Hyperparameter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3bb8a2aa",
   "metadata": {},
   "source": [
    "# Keras Hyperparameter tuner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2781ecb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install keras_tuner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10ff7b49",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2154f564",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c22e706",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install upgrade --numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a3f77e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from keras_tuner.tuners import RandomSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16925183",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Real_Combine.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a281e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,:-1]\n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "58a3f8d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hyper_tune(param):\n",
    "    model=keras.Sequential()\n",
    "    for i in range(param.Int('num_layers',2,20)):\n",
    "        model.add(layers.Dense(units=param.Int('units_'+str(i),\n",
    "                                              min_value=32,\n",
    "                                              max_value=512,\n",
    "                                              step=32),\n",
    "                              activation='tanh'))\n",
    "        model.add(layers.Dense(1,activation='linear'))\n",
    "        model.compile(\n",
    "        optimizer=keras.optimizer.Adam(\n",
    "        param.Choice('learning_rate',[1e-2,1e-3,1e-4])),\n",
    "        loss='mean_absolute_error',\n",
    "        metrics=['mean_absolute_error'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4905a6ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuner=RandomSearch(\n",
    "      hyper_tune,\n",
    "objective')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb7b098b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e988e2c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97a6d07d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eeb3ead",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caa520a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e25c62b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fb308e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}