keras_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/alturas-pesos-mils-train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.fit(df[['Peso', 'Altura']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['Peso_norm', 'Alt_norm']] = sc.transform(df[['Peso', 'Altura']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['coded_gen'] = (df['Genero'] == 'Mujer')*1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Unnamed: 0</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Peso_norm</th>\n",
       "      <th>Alt_norm</th>\n",
       "      <th>coded_gen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7620</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>61.235</td>\n",
       "      <td>162.402</td>\n",
       "      <td>-0.791004</td>\n",
       "      <td>-0.581114</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1837</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>97.432</td>\n",
       "      <td>181.908</td>\n",
       "      <td>1.684210</td>\n",
       "      <td>1.403542</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3311</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>73.324</td>\n",
       "      <td>172.459</td>\n",
       "      <td>0.035663</td>\n",
       "      <td>0.442145</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9478</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>55.193</td>\n",
       "      <td>157.748</td>\n",
       "      <td>-1.204167</td>\n",
       "      <td>-1.054640</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7805</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>56.886</td>\n",
       "      <td>151.798</td>\n",
       "      <td>-1.088396</td>\n",
       "      <td>-1.660028</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7995</th>\n",
       "      <td>6395</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>62.215</td>\n",
       "      <td>160.083</td>\n",
       "      <td>-0.723990</td>\n",
       "      <td>-0.817063</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7996</th>\n",
       "      <td>2714</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>83.840</td>\n",
       "      <td>177.866</td>\n",
       "      <td>0.754765</td>\n",
       "      <td>0.992285</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7997</th>\n",
       "      <td>3476</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>75.461</td>\n",
       "      <td>170.910</td>\n",
       "      <td>0.181795</td>\n",
       "      <td>0.284540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7998</th>\n",
       "      <td>7084</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>65.461</td>\n",
       "      <td>166.137</td>\n",
       "      <td>-0.502023</td>\n",
       "      <td>-0.201093</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7999</th>\n",
       "      <td>3346</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>80.323</td>\n",
       "      <td>171.575</td>\n",
       "      <td>0.514267</td>\n",
       "      <td>0.352201</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  Genero    Peso   Altura  Peso_norm  Alt_norm  coded_gen\n",
       "0           7620   Mujer  61.235  162.402  -0.791004 -0.581114          1\n",
       "1           1837  Hombre  97.432  181.908   1.684210  1.403542          0\n",
       "2           3311  Hombre  73.324  172.459   0.035663  0.442145          0\n",
       "3           9478   Mujer  55.193  157.748  -1.204167 -1.054640          1\n",
       "4           7805   Mujer  56.886  151.798  -1.088396 -1.660028          1\n",
       "...          ...     ...     ...      ...        ...       ...        ...\n",
       "7995        6395   Mujer  62.215  160.083  -0.723990 -0.817063          1\n",
       "7996        2714  Hombre  83.840  177.866   0.754765  0.992285          0\n",
       "7997        3476  Hombre  75.461  170.910   0.181795  0.284540          0\n",
       "7998        7084   Mujer  65.461  166.137  -0.502023 -0.201093          1\n",
       "7999        3346  Hombre  80.323  171.575   0.514267  0.352201          0\n",
       "\n",
       "[8000 rows x 7 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121.68700000000001, 31.206999999999997)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Peso'].max(), df['Peso'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199.824, 138.866)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Altura'].max(), df['Altura'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(df[['Peso', 'Altura']], df['Genero'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-0.4286322 ,  0.18549582]]), array([0.00347453]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_, clf.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import keras.optimizers as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_36\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_36 (Dense)            (None, 1)                 3         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3\n",
      "Trainable params: 3\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([[-0.49595308],\n",
       "        [ 1.3453645 ]], dtype=float32),\n",
       " array([0.], dtype=float32)]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(1, input_shape=(2,), activation='sigmoid'))\n",
    "model.summary()\n",
    "\n",
    "model.compile(optimizer=optim.Adam(learning_rate=0.001), loss='binary_crossentropy', metrics='Accuracy')\n",
    "model.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "250/250 [==============================] - 0s 648us/step - loss: 0.9302 - Accuracy: 0.2794\n",
      "Epoch 2/50\n",
      "250/250 [==============================] - 0s 653us/step - loss: 0.7233 - Accuracy: 0.4893\n",
      "Epoch 3/50\n",
      "250/250 [==============================] - 0s 680us/step - loss: 0.5789 - Accuracy: 0.7846\n",
      "Epoch 4/50\n",
      "250/250 [==============================] - 0s 653us/step - loss: 0.4825 - Accuracy: 0.8954\n",
      "Epoch 5/50\n",
      "250/250 [==============================] - 0s 686us/step - loss: 0.4184 - Accuracy: 0.9153\n",
      "Epoch 6/50\n",
      "250/250 [==============================] - 0s 736us/step - loss: 0.3744 - Accuracy: 0.9146\n",
      "Epoch 7/50\n",
      "250/250 [==============================] - 0s 854us/step - loss: 0.3433 - Accuracy: 0.9125\n",
      "Epoch 8/50\n",
      "250/250 [==============================] - 0s 804us/step - loss: 0.3207 - Accuracy: 0.9090\n",
      "Epoch 9/50\n",
      "250/250 [==============================] - 0s 934us/step - loss: 0.3037 - Accuracy: 0.9072\n",
      "Epoch 10/50\n",
      "250/250 [==============================] - 0s 795us/step - loss: 0.2908 - Accuracy: 0.9047\n",
      "Epoch 11/50\n",
      "250/250 [==============================] - 0s 860us/step - loss: 0.2807 - Accuracy: 0.9038\n",
      "Epoch 12/50\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.2728 - Accuracy: 0.9025\n",
      "Epoch 13/50\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.2665 - Accuracy: 0.9018\n",
      "Epoch 14/50\n",
      "250/250 [==============================] - 0s 788us/step - loss: 0.2614 - Accuracy: 0.9019\n",
      "Epoch 15/50\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.2572 - Accuracy: 0.9010\n",
      "Epoch 16/50\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.2537 - Accuracy: 0.9007\n",
      "Epoch 17/50\n",
      "250/250 [==============================] - 0s 969us/step - loss: 0.2507 - Accuracy: 0.9005\n",
      "Epoch 18/50\n",
      "250/250 [==============================] - 0s 982us/step - loss: 0.2481 - Accuracy: 0.9009\n",
      "Epoch 19/50\n",
      "250/250 [==============================] - 0s 806us/step - loss: 0.2458 - Accuracy: 0.9006\n",
      "Epoch 20/50\n",
      "250/250 [==============================] - 0s 678us/step - loss: 0.2437 - Accuracy: 0.9009\n",
      "Epoch 21/50\n",
      "250/250 [==============================] - 0s 703us/step - loss: 0.2418 - Accuracy: 0.9015\n",
      "Epoch 22/50\n",
      "250/250 [==============================] - 0s 739us/step - loss: 0.2400 - Accuracy: 0.9018\n",
      "Epoch 23/50\n",
      "250/250 [==============================] - 0s 860us/step - loss: 0.2383 - Accuracy: 0.9024\n",
      "Epoch 24/50\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.2366 - Accuracy: 0.9025\n",
      "Epoch 25/50\n",
      "250/250 [==============================] - 0s 972us/step - loss: 0.2350 - Accuracy: 0.9035\n",
      "Epoch 26/50\n",
      "250/250 [==============================] - 0s 720us/step - loss: 0.2334 - Accuracy: 0.9040\n",
      "Epoch 27/50\n",
      "250/250 [==============================] - 0s 879us/step - loss: 0.2319 - Accuracy: 0.9050\n",
      "Epoch 28/50\n",
      "250/250 [==============================] - 0s 703us/step - loss: 0.2304 - Accuracy: 0.9047\n",
      "Epoch 29/50\n",
      "250/250 [==============================] - 0s 698us/step - loss: 0.2290 - Accuracy: 0.9051\n",
      "Epoch 30/50\n",
      "250/250 [==============================] - 0s 850us/step - loss: 0.2276 - Accuracy: 0.9062\n",
      "Epoch 31/50\n",
      "250/250 [==============================] - 0s 712us/step - loss: 0.2263 - Accuracy: 0.9070\n",
      "Epoch 32/50\n",
      "250/250 [==============================] - 0s 666us/step - loss: 0.2251 - Accuracy: 0.9075\n",
      "Epoch 33/50\n",
      "250/250 [==============================] - 0s 664us/step - loss: 0.2239 - Accuracy: 0.9087\n",
      "Epoch 34/50\n",
      "250/250 [==============================] - 0s 686us/step - loss: 0.2229 - Accuracy: 0.9090\n",
      "Epoch 35/50\n",
      "250/250 [==============================] - 0s 689us/step - loss: 0.2219 - Accuracy: 0.9090\n",
      "Epoch 36/50\n",
      "250/250 [==============================] - 0s 714us/step - loss: 0.2210 - Accuracy: 0.9099\n",
      "Epoch 37/50\n",
      "250/250 [==============================] - 0s 728us/step - loss: 0.2201 - Accuracy: 0.9096\n",
      "Epoch 38/50\n",
      "250/250 [==============================] - 0s 848us/step - loss: 0.2193 - Accuracy: 0.9100\n",
      "Epoch 39/50\n",
      "250/250 [==============================] - 0s 850us/step - loss: 0.2186 - Accuracy: 0.9106\n",
      "Epoch 40/50\n",
      "250/250 [==============================] - 0s 932us/step - loss: 0.2180 - Accuracy: 0.9110\n",
      "Epoch 41/50\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.2174 - Accuracy: 0.9121\n",
      "Epoch 42/50\n",
      "250/250 [==============================] - 0s 987us/step - loss: 0.2168 - Accuracy: 0.9121\n",
      "Epoch 43/50\n",
      "250/250 [==============================] - 0s 869us/step - loss: 0.2163 - Accuracy: 0.9124\n",
      "Epoch 44/50\n",
      "250/250 [==============================] - 0s 720us/step - loss: 0.2159 - Accuracy: 0.9133\n",
      "Epoch 45/50\n",
      "250/250 [==============================] - 0s 837us/step - loss: 0.2154 - Accuracy: 0.9131\n",
      "Epoch 46/50\n",
      "250/250 [==============================] - 0s 737us/step - loss: 0.2150 - Accuracy: 0.9131\n",
      "Epoch 47/50\n",
      "250/250 [==============================] - 0s 708us/step - loss: 0.2147 - Accuracy: 0.9130\n",
      "Epoch 48/50\n",
      "250/250 [==============================] - 0s 733us/step - loss: 0.2144 - Accuracy: 0.9134\n",
      "Epoch 49/50\n",
      "250/250 [==============================] - 0s 720us/step - loss: 0.2141 - Accuracy: 0.9137\n",
      "Epoch 50/50\n",
      "250/250 [==============================] - 0s 703us/step - loss: 0.2138 - Accuracy: 0.9135\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(df[['Peso_norm', 'Alt_norm']].values, df['coded_gen'].values, epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[-5.096652],\n",
       "        [ 1.02123 ]], dtype=float32),\n",
       " array([-0.02632075], dtype=float32)]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'binary_crossentropy'"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fda7ca08d90>]"
      ]
     },
     "execution_count": 212,
     "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": [
    "plt.plot(hist.history['Accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fda7cd7b8b0>]"
      ]
     },
     "execution_count": 213,
     "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": [
    "plt.plot(hist.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hombres = df[df['Genero'] == 'Hombre']\n",
    "df_mujeres = df[df['Genero'] == 'Mujer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = model.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[-4.8032055],\n",
       "        [ 0.8074303]], dtype=float32),\n",
       " array([-0.02686926], dtype=float32)]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "w1 = weights[0][0][0]\n",
    "w2 = weights[0][1][0]\n",
    "w0 = weights[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.8032055, 0.8074303, -0.026869263)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w1, w2, w0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "pesos = np.linspace(-1, 1, 100)\n",
    "alturas = - w0/w2 - w1*pesos/w2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alturas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fda7bd0a520>]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(20, 10))\n",
    "plt.scatter(df_hombres['Peso_norm'], df_hombres['Alt_norm'])\n",
    "plt.scatter(df_mujeres['Peso_norm'], df_mujeres['Alt_norm'])\n",
    "plt.plot(pesos, alturas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [80, 170],\n",
    "    [50, 164]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_norm = sc.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.49217931,  0.19195141],\n",
       "       [-1.55927324, -0.41852414]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 37ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.09657089],\n",
       "       [0.9991957 ]], dtype=float32)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 80., 170.],\n",
       "       [ 50., 164.]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.inverse_transform(x_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250/250 [==============================] - 0s 645us/step - loss: 0.2136 - Accuracy: 0.9133\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.21362793445587158, 0.9132500290870667]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(df[['Peso_norm', 'Alt_norm']].values, df['coded_gen'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}