{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CSV Logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "from fastai.vision import *\n",
    "from fastai.gen_doc.nbdoc import *\n",
    "from fastai.callbacks import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h2 id=\"CSVLogger\"><code>class</code> <code>CSVLogger</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L10\" class=\"source_link\">[source]</a></h2>\n",
       "\n",
       "> <code>CSVLogger</code>(**`learn`**:[`Learner`](/basic_train.html#Learner), **`filename`**:`str`=***`'history'`***) :: [`LearnerCallback`](/basic_train.html#LearnerCallback)\n",
       "\n",
       "A [`LearnerCallback`](/basic_train.html#LearnerCallback) that saves history of metrics while training `learn` into CSV `filename`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CSVLogger)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's show an example of use, with a training on the usual MNIST dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.MNIST_TINY)\n",
    "data = ImageDataBunch.from_folder(path)\n",
    "learn = Learner(data, simple_cnn((3, 16, 16, 2)), metrics=[accuracy, error_rate], callback_fns=[CSVLogger])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 00:01 <p><table style='width:375px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "    <th>error_rate</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.586522</th>\n",
       "    <th>0.433411</th>\n",
       "    <th>0.935622</th>\n",
       "    <th>0.064378</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.448884</th>\n",
       "    <th>0.187622</th>\n",
       "    <th>0.949928</th>\n",
       "    <th>0.050072</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.345840</th>\n",
       "    <th>0.130823</th>\n",
       "    <th>0.957082</th>\n",
       "    <th>0.042918</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training details have been saved in 'history.csv'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/export.pkl'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/models'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/valid')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.path.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that, as with all [`LearnerCallback`](/basic_train.html#LearnerCallback), you can access the object as an attribute of `learn` after it has been created. Here it's `learn.csv_logger`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CSVLogger.read_logged_file\"><code>read_logged_file</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L16\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>read_logged_file</code>()\n",
       "\n",
       "Read the content of saved file  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CSVLogger.read_logged_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>error_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.586522</td>\n",
       "      <td>0.433411</td>\n",
       "      <td>0.935622</td>\n",
       "      <td>0.064378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.448884</td>\n",
       "      <td>0.187622</td>\n",
       "      <td>0.949928</td>\n",
       "      <td>0.050072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.345840</td>\n",
       "      <td>0.130823</td>\n",
       "      <td>0.957082</td>\n",
       "      <td>0.042918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   epoch  train_loss  valid_loss  accuracy  error_rate\n",
       "0      1    0.586522    0.433411  0.935622    0.064378\n",
       "1      2    0.448884    0.187622  0.949928    0.050072\n",
       "2      3    0.345840    0.130823  0.957082    0.042918"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.csv_logger.read_logged_file()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calback methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You don't call these yourself - they're called by fastai's [`Callback`](/callback.html#Callback) system automatically to enable the class's functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CSVLogger.on_train_begin\"><code>on_train_begin</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L20\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>on_train_begin</code>(**\\*\\*`kwargs`**:`Any`)\n",
       "\n",
       "Prepare file with metric names.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CSVLogger.on_train_begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CSVLogger.on_epoch_end\"><code>on_epoch_end</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L26\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>on_epoch_end</code>(**`epoch`**:`int`, **`smooth_loss`**:`Tensor`, **`last_metrics`**:`MetricsList`, **\\*\\*`kwargs`**:`Any`) → `bool`\n",
       "\n",
       "Add a line with `epoch` number, `smooth_loss` and `last_metrics`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CSVLogger.on_epoch_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CSVLogger.on_train_end\"><code>on_train_end</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L34\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>on_train_end</code>(**\\*\\*`kwargs`**:`Any`)\n",
       "\n",
       "Close the file.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CSVLogger.on_train_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Undocumented Methods - Methods moved below this line will intentionally be hidden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Methods - Please document or move to the undocumented section"
   ]
  }
 ],
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