history_stats.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y2aU5JguR8D8"
   },
   "source": [
    "## Check training and validation curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eliFBegMTy3A"
   },
   "outputs": [],
   "source": [
    "#@title Mount drive content\n",
    "# Load the Drive helper and mount\n",
    "from google.colab import drive\n",
    "\n",
    "# This will prompt for authorization.\n",
    "drive.mount('/content/drive')\n",
    "%cd /content/drive/My\\ Drive/Work/TSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KBSkFg7vR6t4"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pathlib as pt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7WwYPwKmR7oK"
   },
   "outputs": [],
   "source": [
    "def plot_results(history, graphs_path, metric='dice_coef'):\n",
    "    dice_coef = history[metric]\n",
    "    val_dice_coef = history['val_' + metric]\n",
    "    loss = history['loss']\n",
    "    val_loss = history['val_loss']\n",
    "\n",
    "    epochs = range(1, len(dice_coef) + 1)\n",
    "    plt.figure(figsize=(12, 7))\n",
    "    \n",
    "    plt.plot(epochs, dice_coef, 'b', label='Training {}'.format(metric))\n",
    "    plt.plot(epochs, val_dice_coef, 'g', label='Validation {}'.format(metric))\n",
    "    plt.title('Training and validation {}'.format(metric.upper()))\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    \n",
    "    if not graphs_path.exists():\n",
    "      os.mkdir(str(graphs_path))\n",
    "          \n",
    "    plt.savefig(graphs_path/'train_val_{}.png'.format(metric), bbox_inches='tight', pad_inches=0)\n",
    "    \n",
    "    plt.figure(figsize=(12, 7))\n",
    "\n",
    "    plt.plot(epochs, loss, 'b', label='Training loss')\n",
    "    plt.plot(epochs, val_loss, 'g', label='Validation loss')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.savefig(graphs_path/'train_val_loss_with_{}.png'.format(metric), bbox_inches='tight', pad_inches=0)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vHch5JeRR7wk"
   },
   "outputs": [],
   "source": [
    "RUN_TIME = '2020_08_28-14_40'\n",
    "# RUN_TIME = '2020_08_30-08_49'\n",
    "ROOT_PATH    = pt.Path(\"/content/drive/My Drive/Work/TSM\")\n",
    "HISTORY_FILE = ROOT_PATH/\"trainings\"/RUN_TIME/\"history_{}.csv\".format(RUN_TIME)\n",
    "GRAPHS_PATH  = ROOT_PATH/\"trainings\"/RUN_TIME/\"graphs\"\n",
    "history = pd.read_csv(HISTORY_FILE)\n",
    "# history.loc[history['epoch'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qwKnffcsSR4Y"
   },
   "outputs": [],
   "source": [
    "plot_results(history, graphs_path=GRAPHS_PATH, metric='dice_coef')"
   ]
  }
 ],
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   "authorship_tag": "ABX9TyOn3xpROsQrSu6OiLkHcVhQ",
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