TensorFlow_Keras_regresion lineal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>GRAPHS</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EJEMPLO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 500 valores separados linealmente\n",
    "\n",
    "x_data = np.linspace(0.0,10.0,500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 500 valores random\n",
    "\n",
    "noise = np.random.randn(len(x_data))\n",
    "noise.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aca vamos a mezclar los puntos lineales con los ruidosos \n",
    "# y = mx + b + noise_levels\n",
    "# m = 0.5  ,  b = 5 y ruido para encontrar \"m\" y \"b\" con TensorFlow\n",
    "\n",
    "y_true =  (0.5 * x_data ) + 5 + noise\n",
    "y_true.shape\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>X Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.06012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.08016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X Data\n",
       "0  0.00000\n",
       "1  0.02004\n",
       "2  0.04008\n",
       "3  0.06012\n",
       "4  0.08016"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Paso a Dataframe los datos hechos con numpy\n",
    "\n",
    "x_df = pd.DataFrame(x_data,columns=['X Data'])\n",
    "y_df = pd.DataFrame(y_true,columns=['Y True'])\n",
    "x_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>X Data</th>\n",
       "      <th>Y True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>6.492127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02004</td>\n",
       "      <td>3.948371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04008</td>\n",
       "      <td>6.927520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.06012</td>\n",
       "      <td>4.983693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.08016</td>\n",
       "      <td>5.653284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X Data    Y True\n",
       "0  0.00000  6.492127\n",
       "1  0.02004  3.948371\n",
       "2  0.04008  6.927520\n",
       "3  0.06012  4.983693\n",
       "4  0.08016  5.653284"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = pd.concat([x_df,y_df],axis=1)\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas dataframe_name.sample.plot\n",
    "\n",
    "- **``dataframe_name.sample(n=100)``**: Utiliza 100 pares de valores XY para lo que sigue, que en este caso es graficar\n",
    "\n",
    "\n",
    "- **``dataframe_name.sample(n=100).plot(kind='scatter',x='X Data',y='Y True').``**: Ploteo con los 100 puntos random (tenia 10.000 puntos, y eso iba a hacer que tarde mucho para graficar, por eso usamos solo 100 pares XY."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='X Data', ylabel='Y True'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "all_data.sample(n=100).plot(kind='scatter',x='X Data',y='Y True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow con Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m = 0.498\n",
      "b = 4.9\n"
     ]
    },
    {
     "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": [
    "# Modelo de Keras\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(1, input_shape=[1]))\n",
    "model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.SGD(0.01))\n",
    "#model.summary()         # Comentado para que no salga en la salida y salgan solo \"m\" y \"b\"\n",
    "\n",
    "# Entreno el modelo\n",
    "history = model.fit(x_data, y_true, epochs=250,verbose=0,batch_size=None)\n",
    "\n",
    "# Hago la prediccion de Y utilizando el modelo entrenado y veo los valores de \"m\" y \"b\" calculados\n",
    "y = model.predict(x_data,verbose=0,batch_size=2)\n",
    "tf.print('m =',round(float(model.variables[0]),3))\n",
    "tf.print('b =',round(float(model.variables[1]),3))\n",
    "\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title('ERROR vs EPOCH',size=18)\n",
    "plt.xlabel('EPOCHS')\n",
    "plt.ylabel('ERROR')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Grafico utilizando el DATAFRAME_NAME.sample, que elige pares aleaorios\n",
    "\n",
    "all_data.sample(n=200).plot(kind='scatter',x='X Data',y='Y True',label='Generated data',marker='.',color='b')\n",
    "plt.plot(x_data, y, label='Predicted with model', color='r')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}