Pruebas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, LSTM\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.loadtxt(\"datasets/PruebaY2.0.csv\", delimiter=\",\")\n",
    "X = np.loadtxt(\"datasets/PruebaX2.0.csv\", delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(497, 1100)\n",
      "(497, 5)\n",
      "127.0\n",
      "-128.0\n",
      "6.0\n",
      "0.0\n",
      "(497, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(Y.shape)\n",
    "print(np.amax(X))\n",
    "print(np.amin(X))\n",
    "print(np.amax(Y))\n",
    "print(np.amin(Y))\n",
    "print(Y[0:,4:5].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_formated = tf.keras.utils.normalize(X, axis=1)\n",
    "X_math = (((X - np.amin(X)) * (1)) / (np.amax(X) - np.amin(X)))\n",
    "X_math = np.round(X_math, decimals=1)\n",
    "#X_formated = X_math.reshape((497,11,100))\n",
    "X_formated = (X / 128).reshape((497,100,11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9921875\n",
      "-1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.amax(X_formated))\n",
    "print(np.amin(X_formated))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 1, 1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_formated = Y.astype(int)\n",
    "Y_formated[0:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#config = tf.ConfigProto( device_count = {'GPU': 1})\n",
    "#sess = tf.Session(config=config)\n",
    "#tf.keras.backend.set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(128, input_shape=(X_formated.shape[1:]), activation='relu', return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(128, activation='relu'))\n",
    "model.add(Dropout(0.1))\n",
    "\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(7, activation='softmax'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tensorboard = TensorBoard(log_dir=NAME)\n",
    "#adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.01, amsgrad=False)\n",
    "opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 447 samples, validate on 50 samples\n",
      "Epoch 1/5\n",
      "447/447 [==============================] - 6s 14ms/step - loss: 1.9465 - acc: 0.1342 - val_loss: 1.9456 - val_acc: 0.1400\n",
      "Epoch 2/5\n",
      "447/447 [==============================] - 1s 2ms/step - loss: 1.9457 - acc: 0.1342 - val_loss: 1.9431 - val_acc: 0.1200\n",
      "Epoch 3/5\n",
      "447/447 [==============================] - 1s 3ms/step - loss: 1.9419 - acc: 0.1566 - val_loss: 1.9363 - val_acc: 0.1200\n",
      "Epoch 4/5\n",
      "447/447 [==============================] - 1s 2ms/step - loss: 1.9394 - acc: 0.1454 - val_loss: 1.9131 - val_acc: 0.1600\n",
      "Epoch 5/5\n",
      "447/447 [==============================] - 1s 2ms/step - loss: 1.9354 - acc: 0.1298 - val_loss: 1.8926 - val_acc: 0.2000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1e9a7a8bcc0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_formated, Y_formated[0:,1], epochs=5, validation_split=0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_formated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "np.argmax(predictions[1])\n",
    "for x in predictions:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "497/497 [==============================] - 0s 489us/step\n",
      "1.8974623824029382\n",
      "0.18712273644849328\n"
     ]
    }
   ],
   "source": [
    "val_loss, val_acc = model.evaluate(X_formated, Y_formated[0:,1])\n",
    "print(val_loss)\n",
    "print(val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"modelo.h5\")"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}