Height-Weight-Keras-Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c15fbf70",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "df=pd.read_csv(\"weight-height.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be164ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ed61b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[\"Height\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3658549c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df[\"Weight\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d35a86eb",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3688/175639002.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Create model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m \"\"\"\n\u001b[0;32m     20\u001b[0m \u001b[1;31m# pylint: disable=unused-import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtf2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdistribute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
    }
   ],
   "source": [
    "# Create model \n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b94c8bd0",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Sequential' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3688/3784925524.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mSequential\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'Sequential' is not defined"
     ]
    }
   ],
   "source": [
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12aacdd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now adding layers with Dense function \n",
    "# bias_initializer to initialize bias \n",
    "# kernel_initializer to initialize weights\n",
    "from keras.layers import Dense\n",
    "model.add(Dense(units=1,bias_initializer = \"zeros\", kernel_initializer = \"zeros\",activation=\"linear\",input_shape=(1,)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ced0578",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# To check model insights \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc773d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential',\n",
       " 'layers': [{'class_name': 'InputLayer',\n",
       "   'config': {'batch_input_shape': (None, 1),\n",
       "    'dtype': 'float32',\n",
       "    'sparse': False,\n",
       "    'ragged': False,\n",
       "    'name': 'dense_input'}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense',\n",
       "    'trainable': True,\n",
       "    'batch_input_shape': (None, 1),\n",
       "    'dtype': 'float32',\n",
       "    'units': 1,\n",
       "    'activation': 'linear',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}}]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get details about model \n",
    "model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa94090a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to compiler model and provide optimizer and loss so that it can learn correctly \n",
    "from keras.optimizers import Adam \n",
    "model.compile(optimizer=Adam(), loss=\"mean_squared_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e70ebfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 9s 441us/step - loss: 25385.9125\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1ac98d3fee0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting data \n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "95135d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0.30421597]], dtype=float32), array([0.30414656], dtype=float32)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get information about the weights of each feature \n",
    "model.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79796744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30.725742]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict if Height is 110\n",
    "model.predict([100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a00f8f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'dense_input')>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get information about each layer \n",
    "model.get_layer(\"dense\").input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f82f4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'dense')>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_layer(\"dense\").output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a09a44f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To save a model \n",
    "model.save(\"HW-model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53ef397d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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