AND GATE 3 INPUTS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TRAIN AND TEST MODEL ON 3 INPUT AND GATE\n",
    "import keras\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0]\n",
      " [0 0 1]\n",
      " [0 1 0]\n",
      " [0 1 1]\n",
      " [1 0 0]\n",
      " [1 0 1]\n",
      " [1 1 0]\n",
      " [1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "input = np.array([[0,0,0],\n",
    "                  [0,0,1],\n",
    "                  [0,1,0],\n",
    "                  [0,1,1],\n",
    "                  [1,0,0],\n",
    "                  [1,0,1],\n",
    "                  [1,1,0],\n",
    "                  [1,1,1]])\n",
    "print(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]]\n"
     ]
    }
   ],
   "source": [
    "output = np.array([[0],\n",
    "                     [0],\n",
    "                     [0],\n",
    "                     [0],\n",
    "                     [0],\n",
    "                     [0],\n",
    "                     [0],\n",
    "                     [1]])\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential(layers=[ \n",
    "    keras.layers.Dense(input_dim =3, units = 1),\n",
    "    keras.layers.Activation(keras.activations.sigmoid)\n",
    "])\n",
    "#model.compile(optimizer=keras.optimizers.SGD(lr=.5),loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 1)                 4         \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 4\n",
      "Trainable params: 4\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=keras.optimizers.SGD(lr=.5),loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'dense_4/kernel:0' shape=(3, 1) dtype=float32_ref>,\n",
       " <tf.Variable 'dense_4/bias:0' shape=(1,) dtype=float32_ref>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.weights # shape (2,1) = 2 input neurons 1 output neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-7.906005], dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.backend.eval(model.weights[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0138\n",
      "Epoch 2/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 3/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 4/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 5/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 6/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 7/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 8/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 9/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 10/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 11/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 12/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 13/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 14/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 15/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0137\n",
      "Epoch 16/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 17/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 18/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 19/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 20/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 21/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 22/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 23/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 24/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 25/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 26/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 27/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 28/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 29/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 30/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0136\n",
      "Epoch 31/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 32/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 33/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 34/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 35/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 36/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 37/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 38/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 39/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 40/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 41/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 42/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 43/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 44/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 45/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0135\n",
      "Epoch 46/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 47/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 48/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 49/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 50/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 51/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 52/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 53/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 54/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 55/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 56/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 57/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 58/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 59/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 60/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0134\n",
      "Epoch 61/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 62/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 63/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 64/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 65/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 66/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 67/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 68/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 69/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 70/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 71/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 72/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 73/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 74/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 75/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0133\n",
      "Epoch 76/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 77/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 78/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 79/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 80/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 81/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
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      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 83/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 84/1000\n",
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      "Epoch 86/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 87/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 88/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 89/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 90/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 91/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0132\n",
      "Epoch 92/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 93/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 94/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 95/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 96/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 97/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 98/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 99/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 100/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 101/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 102/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 103/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 104/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 105/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 106/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 107/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0131\n",
      "Epoch 108/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 109/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 110/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 111/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 112/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 113/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 114/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 115/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 116/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 117/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 118/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 119/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 120/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 121/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 122/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 123/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0130\n",
      "Epoch 124/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 125/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 126/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 127/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 128/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 129/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 130/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 131/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 132/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 133/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 134/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 135/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 136/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 137/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 138/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 139/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0129\n",
      "Epoch 140/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 141/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 142/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 143/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 144/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 145/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 146/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 147/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 148/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 149/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 150/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 151/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 152/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 153/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 154/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 155/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0128\n",
      "Epoch 156/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 157/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 158/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 159/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 160/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 161/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 162/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 163/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 164/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 165/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 166/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 167/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 168/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 169/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 170/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 171/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 172/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0127\n",
      "Epoch 173/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 174/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 175/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 176/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 177/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 178/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 179/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 180/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 181/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 182/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 183/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 184/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 185/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 186/1000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 991/1000\n",
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      "Epoch 992/1000\n",
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      "Epoch 993/1000\n",
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      "Epoch 994/1000\n",
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      "Epoch 995/1000\n",
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      "Epoch 996/1000\n",
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      "Epoch 997/1000\n",
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      "Epoch 998/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0090\n",
      "Epoch 999/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0090\n",
      "Epoch 1000/1000\n",
      "8/8 [==============================] - 0s - loss: 0.0090\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11910dae10>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(input, output, epochs = 1000) # if you use , verbose = False than iteration can not show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00368037]], dtype=float32)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array([[0,1,0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.81960815]], dtype=float32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array([[1,1,1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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