regresion_batches_tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresión Lineal con TensorFlow para grandes datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Además de TensorFlow, importamos algunas librerías que pueden resultarnos de utilidad\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generamos un buen número de datos aleatorios de los que hacer uso para realizar la regresión:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Elegimos el número de variables independientes\n",
    "num_features = 1000000\n",
    "# Generamos los valores de x \n",
    "x_data = np.linspace(0.0, 10.0, num_features)\n",
    "# Y les metemos algo de ruido\n",
    "x_data += np.random.uniform(-0.5,0.5,num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.48072575,  -0.02424944,  -0.32384839, ...,  10.17069357,\n",
       "        10.23588842,  10.11950999])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Generamos las variables dependientes:\n",
    "\n",
    "`y_label = m · x_data + b + ruido`\n",
    "\n",
    "Elegimos `m = 2.0` y `b = 1.5`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_label = 2.0 * x_data + 1.5 + np.random.normal(-2, 2, num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pandas nos permite ordenar en formato tabular todas nuestras variables para acceder de manera rápida a ellas\n",
    "my_data = pd.concat([pd.DataFrame(data=x_data,columns=[\"x_data\"]),\n",
    "                     pd.DataFrame(data=y_label,columns=[\"y_label\"])], \n",
    "                    axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.480726</td>\n",
       "      <td>3.379555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.024249</td>\n",
       "      <td>0.376482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.323848</td>\n",
       "      <td>0.755134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.376311</td>\n",
       "      <td>3.218991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.432602</td>\n",
       "      <td>-1.154043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x_data   y_label\n",
       "0  0.480726  3.379555\n",
       "1 -0.024249  0.376482\n",
       "2 -0.323848  0.755134\n",
       "3  0.376311  3.218991\n",
       "4  0.432602 -1.154043"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Una breve muestra de cómo la variable \"my_data\" recoge las variables sobre las que se realizará la regresión\n",
    "my_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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UW1kuTrQIwC5baOh6AG8BOAhgPTP/3uWzNwC4AQA6Ozsv3bp1a9nWaSUcDiMUClXkXtWg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\nAkEQhAZHHIEgCEKDI45AEAShwfn/WK3at5EAL2sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f59ad622710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Elegimos una muestra de 500 pares de datos para su representación. \n",
    "sampling = my_data.sample(n = 500)\n",
    "plt.scatter(sampling[\"x_data\"], sampling[\"y_label\"], s = 7)\n",
    "plt.grid()\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dado el elevado número de variables que estamos manejando, debemos partir nuestro dataset en batches de un determinado tamaño para entrenar nuestro modelo.\n",
    "Elegiremos un tamaño de batch de 10 puntos al azar cada vez."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 10"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Definimos las variables de la pendiente y la intersección:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Inicializamos con valores aleatorios como variables la pendiente y la intersección \n",
    "# de nuestra recta a ajustar a los datos.\n",
    "misc = np.random.rand(2)\n",
    "\n",
    "m = tf.Variable(misc[0], dtype = tf.float32, name = 'slope')\n",
    "b = tf.Variable(misc[1], dtype = tf.float32, name = 'intercept')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Construimos los placeholders indicando el type del dato deseado y su shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [batch_size], name = 'x')\n",
    "y = tf.placeholder(tf.float32, [batch_size], name = 'y')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Se define el grafo de nuestro modelo, que en este caso es muy simple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Se define la operación del ajuste lineal\n",
    "y_model = m * x + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función de pérdida que vamos a usar se puede construir fácilmente con TensorFlow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Elegimos como función de coste la RMS.\n",
    "error = tf.reduce_sum(tf.square(y - y_model), name = 'loss_func')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "El optimizador vuelve a definirse. Indicamos una tasa de aprendizaje inicial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Podemos escoger el optimizador que deseemos. En este caso recurrimos a tf.train.GradientDescentOptimizer\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.0001, name = 'optim')\n",
    "train = optimizer.minimize(error)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "¡Inicializamos las variables con tf.global_variables_initializer()!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Volvemos a crear y a ejecutar una sesión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    \n",
    "    sess.run(init)\n",
    "    # Elegimos un número de batches \n",
    "    num_batches = 10000\n",
    "    \n",
    "    for i in range(num_batches):\n",
    "        \n",
    "        # Creamos conjuntos de índices aleatorios de tamaño batch_size para muestrear en el dataset\n",
    "        rand_ind = np.random.randint(num_features, size = batch_size)\n",
    "        \n",
    "        # Pasamos las variables en un feed dictionary: sess.run(operation, feed_dict={...})\n",
    "        # La operación es la dirigida a minimizar el error a través del optimizador elegido\n",
    "        sess.run(train, feed_dict= {x: x_data[rand_ind], y:y_label[rand_ind]})\n",
    "        \n",
    "    # Los resultados de la pendiente y el punto de intersección    \n",
    "    final_m, final_b = sess.run([m, b])\n",
    "    \n",
    "    # sess.graph contiene la definición del grafo. Con esto habilitaremos el Graph Visualizer en tensorboard.\n",
    "    # Desde un terminal seremos capaces de lanzar tensorboard. Las instrucciones quedan indicadas después de \n",
    "    # este notebook.\n",
    "    file_writer = tf.summary.FileWriter(\"log/regresion_batches_tensorflow/\", sess.graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.98998 -0.503472\n"
     ]
    }
   ],
   "source": [
    "# Los valores estimados para la pendiente \"m\" y el punto de intersección \"b\" son respectivamente:\n",
    "print (final_m, final_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluación del ajuste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Elegimos un conjunto de variables independientes de prueba y obtenemos su predicción \n",
    "x_test = np.linspace(-1, 12, 50)\n",
    "y_test = final_m * x_test + final_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H/ve3cdMPfs2+XsX8v2Ivy7bUsL+xOa4tLTIVZrNYne3agjyDCx5aidNBeLB8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\nDeHnaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f59ad241cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sampling[\"x_data\"], sampling[\"y_label\"], s = 7)\n",
    "plt.plot(x_test, y_test, 'r', linewidth = .75)\n",
    "plt.grid()\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La línea roja estimada por nuestro modelo se ajusta adecuadamente a la nube de puntos. \n",
    "El coeficiente de determinación r-squared para nuestro ajuste a toda la nube de puntos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Suma de los cuadrados de los residuos:\n",
    "SS_res = np.sum((y_label - (final_m * x_data + final_b))**2)\n",
    "# La suma de los cuadrados totales se define como:\n",
    "SS_tot = np.sum((y_label - np.mean(y_label))**2)\n",
    "\n",
    "# El coeficiente r**2 se define como:\n",
    "r_squared = 1 - SS_res/SS_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89381264823729345"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_squared"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "A pesar de la dispersión que presenta nuestro dataset, r**2 es > 0.8. Nuestro ajuste podría considerarse de nuevo correcto."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tf.estimator API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "En esta parte del notebook se proporciona una introducción al uso de los Estimadores en TensorFlow. Estos estimadores son\n",
    "una API de TensorFlow que simplifican la programación de algortimos de Machine Learning. \n",
    "Los Estimadores encapsulan el entrenamiento, evaluación y predicción.\n",
    "Además, son capaces de construir el grafo, inicializar variables, crear checkpoints del modelo y guardar summaries para TensorBoard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feat_cols = [tf.feature_column.numeric_column(\"x\", shape=[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpt20_mtn9\n",
      "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_keep_checkpoint_max': 5, '_tf_random_seed': 1, '_session_config': None, '_model_dir': '/tmp/tmpt20_mtn9', '_save_summary_steps': 100, '_keep_checkpoint_every_n_hours': 10000, '_save_checkpoints_steps': None}\n"
     ]
    }
   ],
   "source": [
    "estimator = tf.estimator.LinearRegressor(feature_columns = feat_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partición del dataset en conjuntos de entrenamiento y evaluación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Hacemos uso de la función train_test_split de sklearn\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Definimos los datos de entrenamiento y evaluación del modelo. Elegimos una razón 3:1\n",
    "x_train, x_eval, y_train, y_eval = train_test_split(x_data, y_label, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(750000,) (750000,)\n",
      "(250000,) (250000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, y_train.shape)\n",
    "print(x_eval.shape, y_eval.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generación de los inputs con los que alimentaremos al estimador"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Haciendo uso de \n",
    "tf.estimator.inputs.numpy_input_fn(\n",
    "    x,\n",
    "    y,\n",
    "    batch_size,\n",
    "    num_epochs,\n",
    "    shuffle,\n",
    "    queue_capacity,\n",
    "    num_threads\n",
    ")\n",
    "se nos devolverá una función que nos permitirá alimentar nuestro modelo como si fuera un feed dictionary con arrays de numpy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_func = tf.estimator.inputs.numpy_input_fn({\"x\": x_train}, y_train, batch_size=10, num_epochs=None, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_input_func = tf.estimator.inputs.numpy_input_fn({\"x\": x_train}, y_train, batch_size=10, num_epochs=10000, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eval_input_func = tf.estimator.inputs.numpy_input_fn({\"x\": x_eval}, y_eval, batch_size=10, num_epochs=10000, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrenamiento del estimador"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Entrenaremos el estimador, que creará primero un checkpoint, realizarálos pasos necesarios y guardará el modelo enese mismo checkpoint, a partir del cual se puede bien retomar el entrenamiento, bien obtener predicciones dadas nuevas variables independientes.\n",
    "\n",
    "Este paso tardará un poco en ejecutarse. Paciencia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpt20_mtn9/model.ckpt.\n",
      "INFO:tensorflow:step = 1, loss = 955.156\n",
      "INFO:tensorflow:global_step/sec: 571.998\n",
      "INFO:tensorflow:step = 101, loss = 40.9235 (0.181 sec)\n",
      "INFO:tensorflow:global_step/sec: 613.515\n",
      "INFO:tensorflow:step = 201, loss = 53.7345 (0.164 sec)\n",
      "INFO:tensorflow:global_step/sec: 504.202\n",
      "INFO:tensorflow:step = 301, loss = 46.6652 (0.193 sec)\n",
      "INFO:tensorflow:global_step/sec: 614.667\n",
      "INFO:tensorflow:step = 401, loss = 20.678 (0.167 sec)\n",
      "INFO:tensorflow:global_step/sec: 461.453\n",
      "INFO:tensorflow:step = 501, loss = 28.1145 (0.211 sec)\n",
      "INFO:tensorflow:global_step/sec: 585.833\n",
      "INFO:tensorflow:step = 601, loss = 61.9846 (0.170 sec)\n",
      "INFO:tensorflow:global_step/sec: 626.145\n",
      "INFO:tensorflow:step = 701, loss = 57.5413 (0.162 sec)\n",
      "INFO:tensorflow:global_step/sec: 559.851\n",
      "INFO:tensorflow:step = 801, loss = 64.4317 (0.176 sec)\n",
      "INFO:tensorflow:global_step/sec: 706.753\n",
      "INFO:tensorflow:step = 901, loss = 51.2241 (0.143 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpt20_mtn9/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 49.2191.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.canned.linear.LinearRegressor at 0x7f59ad25a4a8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.train(input_fn = input_func, steps = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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      "INFO:tensorflow:Finished evaluation at 2018-05-22-10:29:08\n",
      "INFO:tensorflow:Saving dict for global step 1000: average_loss = 4.06223, global_step = 1000, loss = 40.6223\n"
     ]
    }
   ],
   "source": [
    "train_metrics = estimator.evaluate(input_fn = train_input_func, steps = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2018-05-22-10:29:09\n",
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      "INFO:tensorflow:Evaluation [1000/1000]\n",
      "INFO:tensorflow:Finished evaluation at 2018-05-22-10:29:13\n",
      "INFO:tensorflow:Saving dict for global step 1000: average_loss = 4.08338, global_step = 1000, loss = 40.8338\n"
     ]
    }
   ],
   "source": [
    "eval_metrics = estimator.evaluate(input_fn = eval_input_func, steps = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train metrics: {'average_loss': 4.0622344, 'loss': 40.622345, 'global_step': 1000}\n",
      "eval metrics: {'average_loss': 4.0833774, 'loss': 40.833775, 'global_step': 1000}\n"
     ]
    }
   ],
   "source": [
    "# Las estadísticas del entrenamiento y de la evaluación de nuestro test dataset son:\n",
    "print(\"train metrics: {}\".format(train_metrics))\n",
    "print(\"eval metrics: {}\".format(eval_metrics))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obteniendo las predicciones del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Volvemos a hacer uso de tf.estimator.inputs.numpy_input_fn()\n",
    "# Intentamos predecir qué valores obtendríamos del modelo si introducimos, \n",
    "# por ejemplo, un vector np.linspace(0,10,10)\n",
    "input_fn_predict = tf.estimator.inputs.numpy_input_fn({\"x\": np.linspace(0,10,10)}, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /tmp/tmpt20_mtn9/model.ckpt-1000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'predictions': array([-0.00858639], dtype=float32)},\n",
       " {'predictions': array([ 2.17043519], dtype=float32)},\n",
       " {'predictions': array([ 4.34945679], dtype=float32)},\n",
       " {'predictions': array([ 6.52847767], dtype=float32)},\n",
       " {'predictions': array([ 8.70750046], dtype=float32)},\n",
       " {'predictions': array([ 10.88652039], dtype=float32)},\n",
       " {'predictions': array([ 13.06554222], dtype=float32)},\n",
       " {'predictions': array([ 15.24456406], dtype=float32)},\n",
       " {'predictions': array([ 17.42358589], dtype=float32)},\n",
       " {'predictions': array([ 19.60260582], dtype=float32)}]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(estimator.predict(input_fn = input_fn_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /tmp/tmpt20_mtn9/model.ckpt-1000\n"
     ]
    }
   ],
   "source": [
    "# Para mejor manejabilidad a la hora de realizar cálculs, hacemos un append de las predicciones:\n",
    "predictions = []\n",
    "for x in estimator.predict(input_fn = input_fn_predict):\n",
    "    predictions.append(x['predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9uL06KC7h9Xvmnn10RBJXPBdI7diRFBXYcQSqoRqGvKmlI6TG0dVnHclZXxwV\nYeTF5z44ESEQ8oJY7pmK0kIW//AkvnXvy8Hr7TYVaPpiR6Eocdrodvsodtpx2vt2A33lmX2UFvoP\naMv3c/oPS3t6Q0s3f+kwlm5oZs6ysNLNYclZKPxNBp5+mt4bbqT5hEmMeOIpDji4AvCLxXXPbg5e\n7vURrORpNvxoko6IiWbIm3d3Uey00RUQtEP2L5GyD0MIEQIhL4h3WPqlMQfwfKAN4qSxFfxk8WvB\nQ1fDpRJeyx8iXSGlRQ5Ou+VFNP7EKPPh87iDh+PTOngY27C1NegCGl7kwO3x4XTYmPzpe/CVC+iu\nPpKzai+ltXg4jvteDwpHe1dvyNx6PV5m3LM+on5PbfXIlFbnVoa8pqoMp90e0vNAGDqIEAh5QSLx\n4kYbRIhsum6EYYYnkIXX1zfHzZc47dx4Tl+0DxByGDt32eag62XV7DreW/Uyx93/Bwo/GAF//jPr\nuotpXfJGhHiVlRSg8ARDMz0aPD5f1Po9qazOwxvhJJMZLAw+RAiEvCAR33X4GUKiBtS4duWWlpBc\nAwV90T4B5p09jqse9x/G+nQgD6Gkh9Jr5nDYzk9Y+5PLqfn6qf6a/Z0uS/EqdNh47rJTWbqxOdj/\nN179nmQIby4DhISLiggMPUQIhLwhlpFMR80Zw32Ck6iNUmqr+w5jK7o7mLjod7g3v81Pxnyd+pOO\nhDfcDN+ymlWz62KKV3XlMK79+tHMOu3wpGryJHJtaD6BDSBY7iJeZrAUcBuciBAIeU0wCzZwsNuf\nmjOJ7DoqSgv553nH8Mm8BRz/n1cpvvZqVv70Kl75+xsQCM00dxSLt8JPdAeQjNCZ3WjGjsCIIop1\nNiAF3AYvIgTCoKW/q89wF0i0rOJkiGmY3W4677qb7tvu4akTp/KLM6/j+cmnU4M/L8GI0Xc60n8Y\nG1GptKk1eHYRLwTU+HwyO4lUxNT4e+rwWF4h44gQCIOSdKw+G7a2Bmv8FDlsXH3WkRyyf0m/3BqW\n4uTzwZIlcMcd7Dx9Kj/4wc3s8dopRQWN5arZdTRsbQUdea6QDsJX+ebuYVbfXbigJWLQ+1PAzfz3\n/OmRbo415VkImSdjQqCUehCYCnyitR4feK0ceAQYDWwDZmit92RqDMLQJR2rz7nLNgdX4T0eHwuf\nbwr65sOvTWRFHCFOvzqVirUvwu9+B1OmwAsvUK6c6FvrKQ1cYxjLitJCph93aArfROw5WpWSNrKE\n/fkN6asrBjCqAAAgAElEQVQI2p9kspCm9zp/y0HnikzuCP4K3AUsNr12JbBSa71AKXVl4PffZHAM\nwhClv+WDG5vb8elQF0QwiifBOkFW9zSM2cTWLaiv3QQnfBEeewwOPBCACiJDUzNBtHFPOaoyJEu4\n29RiMh2kGrVk/nuivJKnkGUyJgRa6zVKqdFhL08D6gI/PwSsRoRASIFYq0+PTwdr6Mc7EC1x6mDG\nsJWgJLPzqKkqY+ynO/jpyr/S4yxCPXw3jD/ScuyJHvCmWto6Vn0jc5ZwsdMW0WIyF5j/nnrXO+IW\nyjLZPiOo1FrvCvz8MSB7PyFlrAxqW6eLppYO/lT/RswVfHgimDlRKl69ffOzgoZ6zydUzJ3LkpZW\n3rxpPsedMZHyfhizVM9ArOobWZXUTkeWcLpDRY2/5+qWLf2+l5AcSuvMndAHdgRPmc4I2rXWZab3\n92it94/y2YuAiwAqKytPWLJkScbGaaazs5PSQH/Xochgmp/Hp+l2ewOr9fCCPNZ09Hjo6drHx91g\nV/4+usOKEl/veHyappYOjH6Uxko5fBzGdYV793LCvx7jsJ3b2X7++Xxy9Hjau3opKymgMBCDnwod\nPR6ad3fh1TpiHrH+hubP2ZSiorSQA0qdOGwq5Pu0mlMyWH1PqdzHisH0/2iqZGuOkydPfk1rPSHe\nddneEbQopQ7WWu9SSh0MfBLtQq31fcB9ABMmTNB1dXVZGeDq1avJ1rNywWCZX+iK2JfUinjpv57j\n3iZnYCVdG7HKj3WflVta+FP9G8F6+rcfe7SlG+fF196nd/48Ttq6gT9PmsHhN93EpOqRzLr35YBt\n9PDcZaem7HJp63QxJ2RHUBscd6y/YeTn+rqKpfJ9RsPqe6pL0+HuYPl/tD8MtDnGFQKl1C+Av6Up\numc58ANgQeC/y9JwT2EAE26AU8lujeebD79ndeUwbj/26JBnppJMZek26e2F++/n5Psf5N8HfZkZ\n312Az2aneO373LPm/WCBCQ081bgzWLsoWVKNwIn2uXQ3aZdev0OLRHYElcAGpdTrwIPAczoBf5JS\n6h/4D4YrlFI7gLn4BWCpUuqHwHZgRqoDFwY+ifbAtSJRQ2Nl5B02FbI6TcYIRjXAPh8sXQq33w4z\nZvDyA4/y9OPv4Av0KejujfwnMbVmVDJfl+VYUjHW0aqHptNwS9+BoUVcIdBaX6uUmgN8BbgAuEsp\ntRR4QGv93xif+06Ut6akNFJh0BFugJ9q3JmyQQaCkUDGvWuqyiyNvD3sXv0yglrD88/7+wPX1cFz\nz8Hw4RzT6cJpfxecim63F6dd4fL6xcBhg29NqKJ8P2eyX1nGyIThlr4DQ4eEzgi01lop9TH+SB8P\nsD/wqFLqBa31FZkcoDB4CTfAU2tGsXj99oQNsmFoYlXDDK/BX1NVxqaWyPuYI4TiVSA1nnXsziYe\n3PYUziOPgH/+Eyoro95z80efMXf5Zjw+f7TOE2/u5N9vfzyg6u2I4RaikcgZwSXATKANuB/4tda6\nVyllA7YCIgSCJVar0FRWpbGqYUarwW81lvBWjlZGurG5napPtjNr5UNop5MNCxdwxMQaGppaYeeO\nkB68ZsNaXTmM2uqRLF63jT83vE+X2wvOyAQ1QRiIJLIjKAe+qbXebn5Ra+1TSk3NzLCEoUL4KjSV\nVWm8apiJ3jPuWcGOHUy6+Trs67dwZ933+e+osSytOYq637/oL30ADC9yWJahMOY2tWYUd656D4Bu\nty+tWbuCkCkSOSOYG+M9yfwQMk6q1TDD6csm9rv+g0Z6925YsAA2bqRo7lzG33UyPw3cv6GpNSgC\nEGguHygOhyJkhwBG1q6drkCM/kDI2hWEeEj1USGnJBpOmkw1zGglJipKC1k66xSmL3oJjWbmHS+y\nsqCR/Z5/Bq64Am6+GZSiwnz/sBwpm1LMeXJT1B2CP2vXFux3LGGVwmBAhEDIGf0tJW0lIvFKTDTv\n7qLA5+HrG55hxuaVbL94FkevWQMOh+X9jMbyrl4vNpviqq8dwe+e/k/wfuYmMiBhlcLgRIRAyBn9\nSXKyEhGAxeu2UaixvqfPx4kbVvDQQzfy/FGT+PH5C3n6kv8JioC/Ro8XheLBC06ks8dDTVUZq2bX\nhbilFj7fFLOJjETnCIMNEQIhK1ittsPDS6vKS+JWDTWw6rg171/v4PZ6ufgITUl4NdEXXoAbbmB4\nbS2Hvrya4/dqLjBlHi9etw2Xx0t3r9/Af+velylx2nHa/SJjGPa2Thfzpo2jo7uXYcUFEWcEqXwP\ngpBrRAiEjBPNBRQei59o1jFEiggKPD4fXW5/rsGPvjyGmRNHU7HlLbjuOhgzhk8feIg3e4upGTGC\nKYf4BeCJN3Ywd1kg/j8gAgZdbi82064iHa6swdrTVwRsaCNCkAeY/xHn4rmxGsMbbpSVW1qSchNZ\nRRIZwqCUlwsq3Ox/4XngcMCdd9J2UFVEuYtz715HT68XdyAj2GkHd1+AEIWO0BLO8VxZTS0dPNW4\nk6k1oywjhRJ1hQ00ozuYBUxIDBGCIU74P+KFtQVZf24ijeFTKQMR7otfcflp/GfjFkYvvJb9AebP\nh+OOA6AxTGiWbmxmb48n+NlCh6LAbsPt7VOCa75+JGcdMyrElWVTiiKHDZtSIWNsaungq39Ygwbu\nXPWeZeXRROY4EI1uugvWCQMPEYIhTvg/4m7zkjdrz7Uzf9p4hhcXRET49DfrOMiePVQsWMCXX3mF\nN6dN49DLLgu+1dbpYm9Pb4gYlZeECuK5xx/KhDHlXPPE23QFGrocUlYSUjE1VnLYU40741YeTWSO\nA9HoSqXRoY8IwRAn/B+x0ZQk28+trQ49VI3VU9dMNDdJ8PUDnFT85T5Ytgxmz6bt2nk0v7SWQztd\nESWobUoxf9p4aqtH+pPCTEwYU07t2JERnbvMn9caNJoejw+HXYUY6UljK7gjkFFs/G5FvIiigWh0\nJSR26CNCMMQJ/0e8aeP6nDwXCIkIatjaSk+vN1gzyGrlGy1EtGFrK/Mfb+TsN57jwDefp/CqSxm2\nZg1tPV7OuLWei8b2MOfW+uDzzTuT4cUFVJQWBvMD3B4fToctGP0TbvDMZxclTjsKaxdXZ4+HkkBG\ncYnTTqfJ7dSf722gGF0JiR3aiBDkAbn6R2xVPdQ4qJ27bHMwFt8WaMUYHjoaESK6tZV5yzZz2tv1\n3P/y4zxbPZHzZ97MLaefzBSHg8bmT/H4fHi1xuPzBY2p1Qq7orQwJD/AqpAcRK7Ql846xbK/cVV5\nSdoyisXoCtlGhEDICGaXTvjq/6nGnfgCvY2KHDZmf6XaMnQ03AiPfGUtf/7r73j14CM5/3/n4S4d\nTlGBPWh0jevtKrQgXbQVdiIG1+rzxiGwlcCZRUIQBgsiBELaCffL+7QOWf2H9yUYVlxgeUBq1AZ6\n9dHnmf74vdjGjObs/3cVu0r3x6YUN509LuTswTDa69c2hPT47e8KO9rnzTuWEqdf4GZOHC0iIAw6\nRAiEIOmKX7fqHwD+1f+8s8dRXTks4vzAKixz9xub+OC8n3EA8J3Tz+fB332XfxC78mhFaSHDihxZ\nMcbmaqbdbi/3r32fxeu3D4iQT0FIBlv8S4R8wFjFX7LkDc64tZ6mlg5WbmmhrdOV9L36XDp2nA4b\nTof/56ICO7XVI+MKjm3XTpg1C9+ll/LAKedy8dQreH//g2lsbg+uznfvc3Pb8+/S1NLR73lHm6fV\ne+bXjB3Id086DKdd0eX2Bc8mBGEwITuCPCNaieZQN4dm+qKXUIqUkppi9Q8AIiKBGpvb8WmNs3Mv\nv9z4OOrfH8KCG+BLX6bp1npKA9canzcnb92x6j2uOPMIDh5eFBGiGo9YyVvRIpasXlu6sTnYrzg8\n0UwQBgMiBHlErBLN5oNZf7w87IuR1BQvvr+qvIS9Pb00NLVSWz0y+HmrUhI1FYX8aN2j1G15ib9N\nPBfvAw/BsCIqwPKg15y8BXDLs+8Cfb0BEiVW8pbVe3u7eyNCXoGQg+9508aJW0gYdIgQ5BGNze0Q\npUSzVQG4aKGQ4YfB86aNo3bsSIBgKecud18Bt9JCO/PPGU/t2JEhglOoNSetfJxh//dXfvi9mWy4\nbR5XjqmIG9ljtIPUhGL0BoiXMmcWK6vQ0rZOF3u7QzORq8pL/LWJTIfe4TWOHDYb40aNSLiCqiAM\nFEQI8oiaqjLefZOoNX/MRtcsCuErcvNqGeCqx96mqMDO3G8cHawAaqbT5Q1es+Ly01jxq1P5772L\nGbv4XmznfhNefJFhJSWcnuA8qiuH8dxlp7J0QzP/2PAh+wLjMHoDbGqJ/tl4IZ8RmcgBATPcVxC5\n8k+1gqogDBRECPIIIwb+9mOPjl6yIfC6Ecdv5UM3VvWFDh8uT1/JBQJnCoV2X9BnbmBc8+E/n+KY\nB29nsz6Yy868ki49ghU+O9YFGaJTXTmMa6cezay6w4M9hK3OCMLnFe7yMUTAuCYiE7mowDKnwdgB\nGd9rKhVUBWGgIEKQZzhsiroESjlYGc3w+P5z7loL9B2S1o4dyYrLT6NhaytzntxEr9eHDXA47By5\ncyu/XP1/HPXlY3hl/h3c9uLH/vv6fEkZzPCS2o3N7SHNYdo6XbR39/LE6zsYd8iIiBW6VTOc8B1C\ntEzkeKUfBmKdIEFIBBGCPCBeP4LG5vagX7/EqYOGuS9OXqM1IdU3m3d3YbP5O7sb+QGGcZx+3KFB\nd8px7k/Z74bf0t7pwrnkfoqPP4YjO1046j+xNJixQkvbOl2cvnA17sDuwkhWM0fwnL5wNT8e28Ot\n9Y3+vASbossdKmRmg261Q0g1E3mg1gkShHiIEAxxmlo6mL7oJTQap91u2Y+gqryE7oBfv9vtCxp8\nY+Xv/zzMuGd91PIPtdUjQ+5Z0bGbKXdcD9u2wfXXU3niiX3vRTGY0XYmhjh81N7V10PA428c4wqL\n4HF5+s4nejw+Spz2iDMRs0G3WsX3JxNZ6gQJgxERgiFMW6eL6YteYp/Rg8AJ3e7IHMLm3V0UBypn\nFjvtNO/uCtbTad7dhVL+UFJbmHvIcvXb3g6//z2sXQtz5sAZZ1iOzRCTWP57w/c/d/lmfFrjCwsT\nstsiK4HaA7sUgyu/dgSHlJXEzESWVbyQ74gQDAGiuVMam9tDQiwVKqQfQSKVMxP2e3d3w6JF8Nhj\ncNll/u5gtuiJ61arf/OzbEoxd9lm3B5fMGSzxGmnyKHw+qDAobjya0cyrNARckj85M8m8cLKFym0\nKwoL7CEdxqIhq3gh38mJECiltgEdgBfwaK0n5GIcQ4FY2bE1VWU47TZwggKe+Nkkdm55DTC7jMBp\nj145M54bR3t6mf7WSq7ZuZaCH/0Q1qyBgoKQ8UUTKauDaONZe7t7mbNsU1AEihw2HDaFttnRHn+I\n6oJntuC024PnA+CPJvrwoGH86bjIyKh0fueygxCGErncEUzWWrfl8PlDgljZsVZGfOcWa5dR8+6u\nqKtiqxVz44d7OO2dBmaufZQ1R57C2nsfYfLxY0KuiSdSsXYa4w4ZEbI7mDdtHABzntwUPAfocvuw\nFYZ2CgPryKh0MRB7CgtCfxHX0CAnlkGN7TLqcxqpwH0SZvVqTrnmOrbpSn7+3Xl0l5ZxXvWoiMuS\nFalEkr3M1T6Lnbash2kOxJ7CgtBfciUEGlihlPIC92qt78vROAY9yUbggOEysgdcRoonfjYp6qo2\nREy2vgNz5tBTcSDnfOkn7Cg9wO9ymnVKSnH14TsNq1BO8/vhZTBy0QRGcgWEoYjSOrxiSxYeqtQh\nWuuPlFIHAi8Av9Barwm75iLgIoDKysoTlixZkpWxdXZ2UlpaGvMaj08HVqR2HGFRKqkS757JPrOj\nx0Pz7i68WmMPtIIcVuQIzi+R53W6POxs72b4x7s46dG/M8IB23/4Q1oOHGV571TmFX5tU0uHf5mg\n/P7+VL7fRP6GqeLxaX8/YgWlhY60/f2TIZPzGwgM9flB9uY4efLk1xI5g82JEIQMQKnfAp1a64XR\nrpkwYYLeuHFjVsazevVq6urqor6fCR9xvHsm+8y2ThcNW1uZu2xzSMJVRWlhzPmZo4jOvXsdpXta\n+cnaJYzes4u7T5/Jjy//tmUP4vB4//6s0uNVNY12b/P7mzauj/k3TJWBcj4Q7//Rwc5Qnx9kb45K\nqYSEIOuuIaXUfoBNa90R+PkrwPXZHkeqZMJHHO+e0d63KrdgLnxmU4r508YnVKffbORKuvfxo7X/\n5EvNm7hr4rd49QvHU+wsiFluIR1GMtzYm4XJPKd5YS0qw59tlTSXDuR8QBiq5OKMoBJ4QillPP/v\nWutnczCOlMiEjzjePa3eD6+SCf66+FqDT/vo7tWUOG0MLy5IyCA3Nrdjc/fwrfXLmdr0Eg+ccDZ/\nqP0eWtn43glVTBhTzu597hBDnYhYJUJbp4uGptZg4phxUGwYf39/BB2sanrV432VTK0S0bqNaKg0\nI+cDwlAl60KgtX4fqMn2c9NFJjJR493T6n1zpUujL7BRUqG71+/uM5eLiInXy0mrl/HQQ7fxZM0Z\n/PCChbhtdpxejQ3N8rd28cSbH9Ht9lHstOO0R674UzWShqAZDV8ASgv9zWf6OqbZUfh7GvcEEswc\n9r6w0fBnm5Pm0olkIQtDFQkfTYFMZKImUtDM/H54Fq75pKe4wEZ3ry+iXEQEWsPy5XDLLQw76ywO\nfm0dE3f3cnFVGbv3uZm+6CU8Pk2HUd8H6HKHlpqAPpdOtKS0WBir+dDEMRuTxlZwf8P7FDpsoDUP\nXngSO9u7Q849ormqNm1cn9CzU0GykIWhiAhBjujvwWp4KOW5d68DwKbAZrOhlEJB9B3BmjXw29/C\nCSf4xeCAA6gApgRsXGNzO0qBy+OXGKdd0evVgeif0G5eyR5km+ddVV6C1lASyAmYN20c40aN4Ny7\n19HV21dA7scPbWTV7DpWza5LqTKoIAjRESHIAemKPjE3RPFpf4OYUrud2V+t5uZn3kWjQyqGAuz3\n3ntw220wciQ89BBUVYWMyzCyNVVlwbMH8Bdz+78ffYnOHk/UbmXxzgasEsZm3LM+EC2qePTiiVRX\nDmPllhbcntAuZ0YbyilHVYrBF4Q0I0KQA9IdfRLuIx9WWBCoGGoqwVC4D+bOZfSHH8I998BRR/kN\nf6C/LhBppE+s4v/WbcPl1dhtis4eT8Q4kzkbCJ+3cQ7g7xfQ58aqqSrDGTgPMDDaUAqCkH5ECHJA\nOqJPwl0sZh/57n3uoLvlwK7PmHj7PPjgv3D99Wzu7qYuIAJmw2/0G/YfzhIsSOf26qDbxmqcyRyg\nhs970tgKHlj7ASXOyH4Bq2bX0dDUSofLE1FhVBCE9CJCkAP6G30SqyYP+BvIlLj28YN1jzOz532K\n58+Fr34VlILVqwF/rX8jUqe0kGC/4dJCQsI1S5x2fvTlMcycODrqOBP1z4efa/jdQhqFYmlYmYqK\n0kKmH39oUt+LIAipIUKQI/pzuGl2sZQ4NdMXvYQKGPJ5Zx7Ot9Y9xplv1/OXCWczseZ7rP7yZCpM\n/v62Thdzl20Oul7M/YbNRtpWqHDYbCEikI5DbnOj9y63L8QtJAhC9hEhGISYXSz+1Tt0d7v5dlM9\ntY++yFtVE5nxvQX02gsoUZFlmv1C4o8GKnTYmP3V6qBxNzKWf/U/Y/lwTzczJlSFiIDRM9jpsLFq\ndl3K7hpJzhKEgYMIgYm2ThcdPR7aOl1pqR+UqcSjEBfL/sUsuuwPnL92KS+NPRHfCy/wLeVkyaKX\nKAj0KQ43sv4exf7sW5fHxy3PvgsQdDOde/e6YG/gpRuagwa/YWtr8PUej4+Gra1MPy41940kZwnC\nwEGEIIDhd79obA9zbq1PKKQzVoG0TBcnqygtZMqnW+EXv2XBUePZ8Mg/+fYXD6eitJAKoP6KyTQ0\ntfqbDYTh71Fso8vto9Cu8Hh18Kxg6YYPQ0o0mMM2Ca9PaFGvMBkBtHKPSfcvQcg+IgQBDL+7V2s8\nPl/ckE7D2Lu9vmAbSMPHneniZHteeQ3P1dcybNSBFD34IMWHHcapFtfN+9c7MfsR2ApVMFfAYff/\n/MjGHfSausSbwzZrq0cyvMgRdA3VVo+0/E5SFcDw+klGcTlAxEEQMogIQQDDZ21XKiGfdWNzO26v\nPwYe/OGW9VdMpqK0MGn/t1F0DQW1Y2OESW7bRs/V1/Lam9u487Tz2H7QGFaUV1IRZ3wlztjdwYzr\n9/b0MufJTYD/7OC8Uz7HrNMOD47HCOuMZpT7K4Dmz4O/uJwzUEcpvJy2IAjpQ4QggGEc1zasYe43\njop7fU1VWYjXRaODhi8Z/3dbp4u6378YNH7DixyRh7CffAI33ghbtvD2BZdw6Vi739hG2bk0tXSw\nassnQZHqdnsjSk2Eu2WMQ2KzgBkiEO6uiWbc+3sAbHy+yKGDxeUMDNeVlH4WhPQjQhDGzvZu/vTy\nprirz4rSQp742aRA4lXkoWyi4aENTa1BEQBw9Xr7jF1HB9x6K6xcCVdfDX/8I2P2uXE01Uc1tk0t\nHXz1D2tCi9A5bQmFZ/a3z0B/D4CNz5ub6phdVxJdJAiZQYTARGNzO2gSdm1UVw6j/orJSRu+kIYy\nYYe5Npui5sBiuOMO+Mc/4Be/gOuuA5vfRRLP2D7VuDNEBApsyjJyKBr97TPQ3+JvFaWFTD/uUGrH\njoxwXckZgSBkBhECEzVVZbz7JpQW2jO2+rTKCh5e5MDl8VGAjxcqm6n4+lfg+9+H+npwOiPuEcvY\nTq0ZxZ2r3guKwYwTq/hBICGsrdNFe3cvT7y+I+GSDbmK97dyXQmCkBlECExUlBZSXTmM2489mqry\nkrir0L7IIS8KFRI5FI3wFfbmnZ8x7+yjqWxYxYmP3EfB186EFSsghcbWbZ0umnd3seQnJ/PC5haW\nbPiQZW9+xL/f2hXMD/jx2B5urW8MnkUYY4o2T4n3F4ShjwhBGA6bYnxVWdxm8o3N7ezt7sXt9QZb\nKH7jzrX86xdfjikG5hW2Uop/3v4IP1/zMP+tHMMRf3+EA8aklqBlVUTOpwlGDT3VuBOX6fDV7fHR\n0NQaNcTUjNT6F4ShjQiBBbH84hG9gk0OeZfHFxJGaoWxwn7zmQaYcx3tBSX8+qu/YM/IgyndA8N7\nWlJaeYePucPlCSaGdbm9HHZASYgQOOwKFNKMXRAEEQIrYvnFQw2unSvPOoob/70laGSNMNKaqjJr\nd8r27VTMncsXP2rlR3Xn8VbZYQCUaB3SvD3ZePnIngQOigpUsH/xNU9sorjAf+BcaFfMP2c8tWNH\nSr0fQRBECKxKGsTyi4cb3LOOOZiTP39ASBhpVXlJhGtJtbWxb+71HNT8Hs4b5mP74gl8eGs9JYHz\nhd987UhuefY/wdV5w9ZWhhcVJLw7sEoSsykb4A387K8yaleKYqcjmLgWy/8v5R4EIT/IayGwipE3\niOYXtzKeFaWFIWGkDU19tf5Hqm52XHolvuef575JM3j51G+y9PBjaA5r9g5w2/NNwWb05ibtie4O\njKxmYxx9eQ7gtPsjlN594xVWXF4bInpW88xGvSRBEAYGeS0EVmcB9ijXxsuuNV5r63Qxd/lmvC4X\nM998lm9uqefhE6by2LdvwmezU+z1cs6il/D5dEQpZ0Ng9nb3MmfZpqR991Zlog2BqiovCRSbsydk\n0DNdL0kQhIFDXguB1VnAppbI62J1BIuot7N9N197ayXfemU5/x4/me9//2Y6TfLi9YHb63fXRCvl\nPO6QEZa++3iumoamsDLRTa1MP/5QakxRUD890s2xCZTZln4BgpA/5LUQJBojH9oRjGBHMHOFzIr9\nnPDMM5x6w400FR7OrPN+xz5nCT6todd/kFzosHHJGV/glmeb+m4eiDqKJzYJuWrCS06ryPGjE1vd\nZzt/QM4jBCF35LUQQGIx8pEdwTT7XH7jfsWjb/Gllnd5cNu/cR4znoJlTzLFV0R3407GHzqCy5a8\nSXGBDZvyJ5yV7+fkntXvR5RyNhvrIodm887PQnYKVq4a83mAQWmhHY/X73aqHTsyYvwob8rlJjKF\nnEcIQm7JeyFIBKum614HVH28jcsb/sY+ZzEv3jSfr049hbZOFzMC2cbdbh9FBTZ8Gv564YnBRDOr\nUs41VWXBAms9Hh9XPfYW40aNCH4m3FVjjkwyPmcUabvpm+NDSkiYx693vZNWI5uOlbycRwhCbskr\nIeiP0TKvjlfNGEPzL6/g4+0fc1vt93l35Gj+cEgV0GfUjGzj7oBb6MK/bAgmmkU7bJ79lWquW/4O\nAD0ezTl3reXJn3856CIyu2pCdxC2wGf8jeCHFxdEzM945uqWLUl+a9FJ10peziMEIbfkjRDEM1qG\nSGifRf9Fg08/hd/9jvLGRmxXXM1563pxe3wMt3DDFBf4gslcAD4dv+vZsOKCkN89Ph08jzDGbHze\nbDxzVao5XSt5qWckCLklb4QgltFqaukIxtv//KjIqJq2jz/ls5t+z2GvrqHg6qtg4ULKlGLVxOjJ\naIvXbePPDe8HdwQ2Fd1AGyI0btQIhhU56AhE/jhsCg3ssxhztC5j2TSk6VzJJ3MeIQfLgpBeciIE\nSqkzgdsBO3C/1npBpp8ZzWi1dbqYvugl9gXq8mhzVI3bTeddd7PztntYMuHrPPuVOTw/+XQqAivw\nWElnMyeOZvH67SilUMCDF5yYUKP7xy6eyOadn4H2h5HOuGc9tiiGNpOlmhMxtrlYycvBsiCkn6wL\ngVLKDiwC/gfYAWxQSi3XWr+TyedGM1qNze1oQt1BNYcM9zeFueMOdk7+Oj/4wc3s8dopRSXk/jCM\n6NJZp7B552d0dPfy44c2WmYKh4emPtW4k5mB/gFATlwmyXYly+bBrhwsC0L6ycWO4CTgPa31+wBK\nqSXANCCjQgDWRqumqgyn3Q5OUBpO3v42FVMXwumnw3PPUW4rRN9aT2nAKMZz7xhRReZoHrep/264\n8Z3tC5IAAAjbSURBVDJ2KiVOf2/h+9e+z+L124PGNxcloAeysZWDZUFIP0rrGIejmXigUucCZ2qt\nfxT4/TzgS1rrn4dddxFwEUBlZeUJS5YsydiYPD5Nt9vL0UsexvZpGzsvvJDe/fePeL/YacdhC8/a\n8r/f1NIBuq8qtbnfri/wHduUQik4fGQpbo8veD+PT/Npp5u2Thc+rbErRVV5CcOK0q/TnZ2dlMZp\nemOeD8rfktNq3rki3t8jkTkOZmR+g59szXHy5Mmvaa0nxLtuwB4Wa63vA+4DmDBhgq6rq8v8Q786\nhdX19ST7rCfe2MEdq96mx+OjxGlHQTDzGPpEYd60cYwbNSK4Y3DYfMGVf3ifg3nTjmT82MTaSSbD\n6tWrE5rfsYP4QDbROQ5WZH6Dn4E2x1wIwUdAlen3QwOv5R6V/Kq3rdPF3GWbg64fh03x6MUTQ6qK\nmg3qyi0tlm4X4wyjoamVucs3M+fJTTk9DJWuZIKQP+RCCDYAY5VSY/ALwLeB7+ZgHGmhsbk96Pop\nctiYd/Y4qiuHhbSrNBvUWD7uitJChhcX4NN6QPrnBUEYmmRdCLTWHqXUz4Hn8IePPqi13pztcaSL\ncMNu1A6KxdxvHA2KYHOYWPeLdxgqMfWCIPSXnJwRaK2fBp7OxbPTTTKx9LEa4ZgNen/uJ2IgCEKy\n2HI9gIGGx6dZuaWFtk5Xwp8x/OnxjLA5LNPj85ecgD6DfsmSNzjj1nqAft1PEAQhGUQITLR1umhq\n6Qga5GTEIBH63D72ELdPqgY92v0Soa3TlbTgCYIwNBmw4aO5oLG5HTQZO6iN5kZKNUkq1RIP4lIS\nBMGMCIGJmqoy3n2TlFbYiRKtBHWqpSRSCfMcyJnDgiBkHxECExWlhVRXDuP2Y4/OehRONuP2rXYg\nEn0kCPmLCEEYDpuiboivjq1KWIurSBDyFzksHiSk+3DXHOkk0UeCkN/IjiAKVq6SXLlPMn24KxU9\nBSG/ESGwIFriV67cJ5k+3JVWkYKQ34hryAIrV0ku3Sf9yRdIlEST4gRBGHrIjsCCaK6SXLlPZMUu\nCEImGdJCkKpPP5rhzaUxlrLQgiBkiiErBP09YA03vBJnLwjCUGXICkGqB6xG0bnwaCGzqCyddUqw\n8YyIgiAIg50hKwSphEQaRef+VP9GyC7CLColTs30RS+hFJJ8JQjCkGDICkEqB6zRis6ZRUUHGtTv\nkzo9giAMEYasEEDyB6zRis6ZRaWqvIQZ96zHJslXgiAMEYa0ECSLUXRu/pFj/cv+sPcMUZFQTkEQ\nhhIiBBbMW/5OzGgjCeUUBGEokXeZxfGKt3W6PPT0eqUAmyAIeUNe7Qji5Ra0dbrY2d5Nj8cOgE0p\nOQMQBGHIk1c7gnj1goyoIYAih415Z4+TMwBBEIY8eSUE8Yq31VSVgfJHDRUV2KmtHpmjkQqCIGSP\nvHINxcstyGWrSkEQhFyRV0IA8SN+8qFVpSAIgpm8cg0JgiAIkYgQCIIg5DkiBIIgCHmOCIEgCEKe\nI0IgCIKQ54gQCIIg5DlKax3/qhyjlGoFtmfpcRVAW5aelQuG+vxg6M9R5jf4ydYcP6e1jpsZOyiE\nIJsopTZqrSfkehyZYqjPD4b+HGV+g5+BNkdxDQmCIOQ5IgSCIAh5jghBJPflegAZZqjPD4b+HGV+\ng58BNUc5IxAEQchzZEcgCIKQ54gQBFBKnamUelcp9Z5S6spcjyfdKKWqlFIvKqXeUUptVkpdkusx\nZQKllF0p9YZS6qlcjyUTKKXKlFKPKqX+o5TaopQ6JddjSidKqcsC/39uUkr9QylVlOsx9Rel1INK\nqU+UUptMr5UrpV5QSm0N/Hf/XI5RhAC/8QAWAV8Djga+o5Q6OrejSjse4HKt9dHAycDPhuAcAS4B\ntuR6EBnkduBZrfWRQA1DaK5KqUOAXwITtNbjATvw7dyOKi38FTgz7LUrgZVa67HAysDvOUOEwM9J\nwHta6/e11m5gCTAtx2NKK1rrXVrr1wM/d+A3IIfkdlTpRSl1KPB14P5cjyUTKKVGAKcCDwBord1a\n6/bYnxp0OIBipZQDKAF25ng8/UZrvQbYHfbyNOChwM8PAedkdVBhiBD4OQRoNv2+gyFmJM0opUYD\nxwGv5HYkaeePwBWAL9cDyRBjgFbgLwH31/1Kqf1yPah0obX+CFgIfAjsAj7TWj+f21FljEqt9a7A\nzx8DOe2GJUKQZyilSoHHgEu11ntzPZ50oZSaCnyitX4t12PJIA7geOBurfVxwD5y7FJIJwE/+TT8\ngjcK2E8p9f3cjirzaH/oZk7DN0UI/HwEVJl+PzTw2pBCKVWAXwQe1lo/nuvxpJlJwNlKqW34XXun\nK6X+ltshpZ0dwA6ttbGTexS/MAwVzgA+0Fq3aq17gceBiTkeU6ZoUUodDBD47ye5HIwIgZ8NwFil\n1BillBP/AdXyHI8prSilFH7f8hat9W25Hk+60VpfpbU+VGs9Gv/fb5XWekitJrXWHwPNSqkjAi9N\nAd7J4ZDSzYfAyUqpksD/r1MYQofhYSwHfhD4+QfAshyOJf+a11uhtfYopX4OPIc/UuFBrfXmHA8r\n3UwCzgPeVkq9GXjtaq310zkck5A8vwAeDixY3gcuyPF40obW+hWl1KPA6/ij3N5ggGXgpoJS6h9A\nHVChlNoBzAUWAEuVUj/EX1l5Ru5GKJnFgiAIeY+4hgRBEPIcEQJBEIQ8R4RAEAQhzxEhEARByHNE\nCARBEPIcEQJBEIQ8R4RAEAQhzxEhEIQUUEqdqJR6SylVpJTaL1BDf3yuxyUIqSAJZYKQIkqpG4Ai\noBh/DaCbcjwkQUgJEQJBSJFAmYcNQA8wUWvtzfGQBCElxDUkCKlzAFAKDMO/MxCEQYnsCAQhRZRS\ny/GXvB4DHKy1/nmOhyQIKSHVRwUhBZRSM4FerfXfAz2v1ymlTtdar8r12AQhWWRHIAiCkOfIGYEg\nCEKeI0IgCIKQ54gQCIIg5DkiBIIgCHmOCIEgCEKeI0IgCIKQ54gQCIIg5DkiBIIgCHnO/wdl33r0\nN7m1+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5983c1d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Volvemos a representar una muestra de nuestros datos para superponer las predicciones anteriormente calculadas.\n",
    "plt.scatter(sampling[\"x_data\"], sampling[\"y_label\"], s = 7)\n",
    "plt.plot(np.linspace(0,10,10), predictions, 'r', linewidth = .75)\n",
    "plt.grid()\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Con esto hemos conseguido abordar un problema de regresión lineal con un data set extenso de manera satisfactoria haciendo uso de TensorFlow."
   ]
  }
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