Regression Problem and cost function[predicts weight of a person].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I have trained this model on 10,000 datasets which predicts weight of a person corresponding to height."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>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",
       "  </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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('weight-height.csv')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Now X, its input of our model\n",
    "#y_true is true output\n",
    "X=df[['Height']].values\n",
    "y_true=df[['Weight']].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def line(X,w=0,b=0):\n",
    "    return w*X+b\n",
    "def mean_squared_error(y_true,y_pred):    # this should be minimized, its most commonly used in regression problem,\n",
    "    s= (y_true-y_pred)**2\n",
    "    return np.sqrt(s.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "ax1=plt.subplot(121)\n",
    "df.plot(kind='scatter',\n",
    "       x='Height',\n",
    "       y='Weight',\n",
    "       title='Weight and Height in Adults', ax=ax1)\n",
    "bs=np.array([-100,-50,0,50,100,150])  # Supposed values ,, but no need in deep learning\n",
    "mses=[]\n",
    "for b in bs:\n",
    "    y_pred=line(X, w=2, b=b)\n",
    "    mse=mean_squared_error(y_true, y_pred)\n",
    "    mses.append(mse)\n",
    "    plt.plot(X,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'b')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2=plt.subplot(122)\n",
    "plt.plot(bs,mses, 'o-')\n",
    "plt.title('cost as a function of b')\n",
    "plt.xlabel ('b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Now solve this problem by DEEP LEARNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential    #Capital S\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(1,input_shape=(1,)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10000 samples\n",
      "Epoch 1/40\n",
      "10000/10000 [==============================] - 1s 136us/sample - loss: 835.2195\n",
      "Epoch 2/40\n",
      "10000/10000 [==============================] - 1s 53us/sample - loss: 547.6970\n",
      "Epoch 3/40\n",
      "10000/10000 [==============================] - 1s 50us/sample - loss: 516.5361\n",
      "Epoch 4/40\n",
      "10000/10000 [==============================] - 0s 50us/sample - loss: 481.2584\n",
      "Epoch 5/40\n",
      "10000/10000 [==============================] - 1s 51us/sample - loss: 443.6298\n",
      "Epoch 6/40\n",
      "10000/10000 [==============================] - 1s 54us/sample - loss: 383.0040\n",
      "Epoch 7/40\n",
      "10000/10000 [==============================] - 1s 56us/sample - loss: 362.2824\n",
      "Epoch 8/40\n",
      "10000/10000 [==============================] - 1s 57us/sample - loss: 332.1031\n",
      "Epoch 9/40\n",
      "10000/10000 [==============================] - 1s 59us/sample - loss: 298.4701\n",
      "Epoch 10/40\n",
      "10000/10000 [==============================] - 1s 107us/sample - loss: 269.0480\n",
      "Epoch 11/40\n",
      "10000/10000 [==============================] - 1s 118us/sample - loss: 251.1989\n",
      "Epoch 12/40\n",
      "10000/10000 [==============================] - 1s 103us/sample - loss: 238.5993\n",
      "Epoch 13/40\n",
      "10000/10000 [==============================] - 1s 89us/sample - loss: 225.6375\n",
      "Epoch 14/40\n",
      "10000/10000 [==============================] - 1s 91us/sample - loss: 212.1787\n",
      "Epoch 15/40\n",
      "10000/10000 [==============================] - 1s 70us/sample - loss: 204.4522\n",
      "Epoch 16/40\n",
      "10000/10000 [==============================] - 1s 59us/sample - loss: 201.4464\n",
      "Epoch 17/40\n",
      "10000/10000 [==============================] - 1s 54us/sample - loss: 186.3865\n",
      "Epoch 18/40\n",
      "10000/10000 [==============================] - 1s 53us/sample - loss: 186.7283\n",
      "Epoch 19/40\n",
      "10000/10000 [==============================] - 1s 61us/sample - loss: 189.4223\n",
      "Epoch 20/40\n",
      "10000/10000 [==============================] - 1s 54us/sample - loss: 194.9278\n",
      "Epoch 21/40\n",
      "10000/10000 [==============================] - 1s 53us/sample - loss: 178.0416\n",
      "Epoch 22/40\n",
      "10000/10000 [==============================] - 1s 54us/sample - loss: 182.0511\n",
      "Epoch 23/40\n",
      "10000/10000 [==============================] - 1s 82us/sample - loss: 183.9997\n",
      "Epoch 24/40\n",
      "10000/10000 [==============================] - 1s 51us/sample - loss: 181.0064\n",
      "Epoch 25/40\n",
      "10000/10000 [==============================] - 1s 52us/sample - loss: 173.5512\n",
      "Epoch 26/40\n",
      "10000/10000 [==============================] - 1s 59us/sample - loss: 177.4672\n",
      "Epoch 27/40\n",
      "10000/10000 [==============================] - 1s 59us/sample - loss: 178.0011\n",
      "Epoch 28/40\n",
      "10000/10000 [==============================] - 1s 57us/sample - loss: 173.7358\n",
      "Epoch 29/40\n",
      "10000/10000 [==============================] - 1s 59us/sample - loss: 174.0557\n",
      "Epoch 30/40\n",
      "10000/10000 [==============================] - 1s 54us/sample - loss: 173.2439\n",
      "Epoch 31/40\n",
      "10000/10000 [==============================] - 1s 51us/sample - loss: 181.7264\n",
      "Epoch 32/40\n",
      "10000/10000 [==============================] - 1s 52us/sample - loss: 170.0330\n",
      "Epoch 33/40\n",
      "10000/10000 [==============================] - 1s 53us/sample - loss: 181.7329\n",
      "Epoch 34/40\n",
      "10000/10000 [==============================] - 1s 55us/sample - loss: 188.5492\n",
      "Epoch 35/40\n",
      "10000/10000 [==============================] - 1s 59us/sample - loss: 170.5910\n",
      "Epoch 36/40\n",
      "10000/10000 [==============================] - 1s 53us/sample - loss: 182.5443\n",
      "Epoch 37/40\n",
      "10000/10000 [==============================] - 1s 53us/sample - loss: 179.4744\n",
      "Epoch 38/40\n",
      "10000/10000 [==============================] - 1s 54us/sample - loss: 176.4639\n",
      "Epoch 39/40\n",
      "10000/10000 [==============================] - 1s 53us/sample - loss: 172.9913\n",
      "Epoch 40/40\n",
      "10000/10000 [==============================] - 1s 52us/sample - loss: 179.6763\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x201f3069788>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(Adam(lr=0.8),'mean_squared_error')\n",
    "model.fit(X,y_true,epochs=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[215.82767],\n",
       "       [177.14822],\n",
       "       [217.83676],\n",
       "       ...,\n",
       "       [139.62335],\n",
       "       [179.07516],\n",
       "       [124.93274]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x201f38312c8>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',\n",
    "       x='Height',\n",
    "       y='Weight',\n",
    "       title='Weight and Height in Adults')\n",
    "plt.plot(X,y_pred, color='red', linewidth=3 )\n",
    "\n",
    "# Now see the result, machine learning automatically found w and b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.636454]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w,b =model.get_weights()   # Get learned values from model\n",
    "w\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-348.10165], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict the value,   give height, model will tell you weight of person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[140.63141]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z=model.predict([[64]])\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[63.923367]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z/2.20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is my predicted weight which is almost correct (64)"
   ]
  },
  {
   "cell_type": "code",
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