Mixture of Autoencoders.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Expects timeseries.py in same folder as nb. \n",
    "from fastai.vision import *\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",
    "from scipy.signal import resample\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.utils.linear_assignment_ import linear_assignment\n",
    "from scipy.optimize import linear_sum_assignment\n",
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#init UCR archive helper class\n",
    "ucr = UCRArchive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Create dataloaders\n",
    "To run this notebook, you'll need the archive available here <https://www.cs.ucr.edu/~eamonn/time_series_data_2018/>\n",
    "The password to the archive is 'someone'. Place the unziped directory in the same location as this nb.\n",
    "\n",
    "The UCR Time Series Classification Archive contains 128 univariate timeseries data sets, of varying lengths and number of classes. They are presorted into training and validation sets, however the ratio between the two differs wildly from set to set. Below, I combine all the data and make my own validation set, set at 30% of the total data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "#Below are some datasets that seem to have good transfer properties:\n",
    "#CinCECGTorso, MedicalImages, TwoPatterns, InsectWingbeatSound, UWaveGestureLibraryAll, WordSynonyms, SemgHandGenderCh2\n",
    "dataset_name = \"TwoPatterns\"\n",
    "#Visualize class distribution of initial train and validation sets\n",
    "vc = ucr.category_distribution(dataset_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All input is resampled down to a length of 96\n",
    "class Resample(PreProcessor):\n",
    "    def process_one(self,item):\n",
    "        return np.concatenate([item[[0]],resample(item[1:],96)]) if len(item) > 97 else item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here we create the classification dataset. The fastai data_block api doesn't directly support what I am trying to do, namely\n",
    "#remove most of the labels AFTER creating the validation set. Thus, a bit of hackery is required. \n",
    "#Not sure about the warning, seems to be ok...\n",
    "\n",
    "src = TimeSeriesList.from_csv_list(ucr.get_csv_files(dataset_name),labelCol=0,processor=Resample())\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)\n",
    "len(data.train_ds) #We end up with a training data set consisting of 10% of the training data as well as the full validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3500"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here we create the autocoder dataset. The differences are the data is labeled with itself, and that we use \n",
    "#the full amount of training data\n",
    "\n",
    "src = TimeSeriesList.from_csv_list(ucr.get_csv_files(dataset_name),labelCol=0,processor=Resample())\n",
    "dataAE = src.split_by_idx(valIdxs)\n",
    "dataAE = dataAE.label_from_col()\n",
    "dataAE = dataAE.databunch(bs=bs,num_workers=0)\n",
    "len(dataAE.train_ds) #Should be ~10x the classification size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show a few inputs from the dataset\n",
    "data.show_batch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Define models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Helper functions, modified to 1d from fastai\n",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is the hero network, which serves as a backbone to all the other models\n",
    "class HeroConvnet(nn.Module):\n",
    "    def __init__(self, num_layers=3, start_nf=16, drop_p = 0.0):\n",
    "        super().__init__()\n",
    "        \n",
    "        layers = [nn.Sequential(conv1d(1,start_nf,3,1),nn.LeakyReLU(0.2))] #First layer is stride 1, creates initial set of filters\n",
    "        nf = start_nf\n",
    "        for d in np.linspace(drop_p/10,drop_p,num_layers): #Then num_layers stride 2 convs, doubling the number of filters each layer\n",
    "            layers.append(nn.Sequential(conv1d(nf,nf*2,3,2),nn.LeakyReLU(0.2)))\n",
    "            nf *= 2\n",
    "        \n",
    "        self.nf = nf\n",
    "        self.layers = nn.ModuleList(layers)\n",
    "        self.avg = nn.AdaptiveAvgPool1d(1)\n",
    "            \n",
    "            \n",
    "    def forward(self, x):\n",
    "        actvns = [x]\n",
    "        \n",
    "        for l in self.layers:\n",
    "            actvns.append(l(actvns[-1]))\n",
    "\n",
    "        return self.avg(actvns[-1]), actvns[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BasicClassifier(nn.Module):\n",
    "    def __init__(self,numClasses,num_layers=3,start_nf=16,drop_p=0.0):\n",
    "        super().__init__()\n",
    "        self.conv = HeroConvnet(num_layers,start_nf,drop_p=drop_p)\n",
    "        self.out = create_head_1d(self.conv.nf,numClasses,ps=0.0)\n",
    "              \n",
    "    def forward(self,ts):\n",
    "        ts = self.conv(ts.unsqueeze(1))[0].squeeze(-1)\n",
    "        return self.out(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Basic variational autoencoder with hero net serving as encoder and a few linear layers as decoder\n",
    "class TSAutoencoder(nn.Module):\n",
    "    def __init__(self,seqLen,latentDim=12,num_layers=3,start_nf=16):\n",
    "        super().__init__()\n",
    "        self.conv = HeroConvnet(num_layers,start_nf)\n",
    "        self.ls = torch.nn.Linear(self.conv.nf,latentDim)\n",
    "        self.out = create_head_1d(latentDim,seqLen,lin_ftrs=[256,512],ps=0.0)\n",
    "        \n",
    "    def getLS(self,ts):\n",
    "        ts, _ = self.conv(ts.unsqueeze(1))\n",
    "        ts = ts.squeeze(-1)\n",
    "        ls = self.ls(ts)\n",
    "        return ls\n",
    "\n",
    "    def forward(self,ts):\n",
    "        seqLen = ts.shape[1]\n",
    "        ots = ts\n",
    "        ts, _ = self.conv(ts.unsqueeze(1))\n",
    "        ts = ts.squeeze(-1)\n",
    "\n",
    "        ls = self.ls(ts)\n",
    "               \n",
    "        return self.out(ls), ots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Cluster(nn.Module):\n",
    "    def __init__(self,num_classes,num_layers,start_nf):\n",
    "        super().__init__()\n",
    "        self.conv = HeroConvnet(num_layers,start_nf)\n",
    "        self.ps = nn.Linear(self.conv.nf,num_classes)\n",
    "        \n",
    "    def forward(self,ts):\n",
    "        ts, _ = self.conv(ts.unsqueeze(1))\n",
    "        return self.ps(ts.squeeze(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MOA(nn.Module):\n",
    "    def __init__(self,num_classes,seqLen,latentDim=12,num_layers=3,start_nf=16):\n",
    "        super().__init__()\n",
    "        self.aes = nn.ModuleList([TSAutoencoder(seqLen,latentDim,6,4) for _ in range(num_classes)])\n",
    "        self.cluster = Cluster(num_classes,num_layers,start_nf)\n",
    "        \n",
    "    def getCluster(self,ts):\n",
    "        ps = self.cluster(ts)\n",
    "        ps = torch.softmax(ps,dim=1)\n",
    "        return torch.argmax(ps,dim=1)\n",
    "        \n",
    "    def forward(self, ts):\n",
    "        recon = [ae(ts)[0] for ae in self.aes]\n",
    "        ps = self.cluster(ts)\n",
    "        return ps, recon, ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The sidekick network mirrors the structure of the hero, but concats the output of each layer of the hero to the input of each \n",
    "#layer of the sidekick\n",
    "class SidekickConvnet(nn.Module):\n",
    "    def __init__(self, num_classes, num_layers=3, start_nf=16, start_nf_hero=16):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.hero = HeroConvnet(num_layers,start_nf_hero)\n",
    "        \n",
    "        layers = [nn.Sequential(conv1d(1,start_nf,3,1),nn.LeakyReLU(0.2))] \n",
    "        nf = start_nf\n",
    "        nf_hero = start_nf_hero\n",
    "        for _ in range(num_layers):\n",
    "            layers.append(nn.Sequential(conv1d(nf+nf_hero,nf*2,3,2),nn.LeakyReLU(0.2)))\n",
    "            nf *= 2\n",
    "            nf_hero *= 2\n",
    "        \n",
    "        self.layers = nn.ModuleList(layers)\n",
    "        #self.ll = conv1d(nf+nf_hero,nf+nf_hero,3,1)\n",
    "        self.avg = nn.AdaptiveAvgPool1d(1)\n",
    "        self.out = create_head_1d(nf + nf_hero,num_classes,ps=0.0)\n",
    "    \n",
    "    def forward(self,ts):\n",
    "        ts = ts.unsqueeze(1)\n",
    "        pt, actvns = self.hero(ts)\n",
    "        \n",
    "        x = self.layers[0](ts)\n",
    "        for l,a in zip(self.layers[1:],actvns):\n",
    "            x = l(torch.cat([x,a],dim=1))\n",
    "            \n",
    "        x = torch.cat([self.avg(x),pt],dim=1).squeeze(-1)\n",
    "        #x = self.ll(torch.cat([x,actvns[-1]],dim=1))\n",
    "        #x = self.avg(x).squeeze(-1)\n",
    "        return self.out(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Get Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnBase = Learner(data,BasicClassifier(data.train_ds.c,8,8,drop_p=0.0),loss_func=F.cross_entropy,metrics=[accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run one batch through the model to make sure the output size is correct\n",
    "learnBase.model(next(iter(data.train_dl))[0]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Learning rate finder\n",
    "learnBase.lr_find()\n",
    "learnBase.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 00:11 <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>1.394590</th>\n",
       "    <th>1.381884</th>\n",
       "    <th>0.265333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>1.165905</th>\n",
       "    <th>1.358307</th>\n",
       "    <th>0.358667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.941310</th>\n",
       "    <th>1.282704</th>\n",
       "    <th>0.502667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.750283</th>\n",
       "    <th>1.113496</th>\n",
       "    <th>0.600000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.600082</th>\n",
       "    <th>0.865146</th>\n",
       "    <th>0.717333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.488579</th>\n",
       "    <th>0.685421</th>\n",
       "    <th>0.738667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.403138</th>\n",
       "    <th>0.616798</th>\n",
       "    <th>0.750000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.336675</th>\n",
       "    <th>0.608999</th>\n",
       "    <th>0.745333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.284411</th>\n",
       "    <th>0.514250</th>\n",
       "    <th>0.787333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.242883</th>\n",
       "    <th>0.516855</th>\n",
       "    <th>0.790667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.209032</th>\n",
       "    <th>0.526330</th>\n",
       "    <th>0.790000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.181138</th>\n",
       "    <th>0.506886</th>\n",
       "    <th>0.800667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.157855</th>\n",
       "    <th>0.492810</th>\n",
       "    <th>0.811333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.138447</th>\n",
       "    <th>0.487405</th>\n",
       "    <th>0.816000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.121961</th>\n",
       "    <th>0.483576</th>\n",
       "    <th>0.822667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.107782</th>\n",
       "    <th>0.485154</th>\n",
       "    <th>0.819333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.095505</th>\n",
       "    <th>0.484834</th>\n",
       "    <th>0.818667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.084870</th>\n",
       "    <th>0.485612</th>\n",
       "    <th>0.818667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.075765</th>\n",
       "    <th>0.488474</th>\n",
       "    <th>0.815333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.067764</th>\n",
       "    <th>0.487717</th>\n",
       "    <th>0.817333</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learnBase.fit_one_cycle(20,1e-3,wd=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ClusterAccuracy(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([\"cluster_accuracy\"])\n",
    "        \n",
    "    def on_epoch_end(self,**kwargs):\n",
    "        self.learn.model.eval()\n",
    "        dl = torch.utils.data.DataLoader(self.learn.data.train_ds,batch_size=64,collate_fn=data_collate)\n",
    "        labels_pred, labels_true = [],[]\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for ts,labels in iter(dl):\n",
    "                ts = ts.cuda()\n",
    "                labels_pred.append(self.learn.model.getCluster(ts).cpu().detach().numpy())\n",
    "                labels_true.append(labels.numpy())\n",
    "        labels_pred = np.concatenate(labels_pred)\n",
    "        labels_true = np.concatenate(labels_true)\n",
    "        labels_true = labels_true.astype(np.int64)\n",
    "        assert labels_pred.size == labels_true.size\n",
    "        \n",
    "        D = max(labels_pred.max(), labels_true.max()) + 1\n",
    "        w = np.zeros((D, D), dtype=np.int64)\n",
    "        for i in range(labels_pred.size):\n",
    "            w[labels_pred[i], labels_true[i]] += 1\n",
    "        ind = linear_assignment(w.max() - w)\n",
    "        #self.metric = torch.tensor(sum([w[i, j] for i, j in ind]) * 1.0 / labels_pred.size)\n",
    "        self.learn.recorder.add_metrics([torch.tensor(sum([w[i, j] for i, j in ind]) * 1.0 / labels_pred.size)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AELoss(pred,target):\n",
    "    return F.mse_loss(pred[0],pred[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MOALoss(torch.nn.Module):\n",
    "    def forward(self,p,target):\n",
    "        p,recon, ts = p\n",
    "        p = torch.softmax(p,dim=1)\n",
    "        rerrs = torch.stack([torch.exp(-0.5 * ((ts-r)**2).sum(dim=1)) for r in recon],dim=1)\n",
    "        berr = (p * rerrs).sum(dim=1)\n",
    "        berr = torch.log(berr)\n",
    "        return -berr.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnAE = Learner(dataAE,MOA(data.train_ds.c,len(data.train_ds[0][0].data),latentDim=12,num_layers=8,start_nf=8),\n",
    "                  loss_func=MOALoss(),callback_fns=[ClusterAccuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 4])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learnAE.model(next(iter(data.train_dl))[0])[0].size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnAEInit = Learner(dataAE,TSAutoencoder(len(data.train_ds[0][0].data),num_layers=8,start_nf=8),loss_func=AELoss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnAEInit.fit_one_cycle(20,1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnAEInit.model.eval()\n",
    "out = []\n",
    "dl = torch.utils.data.DataLoader(dataAE.train_ds,batch_size=64,collate_fn=data_collate)\n",
    "with torch.no_grad():\n",
    "    for x,y in iter(dl):\n",
    "        ls = learnAEInit.model.getLS(x.cuda())\n",
    "        out.append(ls)\n",
    "out = torch.cat(out).cpu().detach().numpy()\n",
    "\n",
    "kmeans = KMeans(n_clusters=data.train_ds.c,n_init=20)\n",
    "kmeans.fit_predict(out)\n",
    "\n",
    "def assignClusters(x):\n",
    "    x = resample(x[1:],96)\n",
    "    ls = learnAEInit.model.getLS(torch.tensor(x,dtype=torch.float).unsqueeze(0).cuda())\n",
    "    pred =  kmeans.predict(ls.cpu().detach().numpy())[0]\n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "src = TimeSeriesList.from_csv_list(ucr.get_csv_files(dataset_name),labelCol=0,processor=Resample())\n",
    "dataAEInit = src.filter_out_idx(valIdxs)\n",
    "dataAEInit = dataAEInit.no_split()\n",
    "dataAEInit = dataAEInit.label_from_func(assignClusters)\n",
    "dataAEInit = dataAEInit.databunch(bs=bs,num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(dataAEInit.train_ds.y.items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnClusterInit = Learner(data,learnAE.model.cluster,loss_func=F.cross_entropy,metrics=accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 00:10 <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>1.364763</th>\n",
       "    <th>1.349353</th>\n",
       "    <th>0.374667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>1.305988</th>\n",
       "    <th>1.255152</th>\n",
       "    <th>0.454000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.192828</th>\n",
       "    <th>1.130247</th>\n",
       "    <th>0.499333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.039001</th>\n",
       "    <th>0.968712</th>\n",
       "    <th>0.639333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.861988</th>\n",
       "    <th>0.738324</th>\n",
       "    <th>0.743333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.751021</th>\n",
       "    <th>0.685751</th>\n",
       "    <th>0.767333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.641420</th>\n",
       "    <th>0.745737</th>\n",
       "    <th>0.770000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.553338</th>\n",
       "    <th>0.407121</th>\n",
       "    <th>0.856667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.491236</th>\n",
       "    <th>0.469505</th>\n",
       "    <th>0.855333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.424413</th>\n",
       "    <th>0.403795</th>\n",
       "    <th>0.869333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.367818</th>\n",
       "    <th>0.341965</th>\n",
       "    <th>0.882000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.318886</th>\n",
       "    <th>0.407321</th>\n",
       "    <th>0.878667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.278135</th>\n",
       "    <th>0.311053</th>\n",
       "    <th>0.898667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.243151</th>\n",
       "    <th>0.271207</th>\n",
       "    <th>0.911333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.213435</th>\n",
       "    <th>0.256447</th>\n",
       "    <th>0.906667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.188015</th>\n",
       "    <th>0.249709</th>\n",
       "    <th>0.909333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.166136</th>\n",
       "    <th>0.246684</th>\n",
       "    <th>0.909333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.147197</th>\n",
       "    <th>0.245438</th>\n",
       "    <th>0.910667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.130727</th>\n",
       "    <th>0.245039</th>\n",
       "    <th>0.910667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.116344</th>\n",
       "    <th>0.244980</th>\n",
       "    <th>0.910667</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learnClusterInit.fit_one_cycle(20,1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'assignClusters' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-53-3a31523edaf6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mdataExpert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter_out_idx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalIdxs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mdataExpert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataExpert\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mdataExpert\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter_by_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0massignClusters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mdataExpert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataExpert\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel_from_self\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mdataExpert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataExpert\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatabunch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\fastai\\data_block.py\u001b[0m in \u001b[0;36mfilter_by_func\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m    143\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfilter_by_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mCallable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m->\u001b[0m\u001b[1;34m'ItemList'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m         \u001b[1;34m\"Only keep elements for which `func` returns `True`.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 145\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mo\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mo\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[0m\u001b[0;32m    146\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\fastai\\data_block.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    143\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfilter_by_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mCallable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m->\u001b[0m\u001b[1;34m'ItemList'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m         \u001b[1;34m\"Only keep elements for which `func` returns `True`.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 145\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mo\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mo\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[0m\u001b[0;32m    146\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-53-3a31523edaf6>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mdataExpert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter_out_idx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalIdxs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mdataExpert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataExpert\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mdataExpert\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter_by_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0massignClusters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mdataExpert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataExpert\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel_from_self\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mdataExpert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataExpert\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatabunch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'assignClusters' is not defined"
     ]
    }
   ],
   "source": [
    "for c,m in enumerate(learnAE.model.aes):\n",
    "    src = TimeSeriesList.from_csv_list(ucr.get_csv_files(dataset_name),labelCol=0,processor=Resample())\n",
    "    dataExpert = src.filter_out_idx(valIdxs)\n",
    "    dataExpert = dataExpert.no_split()\n",
    "    dataExpert.train.filter_by_func(lambda x: assignClusters(x) == c)\n",
    "    dataExpert = dataExpert.label_from_self()\n",
    "    dataExpert = dataExpert.databunch(bs=4,num_workers=0)\n",
    "    \n",
    "    learnExpert = Learner(dataExpert,m,loss_func=AELoss)\n",
    "    learnExpert.fit_one_cycle(5,1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "Total time: 00:06 <p><table style='width:300px; margin-bottom:10px'>\n",
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       "    <th>5</th>\n",
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       "    <th>6</th>\n",
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       "    <th>19</th>\n",
       "    <th>0.571697</th>\n",
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       "    <th>20</th>\n",
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       "    <th>0.936323</th>\n",
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       "    <th>11</th>\n",
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       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.783582</th>\n",
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       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.753285</th>\n",
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       "    <th>14</th>\n",
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       "    <th>15</th>\n",
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       "    <th>0.685040</th>\n",
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       "    <th>17</th>\n",
       "    <th>0.673156</th>\n",
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       "    <th>18</th>\n",
       "    <th>0.649423</th>\n",
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       "    <th>0.637485</th>\n",
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       "Total time: 00:05 <p><table style='width:300px; margin-bottom:10px'>\n",
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       "    <th>valid_loss</th>\n",
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       "    <th>0.893486</th>\n",
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       "    <th>4</th>\n",
       "    <th>0.894065</th>\n",
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       "    <th>5</th>\n",
       "    <th>0.912813</th>\n",
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       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.924245</th>\n",
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       "    <th>7</th>\n",
       "    <th>0.900583</th>\n",
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       "    <th>8</th>\n",
       "    <th>0.878205</th>\n",
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       "    <th>9</th>\n",
       "    <th>0.850110</th>\n",
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       "    <th>10</th>\n",
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       "    <th>11</th>\n",
       "    <th>0.816960</th>\n",
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       "    <th>12</th>\n",
       "    <th>0.791663</th>\n",
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       "    <th>13</th>\n",
       "    <th>0.762023</th>\n",
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       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.720768</th>\n",
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       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.690687</th>\n",
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       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.668367</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.644947</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.627358</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.609597</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.608222</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Total time: 00:06 <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>0.990740</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.912667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.878397</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.922723</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.928799</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.914603</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.887885</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.835104</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.790682</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.754038</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.730802</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.688566</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.653216</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.634660</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.634656</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.614430</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.585690</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.572084</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.564627</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.558139</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c,m in enumerate(learnAE.model.aes):\n",
    "    d = data.train_ds.x.items.copy()\n",
    "    src = TimeSeriesList.from_numpy(d,labeled=0)\n",
    "    src = src.filter_by_func(lambda x: int(x[0]) - 1 == c)\n",
    "    dataExpert = src.no_split()\n",
    "    dataExpert = dataExpert.label_from_self()\n",
    "    dataExpert = dataExpert.databunch(bs=4,num_workers=0)\n",
    "    \n",
    "    learnExpert = Learner(dataExpert,m,loss_func=AELoss)\n",
    "    learnExpert.fit_one_cycle(20,1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(learnAE.model.state_dict(),\"init.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnAE.model.load_state_dict(torch.load(\"init.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnAE.lr_find()\n",
    "learnAE.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnAE = Learner(dataAE,learnAE.model,loss_func=MOALoss(),callback_fns=[ClusterAccuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 01:04 <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>cluster_accuracy</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>15.248502</th>\n",
       "    <th>14.388304</th>\n",
       "    <th>0.856571</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>13.828208</th>\n",
       "    <th>12.144245</th>\n",
       "    <th>0.794286</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>12.888902</th>\n",
       "    <th>12.307667</th>\n",
       "    <th>0.419429</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>12.328094</th>\n",
       "    <th>10.891732</th>\n",
       "    <th>0.448286</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>12.396356</th>\n",
       "    <th>11.548126</th>\n",
       "    <th>0.328857</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>11.584217</th>\n",
       "    <th>10.423244</th>\n",
       "    <th>0.332571</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>10.642795</th>\n",
       "    <th>9.464816</th>\n",
       "    <th>0.338000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>9.881576</th>\n",
       "    <th>9.058051</th>\n",
       "    <th>0.339429</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>9.465524</th>\n",
       "    <th>8.941978</th>\n",
       "    <th>0.338857</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>9.271304</th>\n",
       "    <th>8.921819</th>\n",
       "    <th>0.338571</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learnAE.fit_one_cycle(10,1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = next(iter(dataAE.train_dl))\n",
    "learnAE.model.eval()\n",
    "ps,recon,ts = learnAE.model(x)\n",
    "ps = torch.softmax(ps,dim=1)\n",
    "ps = torch.argmax(ps,dim=1)\n",
    "fig, axes = plt.subplots(3,3,figsize=(16,14))\n",
    "for i,ax in enumerate(axes.flatten()):\n",
    "    pred = ps[i].item()\n",
    "    ax.plot(x[i])\n",
    "    ax.plot(recon[pred][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Optional: Visualize latent space\n",
    "learnAE.model.eval()\n",
    "out = []\n",
    "classes = []\n",
    "with torch.no_grad():\n",
    "    for x,y in iter(data.valid_dl):\n",
    "        ls = learnAE.model.mean(learnAE.model.conv(x.unsqueeze(1))[0].squeeze(-1))\n",
    "        out.append(ls)\n",
    "        classes.append(y)\n",
    "out = torch.cat(out).cpu().detach().numpy()\n",
    "classes = torch.cat(classes).cpu().detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "f = pca.fit(out.T)\n",
    "f = f.components_.T\n",
    "plt.subplots(figsize=(20,15))\n",
    "style = classes if data.train_ds.c < 8 else None\n",
    "sns.scatterplot(x=f[:,0],y=f[:,1],hue=classes,palette=\"Dark2\",style=style,legend=\"full\",s=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2)\n",
    "f = tsne.fit_transform(out)\n",
    "plt.subplots(figsize=(20,15))\n",
    "sns.scatterplot(x=f[:,0],y=f[:,1],hue=classes,palette=\"Dark2\",style=style,legend=\"full\",s=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Traditional transfer\n",
    "Here we train a classifier by just adding a classifier head onto the encoder from the previous step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnDT = Learner(data,BasicClassifier(data.train_ds.c,8,8),loss_func=F.cross_entropy,metrics=[accuracy],\n",
    "                 callback_fns=BnFreeze,bn_wd=False,train_bn=False)\n",
    "learnDT.split([*learnDT.model.conv.layers,learnDT.model.conv.avg,learnDT.model.out])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the parameters from the encoder\n",
    "learnDT.model.conv.load_state_dict(learnAE.model.cluster.conv.state_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Freeze up to the classifier layer\n",
    "learnDT.freeze_to(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 00:03 <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>2.125969</th>\n",
       "    <th>2.241078</th>\n",
       "    <th>0.546970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>1.732726</th>\n",
       "    <th>1.864955</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.436036</th>\n",
       "    <th>1.431720</th>\n",
       "    <th>0.586364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.262841</th>\n",
       "    <th>1.225137</th>\n",
       "    <th>0.600000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.133190</th>\n",
       "    <th>1.203084</th>\n",
       "    <th>0.595455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.040897</th>\n",
       "    <th>1.210992</th>\n",
       "    <th>0.600000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.979761</th>\n",
       "    <th>1.233358</th>\n",
       "    <th>0.596970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.924566</th>\n",
       "    <th>1.190132</th>\n",
       "    <th>0.593939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.878520</th>\n",
       "    <th>1.147774</th>\n",
       "    <th>0.592424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.834077</th>\n",
       "    <th>1.180569</th>\n",
       "    <th>0.596970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.798154</th>\n",
       "    <th>1.172305</th>\n",
       "    <th>0.603030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.762280</th>\n",
       "    <th>1.196130</th>\n",
       "    <th>0.593939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.730184</th>\n",
       "    <th>1.204204</th>\n",
       "    <th>0.592424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.699043</th>\n",
       "    <th>1.180716</th>\n",
       "    <th>0.590909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.675410</th>\n",
       "    <th>1.167009</th>\n",
       "    <th>0.596970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.651541</th>\n",
       "    <th>1.164799</th>\n",
       "    <th>0.593939</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.627922</th>\n",
       "    <th>1.172497</th>\n",
       "    <th>0.595455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.608236</th>\n",
       "    <th>1.179907</th>\n",
       "    <th>0.595455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.593860</th>\n",
       "    <th>1.181173</th>\n",
       "    <th>0.595455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.577523</th>\n",
       "    <th>1.179439</th>\n",
       "    <th>0.596970</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learnDT.fit_one_cycle(20,1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Fine tune\n",
    "learnDT.unfreeze()\n",
    "learnDT.fit_one_cycle(100,1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6: Sidekick network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 48.00 MiB (GPU 0; 8.00 GiB total capacity; 792.16 MiB already allocated; 0 bytes free; 68.34 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-61-69a6b7ac282d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m learnSidekick = Learner(data,SidekickConvnet(data.train_ds.c,8,8,8), loss_func=F.cross_entropy,metrics=[accuracy],\n\u001b[1;32m----> 2\u001b[1;33m                         callback_fns=BnFreeze,bn_wd=False,train_bn=False)\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mlearnSidekick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlearnSidekick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhero\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlearnSidekick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlearnSidekick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<string>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, model, opt_func, loss_func, metrics, true_wd, bn_wd, wd, train_bn, path, model_dir, callback_fns, callbacks, layer_groups)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\fastai\\basic_train.py\u001b[0m in \u001b[0;36m__post_init__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mifnone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m         \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexist_ok\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[1;32m--> 153\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    154\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mifnone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlistify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\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;36mto\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    379\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    380\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 381\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    382\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    383\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mregister_backward_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhook\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\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\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[0mchildren\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--> 187\u001b[1;33m             \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parameters\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~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\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[0mchildren\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--> 187\u001b[1;33m             \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parameters\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~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\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[0mchildren\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--> 187\u001b[1;33m             \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parameters\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~\\Anaconda3\\envs\\fastai\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m    191\u001b[0m                 \u001b[1;31m# Tensors stored in modules are graph leaves, and we don't\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m                 \u001b[1;31m# want to create copy nodes, so we have to unpack the data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m                 \u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_grad\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m                     \u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\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;36mconvert\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m    377\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    378\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 379\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    380\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    381\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 48.00 MiB (GPU 0; 8.00 GiB total capacity; 792.16 MiB already allocated; 0 bytes free; 68.34 MiB cached)"
     ]
    }
   ],
   "source": [
    "learnSidekick = Learner(data,SidekickConvnet(data.train_ds.c,8,8,8), loss_func=F.cross_entropy,metrics=[accuracy],\n",
    "                        callback_fns=BnFreeze,bn_wd=False,train_bn=False)\n",
    "learnSidekick.split([learnSidekick.model.hero,learnSidekick.model.layers[0],learnSidekick.model.out])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([30, 11])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learnSidekick.model(next(iter(data.train_dl))[0]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnSidekick.model.hero.load_state_dict(learnAE.model.cluster.conv.state_dict())\n",
    "learnSidekick.freeze_to(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnSidekick.lr_find()\n",
    "learnSidekick.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>1.902544</th>\n",
       "    <th>2.168012</th>\n",
       "    <th>0.551515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>1.415446</th>\n",
       "    <th>1.687520</th>\n",
       "    <th>0.587879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.184208</th>\n",
       "    <th>1.270837</th>\n",
       "    <th>0.592424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.037721</th>\n",
       "    <th>1.172526</th>\n",
       "    <th>0.596970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.923211</th>\n",
       "    <th>1.180639</th>\n",
       "    <th>0.607576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.821377</th>\n",
       "    <th>1.187550</th>\n",
       "    <th>0.619697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.731106</th>\n",
       "    <th>1.331244</th>\n",
       "    <th>0.606061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.652346</th>\n",
       "    <th>1.366042</th>\n",
       "    <th>0.600000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.594465</th>\n",
       "    <th>1.419742</th>\n",
       "    <th>0.609091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.531255</th>\n",
       "    <th>1.473732</th>\n",
       "    <th>0.565152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.474514</th>\n",
       "    <th>1.444291</th>\n",
       "    <th>0.612121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.426931</th>\n",
       "    <th>1.423246</th>\n",
       "    <th>0.619697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.382930</th>\n",
       "    <th>1.517657</th>\n",
       "    <th>0.600000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.342972</th>\n",
       "    <th>1.493258</th>\n",
       "    <th>0.589394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.308569</th>\n",
       "    <th>1.499738</th>\n",
       "    <th>0.592424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.276703</th>\n",
       "    <th>1.544050</th>\n",
       "    <th>0.580303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.249370</th>\n",
       "    <th>1.547271</th>\n",
       "    <th>0.583333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.225813</th>\n",
       "    <th>1.543032</th>\n",
       "    <th>0.586364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.205020</th>\n",
       "    <th>1.545226</th>\n",
       "    <th>0.586364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.185759</th>\n",
       "    <th>1.545457</th>\n",
       "    <th>0.584848</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
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   ],
   "source": [
    "learnSidekick.fit_one_cycle(20,1e-3,wd=0.2)"
   ]
  }
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