Red_Neuronal_Xor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicio Red Neuronal función XOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-28T15:32:15.288986Z",
     "start_time": "2018-05-28T15:32:01.155764Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jbagnato/anaconda3/envs/python36/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-28T15:32:15.307942Z",
     "start_time": "2018-05-28T15:32:15.295404Z"
    }
   },
   "outputs": [],
   "source": [
    "# cargamos las 4 combinaciones de las compuertas XOR\n",
    "training_data = np.array([[0,0],[0,1],[1,0],[1,1]], \"float32\")\n",
    "\n",
    "# y estos son los resultados que se obtienen, en el mismo orden\n",
    "target_data = np.array([[0],[1],[1],[0]], \"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-28T15:32:15.413946Z",
     "start_time": "2018-05-28T15:32:15.316734Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(16, input_dim=2, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-28T15:32:15.585922Z",
     "start_time": "2018-05-28T15:32:15.419314Z"
    }
   },
   "outputs": [],
   "source": [
    "model.compile(loss='mean_squared_error',\n",
    "              optimizer='adam',\n",
    "              metrics=['binary_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-28T15:32:23.746657Z",
     "start_time": "2018-05-28T15:32:15.595367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 535/1000\n",
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     ]
    },
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     "text": [
      "Epoch 611/1000\n",
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     ]
    },
    {
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     "text": [
      "Epoch 687/1000\n",
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     ]
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     "text": [
      "Epoch 763/1000\n",
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     ]
    },
    {
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     "text": [
      "Epoch 839/1000\n",
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     ]
    },
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     "text": [
      "Epoch 915/1000\n",
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      "4/4 [==============================] - 0s 1ms/step - loss: 0.0340 - binary_accuracy: 1.0000\n",
      "Epoch 969/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0339 - binary_accuracy: 1.0000\n",
      "Epoch 970/1000\n",
      "4/4 [==============================] - 0s 496us/step - loss: 0.0339 - binary_accuracy: 1.0000\n",
      "Epoch 971/1000\n",
      "4/4 [==============================] - 0s 472us/step - loss: 0.0338 - binary_accuracy: 1.0000\n",
      "Epoch 972/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0337 - binary_accuracy: 1.0000\n",
      "Epoch 973/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0336 - binary_accuracy: 1.0000\n",
      "Epoch 974/1000\n",
      "4/4 [==============================] - 0s 583us/step - loss: 0.0335 - binary_accuracy: 1.0000\n",
      "Epoch 975/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0334 - binary_accuracy: 1.0000\n",
      "Epoch 976/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0333 - binary_accuracy: 1.0000\n",
      "Epoch 977/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0333 - binary_accuracy: 1.0000\n",
      "Epoch 978/1000\n",
      "4/4 [==============================] - 0s 577us/step - loss: 0.0332 - binary_accuracy: 1.0000\n",
      "Epoch 979/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0331 - binary_accuracy: 1.0000\n",
      "Epoch 980/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0330 - binary_accuracy: 1.0000\n",
      "Epoch 981/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0329 - binary_accuracy: 1.0000\n",
      "Epoch 982/1000\n",
      "4/4 [==============================] - 0s 601us/step - loss: 0.0328 - binary_accuracy: 1.0000\n",
      "Epoch 983/1000\n",
      "4/4 [==============================] - 0s 627us/step - loss: 0.0327 - binary_accuracy: 1.0000\n",
      "Epoch 984/1000\n",
      "4/4 [==============================] - 0s 699us/step - loss: 0.0327 - binary_accuracy: 1.0000\n",
      "Epoch 985/1000\n",
      "4/4 [==============================] - 0s 664us/step - loss: 0.0326 - binary_accuracy: 1.0000\n",
      "Epoch 986/1000\n",
      "4/4 [==============================] - 0s 804us/step - loss: 0.0325 - binary_accuracy: 1.0000\n",
      "Epoch 987/1000\n",
      "4/4 [==============================] - 0s 676us/step - loss: 0.0324 - binary_accuracy: 1.0000\n",
      "Epoch 988/1000\n",
      "4/4 [==============================] - 0s 573us/step - loss: 0.0323 - binary_accuracy: 1.0000\n",
      "Epoch 989/1000\n",
      "4/4 [==============================] - 0s 592us/step - loss: 0.0322 - binary_accuracy: 1.0000\n",
      "Epoch 990/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0322 - binary_accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 991/1000\n",
      "4/4 [==============================] - 0s 756us/step - loss: 0.0321 - binary_accuracy: 1.0000\n",
      "Epoch 992/1000\n",
      "4/4 [==============================] - 0s 609us/step - loss: 0.0320 - binary_accuracy: 1.0000\n",
      "Epoch 993/1000\n",
      "4/4 [==============================] - 0s 594us/step - loss: 0.0319 - binary_accuracy: 1.0000\n",
      "Epoch 994/1000\n",
      "4/4 [==============================] - 0s 531us/step - loss: 0.0318 - binary_accuracy: 1.0000\n",
      "Epoch 995/1000\n",
      "4/4 [==============================] - 0s 449us/step - loss: 0.0318 - binary_accuracy: 1.0000\n",
      "Epoch 996/1000\n",
      "4/4 [==============================] - 0s 695us/step - loss: 0.0317 - binary_accuracy: 1.0000\n",
      "Epoch 997/1000\n",
      "4/4 [==============================] - 0s 915us/step - loss: 0.0316 - binary_accuracy: 1.0000\n",
      "Epoch 998/1000\n",
      "4/4 [==============================] - 0s 559us/step - loss: 0.0315 - binary_accuracy: 1.0000\n",
      "Epoch 999/1000\n",
      "4/4 [==============================] - 0s 561us/step - loss: 0.0314 - binary_accuracy: 1.0000\n",
      "Epoch 1000/1000\n",
      "4/4 [==============================] - 0s 714us/step - loss: 0.0314 - binary_accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x10f0edfd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(training_data, target_data, epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-28T15:32:23.802367Z",
     "start_time": "2018-05-28T15:32:23.752178Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 10ms/step\n",
      "\n",
      "binary_accuracy: 100.00%\n"
     ]
    }
   ],
   "source": [
    "# evaluamos el modelo\n",
    "scores = model.evaluate(training_data, target_data)\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-28T15:32:23.844266Z",
     "start_time": "2018-05-28T15:32:23.805234Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]]\n"
     ]
    }
   ],
   "source": [
    "print (model.predict(training_data).round())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que las predicciones se corresponden con las salidas deseadas de la función XOR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lee el artículo completo en www.aprendemachinelearning.com\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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