",
    "                                 nn.AdaptiveAvgPool1d(1))               \n",
    "        \n",
    "        self.split_groups.append(self.out)\n",
    "        self.nf = n_channels[-1]\n",
    "        \n",
    "    def forward(self, x):\n",
    "        actvns = [self.layer0(x)]\n",
    "        \n",
    "        for l in self.groups:\n",
    "            actvns.append(l(actvns[-1]))\n",
    "            \n",
    "        return self.out(actvns[-1]), actvns\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SidekickResnet(nn.Module):\n",
    "    def __init__(self, num_classes:int, num_groups:int, N:int, k:int=1, hero_k:int=1, drop_p:float=0.0, start_nf:int=16, start_nf_hero:int=16):\n",
    "        super().__init__()\n",
    "        n_channels = [start_nf]\n",
    "        \n",
    "        self.hero = HeroResnet(num_groups,N,hero_k,drop_p,start_nf_hero)\n",
    "        \n",
    "        for i in range(num_groups): n_channels.append(start_nf*(2**i)*k)\n",
    "            \n",
    "        n_channels_hero = [start_nf_hero]\n",
    "        for i in range(num_groups): n_channels_hero.append(start_nf_hero*(2**i)*hero_k)   \n",
    "            \n",
    "        self.layer0 = conv1d(1,n_channels[0],3,1)\n",
    "        \n",
    "        groups = [nn.Sequential(*_make_group(N,n_channels[i]+n_channels_hero[i],n_channels[i+1],ResBlock,(1 if i==0 else 2),drop_p)) for i in range(num_groups)]\n",
    "\n",
    "        \n",
    "        self.groups = nn.ModuleList(groups)\n",
    "        \n",
    "        self.avg = nn.Sequential(nn.BatchNorm1d(n_channels[-1]),\n",
    "                                 nn.ReLU(inplace=True),\n",
    "                                 nn.AdaptiveAvgPool1d(1))\n",
    "        \n",
    "        self.out = create_head_1d(n_channels[-1]+n_channels_hero[-1],num_classes,ps=0.3)\n",
    "        \n",
    "    def forward(self,ts):\n",
    "        ts = ts.unsqueeze(1)\n",
    "        pt, actvns = self.hero(ts)\n",
    "        \n",
    "        x = self.layer0(ts)\n",
    "        for l,a in zip(self.groups,actvns):\n",
    "            x = l(torch.cat([x,a],dim=1))\n",
    "            \n",
    "        x = torch.cat([self.avg(x),pt],dim=1).squeeze(-1)\n",
    "        \n",
    "        return self.out(x)"
   ]
  }
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