keras-perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/damminhtien/anaconda3/envs/tf/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "Using TensorFlow backend.\n",
      "/home/damminhtien/anaconda3/envs/tf/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/damminhtien/anaconda3/envs/tf/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import *\n",
    "from keras.models import *\n",
    "from keras import optimizers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1, 2, 3, 4, 5, 6], [2, 3, 6, 5, 10, 13])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1,2,3,4,5,6]\n",
    "y = [2,3,6,5,10,13]\n",
    "x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    Dense(1, input_shape=(1,))\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd=optimizers.SGD(lr=0.0005)\n",
    "model.compile(loss='mse',optimizer=sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
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      "Epoch 2/1000\n",
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      "Epoch 4/1000\n",
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      "Epoch 5/1000\n",
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      "Epoch 6/1000\n",
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      "Epoch 7/1000\n",
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      "Epoch 8/1000\n",
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      "Epoch 9/1000\n",
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      "Epoch 10/1000\n",
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      "Epoch 11/1000\n",
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      "Epoch 12/1000\n",
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      "Epoch 13/1000\n",
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      "Epoch 14/1000\n",
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      "Epoch 15/1000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 102/1000\n",
      "6/6 [==============================] - 0s 310us/step - loss: 1.7600\n",
      "Epoch 103/1000\n",
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     ]
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     ]
    },
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     ]
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     ]
    },
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     "text": [
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     ]
    },
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      "Epoch 696/1000\n",
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     ]
    },
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     "text": [
      "Epoch 795/1000\n",
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     ]
    },
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     ]
    },
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      "Epoch 993/1000\n",
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      "6/6 [==============================] - 0s 151us/step - loss: 1.6716\n",
      "Epoch 998/1000\n",
      "6/6 [==============================] - 0s 174us/step - loss: 1.6716\n",
      "Epoch 999/1000\n",
      "6/6 [==============================] - 0s 179us/step - loss: 1.6715\n",
      "Epoch 1000/1000\n",
      "6/6 [==============================] - 0s 165us/step - loss: 1.6715\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0658a449b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y,epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13.443773],\n",
       "       [15.382045],\n",
       "       [17.320316]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict([7,8,9])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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