Tensorflow_2_0_XOR_Gate.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tensorflow 2.0 XOR Gate.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOkWCKyT/xfgY3DvyATAJAM",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/martinwithaar/notebooks/blob/main/Tensorflow_2_0_XOR_Gate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tensorflow 2.0 XOR Gate\n",
        "\n",
        "This Colab demonstrates how to make a simple XOR gate by training a neural network in Tensorflow 2.0"
      ],
      "metadata": {
        "id": "gPsTuePEdwMz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "print(\"Tensorflow version: \", tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fy-chLHrdtkV",
        "outputId": "b3b52e6b-574e-4777-e7d5-fdd0a43c9355"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tensorflow version:  2.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examples & labels\n",
        "\n",
        "An [XOR gate](https://en.wikipedia.org/wiki/XOR_gate) takes two inputs, A and B, and returns one output. When both inputs are different from each other the output is `1` and when they're both the same the output is `0`. Just like in the table below:\n",
        "\n",
        "|A  |B  |XOR|\n",
        "|:-:|:-:|:-:|\n",
        "|0 \t|0 \t|0  |\n",
        "|0 \t|1 \t|1  |\n",
        "|1 \t|0 \t|1  |\n",
        "|1 \t|1 \t|0  |"
      ],
      "metadata": {
        "id": "02CTz4nndvTh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Examples\n",
        "x = np.array([[0, 0],\n",
        "              [0, 1],\n",
        "              [1, 0],\n",
        "              [1, 1]], dtype=np.float32)\n",
        "\n",
        "# Labels\n",
        "y = np.array([[0],\n",
        "              [1],\n",
        "              [1],\n",
        "              [0]], dtype=np.float32)"
      ],
      "metadata": {
        "id": "1MzTKsAId5R8"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare the model\n",
        "\n",
        "The model uses [sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) activated hidden and output layers. The output is expected to be between 0 and 1. The hidden layer is assigned with a constant kernel initialization value to reduce the occurrence of the [vanishing gradient problem](https://en.wikipedia.org/wiki/Vanishing_gradient_problem).\n",
        "\n",
        "Notice the relatively high learning rate. This greatly reduces the amount of epochs needed because of the straightforward gradient features involved. However, if the learning rate is too high it might also cause the vanishing gradient problem to occur."
      ],
      "metadata": {
        "id": "RSsxDHgKk2sy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5U1CnatZDk1",
        "outputId": "d125749a-2bce-4b99-88fb-71e59a5c36d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 2)                 6         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 3         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 9\n",
            "Trainable params: 9\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Define the model\n",
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.Input(shape=(2,)))\n",
        "model.add(tf.keras.layers.Dense(2, activation=tf.keras.activations.sigmoid, kernel_initializer=tf.initializers.Constant(0.5)))\n",
        "model.add(tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1), loss=tf.keras.losses.MeanSquaredError(), metrics=['mse', 'binary_accuracy'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model\n",
        "\n",
        "Let's train the model and see how well it converges."
      ],
      "metadata": {
        "id": "65UXa5xieBmi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x, y, batch_size=1, epochs=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0js-fu9MeDRm",
        "outputId": "ee9dba4b-8e5f-481e-d160-e93989cf629a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2857 - mse: 0.2857 - binary_accuracy: 0.5000\n",
            "Epoch 2/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2608 - mse: 0.2608 - binary_accuracy: 0.5000\n",
            "Epoch 3/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2535 - mse: 0.2535 - binary_accuracy: 0.5000\n",
            "Epoch 4/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2699 - mse: 0.2699 - binary_accuracy: 0.0000e+00\n",
            "Epoch 5/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2546 - mse: 0.2546 - binary_accuracy: 0.5000\n",
            "Epoch 6/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2550 - mse: 0.2550 - binary_accuracy: 0.5000\n",
            "Epoch 7/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2607 - mse: 0.2607 - binary_accuracy: 0.2500\n",
            "Epoch 8/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2600 - mse: 0.2600 - binary_accuracy: 0.5000\n",
            "Epoch 9/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2603 - mse: 0.2603 - binary_accuracy: 0.2500\n",
            "Epoch 10/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2527 - mse: 0.2527 - binary_accuracy: 0.5000\n",
            "Epoch 11/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2561 - mse: 0.2561 - binary_accuracy: 0.5000\n",
            "Epoch 12/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2543 - mse: 0.2543 - binary_accuracy: 0.5000\n",
            "Epoch 13/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2602 - mse: 0.2602 - binary_accuracy: 0.5000\n",
            "Epoch 14/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2532 - mse: 0.2532 - binary_accuracy: 0.5000\n",
            "Epoch 15/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2610 - mse: 0.2610 - binary_accuracy: 0.5000\n",
            "Epoch 16/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2521 - mse: 0.2521 - binary_accuracy: 0.7500\n",
            "Epoch 17/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2521 - mse: 0.2521 - binary_accuracy: 0.2500\n",
            "Epoch 18/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2510 - mse: 0.2510 - binary_accuracy: 0.5000\n",
            "Epoch 19/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2514 - mse: 0.2514 - binary_accuracy: 0.2500\n",
            "Epoch 20/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2536 - mse: 0.2536 - binary_accuracy: 0.2500\n",
            "Epoch 21/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2511 - mse: 0.2511 - binary_accuracy: 0.5000\n",
            "Epoch 22/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2496 - mse: 0.2496 - binary_accuracy: 0.7500\n",
            "Epoch 23/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2585 - mse: 0.2585 - binary_accuracy: 0.5000\n",
            "Epoch 24/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2506 - mse: 0.2506 - binary_accuracy: 0.5000\n",
            "Epoch 25/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2576 - mse: 0.2576 - binary_accuracy: 0.5000\n",
            "Epoch 26/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2530 - mse: 0.2530 - binary_accuracy: 0.2500\n",
            "Epoch 27/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2477 - mse: 0.2477 - binary_accuracy: 0.5000\n",
            "Epoch 28/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2474 - mse: 0.2474 - binary_accuracy: 0.7500\n",
            "Epoch 29/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2477 - mse: 0.2477 - binary_accuracy: 0.7500\n",
            "Epoch 30/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2521 - mse: 0.2521 - binary_accuracy: 0.5000\n",
            "Epoch 31/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2443 - mse: 0.2443 - binary_accuracy: 0.7500\n",
            "Epoch 32/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2441 - mse: 0.2441 - binary_accuracy: 0.7500\n",
            "Epoch 33/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2433 - mse: 0.2433 - binary_accuracy: 0.7500\n",
            "Epoch 34/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2423 - mse: 0.2423 - binary_accuracy: 0.7500\n",
            "Epoch 35/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2412 - mse: 0.2412 - binary_accuracy: 0.7500\n",
            "Epoch 36/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2457 - mse: 0.2457 - binary_accuracy: 0.7500\n",
            "Epoch 37/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2462 - mse: 0.2462 - binary_accuracy: 0.7500\n",
            "Epoch 38/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2371 - mse: 0.2371 - binary_accuracy: 0.7500\n",
            "Epoch 39/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2354 - mse: 0.2354 - binary_accuracy: 0.7500\n",
            "Epoch 40/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2356 - mse: 0.2356 - binary_accuracy: 0.7500\n",
            "Epoch 41/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2339 - mse: 0.2339 - binary_accuracy: 0.7500\n",
            "Epoch 42/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2359 - mse: 0.2359 - binary_accuracy: 0.7500\n",
            "Epoch 43/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2311 - mse: 0.2311 - binary_accuracy: 0.7500\n",
            "Epoch 44/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2299 - mse: 0.2299 - binary_accuracy: 0.7500\n",
            "Epoch 45/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.2271 - mse: 0.2271 - binary_accuracy: 0.7500\n",
            "Epoch 46/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2327 - mse: 0.2327 - binary_accuracy: 0.7500\n",
            "Epoch 47/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2240 - mse: 0.2240 - binary_accuracy: 0.7500\n",
            "Epoch 48/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2223 - mse: 0.2223 - binary_accuracy: 0.7500\n",
            "Epoch 49/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2192 - mse: 0.2192 - binary_accuracy: 0.7500\n",
            "Epoch 50/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2167 - mse: 0.2167 - binary_accuracy: 0.7500\n",
            "Epoch 51/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2144 - mse: 0.2144 - binary_accuracy: 0.7500\n",
            "Epoch 52/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2216 - mse: 0.2216 - binary_accuracy: 0.7500\n",
            "Epoch 53/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2122 - mse: 0.2122 - binary_accuracy: 0.7500\n",
            "Epoch 54/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2097 - mse: 0.2097 - binary_accuracy: 0.7500\n",
            "Epoch 55/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2090 - mse: 0.2090 - binary_accuracy: 0.7500\n",
            "Epoch 56/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2143 - mse: 0.2143 - binary_accuracy: 0.7500\n",
            "Epoch 57/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2073 - mse: 0.2073 - binary_accuracy: 0.7500\n",
            "Epoch 58/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2027 - mse: 0.2027 - binary_accuracy: 0.7500\n",
            "Epoch 59/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1982 - mse: 0.1982 - binary_accuracy: 0.7500\n",
            "Epoch 60/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1977 - mse: 0.1977 - binary_accuracy: 0.7500\n",
            "Epoch 61/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1959 - mse: 0.1959 - binary_accuracy: 0.7500\n",
            "Epoch 62/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1940 - mse: 0.1940 - binary_accuracy: 0.7500\n",
            "Epoch 63/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1940 - mse: 0.1940 - binary_accuracy: 0.7500\n",
            "Epoch 64/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1901 - mse: 0.1901 - binary_accuracy: 0.7500\n",
            "Epoch 65/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1917 - mse: 0.1917 - binary_accuracy: 0.7500\n",
            "Epoch 66/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1883 - mse: 0.1883 - binary_accuracy: 0.7500\n",
            "Epoch 67/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1886 - mse: 0.1886 - binary_accuracy: 0.7500\n",
            "Epoch 68/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1924 - mse: 0.1924 - binary_accuracy: 0.7500\n",
            "Epoch 69/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1836 - mse: 0.1836 - binary_accuracy: 0.7500\n",
            "Epoch 70/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1871 - mse: 0.1871 - binary_accuracy: 0.7500\n",
            "Epoch 71/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.1801 - mse: 0.1801 - binary_accuracy: 0.7500\n",
            "Epoch 72/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1789 - mse: 0.1789 - binary_accuracy: 0.7500\n",
            "Epoch 73/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1827 - mse: 0.1827 - binary_accuracy: 0.7500\n",
            "Epoch 74/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1754 - mse: 0.1754 - binary_accuracy: 0.7500\n",
            "Epoch 75/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1731 - mse: 0.1731 - binary_accuracy: 0.7500\n",
            "Epoch 76/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1827 - mse: 0.1827 - binary_accuracy: 0.7500\n",
            "Epoch 77/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1726 - mse: 0.1726 - binary_accuracy: 0.7500\n",
            "Epoch 78/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1723 - mse: 0.1723 - binary_accuracy: 0.7500\n",
            "Epoch 79/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1733 - mse: 0.1733 - binary_accuracy: 0.7500\n",
            "Epoch 80/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1683 - mse: 0.1683 - binary_accuracy: 0.7500\n",
            "Epoch 81/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1632 - mse: 0.1632 - binary_accuracy: 0.7500\n",
            "Epoch 82/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1669 - mse: 0.1669 - binary_accuracy: 0.7500\n",
            "Epoch 83/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1610 - mse: 0.1610 - binary_accuracy: 0.7500\n",
            "Epoch 84/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1627 - mse: 0.1627 - binary_accuracy: 0.7500\n",
            "Epoch 85/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1577 - mse: 0.1577 - binary_accuracy: 0.7500\n",
            "Epoch 86/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1573 - mse: 0.1573 - binary_accuracy: 0.7500\n",
            "Epoch 87/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1528 - mse: 0.1528 - binary_accuracy: 0.7500\n",
            "Epoch 88/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1591 - mse: 0.1591 - binary_accuracy: 0.7500\n",
            "Epoch 89/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1583 - mse: 0.1583 - binary_accuracy: 0.7500\n",
            "Epoch 90/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1576 - mse: 0.1576 - binary_accuracy: 0.7500\n",
            "Epoch 91/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1520 - mse: 0.1520 - binary_accuracy: 0.7500\n",
            "Epoch 92/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1451 - mse: 0.1451 - binary_accuracy: 0.7500\n",
            "Epoch 93/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1486 - mse: 0.1486 - binary_accuracy: 1.0000\n",
            "Epoch 94/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1439 - mse: 0.1439 - binary_accuracy: 0.7500\n",
            "Epoch 95/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1462 - mse: 0.1462 - binary_accuracy: 0.7500\n",
            "Epoch 96/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1404 - mse: 0.1404 - binary_accuracy: 0.7500\n",
            "Epoch 97/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1337 - mse: 0.1337 - binary_accuracy: 0.7500\n",
            "Epoch 98/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1382 - mse: 0.1382 - binary_accuracy: 0.7500\n",
            "Epoch 99/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1277 - mse: 0.1277 - binary_accuracy: 1.0000\n",
            "Epoch 100/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1318 - mse: 0.1318 - binary_accuracy: 1.0000\n",
            "Epoch 101/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1261 - mse: 0.1261 - binary_accuracy: 1.0000\n",
            "Epoch 102/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1273 - mse: 0.1273 - binary_accuracy: 1.0000\n",
            "Epoch 103/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1193 - mse: 0.1193 - binary_accuracy: 1.0000\n",
            "Epoch 104/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1169 - mse: 0.1169 - binary_accuracy: 1.0000\n",
            "Epoch 105/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1146 - mse: 0.1146 - binary_accuracy: 1.0000\n",
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            "Epoch 279/500\n",
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            "Epoch 280/500\n",
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            "Epoch 281/500\n",
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            "Epoch 287/500\n",
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            "Epoch 290/500\n",
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            "Epoch 291/500\n",
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            "Epoch 293/500\n",
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            "4/4 [==============================] - 0s 9ms/step - loss: 0.0023 - mse: 0.0023 - binary_accuracy: 1.0000\n",
            "Epoch 400/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0022 - mse: 0.0022 - binary_accuracy: 1.0000\n",
            "Epoch 401/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0022 - mse: 0.0022 - binary_accuracy: 1.0000\n",
            "Epoch 402/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0022 - mse: 0.0022 - binary_accuracy: 1.0000\n",
            "Epoch 403/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0022 - mse: 0.0022 - binary_accuracy: 1.0000\n",
            "Epoch 404/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0022 - mse: 0.0022 - binary_accuracy: 1.0000\n",
            "Epoch 405/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0022 - mse: 0.0022 - binary_accuracy: 1.0000\n",
            "Epoch 406/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0022 - mse: 0.0022 - binary_accuracy: 1.0000\n",
            "Epoch 407/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0022 - mse: 0.0022 - binary_accuracy: 1.0000\n",
            "Epoch 408/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0021 - mse: 0.0021 - binary_accuracy: 1.0000\n",
            "Epoch 409/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0021 - mse: 0.0021 - binary_accuracy: 1.0000\n",
            "Epoch 410/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0021 - mse: 0.0021 - binary_accuracy: 1.0000\n",
            "Epoch 411/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0021 - mse: 0.0021 - binary_accuracy: 1.0000\n",
            "Epoch 412/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0021 - mse: 0.0021 - binary_accuracy: 1.0000\n",
            "Epoch 413/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0021 - mse: 0.0021 - binary_accuracy: 1.0000\n",
            "Epoch 414/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0021 - mse: 0.0021 - binary_accuracy: 1.0000\n",
            "Epoch 415/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0021 - mse: 0.0021 - binary_accuracy: 1.0000\n",
            "Epoch 416/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0021 - mse: 0.0021 - binary_accuracy: 1.0000\n",
            "Epoch 417/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 418/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 419/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 420/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 421/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 422/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 423/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 424/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 425/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 426/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0020 - mse: 0.0020 - binary_accuracy: 1.0000\n",
            "Epoch 427/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 428/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 429/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 430/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 431/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 432/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 433/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 434/500\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 435/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 436/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0019 - mse: 0.0019 - binary_accuracy: 1.0000\n",
            "Epoch 437/500\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 438/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 439/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 440/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 441/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 442/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 443/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 444/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 445/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 446/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 447/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0018 - mse: 0.0018 - binary_accuracy: 1.0000\n",
            "Epoch 448/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 449/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 450/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 451/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 452/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 453/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 454/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 455/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 456/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 457/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 458/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 459/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0017 - mse: 0.0017 - binary_accuracy: 1.0000\n",
            "Epoch 460/500\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 461/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 462/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 463/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 464/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 465/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 466/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 467/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 468/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 469/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 470/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 471/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 472/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 473/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0016 - mse: 0.0016 - binary_accuracy: 1.0000\n",
            "Epoch 474/500\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 475/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 476/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 477/500\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 478/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 479/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 480/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 481/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 482/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 483/500\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 484/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 485/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 486/500\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 487/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 488/500\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0015 - mse: 0.0015 - binary_accuracy: 1.0000\n",
            "Epoch 489/500\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 490/500\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 491/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 492/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 493/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 494/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 495/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 496/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 497/500\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 498/500\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 499/500\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n",
            "Epoch 500/500\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0014 - mse: 0.0014 - binary_accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predict the output\n",
        "\n",
        "The examples are also used to perform predictions. This way we can verify that the model has converged."
      ],
      "metadata": {
        "id": "arcg3T6PeF2R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict_on_batch(x)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDDLcNs8eHoj",
        "outputId": "92bb30ec-ee4c-48ff-e13b-7db833285c33"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.04527503]\n",
            " [0.96575904]\n",
            " [0.9657557 ]\n",
            " [0.03277108]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see, the model predicts the outputs correctly."
      ],
      "metadata": {
        "id": "Wah02kungMZg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "References:\n",
        "\n",
        "https://stackoverflow.com/questions/64681856/tensorflow-for-xor-is-not-predicting-correctly-after-500-epochs"
      ],
      "metadata": {
        "id": "BixaoFUH7_h-"
      }
    }
  ]
}