note_h5scan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from IPython.display import display\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
    "\n",
    "DIR_PROJECT = 'm1s'\n",
    "DIR = os.path.join(os.getcwd(), DIR_PROJECT)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>FILE</th>\n",
       "      <th>LAYERS</th>\n",
       "      <th>INPUT</th>\n",
       "      <th>【1】ACTIV</th>\n",
       "      <th>【1】UNITS</th>\n",
       "      <th>【2】ACTIV</th>\n",
       "      <th>【2】UNITS</th>\n",
       "      <th>【3】ACTIV</th>\n",
       "      <th>【3】UNITS</th>\n",
       "      <th>【4】ACTIV</th>\n",
       "      <th>【4】UNITS</th>\n",
       "      <th>【5】ACTIV</th>\n",
       "      <th>【5】UNITS</th>\n",
       "      <th>【6】ACTIV</th>\n",
       "      <th>【6】UNITS</th>\n",
       "      <th>【7】ACTIV</th>\n",
       "      <th>【7】UNITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ep335_loss0.285_acc0.965_vloss0.278_vacc0.972.h5</td>\n",
       "      <td>4</td>\n",
       "      <td>swish</td>\n",
       "      <td>swish</td>\n",
       "      <td>58</td>\n",
       "      <td>swish</td>\n",
       "      <td>60</td>\n",
       "      <td>softplus</td>\n",
       "      <td>57</td>\n",
       "      <td>softplus</td>\n",
       "      <td>57</td>\n",
       "      <td>softmax</td>\n",
       "      <td>56</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               FILE  LAYERS  INPUT 【1】ACTIV  \\\n",
       "0  ep335_loss0.285_acc0.965_vloss0.278_vacc0.972.h5       4  swish    swish   \n",
       "\n",
       "   【1】UNITS 【2】ACTIV  【2】UNITS  【3】ACTIV  【3】UNITS  【4】ACTIV  【4】UNITS  \\\n",
       "0        58    swish        60  softplus        57  softplus        57   \n",
       "\n",
       "  【5】ACTIV  【5】UNITS 【6】ACTIV 【6】UNITS 【7】ACTIV 【7】UNITS  \n",
       "0  softmax        56                                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = []\n",
    "for file in os.listdir(DIR):\n",
    "    if file.endswith('.h5'):\n",
    "        model = tf.keras.models.load_model(os.path.join(DIR, file))\n",
    "        num_layers = len(model.layers) - 2\n",
    "        row = {\n",
    "            'FILE': file,\n",
    "            'LAYERS': num_layers,\n",
    "            'INPUT': model.layers[0].activation.__name__,\n",
    "        }\n",
    "        for i in range(1, 8):\n",
    "            activ = ''\n",
    "            units = ''\n",
    "            if len(model.layers) > i:\n",
    "                if hasattr(model.layers[i], 'activation'):\n",
    "                    activ = model.layers[i].activation.__name__\n",
    "                if isinstance(model.layers[i], tf.keras.layers.Flatten):\n",
    "                    activ = 'FLATTEN'\n",
    "                if isinstance(model.layers[i], tf.keras.layers.BatchNormalization):\n",
    "                    activ = 'BATCH'\n",
    "                units = model.layers[i].output_shape[1]\n",
    "            row[f'【{i}】ACTIV'] = activ\n",
    "            row[f'【{i}】UNITS'] = units\n",
    "        data.append(row)\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "df_sorted = df.sort_values('LAYERS')\n",
    "display(df_sorted)\n"
   ]
  }
 ],
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