semana08/6-2_nn-keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras\n",
    "\n",
    "Keras es una librería de alto nivel para generar redes neuronales. Fue concebida para funcionar como una interfaz amigable hacia el framework de inteligencia artificial *Tensorflow*, desarrollado por Google.\n",
    "\n",
    "_Código tomado en parte de [este link](https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/)._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Red neuronal secuencial\n",
    "from tensorflow.keras.models import Sequential\n",
    "# Capa completamente conectada\n",
    "from tensorflow.keras.layers import Dense\n",
    "# Optimizador\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       768 non-null    int64  \n",
      " 1   1       768 non-null    int64  \n",
      " 2   2       768 non-null    int64  \n",
      " 3   3       768 non-null    int64  \n",
      " 4   4       768 non-null    int64  \n",
      " 5   5       768 non-null    float64\n",
      " 6   6       768 non-null    float64\n",
      " 7   7       768 non-null    int64  \n",
      " 8   8       768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data_df = pd.read_csv(\"diabetes.csv\", header=None)\n",
    "data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_df.iloc[:,0:8]\n",
    "y = data_df.iloc[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crear red neuronal \n",
    "model = Sequential()\n",
    "\n",
    "# Primer parámetro: cantidad de neuronas\n",
    "# Segundo (solo en la primer capa): cantidad de entrada\n",
    "model.add(Dense(12, input_dim=8, activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 12)                108       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 8)                 104       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 221\n",
      "Trainable params: 221\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/75\n",
      "24/24 - 0s - loss: 10.1036 - accuracy: 0.3607 - 320ms/epoch - 13ms/step\n",
      "Epoch 2/75\n",
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      "Epoch 3/75\n",
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      "Epoch 4/75\n",
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      "Epoch 5/75\n",
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      "Epoch 6/75\n",
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      "Epoch 7/75\n",
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      "Epoch 8/75\n",
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      "Epoch 9/75\n",
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      "Epoch 10/75\n",
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      "Epoch 11/75\n",
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      "Epoch 12/75\n",
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      "Epoch 13/75\n",
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      "Epoch 14/75\n",
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      "Epoch 16/75\n",
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      "Epoch 17/75\n",
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      "Epoch 18/75\n",
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      "Epoch 19/75\n",
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      "Epoch 20/75\n",
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      "Epoch 21/75\n",
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      "Epoch 22/75\n",
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      "Epoch 23/75\n",
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      "Epoch 24/75\n",
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      "Epoch 25/75\n",
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      "Epoch 26/75\n",
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      "Epoch 27/75\n",
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      "Epoch 37/75\n",
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      "Epoch 38/75\n",
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      "Epoch 39/75\n",
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      "Epoch 43/75\n",
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      "Epoch 58/75\n",
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      "Epoch 59/75\n",
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      "Epoch 60/75\n",
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      "Epoch 63/75\n",
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      "Epoch 64/75\n",
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      "Epoch 65/75\n",
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      "Epoch 66/75\n",
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      "Epoch 67/75\n",
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      "Epoch 68/75\n",
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      "Epoch 69/75\n",
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      "Epoch 70/75\n",
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      "Epoch 71/75\n",
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      "Epoch 72/75\n",
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      "Epoch 73/75\n",
      "24/24 - 0s - loss: 0.5495 - accuracy: 0.7253 - 17ms/epoch - 706us/step\n",
      "Epoch 74/75\n",
      "24/24 - 0s - loss: 0.5828 - accuracy: 0.7174 - 17ms/epoch - 706us/step\n",
      "Epoch 75/75\n",
      "24/24 - 0s - loss: 0.5561 - accuracy: 0.7370 - 15ms/epoch - 623us/step\n"
     ]
    }
   ],
   "source": [
    "historial = model.fit(\n",
    "    X.values,\n",
    "    y.values,\n",
    "    epochs=75,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "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": [
    "plt.plot(historial.history['loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "plt.plot(historial.history['accuracy'])\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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}