PlantClassification.ipynb

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   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "from fastai.imports import *\n",
    "from fastai.transforms import *\n",
    "from fastai.conv_learner import *\n",
    "from fastai.model import *\n",
    "from fastai.dataset import *\n",
    "from fastai.sgdr import *\n",
    "from fastai.plots import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "end_time": "2018-03-08T17:38:36.007920Z",
     "start_time": "2018-03-08T17:38:35.991738Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# f1_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)\n",
    "def mod_acc_to_f1(preds, targs):\n",
    "    preds = torch.max(preds, dim=1)[1]\n",
    "    return f1_score(targs, preds, average='micro')\n",
    "#     return (preds==targs).float().mean()\n",
    "\n",
    "metrics=[mod_acc_to_f1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "end_time": "2018-03-08T17:38:36.022241Z",
     "start_time": "2018-03-08T17:38:36.009159Z"
    }
   },
   "outputs": [],
   "source": [
    "PATH = 'data/plant_classification/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "start_time": "2018-03-08T17:38:36.023521Z"
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   "outputs": [],
   "source": [
    "arch = resnet50"
   ]
  },
  {
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   "execution_count": 6,
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     "start_time": "2018-03-08T17:38:36.037368Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_data(sz=150, bs=64):\n",
    "    tfms = tfms_from_model(arch, sz, aug_tfms=transforms_top_down, max_zoom=1.1)\n",
    "    data = ImageClassifierData.from_paths(PATH, bs=bs, tfms=tfms, test_name='test')\n",
    "    return data"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
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     "end_time": "2018-03-08T17:38:42.207010Z",
     "start_time": "2018-03-08T17:38:36.935454Z"
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   "outputs": [],
   "source": [
    "learn = ConvLearner.pretrained(arch, get_data())"
   ]
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   "execution_count": 8,
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   "source": [
    "lr = 1e-2\n",
    "lrs = np.array([lr/9, lr/3, lr])"
   ]
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      "epoch      trn_loss   val_loss   mod_acc_to_f1            \n",
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    "learn.fit(lr, 3, cycle_len=1, metrics=metrics)"
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      "epoch      trn_loss   val_loss   mod_acc_to_f1             \n",
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    "learn.fit(lr, 5, cycle_len=1, metrics=metrics)"
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      "epoch      trn_loss   val_loss   mod_acc_to_f1             \n",
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    "learn.fit(lr, 5, cycle_len=1, metrics=metrics)"
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     "text": [
      "epoch      trn_loss   val_loss   accuracy                  \n",
      "    0      0.472633   0.273685   0.910417  \n",
      "    1      0.303626   0.162908   0.953125                  \n",
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      "                                                           \r"
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   "source": [
    "learn.unfreeze()\n",
    "learn.fit(lrs, 3, cycle_len=1, cycle_mult=2, metrics=metrics)\n",
    "learn.save('150_v4')"
   ]
  },
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      "epoch      trn_loss   val_loss   mod_acc_to_f1             \n",
      "    0      0.139254   0.128817   0.951562  \n",
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    "learn.load('150_v4')\n",
    "learn.set_data(get_data(250))\n",
    "learn.freeze()\n",
    "learn.fit(lr, 3, cycle_len=1, metrics=metrics)"
   ]
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     "text": [
      "epoch      trn_loss   val_loss   mod_acc_to_f1 accuracy    \n",
      "    0      0.1353     0.096606   0.964062   0.964062  \n",
      "    1      0.129837   0.066773   0.979688   0.979688       \n",
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    "learn.unfreeze()\n",
    "learn.fit(lrs, 3, cycle_len=1, cycle_mult=2, metrics=metrics)\n",
    "learn.save('250_v4')"
   ]
  },
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      "epoch      trn_loss   val_loss   mod_acc_to_f1              \n",
      "    0      0.07502    0.042621   0.993333  \n",
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    "learn.load('250_v4')\n",
    "learn.set_data(get_data(350, 50))\n",
    "learn.freeze()\n",
    "learn.fit(lr, 3, cycle_len=1, metrics=metrics)"
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    "learn.set_data(get_data(400, 50))\n",
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     "text": [
      "epoch      trn_loss   val_loss   mod_acc_to_f1                \n",
      "    0      0.069266   0.041566   0.986667  \n",
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    "learn.save('400_v6')\n",
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      "epoch      trn_loss   val_loss   mod_acc_to_f1              \n",
      "    0      0.016362   0.006562   0.998333  \n",
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      "    23     0.009932   0.004738   0.998333                    \n",
      "    24     0.010755   0.005103   0.998333                    \n",
      "    25     0.009387   0.004252   0.998333                    \n",
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      "    27     0.011843   0.004434   0.998333                   \n",
      "    28     0.012396   0.004676   0.998333                    \n",
      "    29     0.008922   0.004443   0.998333                    \n",
      "    30     0.010421   0.004257   0.998333                    \n",
      "\n"
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       "[0.004256842, 0.9983333333333334]"
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     },
     "execution_count": 61,
     "metadata": {},
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    }
   ],
   "source": [
    "learn.fit(lrs, 5, cycle_len=1, cycle_mult=2, metrics=metrics)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-08T22:17:11.325172Z",
     "start_time": "2018-03-08T22:17:11.203032Z"
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   },
   "outputs": [],
   "source": [
    "learn.save('400_v7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-08T22:18:32.617181Z",
     "start_time": "2018-03-08T22:17:13.421508Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    }
   ],
   "source": [
    "log_preds,y = learn.TTA(is_test=True)\n",
    "probs = np.median(np.exp(log_preds),0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create csv with results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-08T22:18:32.633975Z",
     "start_time": "2018-03-08T22:18:32.618718Z"
    }
   },
   "outputs": [],
   "source": [
    "classes = learn.data.classes\n",
    "choices = np.argmax(probs, axis=1)\n",
    "# Returns the indices of the maximum values along an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-08T22:18:32.651905Z",
     "start_time": "2018-03-08T22:18:32.635443Z"
    }
   },
   "outputs": [],
   "source": [
    "res = []\n",
    "for i in np.nditer(choices):\n",
    "    res.append(classes[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-08T22:18:32.666580Z",
     "start_time": "2018-03-08T22:18:32.653368Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-08T22:18:32.682363Z",
     "start_time": "2018-03-08T22:18:32.667893Z"
    }
   },
   "outputs": [],
   "source": [
    "df.insert(0, 'file', [n[5:] for n in learn.data.test_ds.fnames])\n",
    "# subset the string (remove 'test/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-08T22:18:32.697218Z",
     "start_time": "2018-03-08T22:18:32.683643Z"
    }
   },
   "outputs": [],
   "source": [
    "df.columns = ['file','species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-08T22:24:04.775702Z",
     "start_time": "2018-03-08T22:24:04.746825Z"
    }
   },
   "outputs": [],
   "source": [
    "SUBM = f'{PATH}subm/'\n",
    "os.makedirs(SUBM, exist_ok=True)\n",
    "df.to_csv(f'{SUBM}subm_v8.csv', index=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 82,
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list indices must be integers or slices, not str\r\n"
     ]
    }
   ],
   "source": [
    "!kg submit {SUBM}'subm_v8.csv' -c plant-seedlings-classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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