deep learning circle chasing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:52: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5000\n",
      "70/70 [==============================] - 0s 878us/step - loss: 11.8294 - accuracy: 0.4571\n",
      "Epoch 2/5000\n",
      "70/70 [==============================] - 0s 78us/step - loss: 11.5604 - accuracy: 0.3571\n",
      "Epoch 3/5000\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\keras\\callbacks\\callbacks.py:846: RuntimeWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "  (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 1281/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70/70 [==============================] - 0s 93us/step - loss: 0.1069 - accuracy: 1.0000\n",
      "Epoch 1282/5000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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     ]
    },
    {
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     "text": [
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      "Epoch 2061/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70/70 [==============================] - 0s 71us/step - loss: 0.0193 - accuracy: 1.0000\n",
      "Epoch 2062/5000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 2841/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 2997/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 3153/5000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 4926/5000\n",
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      "Epoch 4931/5000\n",
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      "Epoch 4934/5000\n",
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      "Epoch 4935/5000\n",
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      "Epoch 4936/5000\n",
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      "Epoch 4938/5000\n",
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      "Epoch 4939/5000\n",
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      "Epoch 4940/5000\n",
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      "Epoch 4941/5000\n",
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      "Epoch 4945/5000\n",
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      "Epoch 4946/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70/70 [==============================] - 0s 93us/step - loss: 0.0043 - accuracy: 1.0000\n",
      "Epoch 4947/5000\n",
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      "Epoch 4949/5000\n",
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      "Epoch 4950/5000\n",
      "70/70 [==============================] - 0s 64us/step - loss: 0.0024 - accuracy: 1.0000\n",
      "Epoch 4951/5000\n",
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      "Epoch 4952/5000\n",
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      "Epoch 4955/5000\n",
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      "Epoch 4956/5000\n",
      "70/70 [==============================] - 0s 64us/step - loss: 0.0032 - accuracy: 1.0000\n",
      "Epoch 4957/5000\n",
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      "Epoch 4958/5000\n",
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      "70/70 [==============================] - 0s 79us/step - loss: 0.0022 - accuracy: 1.0000\n",
      "Epoch 4960/5000\n",
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      "Epoch 4961/5000\n",
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      "Epoch 4962/5000\n",
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      "Epoch 4963/5000\n",
      "70/70 [==============================] - 0s 64us/step - loss: 0.0046 - accuracy: 0.9857\n",
      "Epoch 4964/5000\n",
      "70/70 [==============================] - 0s 77us/step - loss: 0.0026 - accuracy: 1.0000\n",
      "Epoch 4965/5000\n",
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      "Epoch 4966/5000\n",
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      "Epoch 4967/5000\n",
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      "Epoch 4968/5000\n",
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      "Epoch 4971/5000\n",
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      "Epoch 4972/5000\n",
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      "Epoch 4973/5000\n",
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      "Epoch 4974/5000\n",
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      "Epoch 4975/5000\n",
      "70/70 [==============================] - 0s 57us/step - loss: 0.0079 - accuracy: 1.0000\n",
      "Epoch 4976/5000\n",
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      "Epoch 4977/5000\n",
      "70/70 [==============================] - 0s 79us/step - loss: 0.0199 - accuracy: 0.9857\n",
      "Epoch 4978/5000\n",
      "70/70 [==============================] - 0s 79us/step - loss: 0.0104 - accuracy: 0.9857\n",
      "Epoch 4979/5000\n",
      "70/70 [==============================] - 0s 64us/step - loss: 0.0051 - accuracy: 0.9857\n",
      "Epoch 4980/5000\n",
      "70/70 [==============================] - 0s 79us/step - loss: 0.0132 - accuracy: 0.9857\n",
      "Epoch 4981/5000\n",
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      "Epoch 4983/5000\n",
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      "70/70 [==============================] - 0s 64us/step - loss: 0.0095 - accuracy: 1.0000\n",
      "Epoch 4986/5000\n",
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      "70/70 [==============================] - 0s 114us/step - loss: 0.0028 - accuracy: 1.0000\n",
      "Epoch 4988/5000\n",
      "70/70 [==============================] - 0s 79us/step - loss: 0.0023 - accuracy: 1.0000\n",
      "Epoch 4989/5000\n",
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      "Epoch 4990/5000\n",
      "70/70 [==============================] - 0s 79us/step - loss: 0.0042 - accuracy: 1.0000\n",
      "Epoch 4991/5000\n",
      "70/70 [==============================] - 0s 100us/step - loss: 0.0070 - accuracy: 0.9857\n",
      "Epoch 4992/5000\n",
      "70/70 [==============================] - 0s 64us/step - loss: 0.0080 - accuracy: 0.9857\n",
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      "Epoch 4994/5000\n",
      "70/70 [==============================] - 0s 57us/step - loss: 0.0051 - accuracy: 1.0000\n",
      "Epoch 4995/5000\n",
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      "Epoch 4996/5000\n",
      "70/70 [==============================] - 0s 79us/step - loss: 0.0145 - accuracy: 1.0000\n",
      "Epoch 4997/5000\n",
      "70/70 [==============================] - 0s 57us/step - loss: 0.0040 - accuracy: 0.9857\n",
      "Epoch 4998/5000\n",
      "70/70 [==============================] - 0s 79us/step - loss: 0.0022 - accuracy: 1.0000\n",
      "Epoch 4999/5000\n",
      "70/70 [==============================] - 0s 86us/step - loss: 0.0023 - accuracy: 1.0000\n",
      "Epoch 5000/5000\n",
      "70/70 [==============================] - 0s 79us/step - loss: 0.0033 - accuracy: 1.0000\n",
      "[[ 3.0931077  -4.043112  ]\n",
      " [-1.0223857   4.881736  ]\n",
      " [-0.04342103  5.0654955 ]\n",
      " [-1.3878675  -4.681678  ]\n",
      " [ 4.387747   -2.3331914 ]\n",
      " [ 5.4072065  -0.41562903]\n",
      " [-4.6301646  -1.9044616 ]\n",
      " [ 1.2078211   4.81956   ]\n",
      " [ 4.1862154   2.6286714 ]\n",
      " [ 0.89542     4.882304  ]\n",
      " [-4.9494014  -0.72822857]\n",
      " [-1.6833966  -4.5883904 ]\n",
      " [ 4.766345    1.228915  ]\n",
      " [-2.8165777   4.3263187 ]\n",
      " [ 1.3538196  -4.783613  ]\n",
      " [ 3.8499157   3.1319745 ]\n",
      " [-1.9789269  -4.495103  ]\n",
      " [ 2.6154833   4.361396  ]\n",
      " [-1.3212209   4.7903156 ]\n",
      " [ 4.9627714   0.6494186 ]\n",
      " [-3.3657937  -3.7433913 ]\n",
      " [ 2.8882165   4.2615004 ]\n",
      " [ 5.1182036  -0.95366883]\n",
      " [-5.0102806   0.26034474]\n",
      " [ 4.533838   -2.057288  ]\n",
      " [-0.79680735 -4.8682537 ]\n",
      " [ 4.8260217  -1.5054772 ]\n",
      " [ 4.9721127  -1.2295738 ]\n",
      " [ 2.2563915  -4.474799  ]\n",
      " [-4.3250985  -2.4880164 ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0931077 -4.043112:3.1527633354226143 -3.880732321458783\n",
      "-1.0223857 4.881736:-1.0240333403259536 4.894012231073893\n",
      "-0.04342103 5.0654955:-0.07932981917404014 4.999370638369375\n",
      "-1.3878675 -4.681678:-1.3323690684501732 -4.81921079279971\n",
      "4.387747 -2.3331914:4.514632691433105 -2.1489745604458594\n",
      "5.4072065 -0.41562903:5.0 -1.2246467991473533e-15\n",
      "-4.6301646 -1.9044616:-4.580542287160348 -2.004652677033069\n",
      "1.2078211 4.81956:1.1787946775471365 4.859057841617708\n",
      "4.1862154 2.6286714:4.206267664155906 2.703204087277988\n",
      "0.89542 4.882304:0.8682408883346521 4.92403876506104\n",
      "-4.9494014 -0.72822857:-4.937194443381971 -0.7900069798667504\n",
      "-1.6833966 -4.5883904:-1.6353398165871094 -4.725004093573341\n",
      "4.766345 1.228915:4.839743506981781 1.255739935905396\n",
      "-2.8165777 4.3263187:-2.7696003193305514 4.162849273173857\n",
      "1.3538196 -4.783613:1.4846018766413747 -4.774511207220369\n",
      "3.8499157 3.1319745:3.83022221559489 3.2139380484326963\n",
      "-1.9789269 -4.495103:-1.9317256284656434 -4.611771470522907\n",
      "2.6154833 4.361396:2.636127338052512 4.248627149747572\n",
      "-1.3212209 4.7903156:-1.3323690684501752 4.81921079279971\n",
      "4.9627714 0.6494186:4.959774064153977 0.6329622678687463\n",
      "-3.3657937 -3.7433913:-3.274303669726426 -3.778747871771291\n",
      "2.8882165 4.2615004:2.900284547855991 4.072879760251679\n",
      "5.1182036 -0.95366883:4.959774064153977 -0.6329622678687452\n",
      "-5.0102806 0.26034474:-4.997482711915925 0.15863966749033828\n",
      "4.533838 -2.057288:4.641839665080363 -1.8583122783016361\n",
      "-0.79680735 -4.8682537:-0.7115741913664262 -4.949107209404663\n",
      "4.8260217 -1.5054772:4.839743506981781 -1.255739935905397\n",
      "4.9721127 -1.2295738:4.909643486313534 -0.9462562218020487\n",
      "2.2563915 -4.474799:2.3613553738634145 -4.40726681723791\n",
      "-4.3250985 -2.4880164:-4.289917066174885 -2.568386957867032\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import keras\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.callbacks import EarlyStopping\n",
    "from sklearn.datasets import load_iris\n",
    "from keras.optimizers import SGD\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "theta = np.linspace(0,2*np.pi,100) \n",
    "R=5    \n",
    "x = R*np.cos(theta)\n",
    "y = R*np.sin(theta)\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.show()\n",
    "     #end\n",
    "#print(type(x))\n",
    "X_df=pd.concat([pd.Series(x),pd.Series(y),pd.Series(theta)], axis=1)\n",
    "\n",
    "#print(X_df.head(5))\n",
    "dataset=np.nan_to_num(X_df.values)\n",
    "X=dataset[:,-1]\n",
    "y=dataset[:,0:2]\n",
    "\n",
    "#print(X)\n",
    "#print(y)\n",
    "\n",
    "#min_max_scaler= preprocessing.MinMaxScaler()\n",
    "#X_scale= min_max_scaler.fit_transform(X)\n",
    "   \n",
    "X_train, X_test, y_train, y_test= train_test_split(X,y,test_size=0.3)\n",
    "\n",
    "early_stopping_monitor=EarlyStopping(patience=3)\n",
    "\n",
    "model=Sequential()\n",
    "model.add(Dense(100, activation='relu', input_shape=(1,)))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "#model.add(Dense(100, activation='tanh', input_shape=(1,)))\n",
    "#model.add(Dense(100, activation='tanh'))\n",
    "\n",
    "model.add(Dense(2))\n",
    "\n",
    "#myOptimizer=SGD(0.1)\n",
    "model.compile(optimizer='sgd', loss='mean_squared_error', metrics=['accuracy'])\n",
    "#model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
    "#model.compile(optimizer=myOptimizer,  metrics=['accuracy'])\n",
    "model.fit(X_train, y_train,callbacks=[early_stopping_monitor],  nb_epoch=5000)\n",
    "#model.fit(X, y,callbacks=[early_stopping_monitor],  nb_epoch=20)\n",
    "\n",
    "predictionResults=model.predict(X_test)\n",
    "\n",
    "print(predictionResults)\n",
    "\n",
    "plt.scatter(predictionResults[:,0],predictionResults[:,1])\n",
    "plt.plot(x,y)\n",
    "plt.show()\n",
    "\n",
    "index=0\n",
    "for item in predictionResults:\n",
    "    print(str(predictionResults[index,0])+\" \" + str(predictionResults[index,1]) +\":\"+str(y_test[index,0])+ \" \"+str(y_test[index,1]))\n",
    "    index+=1\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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