xortf2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled11.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPxHimcOOHGBpu45OC3h0ZF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bklooste/tensorflowcollab/blob/master/xortf2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ioLbtB3uGKPX",
        "colab": {}
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4VveH545cmBY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "958845c4-0b5b-44d1-bc80-072f75d98c63"
      },
      "source": [
        "# coding=utf-8\n",
        "import tensorflow as tf\n",
        "print (tf.__version__)\n",
        " \n",
        "# input X vector\n",
        "X = [[0, 0], [0, 1], [1, 0], [1, 1]]\n",
        "# output Y vector\n",
        "Y = [[0], [1], [1], [0]]\n",
        " \n",
        "# Placeholders for input and output\n",
        "x = tf.placeholder(tf.float32, shape=[4,2])\n",
        "y = tf.placeholder(tf.float32, shape=[4,1])\n",
        " \n",
        "# W matrix\n",
        "W1 = tf.Variable([[1.0, 0.0], [1.0, 0.0]], shape=[2,2])\n",
        "W2 = tf.Variable([[0.0], [1.0]], shape=[2,1])\n",
        " \n",
        "# Biases\n",
        "B1 = tf.Variable([0.0, 0.0], shape=[2])\n",
        "B2 = tf.Variable([0.0], shape=1)\n",
        " \n",
        "# Hidden layer and outout layer\n",
        "output =tf.sigmoid(tf.matmul(tf.sigmoid(tf.matmul(x, W1) + B1), W2) + B2)\n",
        " \n",
        "# error estimation\n",
        "e = tf.reduce_mean(tf.squared_difference(y, output))\n",
        "train = tf.train.GradientDescentOptimizer(0.1).minimize(e)\n",
        " \n",
        "init = tf.global_variables_initializer()\n",
        "sess = tf.Session()\n",
        "sess.run(init)\n",
        " \n",
        "for i in range (100001):\n",
        "    error = sess.run(train, feed_dict={x: X, y: Y})\n",
        "    if i % 10000 == 0:\n",
        "        print('\\nEpoch: ' + str(i))\n",
        "        print('\\nError: ' + str(sess.run(e, feed_dict={x: X, y: Y})))\n",
        "        for el in sess.run(output, feed_dict={x: X, y: Y}):\n",
        "            print('    ',el)\n",
        "sess.close()\n",
        " \n",
        "print (\"Complete\")\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0-rc1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-4d080a2872d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Placeholders for input and output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'placeholder'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJjeAUhwdICQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}