hkristen/lesson1-imageclef2013_plant_leaf_types_natural_custom_head.ipynb

## lesson1-imageclef2013_plant_leaf_types_natural_custom_head.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai import *\n",
    "from fastai.vision import *\n",
    "import glob\n",
    "import xml.etree.ElementTree as ET\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data & Task description\n",
    "### https://www.imageclef.org/2013/plant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The task will be based on the Pl@ntView dataset which focuses on 250 herb and tree species from France area. It contains 26077 pictures belonging each to one of the 2 following categories:\n",
    "- SheetAsBackground (or uniform background) (42%): exclusively pictures of leaves in front of a white or colored uniform background produced with a scanner or a camera with a sheet.\n",
    "- NaturalBackground (for most of the time cluttered natural background\") (58%): free natural photographs of different views on different subparts of a plant into the wild.\n",
    "\n",
    "These 2 main categories can be subdivided into 2 and 5 sub-categories:\n",
    "- SheetAsBackground Scan (22%): scan of a single leaf.\n",
    "- SheetAsBackground Scan-like (22%): photograph of a single leaf in front of an uniform artificial background.\n",
    "- NaturalBackground Leaf (16%): photograph of one leaf or more directly on the plant or near the plant on the floor or other non uniform background.\n",
    "- NaturalBackground Flower (18%): photograph of one flower or a group of flowers (inflorescence) directly on the plant.\n",
    "- NaturalBackground Fruit (8%): photograph of one fruit or a group of fruits (infructescence) directly on the plant.\n",
    "- NaturalBackground Stem (8%): photograph of the stalk, or a stipe, or the bark of the trunk or a main branch of the plant.\n",
    "- NaturalBackground Entire (8%): photograph of the entire plant, from the floor to the top.\n",
    "\n",
    "!['muh'](https://www.imageclef.org/system/files/Diapositive1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Select a subset of the data\n",
    "Now we select the leaf **images with a NaturalBackground** and try to use our model trained on the \"Sheetasbackground\" class as baseline for the task -> transfer learning ;) \n",
    "\n",
    "Below we can see that there are 1724 samples//179 classes in total but many classes with just a few samples. So I excluded all classes with samples < 10, which leaves 51 classes and 1230 samples for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'data/ImageCLEF2011/'\n",
    "images_clef2013_pd = pd.read_csv(data_path+'images_clef2013_pd.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_clef2013_pd_leaf = images_clef2013_pd[(images_clef2013_pd['content'] == 'Leaf') & (images_clef2013_pd['backgroundtype'] == 'NaturalBackground')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Leaf    1724\n",
       "Name: content, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_clef2013_pd_leaf['content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hedera helix                  54\n",
       "Pittosporum tobira            50\n",
       "Viburnum tinus                47\n",
       "Cercis siliquastrum           44\n",
       "Quercus ilex                  43\n",
       "Crataegus monogyna            39\n",
       "Aesculus hippocastanum        39\n",
       "Laurus nobilis                36\n",
       "Robinia pseudoacacia          33\n",
       "Cotinus coggygria             31\n",
       "Corylus avellana              31\n",
       "Ilex aquifolium               30\n",
       "Platanus x hispanica          29\n",
       "Acer campestre                28\n",
       "Ginkgo biloba                 28\n",
       "Betula pendula                27\n",
       "Nerium oleander               27\n",
       "Sambucus nigra                27\n",
       "Diospyros kaki                26\n",
       "Acer negundo                  25\n",
       "Quercus pubescens             25\n",
       "Pistacia terebinthus          24\n",
       "Syringa vulgaris              24\n",
       "Quercus robur                 23\n",
       "Eriobotrya japonica           23\n",
       "Populus nigra                 22\n",
       "Celtis australis              22\n",
       "Broussonetia papyrifera       22\n",
       "Magnolia grandiflora          21\n",
       "Pistacia lentiscus            21\n",
       "                              ..\n",
       "Malva moschata                 1\n",
       "Scolymus hispanicus            1\n",
       "Colutea arborescens            1\n",
       "Ruta montana                   1\n",
       "Silene dioica                  1\n",
       "Thalictrum aquilegiifolium     1\n",
       "Malva dendromorpha             1\n",
       "Helleborus foetidus            1\n",
       "Myrtus communis                1\n",
       "Pinus halepensis               1\n",
       "Lithodora fruticosa            1\n",
       "Dactylorhiza maculata          1\n",
       "Stachys sylvatica              1\n",
       "Quercus petraea                1\n",
       "Papaver somniferum             1\n",
       "Rhamnus alaternus              1\n",
       "Rhus coriaria                  1\n",
       "Hyacinthoides non-scripta      1\n",
       "Bryonia cretica                1\n",
       "Quercus palustris              1\n",
       "Aristolochia clematitis        1\n",
       "Clematis vitalba               1\n",
       "Euphorbia paralias             1\n",
       "Epipactis helleborine          1\n",
       "Calystegia soldanella          1\n",
       "Epilobium hirsutum             1\n",
       "Berberis vulgaris              1\n",
       "Plantago media                 1\n",
       "Verbena officinalis            1\n",
       "Convolvulus cantabrica         1\n",
       "Name: classid, Length: 179, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_clef2013_pd_leaf['classid'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select all classes that have more than 10 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "images_clef2013_pd_leaf_filter = images_clef2013_pd_leaf.groupby('classid').filter(lambda x: len(x)>10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(images_clef2013_pd_leaf_filter['classid'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hedera helix               54\n",
       "Pittosporum tobira         50\n",
       "Viburnum tinus             47\n",
       "Cercis siliquastrum        44\n",
       "Quercus ilex               43\n",
       "Aesculus hippocastanum     39\n",
       "Crataegus monogyna         39\n",
       "Laurus nobilis             36\n",
       "Robinia pseudoacacia       33\n",
       "Corylus avellana           31\n",
       "Cotinus coggygria          31\n",
       "Ilex aquifolium            30\n",
       "Platanus x hispanica       29\n",
       "Acer campestre             28\n",
       "Ginkgo biloba              28\n",
       "Betula pendula             27\n",
       "Sambucus nigra             27\n",
       "Nerium oleander            27\n",
       "Diospyros kaki             26\n",
       "Quercus pubescens          25\n",
       "Acer negundo               25\n",
       "Syringa vulgaris           24\n",
       "Pistacia terebinthus       24\n",
       "Eriobotrya japonica        23\n",
       "Quercus robur              23\n",
       "Celtis australis           22\n",
       "Populus nigra              22\n",
       "Broussonetia papyrifera    22\n",
       "Acer monspessulanum        21\n",
       "Pistacia lentiscus         21\n",
       "Magnolia grandiflora       21\n",
       "Acer pseudoplatanus        20\n",
       "Ficus carica               19\n",
       "Vitex agnus-castus         19\n",
       "Olea europaea              17\n",
       "Liquidambar styraciflua    17\n",
       "Fagus sylvatica            16\n",
       "Tilia platyphyllos         15\n",
       "Arbutus unedo              14\n",
       "Populus alba               14\n",
       "Liriodendron tulipifera    14\n",
       "Fraxinus ornus             14\n",
       "Acer opalus                13\n",
       "Ulmus minor                13\n",
       "Pterocarya fraxinifolia    13\n",
       "Morus alba                 13\n",
       "Cornus sanguinea           12\n",
       "Albizia julibrissin        12\n",
       "Gleditsia triacanthos      11\n",
       "Ailanthus altissima        11\n",
       "Cornus mas                 11\n",
       "Name: classid, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_clef2013_pd_leaf_filter['classid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Leaf    1230\n",
       "Name: content, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_clef2013_pd_leaf_filter['content'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Databunch, adding flp_vert to the augmentation options as the orientation of the leaf in the image should not matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ImageDataBunch.from_df(data_path, images_clef2013_pd_leaf_filter, fn_col=1, label_col=5, ds_tfms=get_transforms(flip_vert = True), size=224).normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(rows=3, figsize=(7,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "## Load pretrained model & start training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = create_cnn(data, models.resnet34, metrics=error_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Error(s) in loading state_dict for Sequential:\n\tsize mismatch for 1.8.weight: copying a param with shape torch.Size([124, 512]) from checkpoint, the shape in current model is torch.Size([51, 512]).\n\tsize mismatch for 1.8.bias: copying a param with shape torch.Size([124]) from checkpoint, the shape in current model is torch.Size([51]).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-bce60366c661>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'leaf_types_stage_2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/share/conda3/envs/fastai/lib/python3.7/site-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, name, device)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0;34m\"Load model `name` from `self.model_dir` using `device`, defaulting to `self.data.device`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34mf'{name}.pth'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/conda3/envs/fastai/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mload_state_dict\u001b[0;34m(self, state_dict, strict)\u001b[0m\n\u001b[1;32m    757\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\n\u001b[0;32m--> 759\u001b[0;31m                                self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\n\u001b[0m\u001b[1;32m    760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_named_members\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_members_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for Sequential:\n\tsize mismatch for 1.8.weight: copying a param with shape torch.Size([124, 512]) from checkpoint, the shape in current model is torch.Size([51, 512]).\n\tsize mismatch for 1.8.bias: copying a param with shape torch.Size([124]) from checkpoint, the shape in current model is torch.Size([51])."
     ]
    }
   ],
   "source": [
    "learn.load('leaf_types_stage_2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace the model head with a custom head(124 classes instead of 51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.learner import create_head, cnn_config, num_features_model\n",
    "arch = models.resnet34\n",
    "cut = None\n",
    "meta = cnn_config(arch)\n",
    "body = create_body(arch(pretrained=True), ifnone(cut,meta['cut']))\n",
    "\n",
    "#define number of classes of the \"old\" model\n",
    "nc = 124\n",
    "nf = num_features_model(body) * 2\n",
    "ps=0.5\n",
    "lin_ftrs = None\n",
    "\n",
    "head_124 = create_head(nf, nc, lin_ftrs, ps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): AdaptiveConcatPool2d(\n",
       "    (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "    (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "  )\n",
       "  (1): Lambda()\n",
       "  (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (3): Dropout(p=0.25)\n",
       "  (4): Linear(in_features=1024, out_features=512, bias=True)\n",
       "  (5): ReLU(inplace)\n",
       "  (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (7): Dropout(p=0.5)\n",
       "  (8): Linear(in_features=512, out_features=124, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head_124"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = create_cnn(data,models.resnet34, metrics=error_rate,  custom_head=head_124)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Learner(data=ImageDataBunch;\n",
       "Train: LabelList\n",
       "y: CategoryList (984 items)\n",
       "[Category Populus nigra, Category Cotinus coggygria, Category Corylus avellana, Category Pistacia terebinthus, Category Laurus nobilis]...\n",
       "Path: data/ImageCLEF2011\n",
       "x: ImageItemList (984 items)\n",
       "[Image (3, 750, 800), Image (3, 600, 800), Image (3, 800, 600), Image (3, 450, 800), Image (3, 800, 450)]...\n",
       "Path: data/ImageCLEF2011;\n",
       "Valid: LabelList\n",
       "y: CategoryList (246 items)\n",
       "[Category Laurus nobilis, Category Betula pendula, Category Aesculus hippocastanum, Category Diospyros kaki, Category Liquidambar styraciflua]...\n",
       "Path: data/ImageCLEF2011\n",
       "x: ImageItemList (246 items)\n",
       "[Image (3, 800, 533), Image (3, 529, 800), Image (3, 800, 532), Image (3, 532, 800), Image (3, 600, 800)]...\n",
       "Path: data/ImageCLEF2011;\n",
       "Test: None, model=Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU(inplace)\n",
       "    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (4): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (3): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (6): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (3): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (4): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (5): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (7): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): AdaptiveConcatPool2d(\n",
       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "    )\n",
       "    (1): Lambda()\n",
       "    (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.25)\n",
       "    (4): Linear(in_features=1024, out_features=512, bias=True)\n",
       "    (5): ReLU(inplace)\n",
       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.5)\n",
       "    (8): Linear(in_features=512, out_features=124, bias=True)\n",
       "  )\n",
       "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=<function cross_entropy at 0x7f672d82fd08>, metrics=[<function error_rate at 0x7f6726defa60>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('data/ImageCLEF2011'), model_dir='models', callback_fns=[<class 'fastai.basic_train.Recorder'>], callbacks=[], layer_groups=[Sequential(\n",
       "  (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU(inplace)\n",
       "  (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (8): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (9): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (11): ReLU(inplace)\n",
       "  (12): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (13): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (15): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (16): ReLU(inplace)\n",
       "  (17): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (18): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (19): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (20): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (21): ReLU(inplace)\n",
       "  (22): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (23): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (24): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (25): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (26): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (27): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (28): ReLU(inplace)\n",
       "  (29): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (30): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (31): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (32): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (33): ReLU(inplace)\n",
       "  (34): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (35): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (36): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (37): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (38): ReLU(inplace)\n",
       "  (39): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (40): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "), Sequential(\n",
       "  (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU(inplace)\n",
       "  (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (5): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (9): ReLU(inplace)\n",
       "  (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (11): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (13): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (14): ReLU(inplace)\n",
       "  (15): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (16): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (19): ReLU(inplace)\n",
       "  (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (22): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (23): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (24): ReLU(inplace)\n",
       "  (25): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (26): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (27): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (28): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (29): ReLU(inplace)\n",
       "  (30): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (31): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (32): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (33): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (34): ReLU(inplace)\n",
       "  (35): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (36): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (37): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (39): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (40): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (41): ReLU(inplace)\n",
       "  (42): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (43): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (44): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (45): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (46): ReLU(inplace)\n",
       "  (47): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (48): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "), Sequential(\n",
       "  (0): AdaptiveAvgPool2d(output_size=1)\n",
       "  (1): AdaptiveMaxPool2d(output_size=1)\n",
       "  (2): Lambda()\n",
       "  (3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (4): Dropout(p=0.25)\n",
       "  (5): Linear(in_features=1024, out_features=512, bias=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (8): Dropout(p=0.5)\n",
       "  (9): Linear(in_features=512, out_features=124, bias=True)\n",
       ")])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.load('leaf_types_stage_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 01:23\n",
      "epoch  train_loss  valid_loss  error_rate\n",
      "1      9.007939    6.659494    0.955285    (01:23)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 1e-02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 04:57\n",
      "epoch  train_loss  valid_loss  error_rate\n",
      "1      5.174671    2.957412    0.601626    (01:14)\n",
      "2      3.185586    1.728265    0.410569    (01:14)\n",
      "3      2.281855    1.296025    0.308943    (01:14)\n",
      "4      1.735953    1.159244    0.268293    (01:14)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(4, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastai.defaults.device = torch.device('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 05:15\n",
      "epoch  train_loss  valid_loss  error_rate\n",
      "1      0.821131    1.569144    0.333333    (01:14)\n",
      "2      0.925412    1.969281    0.447154    (01:15)\n",
      "3      0.884400    1.245577    0.308943    (01:31)\n",
      "4      0.793402    1.045452    0.260163    (01:14)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(4, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 05:26\n",
      "epoch  train_loss  valid_loss  error_rate\n",
      "1      0.590450    1.414678    0.378049    (01:37)\n",
      "2      0.670931    1.729311    0.390244    (01:16)\n",
      "3      0.668957    1.146509    0.288618    (01:16)\n",
      "4      0.601572    0.989023    0.247967    (01:15)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(4, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('leaf_types_natural_stage_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 08:36\n",
      "epoch  train_loss  valid_loss  error_rate\n",
      "1      0.400884    1.054175    0.243902    (01:35)\n",
      "2      0.441613    1.626887    0.386179    (01:54)\n",
      "3      0.551158    1.941962    0.382114    (01:17)\n",
      "4      0.595180    1.261080    0.280488    (01:15)\n",
      "5      0.537591    1.000867    0.203252    (01:18)\n",
      "6      0.483004    0.946127    0.182927    (01:14)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(6, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('leaf_types_natural_stage_1_more_training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Learner(data=ImageDataBunch;\n",
       "Train: LabelList\n",
       "y: CategoryList (984 items)\n",
       "[Category Populus nigra, Category Cotinus coggygria, Category Corylus avellana, Category Pistacia terebinthus, Category Laurus nobilis]...\n",
       "Path: data/ImageCLEF2011\n",
       "x: ImageItemList (984 items)\n",
       "[Image (3, 750, 800), Image (3, 600, 800), Image (3, 800, 600), Image (3, 450, 800), Image (3, 800, 450)]...\n",
       "Path: data/ImageCLEF2011;\n",
       "Valid: LabelList\n",
       "y: CategoryList (246 items)\n",
       "[Category Laurus nobilis, Category Betula pendula, Category Aesculus hippocastanum, Category Diospyros kaki, Category Liquidambar styraciflua]...\n",
       "Path: data/ImageCLEF2011\n",
       "x: ImageItemList (246 items)\n",
       "[Image (3, 800, 533), Image (3, 529, 800), Image (3, 800, 532), Image (3, 532, 800), Image (3, 600, 800)]...\n",
       "Path: data/ImageCLEF2011;\n",
       "Test: None, model=Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU(inplace)\n",
       "    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (4): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (3): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (6): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (3): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (4): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (5): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (7): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): AdaptiveConcatPool2d(\n",
       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "    )\n",
       "    (1): Lambda()\n",
       "    (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.25)\n",
       "    (4): Linear(in_features=1024, out_features=512, bias=True)\n",
       "    (5): ReLU(inplace)\n",
       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.5)\n",
       "    (8): Linear(in_features=512, out_features=124, bias=True)\n",
       "  )\n",
       "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=<function cross_entropy at 0x7f672d82fd08>, metrics=[<function error_rate at 0x7f6726defa60>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('data/ImageCLEF2011'), model_dir='models', callback_fns=[<class 'fastai.basic_train.Recorder'>], callbacks=[], layer_groups=[Sequential(\n",
       "  (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU(inplace)\n",
       "  (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (8): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (9): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (11): ReLU(inplace)\n",
       "  (12): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (13): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (15): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (16): ReLU(inplace)\n",
       "  (17): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (18): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (19): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (20): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (21): ReLU(inplace)\n",
       "  (22): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (23): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (24): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (25): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (26): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (27): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (28): ReLU(inplace)\n",
       "  (29): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (30): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (31): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (32): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (33): ReLU(inplace)\n",
       "  (34): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (35): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (36): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (37): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (38): ReLU(inplace)\n",
       "  (39): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (40): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "), Sequential(\n",
       "  (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU(inplace)\n",
       "  (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (5): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (9): ReLU(inplace)\n",
       "  (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (11): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (13): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (14): ReLU(inplace)\n",
       "  (15): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (16): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (19): ReLU(inplace)\n",
       "  (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (22): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (23): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (24): ReLU(inplace)\n",
       "  (25): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (26): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (27): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (28): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (29): ReLU(inplace)\n",
       "  (30): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (31): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (32): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (33): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (34): ReLU(inplace)\n",
       "  (35): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (36): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (37): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (39): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (40): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (41): ReLU(inplace)\n",
       "  (42): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (43): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (44): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (45): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (46): ReLU(inplace)\n",
       "  (47): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (48): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "), Sequential(\n",
       "  (0): AdaptiveAvgPool2d(output_size=1)\n",
       "  (1): AdaptiveMaxPool2d(output_size=1)\n",
       "  (2): Lambda()\n",
       "  (3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (4): Dropout(p=0.25)\n",
       "  (5): Linear(in_features=1024, out_features=512, bias=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (8): Dropout(p=0.5)\n",
       "  (9): Linear(in_features=512, out_features=124, bias=True)\n",
       ")])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.load('leaf_types_natural_stage_1_more_training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.lr_find()\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = lr = 1e-03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 00:51\n",
      "epoch  train_loss  valid_loss  error_rate\n",
      "1      10.963358   6.410128    0.829268    (00:08)\n",
      "2      7.119327    2.927912    0.540650    (00:08)\n",
      "3      4.969768    2.043880    0.434959    (00:08)\n",
      "4      3.654621    1.556045    0.349593    (00:08)\n",
      "5      2.761063    1.279961    0.304878    (00:08)\n",
      "6      2.181263    1.222941    0.300813    (00:08)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(6, slice(lr*10e-2, lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 00:51\n",
      "epoch  train_loss  valid_loss  error_rate\n",
      "1      0.860662    1.173821    0.292683    (00:08)\n",
      "2      0.921671    1.820015    0.369919    (00:08)\n",
      "3      0.984404    1.980631    0.382114    (00:08)\n",
      "4      0.920642    1.229852    0.296748    (00:08)\n",
      "5      0.802395    1.036806    0.247967    (00:08)\n",
      "6      0.665032    0.940457    0.223577    (00:08)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(6, slice(lr*10e-2, lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 01:25\n",
      "epoch  train_loss  valid_loss  error_rate\n",
      "1      0.359213    0.954688    0.231707    (00:08)\n",
      "2      0.312570    1.119786    0.247967    (00:08)\n",
      "3      0.347528    1.626212    0.329268    (00:08)\n",
      "4      0.517475    1.919321    0.378049    (00:08)\n",
      "5      0.613137    1.537711    0.353658    (00:08)\n",
      "6      0.568207    1.123510    0.243902    (00:08)\n",
      "7      0.483670    0.898560    0.207317    (00:08)\n",
      "8      0.415821    0.944457    0.203252    (00:08)\n",
      "9      0.345002    0.885957    0.191057    (00:08)\n",
      "10     0.282003    0.871002    0.199187    (00:08)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(10, slice(lr*10e-2, lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('leaf_types_natural_stage_2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "interp = ClassificationInterpretation.from_learner(learn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp.plot_confusion_matrix(figsize=(15,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Robinia pseudoacacia', 'Pistacia terebinthus', 4),\n",
       " ('Tilia platyphyllos', 'Corylus avellana', 3),\n",
       " ('Quercus pubescens', 'Quercus robur', 2),\n",
       " ('Acer opalus', 'Acer pseudoplatanus', 2),\n",
       " ('Acer campestre', 'Crataegus monogyna', 2)]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interp.most_confused()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 02:52\n",
      "epoch  train_loss  valid_loss  error_rate\n",
      "1      0.168871    0.883860    0.186992    (00:09)\n",
      "2      0.149072    0.788109    0.170732    (00:08)\n",
      "3      0.137098    0.885490    0.203252    (00:08)\n",
      "4      0.160401    1.174955    0.247967    (00:08)\n",
      "5      0.197512    1.730798    0.329268    (00:08)\n",
      "6      0.337823    2.225853    0.394309    (00:08)\n",
      "7      0.433800    2.118726    0.365854    (00:08)\n",
      "8      0.447711    1.590499    0.349593    (00:08)\n",
      "9      0.434659    1.126909    0.243902    (00:08)\n",
      "10     0.380815    1.072827    0.243902    (00:08)\n",
      "11     0.332552    1.038782    0.227642    (00:08)\n",
      "12     0.271788    1.083281    0.223577    (00:08)\n",
      "13     0.225543    0.871944    0.203252    (00:08)\n",
      "14     0.193982    0.760780    0.178862    (00:08)\n",
      "15     0.158792    0.794618    0.174797    (00:08)\n",
      "16     0.126752    0.791848    0.178862    (00:08)\n",
      "17     0.102708    0.819477    0.178862    (00:08)\n",
      "18     0.085054    0.773584    0.170732    (00:08)\n",
      "19     0.072814    0.760188    0.174797    (00:08)\n",
      "20     0.067847    0.771417    0.174797    (00:08)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(20, slice(lr*10e-2, lr))"
   ]
  }
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No results found