3.7 Train VGG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "import cv2                         # To read and manipulate images\n",
    "import matplotlib.pyplot as plt    # Python 2D plotting library\n",
    "%matplotlib inline  \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "from time import time\n",
    "from PIL import Image\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import copy\n",
    "from torch.autograd import Variable\n",
    "import torch.nn.functional as F\n",
    "import pickle\n",
    "from cnn_finetune import make_model\n",
    "from time import sleep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TRAIN_PATH = 'data/CAX_Superhero_Train/'\n",
    "TEST_PATH = 'data/CAX_Superhero_Test/'\n",
    "superhero_dic = {'Ant-Man':'ant_man', 'Aquaman':'aqua_man', 'Avengers':'avengers', 'Batman':'bat_man', \n",
    "                 'Black Panther':'black_panther', 'Captain America':'captain_america', 'Catwoman':'cat_woman',\n",
    "                 'Ghost Rider':'ghostrider', 'Hulk':'hulk', 'Iron Man':'iron_man', 'Spiderman':'spider_man', \n",
    "                 'Superman':'super_man'}\n",
    "\n",
    "labelencoder_dic = {0: 'ant_man', 1: 'aqua_man', 2: 'avengers', 3: 'bat_man', 4: 'black_panther', 5: 'captain_america', \n",
    "                    6: 'cat_woman', 7: 'ghostrider', 8: 'hulk', 9: 'iron_man', 10: 'spider_man', 11: 'super_man'}\n",
    "\n",
    "IMG_HEIGHT = 224\n",
    "IMG_WIDTH = 224\n",
    "IMG_SIZE = 160\n",
    "\n",
    "train_df = pd.read_csv('data/train_df.csv')\n",
    "test_df = pd.read_csv('data/test_df.csv')\n",
    "\n",
    "torch.random.manual_seed(10);\n",
    "torch.cuda.manual_seed(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dataset for PyTorch\n",
    "class SuperheroesDataset(Dataset):\n",
    "    def __init__(self, x, y=None, transform=None):\n",
    "        self.transform = transform\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        img = Image.open(self.x[idx])\n",
    "        img = img.convert('RGB')\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)           \n",
    "        label = self.y[idx]\n",
    "        out = (img, label)\n",
    "        return out\n",
    "\n",
    "#Train model\n",
    "def train_model(model, criterion, optimizer, scheduler=None, num_epochs=2):\n",
    "    since = time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "    best_epoch = 0\n",
    "    loss_log = {'train':{}, 'val':{}}\n",
    "    acc_log = {'train':{}, 'val':{}}\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()\n",
    "                model.train(True)  # Set model to training mode\n",
    "            else:\n",
    "                model.train(False)  # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for data in dataloaders[phase]:\n",
    "                # get the inputs\n",
    "                inputs, labels = data\n",
    "\n",
    "                # wrap them in Variable\n",
    "                if use_gpu:\n",
    "                    inputs = Variable(inputs.cuda())\n",
    "                    labels = Variable(labels.cuda())\n",
    "                else:\n",
    "                    inputs, labels = Variable(inputs), Variable(labels)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                outputs = model(inputs)\n",
    "                _, preds = torch.max(outputs.data, 1)\n",
    "                loss = criterion(outputs, labels)\n",
    "\n",
    "                # backward + optimize only if in training phase\n",
    "                if phase == 'train':\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "\n",
    "                # statistics\n",
    "                running_loss += loss.data[0] * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "            \n",
    "            loss_log[phase][epoch] = epoch_loss\n",
    "            acc_log[phase][epoch] = epoch_acc \n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "                best_epoch = epoch\n",
    "        print('lr: ',optimizer.param_groups[0]['lr'])\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}, epoch {:d}'.format(best_acc, best_epoch))\n",
    "    \n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    \n",
    "    # plot graphs\n",
    "    for phase in loss_log:\n",
    "        lists = sorted(loss_log[phase].items()) # sorted by key, return a list of tuples\n",
    "        x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "        plt.figure(1)\n",
    "        plt.plot(x,y)\n",
    "    plt.legend(['train', 'val'])\n",
    "    plt.title('Loss')\n",
    "    \n",
    "    for phase in acc_log:\n",
    "        lists = sorted(acc_log[phase].items()) # sorted by key, return a list of tuples\n",
    "        x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "        plt.figure(2)\n",
    "        plt.plot(x,y)\n",
    "    plt.legend(['train', 'val'])\n",
    "    plt.title('Accuracy')\n",
    "    \n",
    "    return model, loss_log, acc_log\n",
    "\n",
    "# This is a little utility that we'll use to reset the model if we want to re-initialize all our parameters\n",
    "def reset(m):\n",
    "    if hasattr(m, 'reset_parameters'):\n",
    "        m.reset_parameters()    \n",
    "        \n",
    "def predict(test_loader, model):\n",
    "    model.eval()       #Sets the module in evaluation mode\n",
    "    predictions = []\n",
    "    \n",
    "    for i, data in enumerate(tqdm(test_loader)):\n",
    "        inputs, _ = data\n",
    "        if use_gpu:\n",
    "            inputs = Variable(inputs.cuda())\n",
    "        else:\n",
    "            inputs = Variable(inputs)\n",
    "            \n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs.data, 1)\n",
    "        predictions.append(preds[0])\n",
    "        sleep(0.01)\n",
    "    return predictions\n",
    "\n",
    "def submit_prediction(test_load, model, path):\n",
    "    predictions = predict(test_load, model)\n",
    "    d = {'filename': test_df['img_name'], 'Superhero': predictions}\n",
    "    predict_df = pd.DataFrame(d)\n",
    "    predict_df = predict_df[['filename', 'Superhero']]\n",
    "    predict_df['filename'] = predict_df['filename'].map(lambda x: str(x)[:-4])\n",
    "    predict_df.replace({\"Superhero\": labelencoder_dic}, inplace=True)\n",
    "\n",
    "    submission = pd.read_csv('data/Superhero_3375_SampleSubmission.csv')\n",
    "\n",
    "    # find filename in predict and insert superhero in submission\n",
    "    for i in tqdm(range(submission.shape[0])):\n",
    "        filename = submission['filename'].iloc[i]\n",
    "        submission['Superhero'].iloc[i] = predict_df['Superhero'][predict_df['filename'] == filename].values[0]\n",
    "\n",
    "    submission.to_csv(path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transform_data = transforms.Compose([transforms.Resize(IMG_SIZE),transforms.CenterCrop(IMG_SIZE),transforms.ToTensor()])\n",
    "\n",
    "transform_train = transforms.Compose([transforms.Resize(IMG_SIZE),\n",
    "                                     transforms.CenterCrop(IMG_SIZE),\n",
    "                                     #transforms.RandomHorizontalFlip(),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "                                    ])\n",
    "transform_test = transforms.Compose([transforms.Resize(IMG_SIZE),\n",
    "                                     transforms.CenterCrop(IMG_SIZE),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "                                    ])\n",
    "\n",
    "use_gpu = torch.cuda.is_available()\n",
    "\n",
    "labelencoder = LabelEncoder()\n",
    "\n",
    "x_train_df = train_df['img_path'].values\n",
    "y_train_df = train_df['superhero'].values\n",
    "y_train_df = labelencoder.fit_transform(y_train_df)\n",
    "x_test_df = test_df['img_path'].values\n",
    "\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(x_train_df, y_train_df, test_size=0.2, random_state=42, \n",
    "                                                      shuffle=True, stratify=y_train_df)\n",
    "x_test = x_test_df\n",
    "y_test = np.zeros_like(x_test)\n",
    "\n",
    "batch_size = 32\n",
    "train_set = SuperheroesDataset(x_train, y_train, transform_train)\n",
    "train_load = DataLoader(train_set,batch_size=batch_size, pin_memory=True)\n",
    "valid_set = SuperheroesDataset(x_valid, y_valid, transform_test)\n",
    "valid_load = DataLoader(valid_set,batch_size=batch_size, pin_memory=True)\n",
    "test_set = SuperheroesDataset(x_test, y_test, transform_test)\n",
    "test_load = DataLoader(test_set,batch_size=1, pin_memory=True)\n",
    "\n",
    "image_datasets = {'train':train_set, 'val':valid_set}\n",
    "dataloaders    = {'train':train_load, 'val':valid_load}\n",
    "dataset_sizes  = {'train':len(train_set), 'val':len(valid_set)}\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train model\n",
    "def train_model_batch(model, criterion, optimizer, scheduler=None, num_epochs=2, batch_accum=1):\n",
    "    since = time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "    best_epoch = 0\n",
    "    loss_log = {'train':{}, 'val':{}}\n",
    "    acc_log = {'train':{}, 'val':{}}\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()\n",
    "                model.train(True)  # Set model to training mode\n",
    "            else:\n",
    "                model.train(False)  # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "            loss_mini_batch = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for i, data in enumerate(dataloaders[phase]):\n",
    "\n",
    "                inputs, labels = data\n",
    "\n",
    "                inputs = Variable(inputs.cuda())\n",
    "                labels = Variable(labels.cuda())\n",
    "\n",
    "                outputs = model(inputs)\n",
    "                _, preds = torch.max(outputs.data, 1)\n",
    "                loss = criterion(outputs, labels)                \n",
    "\n",
    "                if phase == 'train':\n",
    "                    loss.backward()\n",
    "\n",
    "                running_loss += loss.data[0]*inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "\n",
    "                if (i+1) % batch_accum == 0:\n",
    "                    optimizer.step()\n",
    "                    optimizer.zero_grad()\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "            \n",
    "            loss_log[phase][epoch] = epoch_loss\n",
    "            acc_log[phase][epoch] = epoch_acc \n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "                best_epoch = epoch\n",
    "        print('lr: ',optimizer.param_groups[0]['lr'])\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}, epoch {:d}'.format(best_acc, best_epoch))\n",
    "    \n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    \n",
    "    # plot graphs\n",
    "    for phase in loss_log:\n",
    "        lists = sorted(loss_log[phase].items()) # sorted by key, return a list of tuples\n",
    "        x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "        plt.figure(1)\n",
    "        plt.plot(x,y)\n",
    "    plt.legend(['train', 'val'])\n",
    "    plt.title('Loss')\n",
    "    \n",
    "    for phase in acc_log:\n",
    "        lists = sorted(acc_log[phase].items()) # sorted by key, return a list of tuples\n",
    "        x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "        plt.figure(2)\n",
    "        plt.plot(x,y)\n",
    "    plt.legend(['train', 'val'])\n",
    "    plt.title('Accuracy')\n",
    "    \n",
    "    return model, loss_log, acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ft = make_model('vgg11_bn', num_classes=12, pretrained=True, input_size=(160, 160))\n",
    "model_ft.cuda();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/29\n",
      "----------\n",
      "train Loss: 4.9379 Acc: 0.1473\n",
      "val Loss: 2.2854 Acc: 0.2254\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 1/29\n",
      "----------\n",
      "train Loss: 2.2835 Acc: 0.2154\n",
      "val Loss: 2.2128 Acc: 0.2447\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 2/29\n",
      "----------\n",
      "train Loss: 2.2188 Acc: 0.2386\n",
      "val Loss: 2.1560 Acc: 0.2576\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 3/29\n",
      "----------\n",
      "train Loss: 2.1761 Acc: 0.2586\n",
      "val Loss: 2.2142 Acc: 0.2429\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 4/29\n",
      "----------\n",
      "train Loss: 2.1485 Acc: 0.2711\n",
      "val Loss: 2.1180 Acc: 0.2668\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 5/29\n",
      "----------\n",
      "train Loss: 2.1116 Acc: 0.2740\n",
      "val Loss: 2.1114 Acc: 0.2613\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 6/29\n",
      "----------\n",
      "train Loss: 2.1050 Acc: 0.2745\n",
      "val Loss: 2.0420 Acc: 0.2640\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 7/29\n",
      "----------\n",
      "train Loss: 2.0694 Acc: 0.2736\n",
      "val Loss: 2.0399 Acc: 0.2695\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 8/29\n",
      "----------\n",
      "train Loss: 2.0343 Acc: 0.2846\n",
      "val Loss: 2.0256 Acc: 0.2815\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 9/29\n",
      "----------\n",
      "train Loss: 2.0390 Acc: 0.2734\n",
      "val Loss: 2.0547 Acc: 0.2787\n",
      "lr:  0.001\n",
      "\n",
      "Epoch 10/29\n",
      "----------\n",
      "train Loss: 1.9898 Acc: 0.3012\n",
      "val Loss: 1.9322 Acc: 0.3036\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 11/29\n",
      "----------\n",
      "train Loss: 1.9429 Acc: 0.3040\n",
      "val Loss: 1.8845 Acc: 0.3183\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 12/29\n",
      "----------\n",
      "train Loss: 1.9144 Acc: 0.3143\n",
      "val Loss: 1.8716 Acc: 0.3441\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 13/29\n",
      "----------\n",
      "train Loss: 1.9089 Acc: 0.3173\n",
      "val Loss: 1.8663 Acc: 0.3321\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 14/29\n",
      "----------\n",
      "train Loss: 1.9001 Acc: 0.3219\n",
      "val Loss: 1.8546 Acc: 0.3459\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 15/29\n",
      "----------\n",
      "train Loss: 1.8680 Acc: 0.3325\n",
      "val Loss: 1.8322 Acc: 0.3579\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 16/29\n",
      "----------\n",
      "train Loss: 1.8705 Acc: 0.3189\n",
      "val Loss: 1.8305 Acc: 0.3560\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 17/29\n",
      "----------\n",
      "train Loss: 1.8599 Acc: 0.3297\n",
      "val Loss: 1.8209 Acc: 0.3477\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 18/29\n",
      "----------\n",
      "train Loss: 1.8427 Acc: 0.3389\n",
      "val Loss: 1.8261 Acc: 0.3441\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 19/29\n",
      "----------\n",
      "train Loss: 1.8367 Acc: 0.3329\n",
      "val Loss: 1.8071 Acc: 0.3652\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 20/29\n",
      "----------\n",
      "train Loss: 1.8413 Acc: 0.3500\n",
      "val Loss: 1.8055 Acc: 0.3772\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Epoch 21/29\n",
      "----------\n",
      "train Loss: 1.8168 Acc: 0.3438\n",
      "val Loss: 1.7955 Acc: 0.3707\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Epoch 22/29\n",
      "----------\n",
      "train Loss: 1.8097 Acc: 0.3543\n",
      "val Loss: 1.7925 Acc: 0.3772\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Epoch 23/29\n",
      "----------\n",
      "train Loss: 1.8112 Acc: 0.3477\n",
      "val Loss: 1.7884 Acc: 0.3753\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Epoch 24/29\n",
      "----------\n",
      "train Loss: 1.8114 Acc: 0.3433\n",
      "val Loss: 1.7878 Acc: 0.3753\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Epoch 25/29\n",
      "----------\n",
      "train Loss: 1.8150 Acc: 0.3426\n",
      "val Loss: 1.7865 Acc: 0.3772\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Epoch 26/29\n",
      "----------\n",
      "train Loss: 1.8136 Acc: 0.3511\n",
      "val Loss: 1.7869 Acc: 0.3753\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Epoch 27/29\n",
      "----------\n",
      "train Loss: 1.8130 Acc: 0.3454\n",
      "val Loss: 1.7850 Acc: 0.3753\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Epoch 28/29\n",
      "----------\n",
      "train Loss: 1.8140 Acc: 0.3532\n",
      "val Loss: 1.7835 Acc: 0.3772\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Epoch 29/29\n",
      "----------\n",
      "train Loss: 1.8109 Acc: 0.3543\n",
      "val Loss: 1.7814 Acc: 0.3753\n",
      "lr:  1.0000000000000003e-05\n",
      "\n",
      "Training complete in 15m 21s\n",
      "Best val Acc: 0.377185, epoch 20\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20eef8c85f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20eef686f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg11_bn, batch_size=128, lr=0.001, pretrained=True\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.001)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)\n",
    "model_ft.apply(reset)\n",
    "model_vgg11, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=30, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/4\n",
      "----------\n",
      "train Loss: 2.1881 Acc: 0.2347\n",
      "val Loss: 3.0245 Acc: 0.2631\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/4\n",
      "----------\n",
      "train Loss: 1.8443 Acc: 0.3757\n",
      "val Loss: 2.1682 Acc: 0.3643\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/4\n",
      "----------\n",
      "train Loss: 1.6470 Acc: 0.4542\n",
      "val Loss: 1.6393 Acc: 0.4443\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/4\n",
      "----------\n",
      "train Loss: 1.5337 Acc: 0.4871\n",
      "val Loss: 1.5525 Acc: 0.5152\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/4\n",
      "----------\n",
      "train Loss: 1.4338 Acc: 0.5274\n",
      "val Loss: 1.7490 Acc: 0.5069\n",
      "lr:  0.0001\n",
      "\n",
      "Training complete in 2m 33s\n",
      "Best val Acc: 0.515179, epoch 3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20eaa77a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSikP8v96q4spTd8gZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ef6491cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg11_bn, batch_size=128, lr=0.001, pretrained=True\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)\n",
    "model_ft.apply(reset)\n",
    "model_vgg11, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=5, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/19\n",
      "----------\n",
      "train Loss: 2.1874 Acc: 0.2372\n",
      "val Loss: 2.9658 Acc: 0.2843\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/19\n",
      "----------\n",
      "train Loss: 1.8413 Acc: 0.3879\n",
      "val Loss: 1.9023 Acc: 0.4140\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/19\n",
      "----------\n",
      "train Loss: 1.6230 Acc: 0.4655\n",
      "val Loss: 1.5733 Acc: 0.4931\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/19\n",
      "----------\n",
      "train Loss: 1.5148 Acc: 0.4956\n",
      "val Loss: 1.6318 Acc: 0.5051\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/19\n",
      "----------\n",
      "train Loss: 1.4087 Acc: 0.5387\n",
      "val Loss: 1.9594 Acc: 0.4600\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/19\n",
      "----------\n",
      "train Loss: 1.3215 Acc: 0.5695\n",
      "val Loss: 1.3850 Acc: 0.5538\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/19\n",
      "----------\n",
      "train Loss: 1.2732 Acc: 0.5792\n",
      "val Loss: 1.6089 Acc: 0.5198\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/19\n",
      "----------\n",
      "train Loss: 1.2088 Acc: 0.6012\n",
      "val Loss: 1.3072 Acc: 0.5842\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/19\n",
      "----------\n",
      "train Loss: 1.0747 Acc: 0.6482\n",
      "val Loss: 1.7327 Acc: 0.5455\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/19\n",
      "----------\n",
      "train Loss: 0.9766 Acc: 0.6896\n",
      "val Loss: 1.3766 Acc: 0.5695\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 10/19\n",
      "----------\n",
      "train Loss: 0.9424 Acc: 0.6988\n",
      "val Loss: 1.6214 Acc: 0.5327\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 11/19\n",
      "----------\n",
      "train Loss: 0.9088 Acc: 0.7080\n",
      "val Loss: 1.2893 Acc: 0.5731\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 12/19\n",
      "----------\n",
      "train Loss: 0.8344 Acc: 0.7278\n",
      "val Loss: 1.4753 Acc: 0.5630\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 13/19\n",
      "----------\n",
      "train Loss: 0.7686 Acc: 0.7483\n",
      "val Loss: 1.1439 Acc: 0.6550\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 14/19\n",
      "----------\n",
      "train Loss: 0.6865 Acc: 0.7768\n",
      "val Loss: 1.3782 Acc: 0.5952\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 15/19\n",
      "----------\n",
      "train Loss: 0.5683 Acc: 0.8159\n",
      "val Loss: 1.6375 Acc: 0.5501\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 16/19\n",
      "----------\n",
      "train Loss: 0.5451 Acc: 0.8201\n",
      "val Loss: 1.4049 Acc: 0.6477\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 17/19\n",
      "----------\n",
      "train Loss: 0.4971 Acc: 0.8357\n",
      "val Loss: 1.4898 Acc: 0.5879\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 18/19\n",
      "----------\n",
      "train Loss: 0.4607 Acc: 0.8539\n",
      "val Loss: 1.9117 Acc: 0.6017\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 19/19\n",
      "----------\n",
      "train Loss: 0.4149 Acc: 0.8659\n",
      "val Loss: 1.7793 Acc: 0.6182\n",
      "lr:  0.0001\n",
      "\n",
      "Training complete in 10m 29s\n",
      "Best val Acc: 0.655014, epoch 13\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20eef6a18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f02898748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg11_bn, batch_size=256, lr=0.0001, pretrained=True\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "model_ft.apply(reset)\n",
    "model_vgg11, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, num_epochs=20, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "torch.save(model_vgg11, 'log/model_vgg11_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/9\n",
      "----------\n",
      "train Loss: 0.6213 Acc: 0.8037\n",
      "val Loss: 0.9694 Acc: 0.6946\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 1/9\n",
      "----------\n",
      "train Loss: 0.4810 Acc: 0.8569\n",
      "val Loss: 0.9246 Acc: 0.7139\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 2/9\n",
      "----------\n",
      "train Loss: 0.4288 Acc: 0.8741\n",
      "val Loss: 0.9229 Acc: 0.7305\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 3/9\n",
      "----------\n",
      "train Loss: 0.3798 Acc: 0.8873\n",
      "val Loss: 0.9372 Acc: 0.7286\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 4/9\n",
      "----------\n",
      "train Loss: 0.3523 Acc: 0.8925\n",
      "val Loss: 0.9501 Acc: 0.7268\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 5/9\n",
      "----------\n",
      "train Loss: 0.3303 Acc: 0.9029\n",
      "val Loss: 0.9383 Acc: 0.7415\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 6/9\n",
      "----------\n",
      "train Loss: 0.2990 Acc: 0.9123\n",
      "val Loss: 0.9594 Acc: 0.7360\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 7/9\n",
      "----------\n",
      "train Loss: 0.2710 Acc: 0.9174\n",
      "val Loss: 0.9731 Acc: 0.7378\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 8/9\n",
      "----------\n",
      "train Loss: 0.2432 Acc: 0.9321\n",
      "val Loss: 0.9948 Acc: 0.7378\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 9/9\n",
      "----------\n",
      "train Loss: 0.2204 Acc: 0.9386\n",
      "val Loss: 0.9870 Acc: 0.7341\n",
      "lr:  1e-05\n",
      "\n",
      "Training complete in 5m 17s\n",
      "Best val Acc: 0.741490, epoch 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEZE4pHAXEYlDCncRkTikcBcRiUP/HzpZqoWrWGkaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20eef686588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f1d1f1080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg11_bn, batch_size=256, lr=0.0001, pretrained=True\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.00001)\n",
    "model_vgg11, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, num_epochs=10, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "torch.save(model_vgg11, 'log/model_vgg11_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_vgg11_2 = torch.load('log/model_vgg11_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/9\n",
      "----------\n",
      "train Loss: 0.3089 Acc: 0.9059\n",
      "val Loss: 0.9550 Acc: 0.7286\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 1/9\n",
      "----------\n",
      "train Loss: 0.2719 Acc: 0.9172\n",
      "val Loss: 0.9623 Acc: 0.7351\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 2/9\n",
      "----------\n",
      "train Loss: 0.2450 Acc: 0.9305\n",
      "val Loss: 0.9722 Acc: 0.7351\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 3/9\n",
      "----------\n",
      "train Loss: 0.2266 Acc: 0.9386\n",
      "val Loss: 0.9865 Acc: 0.7369\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 4/9\n",
      "----------\n",
      "train Loss: 0.1994 Acc: 0.9485\n",
      "val Loss: 1.0079 Acc: 0.7406\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 5/9\n",
      "----------\n",
      "train Loss: 0.1794 Acc: 0.9524\n",
      "val Loss: 0.9972 Acc: 0.7341\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 6/9\n",
      "----------\n",
      "train Loss: 0.1603 Acc: 0.9597\n",
      "val Loss: 1.0147 Acc: 0.7341\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 7/9\n",
      "----------\n",
      "train Loss: 0.1489 Acc: 0.9630\n",
      "val Loss: 1.0979 Acc: 0.7387\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 8/9\n",
      "----------\n",
      "train Loss: 0.1304 Acc: 0.9685\n",
      "val Loss: 1.0960 Acc: 0.7360\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 9/9\n",
      "----------\n",
      "train Loss: 0.1140 Acc: 0.9715\n",
      "val Loss: 1.1825 Acc: 0.7231\n",
      "lr:  1e-05\n",
      "\n",
      "Training complete in 5m 22s\n",
      "Best val Acc: 0.740570, epoch 4\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a2b1ca550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEIW+iEiEKPRFRCJEoS8iEiH/H1/+ESdsFbP4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a14f8dba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg11_bn, batch_size=256, lr=0.0001, pretrained=True\n",
    "optimizer_ft = optim.Adam(model_vgg11_2.parameters(), lr=0.00001)\n",
    "model_vgg11_3, loss_log, acc_log = train_model_batch(model_vgg11_2, criterion, optimizer_ft, num_epochs=10, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/29\n",
      "----------\n",
      "train Loss: 2.2273 Acc: 0.2184\n",
      "val Loss: 2.1374 Acc: 0.3036\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/29\n",
      "----------\n",
      "train Loss: 1.8563 Acc: 0.3801\n",
      "val Loss: 2.5114 Acc: 0.3496\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/29\n",
      "----------\n",
      "train Loss: 1.6781 Acc: 0.4436\n",
      "val Loss: 1.9119 Acc: 0.4057\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/29\n",
      "----------\n",
      "train Loss: 1.5562 Acc: 0.4864\n",
      "val Loss: 1.8987 Acc: 0.4388\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/29\n",
      "----------\n",
      "train Loss: 1.4316 Acc: 0.5214\n",
      "val Loss: 1.7887 Acc: 0.4600\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/29\n",
      "----------\n",
      "train Loss: 1.3719 Acc: 0.5527\n",
      "val Loss: 1.7310 Acc: 0.4986\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/29\n",
      "----------\n",
      "train Loss: 1.2740 Acc: 0.5803\n",
      "val Loss: 2.0045 Acc: 0.4554\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/29\n",
      "----------\n",
      "train Loss: 1.2043 Acc: 0.5994\n",
      "val Loss: 1.3531 Acc: 0.5814\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/29\n",
      "----------\n",
      "train Loss: 1.0888 Acc: 0.6445\n",
      "val Loss: 1.3340 Acc: 0.5842\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/29\n",
      "----------\n",
      "train Loss: 1.0259 Acc: 0.6647\n",
      "val Loss: 1.3582 Acc: 0.5750\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 10/29\n",
      "----------\n",
      "train Loss: 0.9660 Acc: 0.6832\n",
      "val Loss: 1.0344 Acc: 0.6771\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 11/29\n",
      "----------\n",
      "train Loss: 0.7996 Acc: 0.7349\n",
      "val Loss: 0.9763 Acc: 0.6973\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 12/29\n",
      "----------\n",
      "train Loss: 0.7457 Acc: 0.7563\n",
      "val Loss: 0.9725 Acc: 0.6937\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 13/29\n",
      "----------\n",
      "train Loss: 0.7080 Acc: 0.7662\n",
      "val Loss: 0.9694 Acc: 0.6983\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 14/29\n",
      "----------\n",
      "train Loss: 0.6706 Acc: 0.7862\n",
      "val Loss: 0.9811 Acc: 0.6937\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 15/29\n",
      "----------\n",
      "train Loss: 0.6365 Acc: 0.7964\n",
      "val Loss: 0.9608 Acc: 0.7047\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 16/29\n",
      "----------\n",
      "train Loss: 0.6003 Acc: 0.8120\n",
      "val Loss: 0.9442 Acc: 0.7176\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 17/29\n",
      "----------\n",
      "train Loss: 0.5629 Acc: 0.8290\n",
      "val Loss: 0.9517 Acc: 0.7185\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 18/29\n",
      "----------\n",
      "train Loss: 0.5358 Acc: 0.8387\n",
      "val Loss: 0.9572 Acc: 0.7148\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 19/29\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.8465\n",
      "val Loss: 1.0444 Acc: 0.7010\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 20/29\n",
      "----------\n",
      "train Loss: 0.4741 Acc: 0.8548\n",
      "val Loss: 0.9176 Acc: 0.7194\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 21/29\n",
      "----------\n",
      "train Loss: 0.4513 Acc: 0.8649\n",
      "val Loss: 0.9224 Acc: 0.7194\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 22/29\n",
      "----------\n",
      "train Loss: 0.4427 Acc: 0.8679\n",
      "val Loss: 0.9186 Acc: 0.7203\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 23/29\n",
      "----------\n",
      "train Loss: 0.4411 Acc: 0.8716\n",
      "val Loss: 0.9181 Acc: 0.7185\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 24/29\n",
      "----------\n",
      "train Loss: 0.4403 Acc: 0.8751\n",
      "val Loss: 0.9198 Acc: 0.7203\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 25/29\n",
      "----------\n",
      "train Loss: 0.4262 Acc: 0.8764\n",
      "val Loss: 0.9204 Acc: 0.7213\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 26/29\n",
      "----------\n",
      "train Loss: 0.4257 Acc: 0.8744\n",
      "val Loss: 0.9233 Acc: 0.7194\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 27/29\n",
      "----------\n",
      "train Loss: 0.4183 Acc: 0.8827\n",
      "val Loss: 0.9208 Acc: 0.7194\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 28/29\n",
      "----------\n",
      "train Loss: 0.4161 Acc: 0.8808\n",
      "val Loss: 0.9244 Acc: 0.7176\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 29/29\n",
      "----------\n",
      "train Loss: 0.4139 Acc: 0.8836\n",
      "val Loss: 0.9226 Acc: 0.7194\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Training complete in 15m 55s\n",
      "Best val Acc: 0.721251, epoch 25\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xl8VPW9//HXZyaTPZB9gQAJyBKS\nsIatKAKKIrhjLYr7Qm21t3qv/dXebna5ra1Xb+tKXVBrXWrdq4LFIrghskPYCWtCCNkhZM98f3+c\nCQTInplMZvJ5Ph7zOGfmnDnzOY685+R7vud7xBiDUkop/2LzdgFKKaXcT8NdKaX8kIa7Ukr5IQ13\npZTyQxruSinlhzTclVLKD2m4K6WUH9JwV35PRPaLyIXerkOp7qThrpRSfkjDXfVaInKniOwRkRIR\neV9E+rleFxH5PxE5KiLlIrJZRDJcy+aIyDYROS4ieSJyv3f3QqnmabirXklEZgK/B64FkoADwOuu\nxRcB04BhQCTwHaDYtex54LvGmAggA1jejWUr1W4B3i5AKS9ZACw2xqwHEJGfAKUikgLUARHACOAb\nY8z2Ju+rA0aKyCZjTClQ2q1VK9VOeuSueqt+WEfrABhjKrCOzvsbY5YDTwBPAgUi8oyI9HGtOg+Y\nAxwQkZUiMqWb61aqXTTcVW91GBjU+EREwoAYIA/AGPOYMWY8kI7VPPMj1+trjDFXAPHAu8Ab3Vy3\nUu2i4a56C4eIBDc+sEL5VhEZIyJBwO+A1caY/SIyQUQmiYgDOAFUAw0iEigiC0SkrzGmDjgGNHht\nj5RqhYa76i0+AqqaPM4Dfg68BeQDQ4D5rnX7AM9itacfwGqu+V/XshuB/SJyDLgLuKGb6leqQ0Rv\n1qGUUv5Hj9yVUsoPabgrpZQf0nBXSik/pOGulFJ+yGtXqMbGxpqUlBRvfbxSSvmkdevWFRlj4tpa\nz2vhnpKSwtq1a7318Uop5ZNE5EDba2mzjFJK+SUNd6WU8kMa7kop5Yd0yF+llE+pq6sjNzeX6upq\nb5fiUcHBwSQnJ+NwODr1fg13pZRPyc3NJSIigpSUFETE2+V4hDGG4uJicnNzSU1N7dQ2tFlGKeVT\nqquriYmJ8dtgBxARYmJiuvTXSZvhLiIDRORTEdkuIltF5IfNrDPdda/Jja7HLzpdkVJKtcGfg71R\nV/exPc0y9cB/GWPWi0gEsE5Elhljtp2x3ufGmEu7VI277V0JoTGQmOHtSpRSqlu1eeRujMlvvM+k\nMeY4sB3o7+nCuqyhHt64EZb93NuVKKX8SFlZGU899VSH3zdnzhzKyso8UFHzOtTm7rp58FhgdTOL\np4jIJhFZIiLpLbx/oYisFZG1hYWFHS62Qw59DdXlkL8ZdMx6pZSbtBTuDQ2t35Tro48+IjIy0lNl\nnaXd4S4i4Vh3rbnXGHPsjMXrgUHGmNHA41j3ljyLMeYZY0yWMSYrLq7NoRG6ZtdSa1pZBBUFnv0s\npVSv8cADD5CTk8OYMWOYMGECM2bM4PrrryczMxOAK6+8kvHjx5Oens4zzzxz8n0pKSkUFRWxf/9+\n0tLSuPPOO0lPT+eiiy6iqqrK7XW2qyuk616SbwGvGGPePnN507A3xnwkIk+JSKwxpsh9pXbQro8h\nJAqqSuFINkQkeq0UpZRn/OqfW9l2+Mxjza4Z2a8Pv7ys2cYHAB566CGys7PZuHEjK1asYO7cuWRn\nZ5/ssrh48WKio6OpqqpiwoQJzJs3j5iYmNO2sXv3bl577TWeffZZrr32Wt566y1uuMG9d2xsT28Z\nAZ4HthtjHm1hnUTXeojIRNd2i91ZaIcU50DRLpj0Pev5kc1eK0Up5d8mTpx4Wl/0xx57jNGjRzN5\n8mQOHTrE7t27z3pPamoqY8aMAWD8+PHs37/f7XW158h9KtZNgbeIyEbXa/8NDAQwxiwCrgG+JyL1\nWDcfnm+8eXPWXR9b01HXwoa/QUG210pRSnlOa0fY3SUsLOzk/IoVK/jkk09YtWoVoaGhTJ8+vdm+\n6kFBQSfn7Xa7d5pljDFfAK12uDTGPAE84a6iumzXUogbAdGpkJgJR7Z4uyKllJ+IiIjg+PHjzS4r\nLy8nKiqK0NBQduzYwddff93N1Z3if8MPVB+DA1/ClLut54kZsGsJ1FZCYKh3a1NK+byYmBimTp1K\nRkYGISEhJCQknFw2e/ZsFi1axKhRoxg+fDiTJ0/2Wp3+F+45y8FZD8NmW88TM8E44eh2SB7v3dqU\nUn7h1Vdfbfb1oKAglixZ0uyyxnb12NhYsrNPNRXff//9bq8P/HFsmV1LITgSkidazxNcV6cWaNOM\nUqr38K9wdzbA7n/B0IvA7vqjJHIQBPXRdnelVK/iX+Getw4qi2HYxades9kgIV3DXSnVq/hXuO9a\nCmKHcy44/fXETCjYCk6nd+pSSqlu5l/hvnMpDJxiXZnaVGIm1FZA6T7v1KWUUt3Mf8K97CAc3QrD\nZ5+97ORJVb2YSSnVO/hPuDdelTqsmXCPT7Oaa7TdXSnVzcLDw73yuf4V7tGDIeacs5c5QiB2qDWA\nmFJK9QL+cRFT7QnY9xlMuB1aujVVYiYcWNW9dSml/M6Pf/xjBg0axPe//30AHnzwQUSEzz77jNLS\nUurq6vjtb3/LFVdc4dU6/SPc966Ahprmm2QaJWTAln9AZQmERndbaUopD1rygPubWxMz4ZKHWlw8\nf/587r333pPh/sYbb7B06VLuu+8++vTpQ1FREZMnT+byyy/36r1e/SPcdy21LlQaOKXldRKtgfQp\nyIbUad1Tl1LK74wdO5ajR49y+PBhCgsLiYqKIikpifvuu4/PPvsMm81GXl4eBQUFJCZ67z4Svh/u\nTifs+hcMmQkBgS2v1xjuR7ZouCvlL1o5wvaka665hjfffJMjR44wf/58XnnlFQoLC1m3bh0Oh4OU\nlJRmh/rtTr4f7vkboeJI600yAOHxEJ6gJ1WVUl02f/587rzzToqKili5ciVvvPEG8fHxOBwOPv30\nUw4cOODtEv0g3Hd9DIg1nkxbEjK0O6RSqsvS09M5fvw4/fv3JykpiQULFnDZZZeRlZXFmDFjGDFi\nhLdL9IdwXwoDJkJYTNvrJmbCqiehvrb1JhyllGrDli2nDhRjY2NZtar53ngVFRXdVdJpfLuf+7F8\nq1mm6UBhrUnMBGcdFO30bF1KKeVlvh3uu1u5KrU5J0+qaru7Usq/+Vy4f7WniKue+pLyqjqrvb3v\nQIgf2b43x5wDASHa7q6UjzPGeLsEj+vqPvpcuAc5bGw4WMbn2w9ZFy8Nu7jlq1LPZLNb48zoXZmU\n8lnBwcEUFxf7dcAbYyguLiY4OLjT2/C5E6pjBkQRHRZI7vp/QV1l+5tkGiVmwvb3wZj2/ygopXqM\n5ORkcnNzKSws9HYpHhUcHExycnKn3+9z4W63CTOGxxO17RmMIwxJObdjG0jMhPUvwbHD0Le/R2pU\nSnmOw+EgNTXV22X0eD7XLANw4Yg4zjXrKE2cCo4O/tnS9EpVpZTyUz4Z7udHFdJfivnKPr7jb05I\nt6Ya7kopP+aT4R66bxkALx4d1vE3B0VAVKqeVFVK+TWfDHd2LaWoTzprS4LYW9iJq78SdRgCpZR/\n871wryiE3LU40uYAsHzH0Y5vI3EUlOyDmuNuLk4ppXoG3wv3PcsAQ9/RlzI8IYJPthd0fBuJmYCB\ngm3urk4ppXoE3wv3kVfCgjchaTQXpMWzZn8p5ZV1HdtGQoY11XZ3pZSf8r1wDwyFobNAhAvSEmhw\nGlbs6mDTTN9kCI7UdnellN/yvXBvYsyASGLCAvn39g6Gu4jVNKMDiCml/JRPh7vdJswYEc+KnUep\nb3B27M2JmVCwFZwNnilOKaW8yKfDHeDCtHiOVdez9kBpx96YkAH1VVCc45nClFLKi3w+3M8dGkeg\n3ca/O9prpnEYAj2pqpTyQz4f7uFBAUwaHN3xdve44WAL0JOqSim/5PPhDnBhWgJ7i0507GrVgCCI\nG6EnVZVSfskvwv2CtHiAjh+9J2bqkbtSyi+1Ge4iMkBEPhWR7SKyVUR+2Mw6IiKPicgeEdksIuM8\nU27zkqNCGZEYwb93dLDdPSEDKo5YQxoopZQfac+Rez3wX8aYNGAycLeInHnT0kuAoa7HQuBpt1bZ\nDp26WlVPqiql/FSb4W6MyTfGrHfNHwe2A2fewugK4K/G8jUQKSJJbq+2FZ26WvXkjTt8pN29OAfe\nvRvqqr1diVKqh+tQm7uIpABjgdVnLOoPHGryPJezfwAQkYUislZE1rr7/oejkztxtWpoNPTp7zvt\n7p8/Ahv/Boc3eLsSpVQP1+5wF5Fw4C3gXmPMsTMXN/OWs25Nbox5xhiTZYzJiouL61ilbWh6tWpd\nR65WTciAAh84cq8qhey3rPnC7d6tRSnV47Ur3EXEgRXsrxhj3m5mlVxgQJPnycDhrpfXMSevVt3f\ngatVEzOhcGfPb+rY+BrUV1t9849quCulWtee3jICPA9sN8Y82sJq7wM3uXrNTAbKjTH5bqyzXc7r\nzNWqiRlgGnr20bAxsHYxJE+AfmM13JVSbWrPkftU4EZgpohsdD3miMhdInKXa52PgL3AHuBZ4Pue\nKbd1YUEBTB4S07G7MyWOsqY9+aTq/i+geDdk3QbxaXBUbzKilGpdQFsrGGO+oPk29abrGOBudxXV\nFRemxfOL97ayt7CCwXHhbb8hKhUcYT37pOraxdb48+lXQXU5rP+r1Tc/3L3nLZRS/sMvrlBtauaI\nDl6tarNBQnrPPalacRS2/xPGLABHiDVkAujRu1KqVX4X7o1Xq3bo3qqNN+4wZ3Xw8b4NL4OzDrJu\ntZ7Hu64f03Z3pVQr2myW8UUXpMWzaOVeyivr6BvqaPsNiZmw9nn4v3ToO8C6DV+ka9r4vG8yBPf1\nfPFNORtg3YuQch7EDrVeC4+HkGg9cldKtcpPwz2BJz/NYcWuo1wx5qxrqc6WcbXV/FG6H8oPQd5a\n2PaedcTcVFAfK+zH3gBTuuGccc5yKDsIF/7q1Gsi1tF74Q7Pf75Symf5ZbiPSY4kNty6WrVd4R7c\nF6b/+PTXnE6oKIDyXCvwG6f5m+Djn0DNMZj+gGd2oNHaxRAWDyMuPf31+DTY/HerGUlaPdetlOql\n/DLcbTZhxvB4Pt56hLoGJw57J04t2GzQJ8l6DJhw6nVnA7x3D6z4vTU/4789E7DlubBrKZx7HwQE\nnr4sfoT143Isz2ouUkqpM/jdCdVGF6QldPxq1faw2eGKJ62mmc/+CMt/45kTsetesrY77uazl+lJ\nVaVUG/w23M8bGkug3cbHW4+4f+M2G1z2uBW8nz8Cnzzo3oBvqLP6sg+dBVGDzl6u3SGVUm3w23AP\nCwrg0lFJvLr6IDkduf1ee9lscOmfrKtGv/wTLPu5+wJ+5xLrJiJZtzW/PDQaIpLgqJ5UVUo1z2/D\nHeCBOSMIdtj46TtbMJ5oOrHZYO6jMOFO+Opx+Pin7gn4tYuhTzIMvajldeJG6JG7UqpFfh3u8RHB\nPHBJGl/vLeGt9Xme+RARmPMwTLoLvn4Slj7QtYAvzoG9n8L4W6z2/ZbEj7RGs3Q2dP6zlFJ+y6/D\nHWD+hAGMHxTF/3y4jZITtZ75EBGY/RBMvhtWL4KPftT5gF/3Aogdxt3Y+nrxaVBfZfXNV0qpM/h9\nuNtswu+uyuR4dT2/+8iDvUtE4OL/gW/9ANY8Cx/+p9VXviPqqmHDKzBiLkQktr5uY48ZvZhJKdUM\nvw93gOGJESycNpg31+WyKqfYcx8kArN+A1PvtdrNP/gh1FW1//3b34eqEphwe9vrxg23ptrurpRq\nRq8Id4AfzBzKwOhQfvrOFmrqPdhOLQIXPgjn3W91Z3xsLKx5Durb0SS0djFED4GUaW2vGxQOkQO1\nr7tSqlm9JtxDAu385soM9had4OkVOZ79MBG44Odwy0cQlQIf/hc8MR42vgoN9c2/p2AbHFxljf5o\na+fXEj9Sw10p1axeE+4A5w+L4/LR/Xjq0xzP9H0/U8pUuHUJLHjLGsnx3e/B01Mg++2z2+PXLgZ7\nEIy+vv3bj0+Dot3WRU9KKdVErwp3gJ9dmubZvu9nEoGhF8LCFfCdv1k9Yd68Ff4yDXYutXrV1FTA\nptch/UoIi2n/tuPSrJEriz38l4hSyuf0unDvlr7vzRGBtMvge1/C1c9CbQW89h14fhb8+1dQexyy\n2nEitan4NGuqJ1WVUmfodeEO3dT3vSU2O4y6Fu5ZA5c9Bsfy4ZtnID4dBkzs2LZih4HYtN1dKXWW\nXhnu3db3vTV2B4y/Gf5jPVz+BFz5ZMeHDnYEW71rCjXclVKn65XhDt3Y970tAUHW1aj9xnbu/fEj\n9MhdKXWWXhvu0I193z0pfiSU7O3YxVJKKb/Xq8O9W/u+e0p8GhgnFO3ydiVKqR6kV4c7nN73ff1B\nN9+1qTucvCuTjjGjlDql14c7wC8vG0m/yGBue3ENuwuOe7ucjokeDDaHdodUSp1Gwx2ICQ/i5dsn\n4bDbuGnxN+SV+VD7td1hdYnUk6pKqSY03F0GRIfy19smUlFTz03Pr+7+/u9dEZ+m4a6UOo2GexNp\nSX14/uYJ5JZWceuLazhR08IgXz1NfBqUH4QaH2tSUkp5jIb7GSamRvPE9ePIzivnrr+to7a+gzfc\n8IbGYQgKd3q3DqVUj6Hh3oxZIxP4/dWZfL67iP/6xyaczm4YYKwrdIwZpdQZArxdQE91bdYASk7U\n8tCSHUSHOnjw8nSko8MDdJfIFAgI0XZ3pdRJGu6t+O60wRRX1PDs5/uIDQ/iBxcM9XZJzbPZXMMQ\n6JG7Usqi4d4KEeEnl6RRfKKWR5btIjo8kAWTBnm7rObFpUHOcm9XoZTqIbTNvQ02m/CHeaOYOSKe\nn72bzUdb8r1dUvPi06DiCFSWeLsSpVQPoOHeDg67jSevH8e4gVHc+/pGvtpT5O2SznZyGAJtd1dK\nabi3W0ignedvziIlNpSFL69j2+Fj3i7pdNpjRinVhIZ7B0SGBvLSbROJCA7glhe+4VBJpbdLOqVP\nPwjqA4U6gJhSqh3hLiKLReSoiGS3sHy6iJSLyEbX4xfuL7PnSOobwku3TaS6roGbX/iG0p4yTIGI\nDkOglDqpPUfuLwKz21jnc2PMGNfj110vq2cblhDBszdlkVtaxe0vraG6rofc6CM+zWqWMT38oiul\nlMe1Ge7GmM8A7YJxhkmDY/jzd8aw4VAZP3htA/UNPWCYgviRUFUKFQXerkQp5WXuanOfIiKbRGSJ\niKS7aZs93iWZSTx4WTrLthXwi/e3Yrx9xBw3wppq04xSvZ47wn09MMgYMxp4HHi3pRVFZKGIrBWR\ntYWFhW74aO+7+Vsp3HX+EF5dfZAnlu/xbjHaHVIp5dLlcDfGHDPGVLjmPwIcIhLbwrrPGGOyjDFZ\ncXFxXf3oHuPHs4dz9dj+PLJsF2+sOeS9QsLjIDRWu0Mqpbo+/ICIJAIFxhgjIhOxfjCKu1yZDxER\n/nDNKAoravjJO1uIiwhixoh47xSjPWaUUrSvK+RrwCpguIjkisjtInKXiNzlWuUaIFtENgGPAfON\n1xufu5/DbuPpG8aTlhTB919Zz8ZDZd4pJD7N6uve+74CpVQT7ektc50xJskY4zDGJBtjnjfGLDLG\nLHItf8IYk26MGW2MmWyM+crzZfdM4UEBLL5lArERgdz24hr2FZ3o/iLi06C2Asq92DyklPI6vULV\nzeIjgvnrbZMAuPH51XyV083j0OhJVaUUGu4ekRobxuJbJuB0Gq5/djU3Lf6G7Lzy7vnwk90h9aSq\nUr2ZhruHjBkQyfL7p/PTOWlszi3j0se/4D9e28CBYg831YREQkQ/PXJXqpfTcPegYIedO6cNZuWP\nZnD3jCH8a9sRLnhkJb98L5vC4zWe+2DtMaNUr6fh3g36hjj40cUjWPmjGVw7YQB/W32Q8x/+lEeX\n7eJ4dZ37PzA+DQp3grOHjHmjlOp2Gu7dKKFPML+7KpNl901jxvB4Hvv3bs5/eAWLv9hHTb0bgzh+\nJDTUQMk+921TKeVTNNy9YHBcOE8uGMd7d09lRGIEv/5gG99etMp9TTV64w6lej0Ndy8aPSCSV+6Y\nxNMLxrG7oIKrn/6SnMKKrm84brg13bcSTnThYmFjoDwXtr4DH/8UXpgL61/uen1KKY8Tb11MmpWV\nZdauXeuVz+6JNh0q47YX19BgDM/dlEVWSnTXNvjUlFNH7mHx1tH8ycdIq8tkcJ/T31N7Ag5vhNw1\n1iNvHRx33RDcHgSOYAiOhB9usm4OopTqdiKyzhiT1eZ6Gu49x4HiE9zywhryyqr403fGMCczqfMb\nqyqDw+utXjNHt7mmO6CuSVfMvgOssA+LhyOboGAbGFfbf1QqJE+A5CzrkZAJW96A9+6GO5ZD8viu\n7axSqlM03H1UyYla7nhpDRsOlfHTOWnccd5g923c6YTyg2cH/vF8SMx0BfkE6D8ewpoZ2LOqDB4+\nByYuhNm/c19dSql203D3YdV1Ddz7+kaWbj3CrVNT+NnckdhtPaQZ5LXr4PAGuG8b2PSUjVLdrb3h\nrv86e6Bgh50nF4zjtqmpvPDlfu5+ZX3PuU9rxjzrSP/gKm9XopRqhYZ7D2W3Cb+4bCQ/m5vGx9uO\nsOC51ZScqPV2WTBsNgSEQPZb3q5EKdUKDfce7o7zBvPk9ePYklfOvKe/8vzYNG0JCofhs2Hbu9BQ\n791alFIt0nD3AXMyk3jljkmUVtZy1VNf8cHmw969GXfGPKgstvrRK6V6JA13HzEhJZq3vvct+kUG\nc8+rG7jtxTUcKqn0TjHnzILACMh+2zufr5Rqk4a7DxkSF86735/Kzy8dyep9Jcz6v5X8ZWUOdQ3O\n7i3EEQxpl8L2f0K9B0e3VEp1moa7jwmw27j93FQ++c/zOfecOH6/ZAeXP/ElGw6Wdm8hGddATTns\n+Xf3fq5Sql003H1Uv8gQnrs5i7/cOJ7SE7Vc/fRX/OK9bI55Ygjh5gw+H0KiIfvN7vk8pVSHaLj7\nuIvTE1n2n9O4eUoKL399gFmPrmTJlnzPn3C1O2DkFbBziTUmjVKqR9Fw9wMRwQ4evDydd78/lZiw\nIL73ynrueGkth8uqPPvBGfOgrhJ2LfXs5yilOkzD3Y+MHhDJ+/dM5Wdz0/gqp5hvL1pFbqkHe9QM\n+haEJ2qvGaV6IA13PxNgt3HHeYP5x11TOF5dx4LnVnOkvNozH2azQ/pVsPtfUF3umc9QSnWKhruf\nyujfl5dum0hxRS0LnvuaogoPdVnMmAcNtbDjQ89sXynVKRrufmzswCgW3zKBvLIqbnhuNWWVHhib\nJjkL+g7UsWaU6mE03P3cxNRonrtpAnuLTnDj89+4v6ukCGRcDTmfdu2Wfkopt9Jw7wXOHRrLohvG\nsePIMW59YQ0natw84FfGPOsOTtvfc+92lVKdpuHeS8wckcDj141l46Eybn9pDVW1bhwfPjETYoZq\nrxmlehAN915kdkYSj147mtX7Slj48lpq6t0U8CLW0fv+L+BYvnu2qZTqEg33XuaKMf35w7xRfL67\niLtf2eC+QccyrgaMNc67UsrrNNx7oWuzBvCbK9L5ZHsB976+kXp3BHzccEjI1F4zSvUQGu691I1T\nUvjZ3DQ+3JLP/f/Y5J42+IyrIXcNlO7v+raUUl2i4d6L3XHeYH508XDe3XiYmY+s4N0NeTidXRhw\nLONqa7r1HfcUqJTqNA33Xu7uGefw94WTiQ0P4t6/b+Sqp79i7f6Szm0sKgX6Z8EWbZpRyts03BWT\nBsfw3t1TeeTbozlSXsU1i1Zx96vrO3cbv4x5ULAFCne6v1ClVLtpuCsAbDZh3vhkPr1/Oj+8YCj/\n3l7ABY+u5KElOzjekata068CRPu8e0vZQaj10r11VY+i4a5OExoYwH2zhrHi/hlcOiqJRStzmP7w\nCl5ZfaB9vWr6JEHKudYdmpzdfG/X3swY+OZZeGwcvDhXb6CiNNxV8xL7BvPotWN4/56pDIkL56fv\nZDP3sS9YsfNo23d5Gj0fivfAX86z7tTk6btC9Xa1lfDOd+Gj+6H/OMjfCG/eDg1uHmZC+ZQ2w11E\nFovIURHJbmG5iMhjIrJHRDaLyDj3l6m8ZVRyJH//7mSeXjCOqroGbnlhDQueW82mQ2Utv2nMApj3\nvHWXptfmw3MXwt6V3Vd0b1KyF56fBZvfgBk/hVuXwpyHYdcSWPL/9Ie1F5O2jsJEZBpQAfzVGJPR\nzPI5wA+AOcAk4M/GmEltfXBWVpZZu3Ztp4pW3lFb7+TV1Qd4fPkeik/UMjczifsvHk5qbFjzb2io\ng42vwso/wLE8SD0fLviFNUyw6rqdS+HthdbwD/Oeh6EXnlq27Jfw5Z/gwgfh3Pu8VaHyABFZZ4xp\n8x9Rm+Hu2lgK8EEL4f4XYIUx5jXX853AdGNMq4OMaLj7roqaep79bC/Pfr6Xmnon100cwH9cMJT4\niODm31BXDWsXw+ePQGURDJ8DM38GCendW7i/cDZYP5gr/wCJo+A7L1vdUE9bxwlv32md+7j6ORj1\nba+UqtyvO8P9A+AhY8wXruf/Bn5sjDkruUVkIbAQYODAgeMPHDjQ5mernqvweA2PL9/Nq6sP4rDb\nuOO8VBZOG0xEsKP5N9RUwOqn4cvHoeYYZF4D038CMUO6t3BnA1QcheOHrROPscMgPME6Au7pKkus\n0N7zidX8NfcRcIQ0v259DfxtHhz8Gm58B1LP695alUd0Z7h/CPz+jHD/f8aYda1tU4/c/cf+ohM8\nsmwX/9x0mOiwQO6ZcQ4LJg8kKMDe/BsqS+Crx+DrRdYt+uLTICDYejiCW5gPgYAgsAe5poFnTF2v\nN75WXQ7H8+HYYTh+pMl8PlQUgDmjJ09oLCRmQELjI90aLycgyPP/AdsrfxP8/QZr5M05f4Txt7b9\ng1RVBosvtt5z+8fWf2vl07RZRnW7Lbnl/GHpDr7YU8SgmFD+cdeUlptqAI4XwKonoDgH6qutR12V\ndcRZX2U15zR9nU6eHAyOhIgkq5tmRD/XNNGad4RA4Q4oyIYj2dZ8veuG4rYA66i+Mezj0yDmHIgc\nCPYW/jrxBKcTNr8OH9wHoTHVIXUQAAAQP0lEQVRw7V87dt6i7JB1UtsWAHd8Yu2/8lndGe5zgXs4\ndUL1MWPMxLa2qeHuv1bsPMrCl9dx/rA4nrlxPOKO5g5jrBO0DTVQX+ua1lhH/ien1afmgyKsQI9I\ngsDQ9n9OQz2U5JwK+4Kt1vyxvFPr2AIgKtUK+pgh1jR2qDU9s3mnrhqqy6wj6KrSs+drjkNthdVk\nVXvCNX/81HztCVefdQMp58E1L0B4XMf/++VvhhcugehUuHWJ9d9H+SS3hbuIvAZMB2KBAuCXgAPA\nGLNIrH+5TwCzgUrg1uba28+k4e7fnvksh999tIM/fWcMV47t7+1yuq6yxOq7X7Tbmhbvsf7iKMk5\ndaQPEBhu/aDUVlgB3nRZcwIjIDAMgsKtaUvPIwfA6OvBHtD5fdjzCbxyLQw+H65/o3v/+lBu49Yj\nd0/QcPdvDU7Dtxd9RU7hCZbdN434Pq00z/gypxOO5Z4K+6LdUHEEgvpASCSERFnNQmfOB0dCcF+w\ntXBewlPWvwzv3wNjboArnvCNk8jqNO0N9y4cBijVMrtNePjbo5nz58/573e28OxNWe5pnulpbDar\nDT5yIAyZ6e1q2jbuRig/ZHWjDAqHfmOtZp+6SutK17oTrmnlqdfrqqwfgQDXSW1HyBknvZu8bg8E\nsbl+NKT1eeMEZ32TR8PZz43rNeO0muaMEzBNnpvTnzvrTjXbNdS65ps8GpvtnA3WXy62AKtme6D1\nV5E9EGwOa5ndYc2bBtd5n+pTTX/NTZ311n41btcWYP14n5xv8nz0dTDxTo9+1RruymOGxIXzo4uH\n89sPt/POhjyuHpfs7ZIUWN1Py/Ng9aKzl9kDwRFqNQU5Qq3zFY5QKzyry884ye2aNtR4tl6xWY+m\nPxAnnzedp0lQux4BgVZPKnug9eMT3NcV4HYr4E8Gf731o9ZQZoV0Q63rHE+dte5pvbeCILiPq3eW\n64ctINgKdWeD9QPT0o9V43yA5/+S1XBXHnXr1FSWZh/hwfe3MvWcWBL8tXnGl4hYTTJTf2gF18kQ\nD+tcm77TaQV8XZUViqcdYTc3jzUV2xlHtS0c6frjX3zdQMNdeVRj88wlf/6Mn7y9hedv9tPmGV8j\nAnHD3LMtmw1sIS1fTKW8QkeFVB6XGhvGjy4ewfIdR3lrfV7bb1BKdZmGu+oWt34rhYkp0fzqn1s5\nUt5G90ClVJdpuKtuYbMJf7xmFHUNTh54e3PbY8IrpbpEw111m5TYMB6YPYIVOwv5x7pcb5ejlF/T\ncFfd6qYpKUxKjeY3/9xGfnmVt8tRym9puKtuZbMJD18zmnqn4cdvbdHmGaU8RMNddbuBMaH8ZM4I\nPttVyBtrD3m7HKX8koa78oobJg1i8uBofvvBdvLKtHlGKXfTcFde0dg802AMlz72OU8s382x6jpv\nl6WU39BwV14zIDqUN747hXEDo/jff+1i6kPLeeRfOyk5Uevt0pTyeTrkr+oRsvPKeWrFHpZkHyHE\nYeeGyYO447zU1u/kpFQvpOO5K5+0u+A4T63I4b2NeQTYbVw3YQALzx9C/0gdt0Qp0HBXPm5/0QkW\nrczhrfXWxU5Xj03me9OHkBIb5uXKlPIuDXflF/LKqnhmZQ6vrTlEbb2TrEFRXDoqiTmZSf57dyel\nWqHhrvzK0WPV/H3NIT7cks+OI8cRgYkp0Vw6uh+XZCQSGx7k7RKV6hYa7spv7S44zgeb8/lg82Fy\nCk9gE5gyJIZLR/VjdnoiUWGB3i5RKY/RcFd+zxjDzoLjfLDJCvr9xZUE2ISp58Qyd1QSF49MpG+o\nw9tlKuVWGu6qVzHGsPXwsZNH9LmlVTjswrnnxDJ3VD9mjUygb4gGvfJ9Gu6q1zLGsDm3nI+25PPB\n5nzyyqygP29oHHMzk5iVnkCfYA165Zs03JXCCvpNueV8uPkwH27O53B5NYF2G9OGWU03F6YlEKFB\nr3yIhrtSZzDGsOFQGR9tzufDLfnku4J+8pAYZo1M4MK0eJL66sVSqmfTcFeqFU6nFfRLtuTzyfYC\n9hdXApDZvy8XpiUwa2QCaUkRiIiXK1XqdBruSrWTMYacwgqWbTvKsm1H2HCoDGOgf2SI64g+gYmp\n0QQG6Dh7yvs03JXqpMLjNSzfUcCybUf5Yk8h1XVOIoIDmDkinksyEjl/WDwhgXZvl6l6KQ13pdyg\nqraBL/YUsWzbEZZtK6C0so4Qh53pw+O4JDOJmSPiCQ8K8HaZqhdpb7jr/5VKtSIk0M6skVYbfH2D\nk9X7SliSnc/HWwtYkn2EwAAb04bGMjsjiVlpCXrRlOox9MhdqU5ocBrWHyxlyZYjLM22ulgG2IQp\nQ2KYk5nExemJROswCMoDtFlGqW7S2Jd+SXY+S7OPcKC4ErtN+NaQGC4dlcRFI3W8G+U+Gu5KeUHj\nMAiNV8ceLNHxbpR7abgr5WVNx7v5cMthDpXoeDeq6zTclepBjDFsySvnw82nj3eTNSiaianRTBoc\nzdgBUdrFUrVJw12pHupkG/2WfL7MKWLb4WM4DTjswujkSCamWoGflRKt3SzVWTTclfIRx6rrWLe/\nlNX7Sli9r5gtueXUOw12m5Derw+TUqOZlBrDxMHROpql0nBXyldV1taz/kAZ3+wr5ut9JWw8VEZt\nvRObQGZyJFOHxPCtIbFkpUQR7NBmnN7GreEuIrOBPwN24DljzENnLL8FeBjIc730hDHmuda2qeGu\nVPtU1zWw8VAZX+UU89WeIjYeKqPeaQi02xg3KJJvDYll6jkxjEqOxGHX8W/8ndvCXUTswC5gFpAL\nrAGuM8Zsa7LOLUCWMeae9hao4a5U55yoqeeb/SWsyinmyz1FbMs/hjEQGmhnYmo004bGMXNEPCmx\nYd4uVXmAO4cfmAjsMcbsdW34deAKYFur71JKeURYUAAzhsczY3g8AKUnalm9r5gv9xTzZU4Rv/5g\nG7/+YBuDY8OYOSKemSPiyUrRUS17m/aEe3/gUJPnucCkZtabJyLTsI7y7zPGHDpzBRFZCCwEGDhw\nYMerVUqdJSoskNkZSczOSALgYHEly3cUsHxnIX9ddYDnvthHeFAA04bFMmN4PNOHxxMXEeTlqpWn\ntadZ5tvAxcaYO1zPbwQmGmN+0GSdGKDCGFMjIncB1xpjZra2XW2WUcrzTtTU8+WeIj7deZTlO45S\ncKwGERiVHMn5w+IYOyCSUcl9iQnXsPcV7myWyQUGNHmeDBxuuoIxprjJ02eBP7SnSKWUZ4UFBXBR\neiIXpSeevGL20x1HWb7zKI8v303jsV3/yBAy+/dl1IC+jOofSWb/vjpMgo9rT7ivAYaKSCpWb5j5\nwPVNVxCRJGNMvuvp5cB2t1aplOoyESGjf18y+vflBxcMpaKmnuy8crbklrM5r5zNuWUs3Xrk5Pop\nMaFkJkcyqn9fBkSHEh0WSHSYg6jQQCJDA7Hb9BaEPVmb4W6MqReRe4CPsbpCLjbGbBWRXwNrjTHv\nA/8hIpcD9UAJcIsHa1ZKuUF4UACTB8cweXDMydfKKmvZklfO5lwr9NftL+Gfmw6f9V4R6BviIDo0\nkKiwQKJCreDvE+zAbhcCbILdZsMuQoBdsNsEu1jTALtgc80LYBNBxJrabNYU1+s2EYICbPSPCiE5\nKoQIvYir3fQiJqVUq4orajhyrJrSE3WUVNZSeqKWkhO1lFY2ndZReqKWY9V11DsNDa6Hu0WGOkiO\nCmFAVKg1jbamya7noYH+P1yD3olJKeUWMeFBnTrhaozBaaDe6cTpPH3a4LSWOY3BAE6nwbieO13v\nA2taWdtAXmkVh0oryS2t5FBJFbsKjrN8x1Fq6p2nfaYICFYTlDQ+bzqP9VdCgE3oE+Kgr+vRJ9g1\nH+p63mRZUIANh92Gwy4E2FxTu40Am+Cw2wiwCw6bNW2ct/WAJisNd6WUR4gIdgG7rXGIhM4PlTBm\nQORZrzmdhqKKGg6VVpFbWkluaRXVdQ0YAwbjmmJNXT8ixliv1zU4OVZdT3lVHceq6sgprKC8qo7y\nqrqzfjA6w26Tk+Hf+GPgsLmmduG6iQO547zBXf6c1mi4K6V8ks0mxPcJJr5PMOMHRbltu9V1DRxz\nBX1j2Nc1OKlvMNQ7ndQ1nZ4276TeaahrOLV+3RnL6xqc1DlNt1xnoOGulFJNBDvsBDvsxPcJ9nYp\nXaLXIyullB/ScFdKKT+k4a6UUn5Iw10ppfyQhrtSSvkhDXellPJDGu5KKeWHNNyVUsoPeW3gMBEp\nBA508u2xQJEby+kJ/G2f/G1/wP/2yd/2B/xvn5rbn0HGmLi23ui1cO8KEVnbnlHRfIm/7ZO/7Q/4\n3z752/6A/+1TV/ZHm2WUUsoPabgrpZQf8tVwf8bbBXiAv+2Tv+0P+N8++dv+gP/tU6f3xyfb3JVS\nSrXOV4/clVJKtULDXSml/JDPhbuIzBaRnSKyR0Qe8HY97iAi+0Vki4hsFBGfu2u4iCwWkaMikt3k\ntWgRWSYiu11T990qpxu0sE8Pikie63vaKCJzvFljR4jIABH5VES2i8hWEfmh63Wf/J5a2R9f/o6C\nReQbEdnk2qdfuV5PFZHVru/o7yIS2K7t+VKbu4jYgV3ALCAXWANcZ4zZ5tXCukhE9gNZxhifvPhC\nRKYBFcBfjTEZrtf+CJQYYx5y/QhHGWN+7M06O6KFfXoQqDDG/K83a+sMEUkCkowx60UkAlgHXAnc\ngg9+T63sz7X47nckQJgxpkJEHMAXwA+B/wTeNsa8LiKLgE3GmKfb2p6vHblPBPYYY/YaY2qB14Er\nvFxTr2eM+QwoOePlK4CXXPMvYf3D8xkt7JPPMsbkG2PWu+aPA9uB/vjo99TK/vgsY6lwPXW4HgaY\nCbzper3d35GvhXt/4FCT57n4+BfqYoB/icg6EVno7WLcJMEYkw/WP0Qg3sv1uMs9IrLZ1WzjE00Y\nZxKRFGAssBo/+J7O2B/w4e9IROwishE4CiwDcoAyY0y9a5V2Z56vhbs085rvtCu1bKoxZhxwCXC3\nq0lA9TxPA0OAMUA+8Ih3y+k4EQkH3gLuNcYc83Y9XdXM/vj0d2SMaTDGjAGSsVoq0ppbrT3b8rVw\nzwUGNHmeDBz2Ui1uY4w57JoeBd7B+lJ9XYGrXbSxffSol+vpMmNMgesfnxN4Fh/7nlztuG8Brxhj\n3na97LPfU3P74+vfUSNjTBmwApgMRIpIgGtRuzPP18J9DTDUdfY4EJgPvO/lmrpERMJcJ4QQkTDg\nIiC79Xf5hPeBm13zNwPvebEWt2gMQZer8KHvyXWy7nlguzHm0SaLfPJ7aml/fPw7ihORSNd8CHAh\n1rmET4FrXKu1+zvyqd4yAK6uTX8C7MBiY8z/eLmkLhGRwVhH6wABwKu+tk8i8howHWt40gLgl8C7\nwBvAQOAg8G1jjM+coGxhn6Zj/blvgP3Adxvbq3s6ETkX+BzYAjhdL/83Vju1z31PrezPdfjudzQK\n64SpHevA+w1jzK9dGfE6EA1sAG4wxtS0uT1fC3ellFJt87VmGaWUUu2g4a6UUn5Iw10ppfyQhrtS\nSvkhDXellPJDGu5KKeWHNNyVUsoP/X9JU6TfFNHbkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a50827550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n473RoaKYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a1fcae4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg11_bn, batch_size=128, lr=0.0001, pretrained=True\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)\n",
    "model_ft.apply(reset)\n",
    "model_vgg11, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=30, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_vgg11_2 = torch.load('log/model_vgg11_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 3375/3375 [01:11<00:00, 47.05it/s]\n"
     ]
    }
   ],
   "source": [
    "pred_test = predict(test_load, model_vgg11_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 3375/3375 [00:57<00:00, 58.67it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 3375/3375 [00:02<00:00, 1491.57it/s]\n"
     ]
    }
   ],
   "source": [
    "submit_prediction(test_load, model_vgg11_2, 'submit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ft = make_model('vgg11_bn', num_classes=12, pretrained=True, input_size=(160, 160), dropout_p=0.5)\n",
    "model_ft.cuda();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/29\n",
      "----------\n",
      "train Loss: 2.2616 Acc: 0.2101\n",
      "val Loss: 2.2083 Acc: 0.2861\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/29\n",
      "----------\n",
      "train Loss: 1.9591 Acc: 0.3362\n",
      "val Loss: 2.0239 Acc: 0.3579\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/29\n",
      "----------\n",
      "train Loss: 1.7988 Acc: 0.3960\n",
      "val Loss: 2.3892 Acc: 0.2944\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/29\n",
      "----------\n",
      "train Loss: 1.6210 Acc: 0.4611\n",
      "val Loss: 2.2571 Acc: 0.3698\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/29\n",
      "----------\n",
      "train Loss: 1.5122 Acc: 0.4952\n",
      "val Loss: 1.6099 Acc: 0.5161\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/29\n",
      "----------\n",
      "train Loss: 1.3947 Acc: 0.5407\n",
      "val Loss: 1.5318 Acc: 0.5271\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/29\n",
      "----------\n",
      "train Loss: 1.3095 Acc: 0.5683\n",
      "val Loss: 1.6743 Acc: 0.5189\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/29\n",
      "----------\n",
      "train Loss: 1.2531 Acc: 0.5877\n",
      "val Loss: 1.6540 Acc: 0.5216\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/29\n",
      "----------\n",
      "train Loss: 1.1968 Acc: 0.6111\n",
      "val Loss: 1.5078 Acc: 0.5235\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/29\n",
      "----------\n",
      "train Loss: 1.1452 Acc: 0.6208\n",
      "val Loss: 1.4876 Acc: 0.5106\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 10/29\n",
      "----------\n",
      "train Loss: 1.0692 Acc: 0.6457\n",
      "val Loss: 1.0815 Acc: 0.6615\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 11/29\n",
      "----------\n",
      "train Loss: 0.8936 Acc: 0.7082\n",
      "val Loss: 1.0081 Acc: 0.6863\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 12/29\n",
      "----------\n",
      "train Loss: 0.8295 Acc: 0.7356\n",
      "val Loss: 0.9937 Acc: 0.6863\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 13/29\n",
      "----------\n",
      "train Loss: 0.7890 Acc: 0.7469\n",
      "val Loss: 0.9758 Acc: 0.6964\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 14/29\n",
      "----------\n",
      "train Loss: 0.7575 Acc: 0.7563\n",
      "val Loss: 0.9743 Acc: 0.7001\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 15/29\n",
      "----------\n",
      "train Loss: 0.7265 Acc: 0.7676\n",
      "val Loss: 0.9768 Acc: 0.7038\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 16/29\n",
      "----------\n",
      "train Loss: 0.6946 Acc: 0.7770\n",
      "val Loss: 0.9988 Acc: 0.7001\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 17/29\n",
      "----------\n",
      "train Loss: 0.6658 Acc: 0.7892\n",
      "val Loss: 0.9845 Acc: 0.7010\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 18/29\n",
      "----------\n",
      "train Loss: 0.6311 Acc: 0.7991\n",
      "val Loss: 1.0067 Acc: 0.6983\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 19/29\n",
      "----------\n",
      "train Loss: 0.5998 Acc: 0.8102\n",
      "val Loss: 0.9944 Acc: 0.7029\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 20/29\n",
      "----------\n",
      "train Loss: 0.5703 Acc: 0.8279\n",
      "val Loss: 0.9389 Acc: 0.7176\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 21/29\n",
      "----------\n",
      "train Loss: 0.5457 Acc: 0.8336\n",
      "val Loss: 0.9387 Acc: 0.7139\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 22/29\n",
      "----------\n",
      "train Loss: 0.5524 Acc: 0.8318\n",
      "val Loss: 0.9389 Acc: 0.7176\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 23/29\n",
      "----------\n",
      "train Loss: 0.5359 Acc: 0.8332\n",
      "val Loss: 0.9400 Acc: 0.7167\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 24/29\n",
      "----------\n",
      "train Loss: 0.5285 Acc: 0.8329\n",
      "val Loss: 0.9405 Acc: 0.7194\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 25/29\n",
      "----------\n",
      "train Loss: 0.5246 Acc: 0.8408\n",
      "val Loss: 0.9397 Acc: 0.7167\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 26/29\n",
      "----------\n",
      "train Loss: 0.5228 Acc: 0.8488\n",
      "val Loss: 0.9415 Acc: 0.7213\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 27/29\n",
      "----------\n",
      "train Loss: 0.5241 Acc: 0.8353\n",
      "val Loss: 0.9417 Acc: 0.7139\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 28/29\n",
      "----------\n",
      "train Loss: 0.5174 Acc: 0.8417\n",
      "val Loss: 0.9422 Acc: 0.7185\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 29/29\n",
      "----------\n",
      "train Loss: 0.5122 Acc: 0.8426\n",
      "val Loss: 0.9420 Acc: 0.7194\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Training complete in 15m 18s\n",
      "Best val Acc: 0.721251, epoch 26\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRhO/Ukq1Mv8PExu1jeaHDxAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db2f093390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCUIW7sYYE4T+P3Bj4kCuEd7UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db3239d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg11_bn, batch_size=128, lr=0.0001, pretrained=True, dropout_p=0.5\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)\n",
    "model_ft.apply(reset)\n",
    "model_vgg11, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=30, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ft = make_model('vgg13_bn', num_classes=12, pretrained=True, input_size=(160, 160))\n",
    "model_ft.cuda();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/39\n",
      "----------\n",
      "train Loss: 2.2463 Acc: 0.2151\n",
      "val Loss: 2.3799 Acc: 0.2751\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/39\n",
      "----------\n",
      "train Loss: 1.9450 Acc: 0.3426\n",
      "val Loss: 1.8162 Acc: 0.3689\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/39\n",
      "----------\n",
      "train Loss: 1.7263 Acc: 0.4243\n",
      "val Loss: 1.9218 Acc: 0.4112\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/39\n",
      "----------\n",
      "train Loss: 1.6253 Acc: 0.4641\n",
      "val Loss: 1.8664 Acc: 0.4517\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/39\n",
      "----------\n",
      "train Loss: 1.4952 Acc: 0.5064\n",
      "val Loss: 1.7905 Acc: 0.4379\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/39\n",
      "----------\n",
      "train Loss: 1.3854 Acc: 0.5442\n",
      "val Loss: 1.5178 Acc: 0.5455\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/39\n",
      "----------\n",
      "train Loss: 1.3128 Acc: 0.5711\n",
      "val Loss: 1.7764 Acc: 0.4434\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/39\n",
      "----------\n",
      "train Loss: 1.2341 Acc: 0.5985\n",
      "val Loss: 1.5487 Acc: 0.5235\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/39\n",
      "----------\n",
      "train Loss: 1.1413 Acc: 0.6323\n",
      "val Loss: 1.5152 Acc: 0.5189\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/39\n",
      "----------\n",
      "train Loss: 1.1030 Acc: 0.6440\n",
      "val Loss: 2.0382 Acc: 0.4913\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 10/39\n",
      "----------\n",
      "train Loss: 1.0526 Acc: 0.6624\n",
      "val Loss: 1.0551 Acc: 0.6633\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 11/39\n",
      "----------\n",
      "train Loss: 0.8637 Acc: 0.7230\n",
      "val Loss: 1.0091 Acc: 0.6881\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 12/39\n",
      "----------\n",
      "train Loss: 0.8041 Acc: 0.7446\n",
      "val Loss: 0.9867 Acc: 0.7001\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 13/39\n",
      "----------\n",
      "train Loss: 0.7655 Acc: 0.7552\n",
      "val Loss: 0.9744 Acc: 0.7029\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 14/39\n",
      "----------\n",
      "train Loss: 0.7342 Acc: 0.7667\n",
      "val Loss: 0.9784 Acc: 0.7029\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 15/39\n",
      "----------\n",
      "train Loss: 0.7016 Acc: 0.7805\n",
      "val Loss: 0.9663 Acc: 0.7148\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 16/39\n",
      "----------\n",
      "train Loss: 0.6736 Acc: 0.7925\n",
      "val Loss: 0.9717 Acc: 0.7093\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 17/39\n",
      "----------\n",
      "train Loss: 0.6481 Acc: 0.7984\n",
      "val Loss: 0.9923 Acc: 0.7029\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 18/39\n",
      "----------\n",
      "train Loss: 0.6152 Acc: 0.8104\n",
      "val Loss: 1.0043 Acc: 0.7001\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 19/39\n",
      "----------\n",
      "train Loss: 0.5803 Acc: 0.8212\n",
      "val Loss: 0.9896 Acc: 0.7084\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 20/39\n",
      "----------\n",
      "train Loss: 0.5504 Acc: 0.8316\n",
      "val Loss: 0.9604 Acc: 0.7121\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 21/39\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.8401\n",
      "val Loss: 0.9600 Acc: 0.7093\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 22/39\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.8447\n",
      "val Loss: 0.9599 Acc: 0.7121\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 23/39\n",
      "----------\n",
      "train Loss: 0.5194 Acc: 0.8447\n",
      "val Loss: 0.9619 Acc: 0.7102\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 24/39\n",
      "----------\n",
      "train Loss: 0.5077 Acc: 0.8500\n",
      "val Loss: 0.9619 Acc: 0.7111\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 25/39\n",
      "----------\n",
      "train Loss: 0.5083 Acc: 0.8491\n",
      "val Loss: 0.9631 Acc: 0.7121\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 26/39\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.8470\n",
      "val Loss: 0.9635 Acc: 0.7121\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 27/39\n",
      "----------\n",
      "train Loss: 0.5021 Acc: 0.8504\n",
      "val Loss: 0.9645 Acc: 0.7148\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 28/39\n",
      "----------\n",
      "train Loss: 0.5105 Acc: 0.8451\n",
      "val Loss: 0.9643 Acc: 0.7121\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 29/39\n",
      "----------\n",
      "train Loss: 0.4933 Acc: 0.8541\n",
      "val Loss: 0.9664 Acc: 0.7111\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 30/39\n",
      "----------\n",
      "train Loss: 0.4960 Acc: 0.8557\n",
      "val Loss: 0.9675 Acc: 0.7121\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Epoch 31/39\n",
      "----------\n",
      "train Loss: 0.4923 Acc: 0.8546\n",
      "val Loss: 0.9682 Acc: 0.7111\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Epoch 32/39\n",
      "----------\n",
      "train Loss: 0.4876 Acc: 0.8585\n",
      "val Loss: 0.9676 Acc: 0.7102\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Epoch 33/39\n",
      "----------\n",
      "train Loss: 0.4828 Acc: 0.8594\n",
      "val Loss: 0.9684 Acc: 0.7111\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Epoch 34/39\n",
      "----------\n",
      "train Loss: 0.4812 Acc: 0.8576\n",
      "val Loss: 0.9685 Acc: 0.7111\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Epoch 35/39\n",
      "----------\n",
      "train Loss: 0.4869 Acc: 0.8596\n",
      "val Loss: 0.9684 Acc: 0.7111\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Epoch 36/39\n",
      "----------\n",
      "train Loss: 0.4863 Acc: 0.8622\n",
      "val Loss: 0.9693 Acc: 0.7130\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Epoch 37/39\n",
      "----------\n",
      "train Loss: 0.4892 Acc: 0.8550\n",
      "val Loss: 0.9681 Acc: 0.7111\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Epoch 38/39\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.8543\n",
      "val Loss: 0.9694 Acc: 0.7111\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Epoch 39/39\n",
      "----------\n",
      "train Loss: 0.4878 Acc: 0.8537\n",
      "val Loss: 0.9684 Acc: 0.7111\n",
      "lr:  1.0000000000000002e-07\n",
      "\n",
      "Training complete in 27m 16s\n",
      "Best val Acc: 0.714811, epoch 15\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e84ce2c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e959acdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg13_bn, batch_size=32, lr=0.0001, pretrained=True\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)\n",
    "model_ft.apply(reset)\n",
    "model_vgg11, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=40, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ft = make_model('vgg11_bn', num_classes=12, pretrained=True, input_size=(160, 160))\n",
    "model_ft.cuda();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/29\n",
      "----------\n",
      "train Loss: 2.1990 Acc: 0.2428\n",
      "val Loss: 2.3634 Acc: 0.3146\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/29\n",
      "----------\n",
      "train Loss: 1.8458 Acc: 0.3801\n",
      "val Loss: 2.5638 Acc: 0.3707\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/29\n",
      "----------\n",
      "train Loss: 1.6507 Acc: 0.4473\n",
      "val Loss: 2.3135 Acc: 0.3533\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/29\n",
      "----------\n",
      "train Loss: 1.5682 Acc: 0.4798\n",
      "val Loss: 1.5832 Acc: 0.4775\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/29\n",
      "----------\n",
      "train Loss: 1.3942 Acc: 0.5439\n",
      "val Loss: 1.6089 Acc: 0.4738\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/29\n",
      "----------\n",
      "train Loss: 1.2865 Acc: 0.5732\n",
      "val Loss: 1.7709 Acc: 0.4756\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/29\n",
      "----------\n",
      "train Loss: 1.2233 Acc: 0.6012\n",
      "val Loss: 1.4825 Acc: 0.5179\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/29\n",
      "----------\n",
      "train Loss: 1.0773 Acc: 0.6417\n",
      "val Loss: 2.1844 Acc: 0.4903\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/29\n",
      "----------\n",
      "train Loss: 0.9958 Acc: 0.6776\n",
      "val Loss: 1.7311 Acc: 0.4692\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/29\n",
      "----------\n",
      "train Loss: 0.8817 Acc: 0.7027\n",
      "val Loss: 1.5467 Acc: 0.5593\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 10/29\n",
      "----------\n",
      "train Loss: 0.7963 Acc: 0.7474\n",
      "val Loss: 1.1067 Acc: 0.6550\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 11/29\n",
      "----------\n",
      "train Loss: 0.5726 Acc: 0.8226\n",
      "val Loss: 0.9804 Acc: 0.7001\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 12/29\n",
      "----------\n",
      "train Loss: 0.5066 Acc: 0.8456\n",
      "val Loss: 0.9851 Acc: 0.7093\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 13/29\n",
      "----------\n",
      "train Loss: 0.4465 Acc: 0.8675\n",
      "val Loss: 0.9747 Acc: 0.7102\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 14/29\n",
      "----------\n",
      "train Loss: 0.3915 Acc: 0.8893\n",
      "val Loss: 0.9930 Acc: 0.7093\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 15/29\n",
      "----------\n",
      "train Loss: 0.3580 Acc: 0.8962\n",
      "val Loss: 0.9988 Acc: 0.7121\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 16/29\n",
      "----------\n",
      "train Loss: 0.3131 Acc: 0.9158\n",
      "val Loss: 1.0245 Acc: 0.7111\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 17/29\n",
      "----------\n",
      "train Loss: 0.2791 Acc: 0.9243\n",
      "val Loss: 1.0516 Acc: 0.7001\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 18/29\n",
      "----------\n",
      "train Loss: 0.2451 Acc: 0.9379\n",
      "val Loss: 1.0815 Acc: 0.7065\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 19/29\n",
      "----------\n",
      "train Loss: 0.2037 Acc: 0.9505\n",
      "val Loss: 1.1001 Acc: 0.7075\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 20/29\n",
      "----------\n",
      "train Loss: 0.1925 Acc: 0.9517\n",
      "val Loss: 1.0412 Acc: 0.7222\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 21/29\n",
      "----------\n",
      "train Loss: 0.1675 Acc: 0.9678\n",
      "val Loss: 1.0411 Acc: 0.7194\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 22/29\n",
      "----------\n",
      "train Loss: 0.1610 Acc: 0.9669\n",
      "val Loss: 1.0374 Acc: 0.7222\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 23/29\n",
      "----------\n",
      "train Loss: 0.1586 Acc: 0.9673\n",
      "val Loss: 1.0433 Acc: 0.7213\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 24/29\n",
      "----------\n",
      "train Loss: 0.1584 Acc: 0.9657\n",
      "val Loss: 1.0436 Acc: 0.7176\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 25/29\n",
      "----------\n",
      "train Loss: 0.1547 Acc: 0.9694\n",
      "val Loss: 1.0466 Acc: 0.7194\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 26/29\n",
      "----------\n",
      "train Loss: 0.1548 Acc: 0.9682\n",
      "val Loss: 1.0457 Acc: 0.7213\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 27/29\n",
      "----------\n",
      "train Loss: 0.1483 Acc: 0.9735\n",
      "val Loss: 1.0447 Acc: 0.7240\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 28/29\n",
      "----------\n",
      "train Loss: 0.1434 Acc: 0.9705\n",
      "val Loss: 1.0510 Acc: 0.7222\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 29/29\n",
      "----------\n",
      "train Loss: 0.1404 Acc: 0.9726\n",
      "val Loss: 1.0501 Acc: 0.7222\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Training complete in 15m 11s\n",
      "Best val Acc: 0.724011, epoch 27\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlVIqCGm4K6VUENJwV0qpIPT/nB0r3L0S/Y0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e92f468d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmBBk4W6MMSHo/wPJnMtu1WNriwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21eecee3668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg11_bn, batch_size=128, lr=0.0001, pretrained=True, no transforms.RandomHorizontalFlip()\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)\n",
    "model_ft.apply(reset)\n",
    "model_vgg11, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=30, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/9\n",
      "----------\n",
      "train Loss: 2.2064 Acc: 0.2329\n",
      "val Loss: 1.9440 Acc: 0.3533\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/9\n",
      "----------\n",
      "train Loss: 1.8319 Acc: 0.4001\n",
      "val Loss: 3.2771 Acc: 0.3100\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/9\n",
      "----------\n",
      "train Loss: 1.6734 Acc: 0.4478\n",
      "val Loss: 1.8551 Acc: 0.4204\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/9\n",
      "----------\n",
      "train Loss: 1.4987 Acc: 0.5044\n",
      "val Loss: 1.7612 Acc: 0.4397\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/9\n",
      "----------\n",
      "train Loss: 1.4112 Acc: 0.5361\n",
      "val Loss: 1.7421 Acc: 0.4554\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/9\n",
      "----------\n",
      "train Loss: 1.2889 Acc: 0.5826\n",
      "val Loss: 1.8841 Acc: 0.4857\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/9\n",
      "----------\n",
      "train Loss: 1.1837 Acc: 0.6160\n",
      "val Loss: 1.4970 Acc: 0.5161\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/9\n",
      "----------\n",
      "train Loss: 1.1115 Acc: 0.6434\n",
      "val Loss: 1.8840 Acc: 0.5023\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/9\n",
      "----------\n",
      "train Loss: 1.0844 Acc: 0.6576\n",
      "val Loss: 1.6367 Acc: 0.4609\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/9\n",
      "----------\n",
      "train Loss: 0.9449 Acc: 0.6940\n",
      "val Loss: 1.3950 Acc: 0.5759\n",
      "lr:  0.0001\n",
      "\n",
      "Training complete in 5m 3s\n",
      "Best val Acc: 0.575897, epoch 9\n",
      "Epoch 0/9\n",
      "----------\n",
      "train Loss: 0.8240 Acc: 0.7354\n",
      "val Loss: 1.0442 Acc: 0.6762\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 1/9\n",
      "----------\n",
      "train Loss: 0.6419 Acc: 0.8067\n",
      "val Loss: 1.0090 Acc: 0.6826\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 2/9\n",
      "----------\n",
      "train Loss: 0.5684 Acc: 0.8327\n",
      "val Loss: 1.0030 Acc: 0.6808\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 3/9\n",
      "----------\n",
      "train Loss: 0.5136 Acc: 0.8472\n",
      "val Loss: 1.0211 Acc: 0.6799\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 4/9\n",
      "----------\n",
      "train Loss: 0.4636 Acc: 0.8665\n",
      "val Loss: 1.0192 Acc: 0.6789\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 5/9\n",
      "----------\n",
      "train Loss: 0.4094 Acc: 0.8852\n",
      "val Loss: 1.0159 Acc: 0.6900\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 6/9\n",
      "----------\n",
      "train Loss: 0.3668 Acc: 0.8951\n",
      "val Loss: 1.0156 Acc: 0.6927\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 7/9\n",
      "----------\n",
      "train Loss: 0.3227 Acc: 0.9110\n",
      "val Loss: 1.0114 Acc: 0.7056\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 8/9\n",
      "----------\n",
      "train Loss: 0.2796 Acc: 0.9296\n",
      "val Loss: 1.0273 Acc: 0.6983\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 9/9\n",
      "----------\n",
      "train Loss: 0.2470 Acc: 0.9390\n",
      "val Loss: 1.0444 Acc: 0.7010\n",
      "lr:  1e-05\n",
      "\n",
      "Training complete in 5m 10s\n",
      "Best val Acc: 0.705612, epoch 7\n",
      "Epoch 0/9\n",
      "----------\n",
      "train Loss: 0.2835 Acc: 0.9275\n",
      "val Loss: 1.0069 Acc: 0.7047\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Epoch 1/9\n",
      "----------\n",
      "train Loss: 0.2825 Acc: 0.9280\n",
      "val Loss: 1.0064 Acc: 0.7047\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Epoch 2/9\n",
      "----------\n",
      "train Loss: 0.2811 Acc: 0.9301\n",
      "val Loss: 1.0062 Acc: 0.7047\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Epoch 3/9\n",
      "----------\n",
      "train Loss: 0.2824 Acc: 0.9268\n",
      "val Loss: 1.0064 Acc: 0.7038\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Epoch 4/9\n",
      "----------\n",
      "train Loss: 0.2742 Acc: 0.9275\n",
      "val Loss: 1.0072 Acc: 0.7019\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Epoch 5/9\n",
      "----------\n",
      "train Loss: 0.2733 Acc: 0.9305\n",
      "val Loss: 1.0077 Acc: 0.7010\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Epoch 6/9\n",
      "----------\n",
      "train Loss: 0.2731 Acc: 0.9321\n",
      "val Loss: 1.0084 Acc: 0.7019\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Epoch 7/9\n",
      "----------\n",
      "train Loss: 0.2715 Acc: 0.9337\n",
      "val Loss: 1.0090 Acc: 0.6992\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Epoch 8/9\n",
      "----------\n",
      "train Loss: 0.2785 Acc: 0.9273\n",
      "val Loss: 1.0094 Acc: 0.7010\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Epoch 9/9\n",
      "----------\n",
      "train Loss: 0.2761 Acc: 0.9275\n",
      "val Loss: 1.0097 Acc: 0.7019\n",
      "lr:  1.0000000000000001e-07\n",
      "\n",
      "Training complete in 5m 5s\n",
      "Best val Acc: 0.704692, epoch 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21ee078fe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npOGulFJjkIa7UkqNQRruSik1Bv1/AuxeskBBk14AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21ef7c757f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_ft.apply(reset)\n",
    "lr = 0.0001\n",
    "loss_log_all = []\n",
    "acc_log_all = []\n",
    "for i in range(3):\n",
    "    lr *= 10**(-i)\n",
    "    optimizer_ft = optim.Adam(model_ft.parameters(), lr=lr)\n",
    "    model_vgg11, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=10, batch_accum=8)\n",
    "    loss_log_all.append(loss_log)\n",
    "    acc_log_all.append(acc_log)\n",
    "    model_ft = model_vgg11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg16_bn-6c64b313.pth\" to C:\\Users\\Андрей/.torch\\models\\vgg16_bn-6c64b313.pth\n",
      "100%|███████████████████████████████████████████████████████████████| 553507836/553507836 [01:22<00:00, 6746448.87it/s]\n"
     ]
    }
   ],
   "source": [
    "model_ft = make_model('vgg16_bn', num_classes=12, pretrained=True, input_size=(160, 160))\n",
    "model_ft.cuda();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/29\n",
      "----------\n",
      "train Loss: 2.2416 Acc: 0.2145\n",
      "val Loss: 3.7934 Acc: 0.2493\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/29\n",
      "----------\n",
      "train Loss: 1.9231 Acc: 0.3440\n",
      "val Loss: 2.7477 Acc: 0.3054\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/29\n",
      "----------\n",
      "train Loss: 1.7139 Acc: 0.4202\n",
      "val Loss: 2.1901 Acc: 0.4112\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/29\n",
      "----------\n",
      "train Loss: 1.6381 Acc: 0.4554\n",
      "val Loss: 1.8266 Acc: 0.4664\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/29\n",
      "----------\n",
      "train Loss: 1.4866 Acc: 0.5085\n",
      "val Loss: 2.3755 Acc: 0.4425\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/29\n",
      "----------\n",
      "train Loss: 1.3752 Acc: 0.5534\n",
      "val Loss: 1.3949 Acc: 0.5483\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/29\n",
      "----------\n",
      "train Loss: 1.2697 Acc: 0.5851\n",
      "val Loss: 1.7563 Acc: 0.4793\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/29\n",
      "----------\n",
      "train Loss: 1.1261 Acc: 0.6369\n",
      "val Loss: 1.3074 Acc: 0.5713\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/29\n",
      "----------\n",
      "train Loss: 1.0104 Acc: 0.6677\n",
      "val Loss: 1.3064 Acc: 0.5851\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/29\n",
      "----------\n",
      "train Loss: 0.9390 Acc: 0.6954\n",
      "val Loss: 2.1195 Acc: 0.5198\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 10/29\n",
      "----------\n",
      "train Loss: 0.9598 Acc: 0.6931\n",
      "val Loss: 1.0590 Acc: 0.6725\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 11/29\n",
      "----------\n",
      "train Loss: 0.6812 Acc: 0.7858\n",
      "val Loss: 0.9841 Acc: 0.6964\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 12/29\n",
      "----------\n",
      "train Loss: 0.5756 Acc: 0.8240\n",
      "val Loss: 0.9665 Acc: 0.7148\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 13/29\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.8454\n",
      "val Loss: 0.9696 Acc: 0.7102\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 14/29\n",
      "----------\n",
      "train Loss: 0.4506 Acc: 0.8661\n",
      "val Loss: 0.9869 Acc: 0.7194\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 15/29\n",
      "----------\n",
      "train Loss: 0.3954 Acc: 0.8847\n",
      "val Loss: 1.0002 Acc: 0.7065\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 16/29\n",
      "----------\n",
      "train Loss: 0.3443 Acc: 0.8990\n",
      "val Loss: 1.0239 Acc: 0.7093\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 17/29\n",
      "----------\n",
      "train Loss: 0.2889 Acc: 0.9190\n",
      "val Loss: 1.0901 Acc: 0.6992\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 18/29\n",
      "----------\n",
      "train Loss: 0.2398 Acc: 0.9374\n",
      "val Loss: 1.1095 Acc: 0.6927\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 19/29\n",
      "----------\n",
      "train Loss: 0.2020 Acc: 0.9517\n",
      "val Loss: 1.1179 Acc: 0.6918\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 20/29\n",
      "----------\n",
      "train Loss: 0.1686 Acc: 0.9586\n",
      "val Loss: 1.1132 Acc: 0.7029\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 21/29\n",
      "----------\n",
      "train Loss: 0.1508 Acc: 0.9662\n",
      "val Loss: 1.1246 Acc: 0.7019\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 22/29\n",
      "----------\n",
      "train Loss: 0.1459 Acc: 0.9705\n",
      "val Loss: 1.1216 Acc: 0.7029\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 23/29\n",
      "----------\n",
      "train Loss: 0.1388 Acc: 0.9705\n",
      "val Loss: 1.1270 Acc: 0.7010\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 24/29\n",
      "----------\n",
      "train Loss: 0.1402 Acc: 0.9717\n",
      "val Loss: 1.1312 Acc: 0.7019\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 25/29\n",
      "----------\n",
      "train Loss: 0.1334 Acc: 0.9705\n",
      "val Loss: 1.1361 Acc: 0.7065\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 26/29\n",
      "----------\n",
      "train Loss: 0.1321 Acc: 0.9740\n",
      "val Loss: 1.1390 Acc: 0.7047\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 27/29\n",
      "----------\n",
      "train Loss: 0.1280 Acc: 0.9724\n",
      "val Loss: 1.1426 Acc: 0.7084\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 28/29\n",
      "----------\n",
      "train Loss: 0.1250 Acc: 0.9745\n",
      "val Loss: 1.1444 Acc: 0.7047\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 29/29\n",
      "----------\n",
      "train Loss: 0.1226 Acc: 0.9745\n",
      "val Loss: 1.1488 Acc: 0.7065\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Training complete in 24m 29s\n",
      "Best val Acc: 0.719411, epoch 14\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqTCk4a6UUmHo/wPRVrhwSn6rGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15eb5732be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ecf5b97f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg16_bn, batch_size=128, lr=0.0001, pretrained=True, no transforms.RandomHorizontalFlip()\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optaimizer_ft, step_size=10, gamma=0.1)\n",
    "model_ft.apply(reset)\n",
    "model_vgg16, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=30, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/9\n",
      "----------\n",
      "train Loss: 2.2555 Acc: 0.2075\n",
      "val Loss: 2.3692 Acc: 0.2971\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/9\n",
      "----------\n",
      "train Loss: 1.9596 Acc: 0.3320\n",
      "val Loss: 2.3052 Acc: 0.3036\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/9\n",
      "----------\n",
      "train Loss: 1.7496 Acc: 0.4089\n",
      "val Loss: 2.1921 Acc: 0.3698\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/9\n",
      "----------\n",
      "train Loss: 1.6112 Acc: 0.4632\n",
      "val Loss: 1.7785 Acc: 0.4517\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/9\n",
      "----------\n",
      "train Loss: 1.4976 Acc: 0.4995\n",
      "val Loss: 2.1938 Acc: 0.3901\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/9\n",
      "----------\n",
      "train Loss: 1.4399 Acc: 0.5306\n",
      "val Loss: 2.1142 Acc: 0.4269\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/9\n",
      "----------\n",
      "train Loss: 1.3373 Acc: 0.5557\n",
      "val Loss: 1.4508 Acc: 0.5290\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/9\n",
      "----------\n",
      "train Loss: 1.2151 Acc: 0.6008\n",
      "val Loss: 1.7272 Acc: 0.4793\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/9\n",
      "----------\n",
      "train Loss: 1.0635 Acc: 0.6484\n",
      "val Loss: 1.5948 Acc: 0.4710\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/9\n",
      "----------\n",
      "train Loss: 0.9505 Acc: 0.6903\n",
      "val Loss: 1.9129 Acc: 0.4324\n",
      "lr:  0.0001\n",
      "\n",
      "Training complete in 8m 10s\n",
      "Best val Acc: 0.528979, epoch 6\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+Pa8AAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ebe45fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ebe4d7e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg16_bn, batch_size=128, lr=0.0001, pretrained=True, no transforms.RandomHorizontalFlip()\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "model_ft.apply(reset)\n",
    "model_vgg16, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, num_epochs=10, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/9\n",
      "----------\n",
      "train Loss: 1.1918 Acc: 0.6125\n",
      "val Loss: 1.1612 Acc: 0.6311\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 1/9\n",
      "----------\n",
      "train Loss: 1.0235 Acc: 0.6643\n",
      "val Loss: 1.1261 Acc: 0.6421\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 2/9\n",
      "----------\n",
      "train Loss: 0.9516 Acc: 0.6889\n",
      "val Loss: 1.1036 Acc: 0.6541\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 3/9\n",
      "----------\n",
      "train Loss: 0.9006 Acc: 0.7052\n",
      "val Loss: 1.0969 Acc: 0.6569\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 4/9\n",
      "----------\n",
      "train Loss: 0.8531 Acc: 0.7220\n",
      "val Loss: 1.0944 Acc: 0.6559\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 5/9\n",
      "----------\n",
      "train Loss: 0.7952 Acc: 0.7448\n",
      "val Loss: 1.1193 Acc: 0.6486\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 6/9\n",
      "----------\n",
      "train Loss: 0.7408 Acc: 0.7648\n",
      "val Loss: 1.1308 Acc: 0.6550\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 7/9\n",
      "----------\n",
      "train Loss: 0.6828 Acc: 0.7879\n",
      "val Loss: 1.1324 Acc: 0.6587\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 8/9\n",
      "----------\n",
      "train Loss: 0.6186 Acc: 0.8113\n",
      "val Loss: 1.1476 Acc: 0.6661\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 9/9\n",
      "----------\n",
      "train Loss: 0.5524 Acc: 0.8318\n",
      "val Loss: 1.1794 Acc: 0.6587\n",
      "lr:  1e-05\n",
      "\n",
      "Training complete in 8m 12s\n",
      "Best val Acc: 0.666053, epoch 8\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4a6UUh5Iw10ppTyQhrtSSnmg/w/k8USIaBfGzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15eb57d17b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ebe482ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg16_bn, batch_size=128, lr=0.0001, pretrained=True, no transforms.RandomHorizontalFlip()\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.00001)\n",
    "model_vgg16, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, num_epochs=10, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg19_bn-c79401a0.pth\" to C:\\Users\\Андрей/.torch\\models\\vgg19_bn-c79401a0.pth\n",
      "100%|███████████████████████████████████████████████████████████████| 574769405/574769405 [01:26<00:00, 6646209.45it/s]\n"
     ]
    }
   ],
   "source": [
    "model_ft = make_model('vgg19_bn', num_classes=12, pretrained=True, input_size=(160, 160))\n",
    "model_ft.cuda();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/29\n",
      "----------\n",
      "train Loss: 2.3095 Acc: 0.1829\n",
      "val Loss: 4.9430 Acc: 0.2024\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/29\n",
      "----------\n",
      "train Loss: 2.0545 Acc: 0.2809\n",
      "val Loss: 2.7675 Acc: 0.3063\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/29\n",
      "----------\n",
      "train Loss: 1.8326 Acc: 0.3755\n",
      "val Loss: 2.5006 Acc: 0.3772\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/29\n",
      "----------\n",
      "train Loss: 1.6512 Acc: 0.4468\n",
      "val Loss: 1.6687 Acc: 0.4379\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/29\n",
      "----------\n",
      "train Loss: 1.5225 Acc: 0.5002\n",
      "val Loss: 1.4983 Acc: 0.5161\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/29\n",
      "----------\n",
      "train Loss: 1.4585 Acc: 0.5253\n",
      "val Loss: 1.8046 Acc: 0.4269\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/29\n",
      "----------\n",
      "train Loss: 1.2988 Acc: 0.5769\n",
      "val Loss: 1.5085 Acc: 0.5455\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/29\n",
      "----------\n",
      "train Loss: 1.1963 Acc: 0.6086\n",
      "val Loss: 4.0727 Acc: 0.3569\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/29\n",
      "----------\n",
      "train Loss: 1.3019 Acc: 0.5796\n",
      "val Loss: 4.1808 Acc: 0.3707\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/29\n",
      "----------\n",
      "train Loss: 1.1793 Acc: 0.6160\n",
      "val Loss: 2.7285 Acc: 0.4167\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 10/29\n",
      "----------\n",
      "train Loss: 1.1206 Acc: 0.6371\n",
      "val Loss: 1.1541 Acc: 0.6348\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 11/29\n",
      "----------\n",
      "train Loss: 0.8815 Acc: 0.7195\n",
      "val Loss: 1.0723 Acc: 0.6707\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 12/29\n",
      "----------\n",
      "train Loss: 0.7794 Acc: 0.7526\n",
      "val Loss: 1.0533 Acc: 0.6872\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 13/29\n",
      "----------\n",
      "train Loss: 0.7209 Acc: 0.7731\n",
      "val Loss: 1.0464 Acc: 0.6817\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 14/29\n",
      "----------\n",
      "train Loss: 0.6634 Acc: 0.7922\n",
      "val Loss: 1.0482 Acc: 0.6918\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 15/29\n",
      "----------\n",
      "train Loss: 0.6091 Acc: 0.8116\n",
      "val Loss: 1.0669 Acc: 0.6918\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 16/29\n",
      "----------\n",
      "train Loss: 0.5477 Acc: 0.8373\n",
      "val Loss: 1.0956 Acc: 0.6891\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 17/29\n",
      "----------\n",
      "train Loss: 0.4852 Acc: 0.8500\n",
      "val Loss: 1.1242 Acc: 0.6900\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 18/29\n",
      "----------\n",
      "train Loss: 0.4178 Acc: 0.8780\n",
      "val Loss: 1.1641 Acc: 0.6863\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 19/29\n",
      "----------\n",
      "train Loss: 0.3543 Acc: 0.8969\n",
      "val Loss: 1.2485 Acc: 0.6753\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 20/29\n",
      "----------\n",
      "train Loss: 0.3021 Acc: 0.9135\n",
      "val Loss: 1.2071 Acc: 0.6845\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 21/29\n",
      "----------\n",
      "train Loss: 0.2769 Acc: 0.9248\n",
      "val Loss: 1.2095 Acc: 0.6845\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 22/29\n",
      "----------\n",
      "train Loss: 0.2651 Acc: 0.9284\n",
      "val Loss: 1.2154 Acc: 0.6854\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 23/29\n",
      "----------\n",
      "train Loss: 0.2597 Acc: 0.9358\n",
      "val Loss: 1.2226 Acc: 0.6845\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 24/29\n",
      "----------\n",
      "train Loss: 0.2494 Acc: 0.9379\n",
      "val Loss: 1.2314 Acc: 0.6854\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 25/29\n",
      "----------\n",
      "train Loss: 0.2497 Acc: 0.9358\n",
      "val Loss: 1.2390 Acc: 0.6863\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 26/29\n",
      "----------\n",
      "train Loss: 0.2370 Acc: 0.9402\n",
      "val Loss: 1.2439 Acc: 0.6854\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 27/29\n",
      "----------\n",
      "train Loss: 0.2342 Acc: 0.9402\n",
      "val Loss: 1.2498 Acc: 0.6863\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 28/29\n",
      "----------\n",
      "train Loss: 0.2277 Acc: 0.9457\n",
      "val Loss: 1.2596 Acc: 0.6881\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 29/29\n",
      "----------\n",
      "train Loss: 0.2223 Acc: 0.9473\n",
      "val Loss: 1.2662 Acc: 0.6835\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Training complete in 29m 6s\n",
      "Best val Acc: 0.691812, epoch 14\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEICAYAAAB/Dx7IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f3d5c0908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xd8VfX9x/HXJ3sDGUBMgESG7CEx\noLhXQStuBcHRVmmrtNaftWqHtba/1g5tK66fWiwORAQHVuoWcYAmIHuGMBIgIQmQAWTez++Pc4EI\nCbmZd+TzfDzuI/eee+65n5ObvPPN93zP94iqYowxJrAEebsAY4wxbc/C3RhjApCFuzHGBCALd2OM\nCUAW7sYYE4As3I0xJgBZuBtjTACycDd+R0QWicg+EQn3di3G+CoLd+NXRCQNOAtQYGIHvm9IR72X\nMW3Bwt34m5uApcC/gZsPLxSRSBF5RES2i0ipiHwuIpHu584UkS9FZL+I5InILe7li0Tk1nrbuEVE\nPq/3WEXkDhHZDGx2L/unextlIrJMRM6qt36wiPxSRLaISLn7+V4i8oSIPFJ/J0TkbRH5WXt8g4wB\nC3fjf24CXnbfviMiPdzL/waMBs4A4oFfAC4R6Q38F5gBJAEjgRXNeL8rgDHAYPfjLPc24oHZwGsi\nEuF+7n+AycAlQBzwfeAgMAuYLCJBACKSCFwAvNKcHTemOSzcjd8QkTOBPsBcVV0GbAFucIfm94E7\nVXWnqtap6peqWgVMAT5U1VdUtUZVS1S1OeH+J1Xdq6qHAFT1Jfc2alX1ESAcOMW97q3Ar1V1ozpW\nutf9GijFCXSAScAiVS1s5bfEmEZZuBt/cjPwvqoWux/Pdi9LBCJwwv5YvRpZ7qm8+g9E5G4RWe/u\n+tkPdHG/f1PvNQuY6r4/FXixFTUZ0yQ7SGT8grv//DogWEQK3IvDga5AMlAJ9AVWHvPSPCCzkc0e\nAKLqPe7ZwDpHpk1196/fi9MCX6uqLhHZB0i99+oLrGlgOy8Ba0RkBDAIeLORmoxpE9ZyN/7iCqAO\np+97pPs2CPgMpx9+JvCoiJzkPrB5unuo5MvAhSJynYiEiEiCiIx0b3MFcJWIRIlIP+AHTdQQC9QC\nRUCIiDyA07d+2HPA70WkvziGi0gCgKrm4/TXvwjMP9zNY0x7sXA3/uJm4HlV3aGqBYdvwOM4/er3\nAatxAnQv8GcgSFV34BzgvNu9fAUwwr3NvwPVQCFOt8nLTdTwHs7B2U3Adpz/Fup32zwKzAXeB8qA\nfwGR9Z6fBQzDumRMBxC7WIcxHUNEzsbpnklTVZe36zGBzVruxnQAEQkF7gSes2A3HcHC3Zh2JiKD\ngP04B37/4eVyTCdh3TLGGBOArOVujDEByGvj3BMTEzUtLc1bb2+MMX5p2bJlxaqa1NR6Xgv3tLQ0\nsrOzvfX2xhjjl0RkuyfrWbeMMcYEIAt3Y4wJQBbuxhgTgHxq4rCamhry8/OprKz0dintKiIigtTU\nVEJDQ71dijEmQPlUuOfn5xMbG0taWhoi0vQL/JCqUlJSQn5+Punp6d4uxxgToHyqW6ayspKEhISA\nDXYAESEhISHg/zsxxniXT4U7ENDBflhn2EdjjHf5VLeMMcb4o+paF3sPVHOopo6D1bVU1tRxqNrF\noZo6DtXUUVld537O+XrBwO6M6NW1XWuycK9n//79zJ49m9tvv71Zr7vkkkuYPXs2Xbu274dljPGe\nssoadpQcZMfeg2wvOciOvQfYXuLc3116CFczpunqHhtu4d6R9u/fz5NPPnlcuNfV1REcHNzo6xYu\nXNjepRnT6agqlTUual0uosNCCApqXnemqlJeVUtBaeXRW1klu0srKaus8WgbLpeyq7SSHSUH2Hfw\n269JiA6jd0IUp6V1o3dCKj3jIogKCyYiNJjIsGCiwoKJDD36ODLUuYWHBDV7X1rCwr2e++67jy1b\ntjBy5EhCQ0OJiYkhOTmZFStWsG7dOq644gry8vKorKzkzjvvZNq0acDRqRQqKiqYMGECZ555Jl9+\n+SUpKSm89dZbREZGNvHOxgS+OpeyveQA63eXs7GwnP0Hq6moquVAVS0Hqurq3a+lvKqWg9V11NVr\nDkeFBRMdHkJMeAjR4cFEhx2+79yiwoLZd7CaQneAF5RWcrC67rg6EqLD6BIViifxKiL0jItgwrBk\nesdH0Sc+it4JUfSOjyI2wreHMvtsuP/u7bWs21XWptscfFIcv71sSKPPP/zww6xZs4YVK1awaNEi\nLr30UtasWXNkyOLMmTOJj4/n0KFDnHbaaVx99dUkJCR8axubN2/mlVde4dlnn+W6665j/vz5TJ06\ntaG3MyZglR6sYUNBGet3l7GhoJz1u8vYWFhOZY1znZIggbjI0HoBHUxsRAjJXSK+HeDhIYQECQeq\n6pzgr66lvPLoH4TdpZUcqHYeH6yuo2tkKD26RDCwZyznDEgiuUsEPeIiSO4SSXKXCLrHhRMe0vh/\n4YHEZ8PdF2RmZn5rLPpjjz3GG2+8AUBeXh6bN28+LtzT09MZOdK5/vLo0aPZtm1bh9VrTEeoqq2j\nuKKa4vIqisqrKK44fKsmb+9BNhSUs3P/0et/d4sKZVByHFPG9GFgz1gGJcfRr3sMEaGdI2S9xWfD\n/UQt7I4SHR195P6iRYv48MMPWbJkCVFRUZx77rkNjlUPDw8/cj84OJhDh+wi98Y/FZZVsnhTEUu2\nlJC//xDFFU6Yl1fWNrh+bEQIJ3WJJCOtG1N79mFQshPk3WPDbfivF/hsuHtDbGws5eXlDT5XWlpK\nt27diIqKYsOGDSxdurSDqzOmfVXV1pG1dR+LNxexeFMRGwqc34XEmHBOTopmUM84zuoXRmJMOEmx\n4STGhJMYG05ijLPMWuK+xcK9noSEBMaNG8fQoUOJjIykR48eR54bP348Tz/9NMOHD+eUU05h7Nix\nXqzUmNZTVXKLD7B4kxPmS3P3cqimjtBg4bS0eO6bMJCz+ycxKDnWWt5+yGvXUM3IyNBjL9axfv16\nBg0a5JV6Olpn2lfjW7aXHGD21zt4Z9Vu8vc53YbpidGc3T+RswckMfbkBKLDrd3nq0RkmapmNLWe\nR5+giIwH/gkEA8+p6sPHPN8HmAkkAXuBqaqa3+yqjTHtorbOxUcb9vDS0u18trmY4CDh3AFJ/Oic\nvpwzIIle8VHeLtG0sSbDXUSCgSeAi4B8IEtEFqjqunqr/Q14QVVnicj5wJ+AG9ujYGOM5wpKK5mT\ntYM5X+dRUFZJz7gI7rpwANef1oueXSK8XZ5pR5603DOBHFXNBRCROcDlQP1wHwzc5b7/CfBmWxZp\njPGcy6V8nlPMy19t58P1e6hzKWcPSOKhy4dw/sDuhAT73HyBph14Eu4pQF69x/nAmGPWWQlcjdN1\ncyUQKyIJqlpSfyURmQZMA+jdu3dLazbGNKCssoY5X+/g5a92sL3kIPHRYdx6VjpTMvvQO8G6XTob\nT8K9ocPkxx6F/TnwuIjcAiwGdgLHDYZV1WeAZ8A5oNqsSo0xDdpTVsm/vtjK7KU7KK+qJTMtnv+5\naADjh/bsNGdjmuN5Eu75QK96j1OBXfVXUNVdwFUAIhIDXK2qpW1VpDHmeLlFFTyzOJfXl++k1uVi\nwrBkfnR2X4aldvF2acYHeBLuWUB/EUnHaZFPAm6ov4KIJAJ7VdUF3I8zcibgxcTEUFFR4e0yTCez\nIm8/Ty/awnvrCggNDuLajFRuO+tk0hKjm36x6TSaDHdVrRWR6cB7OEMhZ6rqWhF5CMhW1QXAucCf\nRERxumXuaMeajel0VJVPNxXx9KdbWJq7l7iIEG4/ty+3nJFOUmx40xswnY5H49xVdSGw8JhlD9S7\nPw+Y17aldbx7772XPn36HJnP/cEHH0REWLx4Mfv27aOmpoY//OEPXH755V6u1HQmn20u4o8LN7B+\ndxk94sL51SWDmDymNzF2opE5Ad/96fjvfVCwum232XMYTHi40acnTZrEz372syPhPnfuXN59913u\nuusu4uLiKC4uZuzYsUycONFOxzbt7lB1HX/673peWLKdtIQo/nL1cC4fdZIdJDUe8d1w94JRo0ax\nZ88edu3aRVFREd26dSM5OZm77rqLxYsXExQUxM6dOyksLKRnz57eLtcEsG927OPuuSvJLT7AD85M\n557vnGITc5lm8d1wP0ELuz1dc801zJs3j4KCAiZNmsTLL79MUVERy5YtIzQ0lLS0tAan+jWmLdTU\nuZjx0WaeWLSFHrHhzL5tDGf0TfR2WcYP+W64e8mkSZO47bbbKC4u5tNPP2Xu3Ll0796d0NBQPvnk\nE7Zv3+7tEk2AytlTzl2vrmT1zlKuOjWFBycOIc7HL+VmfJeF+zGGDBlCeXk5KSkpJCcnM2XKFC67\n7DIyMjIYOXIkAwcO9HaJJsC4XMq/v9zGn9/dQFRYME9NOZUJw5K9XZbxcxbuDVi9+uiB3MTERJYs\nWdLgejbG3bTWrv2HuGfeSr7IKeG8U5L48zXD6R5rE3qZ1rNwN8YLDlXX8fbKXfz+nXXUuZQ/XjmM\nyZm9bBSWaTMW7sZ0kL0HqvlofSHvryvks81FVNa4GN2nG49eN4I+CXZ2qWlbPhfuqhrwrRdvXf3K\ndLy8vQd5f10h768tIGvbXlwKyV0iuD6jFxcN7snpfRMIDgrsn3fjHT4V7hEREZSUlJCQkBCwAa+q\nlJSUEBFh/aqBSFVZu6vsSKAfvsj0wJ6x3HFePy4e3JOhKXEB+/NtfIdPhXtqair5+fkUFRV5u5R2\nFRERQWpqqrfLMG0st6iCX72xhiW5JQQJZPSJ59eXDuKiwT2s28V0OJ8K99DQUNLT071dhjHNUl3r\n4pnFW3js4xwiQoL47WWDmTjiJBJibEIv4z0+Fe7G+JvlO/Zx//zVbCws59Lhyfz2ssE2lNH4BAt3\nY1qgoqqWv767gReWbqdnXATP3ZTBhYN7eLssY46wcDemmT5YV8gDb62hoKySm09P4+ffOcWm3zU+\nx34ijfHQnrJKHnx7LQtXFzCwZyxPTjmVUb27ebssYxpk4W5ME2rqXLyalcef391AVa2Le75zCtPO\nPpnQ4CBvl2ZMozwKdxEZD/wT5zJ7z6nqw8c83xuYBXR1r3Of++pNxvitypo6Xs3K45nFuezcf4jT\nT07gj1cNI92uVWr8QJPhLiLBwBPARUA+kCUiC1R1Xb3Vfg3MVdWnRGQwziX50tqhXmPaXVllDS8u\n2c7zX2yluKKajD7d+MOVQzl3QJKdfGT8hict90wgR1VzAURkDnA5UD/cFYhz3+8C7GrLIo3pCCUV\nVcz8YisvfLmd8qpazhmQxB3n9SMzPd7bpRnTbJ6EewqQV+9xPjDmmHUeBN4XkZ8A0cCFDW1IRKYB\n0wB69+7d3FqNaRe79h/imcW5zMnaQVWti0uGJvPjc/syNKWLt0szpsU8CfeG/g89duarycC/VfUR\nETkdeFFEhqqq61svUn0GeAYgIyPDZs8yXpVbVMHTn27hjW92ogpXjkrhR+f2pW9SjLdLM6bVPAn3\nfKBXvcepHN/t8gNgPICqLhGRCCAR2NMWRRrTllbnl/LkohzeXVtAWHAQU8b04bazTyala6S3SzOm\nzXgS7llAfxFJB3YCk4AbjllnB3AB8G8RGQREAIE9+5fxK6rKki0lPLloC5/nFBMbEcLt5/ble+PS\nSbQ5YEwAajLcVbVWRKYD7+EMc5ypqmtF5CEgW1UXAHcDz4rIXThdNreoTVpufIDLpby/rpCnPt3C\nyrz9JMWGc/+EgdwwpjexdvFpE8DEWxmckZGh2dnZXnlvE/iqa128uWInT3+6hdyiA/RJiOKHZ/fl\nqlNTiAgN9nZ5xrSYiCxT1Yym1rMzVE1AqaypY/ZXO3j2s1x2l1YyKDmOGZNHMWFoT0LsjFLTiVi4\nm4BQWVPHK1/v4MlFWygqryIzPZ4/XTWMc+zEI9NJWbgbv3Z4ioAnF+VQWFbFmPR4ZkwexdiTE7xd\nmjFeZeFu/FJVbR1zs/J44pMtFJRVkpkWz9+vH8kZfRO9XZoxPsHC3fiV6loXry3L44mPc9hVWsno\nPt3427UjGNcvcC+qbkxLWLgbv1BT52Lesnwe/ziHnfsPMap3Vx6+ejhn9U+0UDemARbuxucVlVfx\n45eWkb19HyN6deV/rxxqB0qNaYKFu/Fpa3aWMu2FbPYerObv14/gipEpFurGeMDC3fist1fu4p55\nK4mPCmPej86wWRqNaQYLd+NzXC7lb+9v5MlFW8jo042npo4mKdbmfzGmOSzcjU8pr6zhZ3NW8NGG\nPUzO7MXvJg4lLMTOLDWmuSzcjc/YWnyA217IZmvxAR66fAg3ju1j/evGtJCFu/EJizcVMX32coKD\nhBd/kGknIxnTShbuxqtUlX99vpU/LlzPgB6xPHtTBr3io7xdljF+z8LdeI2qcv/rq5mTlcd3hvTg\n0etGEh1uP5LGtAX7TTJe80VOCXOy8rjtrHTunzCIoCDrXzemrdgwBOMVqso/P9pEz7gIfv6dUyzY\njWljHoW7iIwXkY0ikiMi9zXw/N9FZIX7tklE9rd9qSaQLMktIWvbPn58bl/CQ+zKSMa0tSa7ZUQk\nGHgCuAjIB7JEZIGqrju8jqreVW/9nwCj2qFWE0Ae+2gz3WPDuf60Xt4uxZiA5EnLPRPIUdVcVa0G\n5gCXn2D9ycArbVGcCUxf5ZawNHcvPzqnr13P1Jh24km4pwB59R7nu5cdR0T6AOnAx408P01EskUk\nu6ioqLm1mgAx4+McEmPCmZzZ29ulGBOwPBkt09CRLm1k3UnAPFWta+hJVX0GeAYgIyOjsW2YALZs\n+14+zynmV5cMIjLMi632qgrIWwrFmz1/TVg0RHaDiK4Q2fXo/bBosDNpjY/xJNzzgfodo6nArkbW\nnQTc0dqiTOB67KMc4qPDmDK2ma32yjIoyYGuvSEqoflhejjMt33u3HYuh4bbIM0XFOqE/eHQD40C\nVy3UVUNdjftW7dyOLK+GulqI6AIxSRDdHWK6Q3SS8zWmx9H70d2dPyRBNrjthNTdXmzLP7QuF9Qc\ngOqDUF0BNQeh+sDRW417efVB533jToK4VOiSAjE9Idh7o809eecsoL+IpAM7cQL8hmNXEpFTgG7A\nkjat0ASMFXn7+XRTEfeOH0hUmIc/9LtXQvZMWPWa80sGEBYL8WnQLQ26pUN8+tGvcanOL1RVOez4\nCrZ95oT5rm+cMA8KgZTRcObPIO1M6Dkcgjz4D0LV+SU+tB8O7YPK/Y3frz4IwWEQFgPBoc79Y78G\nhTq1VJbCgT1QsQf2rHO+umqOf//wOEg/G04+F/qeD/EntyzEXC4o3gh5X8HerR6+SMFVV++PUk3D\nf7jqakBd9fY1zPksjtyvtzwoxFnXk23WVTvfk+OWH7NM6wA55v0a+f4jzdhmC0kwxPaEuBQn7ONS\noEuq8zXlVOd+O2ryN0xVa0VkOvAeEAzMVNW1IvIQkK2qC9yrTgbmqKp1t5gGzfhoM92iQrnp9D4n\nXrHmEKx53Qn1ndkQEglDr4b+F0J5gRNK+7bCng2w6T3nl/CwoBCIPQnKdrrDPNQd5nc5Yd4r0+lG\naYmoeOc/h/ak6vyROFDkBP2R4F8PWz6BDf9x1uvaB/qe5wR9+tlOy74hVeWwcxnkfe0Een6W8wcF\nnO+NePjfQP2gDAo9JsDrBaeEOP+R1JQeDUxXTQMBXu18VscGfkPbDImFkPCm3/9bfzAa+2/JvVxd\nx/+xPW6b7sehURAW5fyxDo1yfn7CYtzLoiE02vnqqnV+7sp2QWm+c790J5Tlw+5VsPG/UFvpfD8v\nfRRO+0HrflaaIN7K4oyMDM3OzvbKe5uOtzq/lMse/5x7vnMKd5zXr+GVijbBsudhxctOACUOgIzv\nw4hJjYeXywXlu44G/t6tUJrntOrTzoTUTOeXMBCowt5c2PKxE/RbF0N1uRPQJ53qhH3aWVBR6AR5\n3ldQuNYJMgS6D3L+uPUa49xa2vo3LaMKB/c6YR+b7HS5tYCILFPVjCbXs3A3HeG2F7L5KreEL+47\nn9iI0KNP1FY7rdHsmU4XSlAoDLrMadX0GWfhcyJ1NU6r/HDY78x2BzlOyzI1wx3kmZCS4RwPMH7P\n03C3uWVMu1u7q5QP1hVy14UDvh3sBWvglclQugO69IYLHoBRN7a4RdPpBIdC77HO7bxfOv3++VlO\nP2/3wZ4dSzABy8LdtLvHP84hNjyEW8alHV2Y+ym8OtVpYd4wF/pdaGHUWpFdof9F3q7C+AgLd9Ou\nNhaU8981Bfz0/H50iXS32lfNhTdvh4R+MHVeu48aMKYzsoGzpl3N+HgzMeEhfP/MdOeA0mePwuu3\nOV0J33/Xgt2YdmItd9NucvaU887q3dx+bl+6RgTDwp9D1nPOsMYrnnKGtxlj2oWFu2k3j3+cQ2Ro\nMD8Y0xNevRE2vgPj7oQLHrSzLY1pZxbupl3kFlWwYOUufnp6PPHzroH8bJjwVxgzzdulGdMpWLib\ndvH4Jzn0DdnDT7b+2jnJ6PoXnfHrxpgOYeFu2tzW4gPkrvyMNyMfIbgSuGkB9B7j7bKM6VQs3E2b\nqaiq5fnPt/LVZ+8yO+T3hEf3gBtfh8T+3i7NmE7Hwt20WmVNHS8t3c5Ti7ZQcqCaWUlLCK8KJ/jW\nDyG2h7fLM6ZTsnA3LVZT5+K17HxmfLyZ3aWVnNkvkbsvHsCod/8GCSMs2I3xIgt302wul/L2ql08\n+sEmtpcc5NTeXXnkuhGc0TfRme51zzrIaN/pTI0xJ2bhbjymqnywrpBH3t/ExsJyBiXH8a+bMzh/\nYHfk8OyNe7c4c1b3HOrdYo3p5CzcjUf2lFdy+0vLyd6+j5MTo5kxeRSXDksmKOiYKXkLVjtfew7r\n+CKNMUdYuJsm5RZVcPPzX1NcXs3DVw3jmtGphAQ3coZpwWpnTvbEUzq2SGPMt3h0DriIjBeRjSKS\nIyL3NbLOdSKyTkTWisjsti3TNMf63WVMfPxzfv+fdVTWtO4i0N/s2Mc1Ty/hYFUdc6aNZVJm78aD\nHZxwTxoIIWGtel9jTOs02XIXkWDgCeAiIB/IEpEFqrqu3jr9gfuBcaq6T0Tsagte8sY3+dz/+mrC\ngoNYlV/KFznFPDZ5FAN6xDZ7Wx9vKOSOl78hKTacF76fSVqiB9ceLVwDfS9oQeXGmLbkScs9E8hR\n1VxVrQbmAJcfs85twBOqug9AVfe0bZmmKdW1Ln771hruenUlw1O78uHd5/D8LadRXFHFZTM+Z9aX\n22jOJRXnZuVx2wvL6Ns9mvk/PsOzYC8vdK7faf3txnidJ+GeAuTVe5zvXlbfAGCAiHwhIktFZHxD\nGxKRaSKSLSLZRUVFLavYHKewrJLJzy5l1pLt3HpmOi/fOobusRGcN7A7/73zbE7vm8BvF6zl1lnZ\nFFdUnXBbqsqMjzbzi/mrOKNvAnOmnU5SrIdT8xYePphqI2WM8TZPwr2hKxQf2wQMAfoD5wKTgedE\n5Lir8arqM6qaoaoZSUlJza3VNGBpbgmXPvY563eXMWPyKH793cGE1usTT4oN5/lbTuPBywbzWU4x\n4//xGYs2NvyPVZ1L+c1ba3jkg01cOSqFf918GjHhzTjmXrDG+drDwt0Yb/Mk3POBXvUepwK7Gljn\nLVWtUdWtwEacsDftRFV57rNcpjz3FXERIbx5xzguG3FSg+uKCLeMS2fB9HHER4dyy/NZ/O7ttd86\n2FpZU8ftLy/jpaU7+OE5J/PItSMIC2nmnOsFq6FLL4iKb82uGWPagCe/vVlAfxFJF5EwYBKw4Jh1\n3gTOAxCRRJxumty2LNQcdaCqlumvfMMf3lnPhYO689b0cR4dMB3YM44F08/kljPSeP6LbVzxxBds\nKixn/8Fqpj73Fe+vK+SB7w7m/gmDjh+/7omC1dZqN8ZHNPk/t6rWish04D0gGJipqmtF5CEgW1UX\nuJ+7WETWAXXAPapa0p6Fd1Zbiir40YvL2FJUwb3jB/Kjc04+enaoByJCg3lw4hDOGZDEPfNWctmM\nz+kRF0FBaSUzJo/iu8Mbbv03qeYQlGyGwcceazfGeIM0ZwRFW8rIyNDs7GyvvLe/+nJLMdNeWEZY\nSBAzJo9iXL/EVm2vqLyKX8xbybLt+3j6xtHO3DAttXMZPHs+XPciDJ7YqrqMMY0TkWWqmtHUenaG\nqp/I23uQ219eTnKXCP79/UxSuka2eptJseE8/71Mqmtdze9fP1aBjZQxxpfYVYr9QGVNHT96aRl1\nLuXZmzLaJNjra3WwgzNSJiwWuqa1flvGmFazlruPU1V++cZq1u4qY+YtGZ6dTOQNBaudVnuQtReM\n8QX2m+jjXlq6ndeX7+TOC/pz/kAfvfiFy+VMO2AjZYzxGRbuPmzZ9r089J91nHdKEnde4MOnDezf\nBtUVNu2AMT7Ewt1H7Smv5McvLSe5SyT/uH5Uy8addxSbw90Yn2Ph7oNq6lxMf/kbyipr+L8bR9Ml\nKtTbJZ1YwWqQIOg+yNuVGGPcLNx90B8XrufrbXv589XDGZQcd/SJ4s2w5EnvFdaYgjWQOABC23YU\njzGm5SzcfcxbK3by/Bfb+N64NC4feczkm4v+BO/dD6U7vVNcYwpWW5eMMT7Gwt2HrN9dxr3zV5GZ\nFs8vLzmmi6P6AGz8r3N/pw+d2XtwL5Tl20gZY3yMhbuPKD1Yww9fXEaXyFAenzLqW9P2ArDpPag5\n6NzPz+r4AhtT6J7m11ruxvgUO4nJB7hcys9e/YbdpYeYM+10usdGHL/SmvkQ0wPiUiB/WccX2Rgb\nKWOMT7KWu5epKo9+sIlPNhbxwHcHM7pPt+NXqiyDzR/AkCuhVybsXgF1tR1fbEMK1jh/dGLssrnG\n+BILdy/aWnyAm2Z+zeOf5HD1qalMHdun4RU3LoS6KhhyFaRkON0ze9Y1vG5Hs4Opxvgk65bxgqra\nOp5elMsTi3IIDw7iocuHMGVMn8bnZV/zunOFo9TTIMZ9ecKd2ZA8vOOKbkhtNRRtgH4XeLcOY8xx\nrOXeXpbNghmjoar8W4u/zClmwj8+4+8fbuLiwT348O5zuOn0NIIbOwP14F7Y8hEMucKZlKtbOkQl\n+Ea/e/FGcNVYy90YH2Qt9/a4fyBJAAAVbklEQVRQVQEfPQQHi2H5i3D67RSVV/HHhet545ud9I6P\nYtb3MzlngAcXCd/wH3DVwtCrnccikDLaN4ZDHjmY6uX/IIwxx/Go5S4i40Vko4jkiMh9DTx/i4gU\nicgK9+3Wti/Vj3z1lBPsXfugS59g9pItXPDIIv6zahc/Ob8f7991tmfBDs4omW7pkDzy6LKUDCja\n6Bxo9aaC1RASCQl9vVuHMeY4TYa7iAQDTwATgMHAZBEZ3MCqr6rqSPftuTau038c2gdfzIABE9iR\n+QBSms+St2cy+KQ4/nvn2dx98SlEhAZ7tq2KIti62Gm11++PTx0NKOxa3i674LGC1dBjMAR5uD/G\nmA7jScs9E8hR1VxVrQbmAHYV5MZ8+ThUlfJ+zx9w/n8i2MpJPJT0Ma/cOoZ+3WOat611b4K6jnbJ\nHJYy2vma78WuGVUbKWOMD/Mk3FOAvHqP893LjnW1iKwSkXki0quhDYnINBHJFpHsoqKiFpTr4yqK\nYOlT7D/5MqZ/XMtpaYn0+M7P6Va6Dtn+efO3t/YNSBrotI7ri+wGCf2di1J7S2k+VO63aQeM8VGe\nhHtDwzj0mMdvA2mqOhz4EJjV0IZU9RlVzVDVjKQkD/uc/cnnf0drD3HHrvHER4Xx+A2jiMqYAtFJ\n8MVjzdtW6U7Y/uXxrfbDUjOclrse+1F0kCPTDtjBVGN8kSfhng/Ub4mnArvqr6CqJapa5X74LDC6\nbcrzI6U70aznWBJzEUvL4nn8hlEkxIRDaARk/hByPoDCZpx4tO5NQJ0TlxqSMhoO7IHSvIafb28F\nqwE5/r8KY4xP8CTcs4D+IpIuImHAJGBB/RVEJLnew4nA+rYr0U8s/isuVx2/KL6E+8YPJCMt/uhz\np/0AQqNgyeOeb2/N606rOLFfw8+nZjhfvTWJWMEqiE+H8FjvvL8x5oSaDHdVrQWmA+/hhPZcVV0r\nIg+JyET3aj8VkbUishL4KXBLexXsk/ZuRZe/yCu15zFo0FBuPSv9289HxcOoqbBqLpTtbnp7+7Y5\n49iHNtJqB6evOyTCeyczFayxg6nG+DCPxrmr6kJVHaCqfVX1f93LHlDVBe7796vqEFUdoarnqeqG\n9iza11R99EeqNYh50ZP427UjGp5GYOztoHXw9f81vcG1bzhfG+uSAQgOheQR3jmZqbIM9m21cDfG\nh9n0A63kKtxA6Np5vFh3Mb+fehFdIhu53ml8OgyaCFkzj5uS4Dhr5jsnKnVrZCKxw1IyYPdKqKtp\nWfEtVbjW+drDwt0YX2Xh3kq5r/2SAxpO3EX3MCy1y4lXPuMnUFXqTEnQmOLNzsHKxkbJ1Jc6Gmor\nj45c6Sh2gQ5jfJ6FeyuszPqUfsUf8XnidVx71oimX5CaAb3PgKVPNt7aXvM6IM5EYU1JOXxQtYO7\nZgpWQWQ8xJ3Use9rjPGYhXsL7SmrpHzhg5QRw9k3Pdj4dL3HGvdTZ/jiureOf07V6ZLpc4Znwdm1\ntzOGvqNPZipYAz2HfntKBGOMT7Fwb4HaOhczZr3MmbqcqjHTie4S3/SLDuv/Hefs0i/+efwJSHvW\nOdPonmiUTH0iTuu9I1vudbVOnXbykjE+zcK9Bf7+wUYu2fMcleEJJF3w0+a9OCgIzpjudG1sXfzt\n59a8DhIEg5oxdU9qBpRsdiYs6wglOU4/v/W3G+PTLNyb6ZONe/hm8QJOD15HxHm/gLDo5m9k+CSn\nO+XLGUeXHe6SST/n6NWWPHH4ZKadHTRD5OGDqTanjDE+zcK9GUoP1nD3qyt4IHI+GpcCGd9r2YYa\nmpJg9wpn7LinXTKHnXQqIB3X716wCoLDIHFAx7yfMaZFLNyb4Z8fbebUqqUMrNuInHMvhIS3fGPH\nTkmwZj4EhcLA7zZvOxFxkHRKx/W7F6x2ZqoMCeuY9zPGtIiFu4e2FFXw8pIt/D72TYg/GUbe0LoN\n1p+SoHQnrHkD+p7vLG+ulAznTNWOmCHSph0wxi9YuHvoj++s58eh75BcuQUufNA5/b+1Dk9J8MYP\noSzfsxOXGpI6Gg6WON067am80JmJ0sLdGJ9n4e6BzzYXkbdxGT8Jeg0GXwGD2+hCVIenJNj2GQSH\nwykTWradIycztXO/+5ELYlu4G+PrLNybUFvn4k9vr+axyGcIiuwKlz7Stm9whnso5YCLnf7zlug+\n2Om/b+9JxArd4d5jSPu+jzGm1UK8XYCvm5OVx3klrzAwdAtcOguiE9v2DVJHO38w+pzZ8m0Eh0Dy\nyPY/qFqwGrr0di7zZ4zxadZyP4GyyhoWvP8Bd4W+jg65yrP5XlritFuh+8DWbSN1tDNMsbaq6XVb\nyi6IbYzfsHA/gSc/3MBvamdAZFfkkr95u5wTS8mAumpnNEt72PCOM2Nlyqj22b4xpk1ZuDdiW/EB\nwr/6J8OCthFy2d8hOsHbJZ3YkTNV26FrJu9rmPd957qtY+9o++0bY9qcR+EuIuNFZKOI5IjIfSdY\n7xoRURHJaLsSveOFN//D9KDXqTzlChg8sekXeFtcCsQmt32/e8kWmH29M0vlDa9CWFTbbt8Y0y6a\nDHcRCQaeACYAg4HJInLcJe9FJBbn+qlftXWRHW3J5t1cteOP1IR1IWLio94uxzMiTsu6LVvuFUXw\n0tXOtqfMa/uDycaYduNJyz0TyFHVXFWtBuYADQ30/j3wF6CyDevrcHUuJWf+7xkatI2Qif/w/e6Y\n+lIzYG8uHNzb+m1VH4DZ10F5AdwwFxL6tn6bxpgO40m4pwB59R7nu5cdISKjgF6q+p8TbUhEpolI\ntohkFxUVNbvYjvDhJx8y6dAc8lMvJWxYG52s1FEOn8zU2knE6mqdPvbdK+CamUf7840xfsOTcG/o\ncjtHJjERkSDg78DdTW1IVZ9R1QxVzUhKasa0th2k4uAh0j67h4rgOFImz2j6Bb7mpFHOfPCt6XdX\nhYU/h03vwiV/hYGXtF19xpgO40m45wO96j1OBXbVexwLDAUWicg2YCywwB8Pqq6Y/RtOYSv7z/sz\n4k/dMYeFx0DSIMjPavk2Pn8Ulj0PZ97ljL83xvglT8I9C+gvIukiEgZMAhYcflJVS1U1UVXTVDUN\nWApMVNUOvmpz6xRs/JoxeTNZFncR6Wdd7+1yWi51tNMt05IZIlfOgY8egmHXwvkPtH1txpgO02S4\nq2otMB14D1gPzFXVtSLykIj4wRhBD9RWUzv/h+wnlpQpj3m7mtZJyYDK/c4QxubY8gm8dQeknQWX\nP+FcDtAY47c8mltGVRcCC49Z1mDTTlXPbX1ZHSt/4Z9Jrc5lweBHmNjjJG+X0zr1T2ZK7OfZawpW\nw6s3OldXuv6l1l2ExBjjE6x5VrabxG8e52PJ5KIrW3jZPF+SNBDCYjw7qKrqdOG8fC2Ex8KU1yCy\na/vXaIxpd51+VsiKd35DqKuWrRn3c35YsLfLab2gYGfUzIlOZipc51zWb+3rzrj4iC5wy0Loktpx\ndRpj2lXnDvedy4jZ+Br/55rIleec4e1q2k7KaFjyBNRUOhfjBmfSrzWvO4FetMEZMpl2Foy707lg\nSEsu72eM8VmdN9xVqVt4H/u0C1sG/pDucRHerqjtpJ4GrhrY8B/Yv925PmvhakCgzxlwyd+cq0nF\ndPd2pcaYdtJ5w33NfIJ3fs1fam9j8lkBdmWhwwdV5//A/TgTxj/sXCIwLtl7dRljOkznDPfqg+gH\nvyUn6GQ2J09kVO8Au7JQbE84/9fOdVmHXAFde3u7ImNMB+uc4f7lDKQsn19V/YabxwXohFhn3+Pt\nCowxXtT5wr10J3zxD7KiziI3ZCSXDLNuCmNM4Ol849w/+h0uVx137buaKWN6ExbS+b4FxpjA17mS\nLS8LVr3KF0nXUxjUgyljrC/aGBOYOk+4u1zw7n24ontw9+4LuHRYcmANfzTGmHo6T7ivmQc7s/ky\n7Q72VIVyy7h0b1dkjDHtpnMcUK0+AB/8Fk0eyW+3DWNkr3BG9rI5VIwxgatztNy/+CeU72LFkHvZ\nUnKI741L83ZFxhjTrgI/3PfnOeE+5Er+uTmRpNhwJgy14Y/GmMAW+OH+4YMA5I2+n0Ubi2z4ozGm\nU/Ao5URkvIhsFJEcEbmvged/JCKrRWSFiHwuIoPbvtQW2PGVcyD1jJ/wrzW1hAYLN9jwR2NMJ9Bk\nuItIMPAEMAEYDExuILxnq+owVR0J/AV4tM0rbS5VeO9+iOlJecYdzFuWz3eHn0T3WBv+aIwJfJ60\n3DOBHFXNVdVqYA5wef0VVLWs3sNooAVXZ25jO5c5t3PuYf7q/VRU1XLLGWnersoYYzqEJ+GeAuTV\ne5zvXvYtInKHiGzBabn/tG3Ka4XlsyA0CtfQa5m1ZDujendlhA1/NMZ0Ep6EuzSw7LiWuao+oap9\ngXuBXze4IZFpIpItItlFRUXNq7Q5qsph9XwYchWf7qhia/EBa7UbYzoVT8I9H+hV73EqsOsE688B\nrmjoCVV9RlUzVDUjKSnJ8yqba+0bUHMATr2JWV9us+GPxphOx5NwzwL6i0i6iIQBk4AF9VcQkf71\nHl4KbG67Eltg+QuQeAq5EYNZtLGIqWP62PBHY0yn0uT0A6paKyLTgfeAYGCmqq4VkYeAbFVdAEwX\nkQuBGmAfcHN7Fn1ChesgPwsu/l9eWLrDhj8aYzolj+aWUdWFwMJjlj1Q7/6dbVxXy33zIgSFUjHo\nGub9YyXfHX4SSbHh3q7KGGM6VGD1VdRWwcpXYOClvLGxioqqWm46vY+3qzLGmA4XWOG+4R04tA89\n9SZeWrKdoSlxNvujMaZTCqxwX/4CdOlNVtAINhaWc+PYPog0NJLTGGMCW+CE+75tkPsJjJrKi1/l\nERsRwsQRx51rZYwxnULghPs3LwNCcf9reHfNbq4d3YvIsGBvV2WMMV4RGOHuqoNvXoJ+FzJno4ua\nOmXKWBv+aIzpvAIj3HM+gvJd1I28kdlf7eDMfon0TYrxdlXGGOM1gRHuy2dBVCIf6yh2lVYydawN\nfzTGdG7+H+7lhbDpXRg5mRe+3k1ylwguHNTd21UZY4xX+X+4r3wFXLXkp1/DZ5uLuSGzNyHB/r9b\nxhjTGv6dgqrO2Pbep/PvjWGEBAnXZ/Zq+nXGGBPg/Dvct38Je7dQPXwqry3LZ/zQnnYZPWOMwd/D\nffkLEB7H27WnUXqohhvtQKoxxgD+HO6H9sO6N2HYNczKLmJAjxgy0+O9XZUxxvgE/w331a9BbSWb\nUq5iVX6pzSNjjDH1+G+4L38Beg7jmc2xRIcFc8Uom0fGGGMO889w37UCClZxcMgU3l61mytPTSE2\nItTbVRljjM/wKNxFZLyIbBSRHBG5r4Hn/0dE1onIKhH5SETa98jm8hcgJILXqk+nqtZlZ6QaY8wx\nmgx3EQkGngAmAIOBySIy+JjVvgEyVHU4MA/4S1sXekT1QVj9GjpoIjOX7yMzLZ6BPePa7e2MMcYf\nedJyzwRyVDVXVauBOcDl9VdQ1U9U9aD74VIgtW3LrGfdW1BVxsrul7O95CBT7TJ6xhhzHE/CPQXI\nq/c4372sMT8A/tuaok4oOhGGXs3jW3qQGBPG+CE92+2tjDHGX3kS7g2NL9QGVxSZCmQAf23k+Wki\nki0i2UVFRZ5XWV//i8i/4HE+3riHSaf1JizEP48JG2NMe/IkGfOB+hO2pAK7jl1JRC4EfgVMVNWq\nhjakqs+oaoaqZiQlJbWkXgBe+XoHAJPH2AU5jDGmIZ6EexbQX0TSRSQMmAQsqL+CiIwC/g8n2Pe0\nfZlHVdXW8WpWHhcM6kFK18j2fCtjjPFbTYa7qtYC04H3gPXAXFVdKyIPichE92p/BWKA10RkhYgs\naGRzrfbumgKKK6ptHhljjDmBEE9WUtWFwMJjlj1Q7/6FbVxXo6LDQrh4cA/O7JfYUW9pjDF+x6Nw\n9yUXDu7BhYN7eLsMY4zxaTbUxBhjApCFuzHGBCALd2OMCUAW7sYYE4As3I0xJgBZuBtjTACycDfG\nmABk4W6MMQFIVBuc4LH931ikCNjewpcnAsVtWI4vCLR9CrT9gcDbp0DbHwi8fWpof/qoapMzL3ot\n3FtDRLJVNcPbdbSlQNunQNsfCLx9CrT9gcDbp9bsj3XLGGNMALJwN8aYAOSv4f6MtwtoB4G2T4G2\nPxB4+xRo+wOBt08t3h+/7HM3xhhzYv7acjfGGHMCFu7GGBOA/C7cRWS8iGwUkRwRuc/b9bSWiGwT\nkdXuyxNme7uelhCRmSKyR0TW1FsWLyIfiMhm99du3qyxORrZnwdFZKf7c1ohIpd4s8bmEpFeIvKJ\niKwXkbUicqd7uV9+TifYH7/9nEQkQkS+FpGV7n36nXt5uoh85f6MXnVfy7rp7flTn7uIBAObgIuA\nfJyLd09W1XVeLawVRGQbkKGqfnvihYicDVQAL6jqUPeyvwB7VfVh9x/hbqp6rzfr9FQj+/MgUKGq\nf/NmbS0lIslAsqouF5FYYBlwBXALfvg5nWB/rsNPPycRESBaVStEJBT4HLgT+B/gdVWdIyJPAytV\n9ammtudvLfdMIEdVc1W1GpgDXO7lmjo9VV0M7D1m8eXALPf9WTi/eH6hkf3xa6q6W1WXu++X41zs\nPgU//ZxOsD9+Sx0V7oeh7psC5wPz3Ms9/oz8LdxTgLx6j/Px8w8U58N7X0SWicg0bxfThnqo6m5w\nfhGB7l6upy1MF5FV7m4bv+i+aIiIpAGjgK8IgM/pmP0BP/6cRCRYRFYAe4APgC3AflWtda/iceb5\nW7hLA8v8p1+pYeNU9VRgAnCHu0vA+J6ngL7ASGA38Ih3y2kZEYkB5gM/U9Uyb9fTWg3sj19/Tqpa\np6ojgVScnopBDa3mybb8LdzzgV71HqcCu7xUS5tQ1V3ur3uAN3A+0EBQ6O4XPdw/usfL9bSKqha6\nf/FcwLP44efk7sedD7ysqq+7F/vt59TQ/gTC5wSgqvuBRcBYoKuIhLif8jjz/C3cs4D+7qPHYcAk\nYIGXa2oxEYl2HwxCRKKBi4E1J36V31gA3Oy+fzPwlhdrabXDAeh2JX72ObkP1v0LWK+qj9Z7yi8/\np8b2x58/JxFJEpGu7vuRwIU4xxI+Aa5xr+bxZ+RXo2UA3EOb/gEEAzNV9X+9XFKLicjJOK11gBBg\ntj/uj4i8ApyLMz1pIfBb4E1gLtAb2AFcq6p+cZCykf05F+dffQW2AT883FftD0TkTOAzYDXgci/+\nJU4/td99TifYn8n46eckIsNxDpgG4zS856rqQ+6cmAPEA98AU1W1qsnt+Vu4G2OMaZq/dcsYY4zx\ngIW7McYEIAt3Y4wJQBbuxhgTgCzcjTEmAFm4G2NMALJwN8aYAPT/8A1y5xfvlEAAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f3d565be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg19_bn, batch_size=32, lr=0.0001, pretrained=True, no transforms.RandomHorizontalFlip()\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)\n",
    "model_ft.apply(reset)\n",
    "model_vgg16, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=30, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ft = make_model('vgg19_bn', num_classes=12, pretrained=True, input_size=(160, 160), dropout_p=0.5)\n",
    "model_ft.cuda();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/29\n",
      "----------\n",
      "train Loss: 2.2944 Acc: 0.1864\n",
      "val Loss: 2.2177 Acc: 0.2760\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 1/29\n",
      "----------\n",
      "train Loss: 2.0286 Acc: 0.3021\n",
      "val Loss: 2.1252 Acc: 0.3349\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 2/29\n",
      "----------\n",
      "train Loss: 1.8858 Acc: 0.3537\n",
      "val Loss: 2.1029 Acc: 0.3293\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 3/29\n",
      "----------\n",
      "train Loss: 1.7868 Acc: 0.3958\n",
      "val Loss: 2.1895 Acc: 0.3956\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 4/29\n",
      "----------\n",
      "train Loss: 1.6382 Acc: 0.4533\n",
      "val Loss: 1.6370 Acc: 0.4738\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 5/29\n",
      "----------\n",
      "train Loss: 1.5416 Acc: 0.4915\n",
      "val Loss: 1.4768 Acc: 0.5189\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 6/29\n",
      "----------\n",
      "train Loss: 1.4027 Acc: 0.5359\n",
      "val Loss: 1.7555 Acc: 0.4729\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 7/29\n",
      "----------\n",
      "train Loss: 1.3242 Acc: 0.5626\n",
      "val Loss: 1.5849 Acc: 0.5051\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 8/29\n",
      "----------\n",
      "train Loss: 1.2036 Acc: 0.6079\n",
      "val Loss: 1.7308 Acc: 0.4839\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 9/29\n",
      "----------\n",
      "train Loss: 1.1444 Acc: 0.6238\n",
      "val Loss: 1.8010 Acc: 0.4747\n",
      "lr:  0.0001\n",
      "\n",
      "Epoch 10/29\n",
      "----------\n",
      "train Loss: 1.1403 Acc: 0.6371\n",
      "val Loss: 1.2197 Acc: 0.6329\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 11/29\n",
      "----------\n",
      "train Loss: 0.8649 Acc: 0.7283\n",
      "val Loss: 1.1280 Acc: 0.6412\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 12/29\n",
      "----------\n",
      "train Loss: 0.7520 Acc: 0.7653\n",
      "val Loss: 1.1125 Acc: 0.6467\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 13/29\n",
      "----------\n",
      "train Loss: 0.6853 Acc: 0.7858\n",
      "val Loss: 1.1200 Acc: 0.6596\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 14/29\n",
      "----------\n",
      "train Loss: 0.6120 Acc: 0.8162\n",
      "val Loss: 1.1693 Acc: 0.6605\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 15/29\n",
      "----------\n",
      "train Loss: 0.5436 Acc: 0.8355\n",
      "val Loss: 1.1595 Acc: 0.6559\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 16/29\n",
      "----------\n",
      "train Loss: 0.4720 Acc: 0.8610\n",
      "val Loss: 1.1731 Acc: 0.6615\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 17/29\n",
      "----------\n",
      "train Loss: 0.4001 Acc: 0.8845\n",
      "val Loss: 1.3016 Acc: 0.6440\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 18/29\n",
      "----------\n",
      "train Loss: 0.3342 Acc: 0.9070\n",
      "val Loss: 1.2465 Acc: 0.6642\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 19/29\n",
      "----------\n",
      "train Loss: 0.2703 Acc: 0.9250\n",
      "val Loss: 1.4973 Acc: 0.6210\n",
      "lr:  1e-05\n",
      "\n",
      "Epoch 20/29\n",
      "----------\n",
      "train Loss: 0.2525 Acc: 0.9296\n",
      "val Loss: 1.3183 Acc: 0.6541\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 21/29\n",
      "----------\n",
      "train Loss: 0.2020 Acc: 0.9526\n",
      "val Loss: 1.2638 Acc: 0.6707\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 22/29\n",
      "----------\n",
      "train Loss: 0.1887 Acc: 0.9535\n",
      "val Loss: 1.2732 Acc: 0.6670\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 23/29\n",
      "----------\n",
      "train Loss: 0.1795 Acc: 0.9597\n",
      "val Loss: 1.2839 Acc: 0.6679\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 24/29\n",
      "----------\n",
      "train Loss: 0.1677 Acc: 0.9636\n",
      "val Loss: 1.2917 Acc: 0.6661\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 25/29\n",
      "----------\n",
      "train Loss: 0.1661 Acc: 0.9653\n",
      "val Loss: 1.2961 Acc: 0.6688\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 26/29\n",
      "----------\n",
      "train Loss: 0.1636 Acc: 0.9632\n",
      "val Loss: 1.2970 Acc: 0.6651\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 27/29\n",
      "----------\n",
      "train Loss: 0.1562 Acc: 0.9669\n",
      "val Loss: 1.3070 Acc: 0.6651\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 28/29\n",
      "----------\n",
      "train Loss: 0.1500 Acc: 0.9712\n",
      "val Loss: 1.3137 Acc: 0.6596\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Epoch 29/29\n",
      "----------\n",
      "train Loss: 0.1455 Acc: 0.9722\n",
      "val Loss: 1.3181 Acc: 0.6615\n",
      "lr:  1.0000000000000002e-06\n",
      "\n",
      "Training complete in 29m 12s\n",
      "Best val Acc: 0.670653, epoch 21\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wMVkqU+ZoGVmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x241725ea550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJghZuBtjTBCycDfGmCBk4W6MMUHo/wMaF22JBMuvNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2412dc32668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vgg19_bn, batch_size=32, lr=0.0001, pretrained=True, no transforms.RandomHorizontalFlip()\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.0001)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)\n",
    "model_ft.apply(reset)\n",
    "model_vgg19, loss_log, acc_log = train_model_batch(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=30, batch_accum=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "model_name vgg not found. Available model_name values: resnet18, resnet34, resnet50, resnet101, resnet152, densenet121, densenet169, densenet201, densenet161, alexnet, vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn, squeezenet1_0, squeezenet1_1, inception_v3, resnext101_32x4d, resnext101_64x4d, nasnetalarge, inceptionresnetv2, dpn68, dpn68b, dpn92, dpn98, dpn131, dpn107, inception_v4, xception",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-b0ee9ffbdee4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel_ft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'vgg'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpretrained\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m160\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m160\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      2\u001b[0m \u001b[0mmodel_ft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuda\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;32mD:\\Anaconda3\\lib\\site-packages\\cnn_finetune\\base.py\u001b[0m in \u001b[0;36mmake_model\u001b[1;34m(model_name, num_classes, pretrained, dropout_p, pool, classifier_factory, use_original_classifier, input_size, original_model_state_dict)\u001b[0m\n\u001b[0;32m    233\u001b[0m             'Available model_name values: {model_names}'.format(\n\u001b[0;32m    234\u001b[0m                 \u001b[0mmodel_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 235\u001b[1;33m                 \u001b[0mmodel_names\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m', '\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMODEL_REGISTRY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\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    236\u001b[0m             )\n\u001b[0;32m    237\u001b[0m         )\n",
      "\u001b[1;31mValueError\u001b[0m: model_name vgg not found. Available model_name values: resnet18, resnet34, resnet50, resnet101, resnet152, densenet121, densenet169, densenet201, densenet161, alexnet, vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn, squeezenet1_0, squeezenet1_1, inception_v3, resnext101_32x4d, resnext101_64x4d, nasnetalarge, inceptionresnetv2, dpn68, dpn68b, dpn92, dpn98, dpn131, dpn107, inception_v4, xception"
     ]
    }
   ],
   "source": [
    "model_ft = make_model('vgg', num_classes=12, pretrained=True, input_size=(160, 160))\n",
    "model_ft.cuda();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}