AND GATE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TRAIN AND TEST MODEL ON AND GATE\n",
    "import keras\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0]\n",
      " [0 1]\n",
      " [1 0]\n",
      " [1 1]]\n"
     ]
    }
   ],
   "source": [
    "input = np.array([[0,0],\n",
    "                  [0,1],\n",
    "                  [1,0],\n",
    "                  [1,1]])\n",
    "print(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [0]\n",
      " [0]\n",
      " [1]]\n"
     ]
    }
   ],
   "source": [
    "output = np.array([[0],\n",
    "                     [0],\n",
    "                     [0],\n",
    "                     [1]])\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential(layers=[ \n",
    "    keras.layers.Dense(input_dim =2, units = 1),\n",
    "    keras.layers.Activation(keras.activations.sigmoid)\n",
    "\n",
    "])\n",
    "\n",
    "\n",
    "#model.compile(optimizer=keras.optimizers.SGD(lr=.5),loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 1)                 3         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 3\n",
      "Trainable params: 3\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=keras.optimizers.SGD(lr=.5),loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'dense_1/kernel:0' shape=(2, 1) dtype=float32_ref>,\n",
       " <tf.Variable 'dense_1/bias:0' shape=(1,) dtype=float32_ref>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.weights # shape (2,1) = 2 input neurons 1 output neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.backend.eval(model.weights[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2655\n",
      "Epoch 2/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2614\n",
      "Epoch 3/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2575\n",
      "Epoch 4/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2538\n",
      "Epoch 5/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2503\n",
      "Epoch 6/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2469\n",
      "Epoch 7/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2437\n",
      "Epoch 8/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2406\n",
      "Epoch 9/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2376\n",
      "Epoch 10/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2347\n",
      "Epoch 11/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2319\n",
      "Epoch 12/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2292\n",
      "Epoch 13/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2266\n",
      "Epoch 14/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2240\n",
      "Epoch 15/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2215\n",
      "Epoch 16/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2190\n",
      "Epoch 17/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2166\n",
      "Epoch 18/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2143\n",
      "Epoch 19/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2120\n",
      "Epoch 20/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2097\n",
      "Epoch 21/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2074\n",
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      "4/4 [==============================] - 0s - loss: 0.2052\n",
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      "4/4 [==============================] - 0s - loss: 0.2031\n",
      "Epoch 24/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2009\n",
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      "4/4 [==============================] - 0s - loss: 0.1988\n",
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      "Epoch 27/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1946\n",
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      "Epoch 46/1000\n",
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      "4/4 [==============================] - 0s - loss: 0.1453\n",
      "Epoch 57/1000\n",
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      "Epoch 61/1000\n",
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      "Epoch 64/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1354\n",
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      "Epoch 66/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1331\n",
      "Epoch 67/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1320\n",
      "Epoch 68/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1309\n",
      "Epoch 69/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1298\n",
      "Epoch 70/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1288\n",
      "Epoch 71/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1277\n",
      "Epoch 72/1000\n",
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      "Epoch 73/1000\n",
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      "Epoch 74/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1247\n",
      "Epoch 75/1000\n",
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      "Epoch 76/1000\n",
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      "Epoch 77/1000\n",
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      "Epoch 78/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1209\n",
      "Epoch 79/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1200\n",
      "Epoch 80/1000\n",
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      "Epoch 89/1000\n",
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      "Epoch 90/1000\n",
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      "Epoch 93/1000\n",
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      "Epoch 96/1000\n",
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      "Epoch 100/1000\n",
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      "Epoch 101/1000\n",
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      "Epoch 105/1000\n",
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      "Epoch 106/1000\n",
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      "Epoch 108/1000\n",
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      "Epoch 109/1000\n",
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      "Epoch 110/1000\n",
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      "Epoch 111/1000\n",
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      "Epoch 114/1000\n",
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      "Epoch 115/1000\n",
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      "Epoch 118/1000\n",
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      "Epoch 119/1000\n",
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      "Epoch 120/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0919\n",
      "Epoch 121/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0914\n",
      "Epoch 122/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0909\n",
      "Epoch 123/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0904\n",
      "Epoch 124/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0899\n",
      "Epoch 125/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0894\n",
      "Epoch 126/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0889\n",
      "Epoch 127/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0884\n",
      "Epoch 128/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0879\n",
      "Epoch 129/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0874\n",
      "Epoch 130/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0870\n",
      "Epoch 131/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0865\n",
      "Epoch 132/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0861\n",
      "Epoch 133/1000\n",
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      "Epoch 134/1000\n",
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      "Epoch 135/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0847\n",
      "Epoch 136/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0843\n",
      "Epoch 137/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0838\n",
      "Epoch 138/1000\n",
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      "Epoch 139/1000\n",
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      "Epoch 141/1000\n",
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      "Epoch 143/1000\n",
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      "Epoch 151/1000\n",
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      "Epoch 156/1000\n",
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      "Epoch 157/1000\n",
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      "Epoch 158/1000\n",
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      "4/4 [==============================] - 0s - loss: 0.0752\n",
      "Epoch 160/1000\n",
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      "Epoch 168/1000\n",
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      "Epoch 169/1000\n",
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      "Epoch 171/1000\n",
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      "Epoch 182/1000\n",
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      "Epoch 183/1000\n",
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      "Epoch 188/1000\n",
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      "Epoch 189/1000\n",
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      "Epoch 190/1000\n",
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      "Epoch 191/1000\n",
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      "Epoch 192/1000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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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",
      "4/4 [==============================] - 0s - loss: 0.0126\n",
      "Epoch 999/1000\n",
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      "Epoch 1000/1000\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x5c31f47080>"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12101924]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array([[0,1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.8546715]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array([[1,1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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