5 times sin 1.5 times x plus pi divided 4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \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 keras.optimizers import SGD\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from keras.layers import LeakyReLU\n",
    "from keras.utils import plot_model\n",
    "\n",
    "range = np.linspace(-np.pi,np.pi,100) \n",
    "\n",
    "x = range\n",
    "y = 5*np.sin((3/2)*range+np.pi/4)\n",
    "\n",
    "#coordinates_df=pd.DataFrame({'x':x,'y':y})\n",
    "\n",
    "#print(coordinates_df)\n",
    "\n",
    "#scaler = StandardScaler()\n",
    "\n",
    "#scaler.fit(y)\n",
    "\n",
    "#StandardScaler(copy=True, with_mean=True,with_std=True)\n",
    "#coordinates_df=scaler.transform(y)\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(100, input_shape=(1,)))\n",
    "model.add(LeakyReLU(alpha=0.03))\n",
    "# Add two Dense layers with 50 neurons and relu activation\n",
    "model.add(Dense(100))\n",
    "model.add(LeakyReLU(alpha=0.03))\n",
    "\n",
    "# End your model with a Dense layer and no activation\n",
    "model.add(Dense(1))\n",
    "\n",
    "# Compile your model\n",
    "model.compile(optimizer='sgd', loss='mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "plot_model(model, to_file='model.png')\n",
    "img=plt.imread('model.png')\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,3)) #fig size same as before\n",
    "ax = plt.gca() #you first need to get the axis handle\n",
    "ax.set_aspect(1.5) #sets the height to width ratio to 1.5. \n",
    "#Use help(ax.set_aspect) for more options.\n",
    "\n",
    "ax.plot(x,y)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 11.5928 - accuracy: 0.0000e+00\n",
      "Epoch 2/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 10.9483 - accuracy: 0.0000e+00\n",
      "Epoch 3/600\n",
      "100/100 [==============================] - 0s 115us/step - loss: 10.3679 - accuracy: 0.0000e+00\n",
      "Epoch 4/600\n",
      "100/100 [==============================] - 0s 125us/step - loss: 10.1941 - accuracy: 0.0000e+00\n",
      "Epoch 5/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 9.7843 - accuracy: 0.0000e+00\n",
      "Epoch 6/600\n",
      "100/100 [==============================] - 0s 90us/step - loss: 9.5547 - accuracy: 0.0000e+00\n",
      "Epoch 7/600\n",
      "100/100 [==============================] - 0s 110us/step - loss: 9.3406 - accuracy: 0.0000e+00\n",
      "Epoch 8/600\n",
      "100/100 [==============================] - 0s 95us/step - loss: 9.1399 - accuracy: 0.0000e+00\n",
      "Epoch 9/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 8.9453 - accuracy: 0.0000e+00\n",
      "Epoch 10/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 8.7538 - accuracy: 0.0000e+00\n",
      "Epoch 11/600\n",
      "100/100 [==============================] - 0s 120us/step - loss: 8.4298 - accuracy: 0.0000e+00\n",
      "Epoch 12/600\n",
      "100/100 [==============================] - 0s 105us/step - loss: 8.1362 - accuracy: 0.0000e+00\n",
      "Epoch 13/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 8.1240 - accuracy: 0.0000e+00\n",
      "Epoch 14/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 7.8558 - accuracy: 0.0000e+00\n",
      "Epoch 15/600\n",
      "100/100 [==============================] - 0s 100us/step - loss: 7.7005 - accuracy: 0.0000e+00\n",
      "Epoch 16/600\n",
      "100/100 [==============================] - 0s 94us/step - loss: 7.5053 - accuracy: 0.0000e+00\n",
      "Epoch 17/600\n",
      "100/100 [==============================] - 0s 90us/step - loss: 7.4537 - accuracy: 0.0000e+00\n",
      "Epoch 18/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 7.3216 - accuracy: 0.0000e+00\n",
      "Epoch 19/600\n",
      "100/100 [==============================] - 0s 85us/step - loss: 7.0772 - accuracy: 0.0000e+00\n",
      "Epoch 20/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 6.8205 - accuracy: 0.0000e+00\n",
      "Epoch 21/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 6.6053 - accuracy: 0.0000e+00\n",
      "Epoch 22/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 6.4673 - accuracy: 0.0000e+00\n",
      "Epoch 23/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 6.1003 - accuracy: 0.0000e+00\n",
      "Epoch 24/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 5.8716 - accuracy: 0.0000e+00\n",
      "Epoch 25/600\n",
      "100/100 [==============================] - 0s 85us/step - loss: 5.7423 - accuracy: 0.0000e+00\n",
      "Epoch 26/600\n",
      "100/100 [==============================] - 0s 100us/step - loss: 5.7512 - accuracy: 0.0000e+00\n",
      "Epoch 27/600\n",
      "100/100 [==============================] - 0s 130us/step - loss: 5.5152 - accuracy: 0.0000e+00\n",
      "Epoch 28/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 5.5903 - accuracy: 0.0000e+00\n",
      "Epoch 29/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 5.2656 - accuracy: 0.0000e+00\n",
      "Epoch 30/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 5.1766 - accuracy: 0.0000e+00\n",
      "Epoch 31/600\n",
      "100/100 [==============================] - 0s 90us/step - loss: 5.2328 - accuracy: 0.0000e+00\n",
      "Epoch 32/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 4.7627 - accuracy: 0.0000e+00\n",
      "Epoch 33/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 4.7281 - accuracy: 0.0000e+00\n",
      "Epoch 34/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 5.4825 - accuracy: 0.0000e+00\n",
      "Epoch 35/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 4.8240 - accuracy: 0.0000e+00\n",
      "Epoch 36/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 4.5546 - accuracy: 0.0000e+00\n",
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     ]
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     ]
    },
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     "text": [
      "Epoch 527/600\n",
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      "Epoch 535/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 0.0228 - accuracy: 0.0000e+00\n",
      "Epoch 536/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 0.0234 - accuracy: 0.0000e+00\n",
      "Epoch 537/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 0.0529 - accuracy: 0.0000e+00\n",
      "Epoch 538/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 0.0267 - accuracy: 0.0000e+00\n",
      "Epoch 539/600\n",
      "100/100 [==============================] - 0s 110us/step - loss: 0.0233 - accuracy: 0.0000e+00\n",
      "Epoch 540/600\n",
      "100/100 [==============================] - 0s 85us/step - loss: 0.0218 - accuracy: 0.0000e+00\n",
      "Epoch 541/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0426 - accuracy: 0.0000e+00\n",
      "Epoch 542/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 0.0263 - accuracy: 0.0000e+00\n",
      "Epoch 543/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0254 - accuracy: 0.0000e+00\n",
      "Epoch 544/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0290 - accuracy: 0.0000e+00\n",
      "Epoch 545/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0287 - accuracy: 0.0000e+00\n",
      "Epoch 546/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 0.0000e+00\n",
      "Epoch 547/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 0.0259 - accuracy: 0.0000e+00\n",
      "Epoch 548/600\n",
      "100/100 [==============================] - 0s 90us/step - loss: 0.0225 - accuracy: 0.0000e+00\n",
      "Epoch 549/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0215 - accuracy: 0.0000e+00\n",
      "Epoch 550/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 0.0277 - accuracy: 0.0000e+00\n",
      "Epoch 551/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 0.0595 - accuracy: 0.0000e+00\n",
      "Epoch 552/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 0.0201 - accuracy: 0.0000e+00\n",
      "Epoch 553/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0261 - accuracy: 0.0000e+00\n",
      "Epoch 554/600\n",
      "100/100 [==============================] - 0s 55us/step - loss: 0.0214 - accuracy: 0.0000e+00\n",
      "Epoch 555/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0218 - accuracy: 0.0000e+00\n",
      "Epoch 556/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 0.0257 - accuracy: 0.0000e+00\n",
      "Epoch 557/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0236 - accuracy: 0.0000e+00\n",
      "Epoch 558/600\n",
      "100/100 [==============================] - 0s 55us/step - loss: 0.0202 - accuracy: 0.0000e+00\n",
      "Epoch 559/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0223 - accuracy: 0.0000e+00\n",
      "Epoch 560/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 0.0516 - accuracy: 0.0000e+00\n",
      "Epoch 561/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0210 - accuracy: 0.0000e+00\n",
      "Epoch 562/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 0.0231 - accuracy: 0.0000e+00\n",
      "Epoch 563/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0230 - accuracy: 0.0000e+00\n",
      "Epoch 564/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 0.0235 - accuracy: 0.0000e+00\n",
      "Epoch 565/600\n",
      "100/100 [==============================] - 0s 85us/step - loss: 0.0449 - accuracy: 0.0000e+00\n",
      "Epoch 566/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 0.0224 - accuracy: 0.0000e+00\n",
      "Epoch 567/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0306 - accuracy: 0.0000e+00\n",
      "Epoch 568/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0188 - accuracy: 0.0000e+00\n",
      "Epoch 569/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 0.0227 - accuracy: 0.0000e+00\n",
      "Epoch 570/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 0.0178 - accuracy: 0.0000e+00\n",
      "Epoch 571/600\n",
      "100/100 [==============================] - 0s 90us/step - loss: 0.0198 - accuracy: 0.0000e+00\n",
      "Epoch 572/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 0.0199 - accuracy: 0.0000e+00\n",
      "Epoch 573/600\n",
      "100/100 [==============================] - 0s 85us/step - loss: 0.0243 - accuracy: 0.0000e+00\n",
      "Epoch 574/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0227 - accuracy: 0.0000e+00\n",
      "Epoch 575/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 0.0198 - accuracy: 0.0000e+00\n",
      "Epoch 576/600\n",
      "100/100 [==============================] - 0s 90us/step - loss: 0.0256 - accuracy: 0.0000e+00\n",
      "Epoch 577/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0203 - accuracy: 0.0000e+00\n",
      "Epoch 578/600\n",
      "100/100 [==============================] - 0s 74us/step - loss: 0.0178 - accuracy: 0.0000e+00\n",
      "Epoch 579/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0423 - accuracy: 0.0000e+00\n",
      "Epoch 580/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 0.0298 - accuracy: 0.0000e+00\n",
      "Epoch 581/600\n",
      "100/100 [==============================] - 0s 85us/step - loss: 0.0202 - accuracy: 0.0000e+00\n",
      "Epoch 582/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 0.0915 - accuracy: 0.0000e+00\n",
      "Epoch 583/600\n",
      "100/100 [==============================] - 0s 115us/step - loss: 0.0327 - accuracy: 0.0000e+00\n",
      "Epoch 584/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 0.0186 - accuracy: 0.0000e+00\n",
      "Epoch 585/600\n",
      "100/100 [==============================] - 0s 85us/step - loss: 0.0181 - accuracy: 0.0000e+00\n",
      "Epoch 586/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0603 - accuracy: 0.0000e+00\n",
      "Epoch 587/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0244 - accuracy: 0.0000e+00\n",
      "Epoch 588/600\n",
      "100/100 [==============================] - 0s 65us/step - loss: 0.0185 - accuracy: 0.0000e+00\n",
      "Epoch 589/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 0.0198 - accuracy: 0.0000e+00\n",
      "Epoch 590/600\n",
      "100/100 [==============================] - 0s 55us/step - loss: 0.0178 - accuracy: 0.0000e+00\n",
      "Epoch 591/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0185 - accuracy: 0.0000e+00\n",
      "Epoch 592/600\n",
      "100/100 [==============================] - 0s 85us/step - loss: 0.0288 - accuracy: 0.0000e+00\n",
      "Epoch 593/600\n",
      "100/100 [==============================] - 0s 80us/step - loss: 0.0224 - accuracy: 0.0000e+00\n",
      "Epoch 594/600\n",
      "100/100 [==============================] - 0s 105us/step - loss: 0.0219 - accuracy: 0.0000e+00\n",
      "Epoch 595/600\n",
      "100/100 [==============================] - 0s 55us/step - loss: 0.0176 - accuracy: 0.0000e+00\n",
      "Epoch 596/600\n",
      "100/100 [==============================] - 0s 55us/step - loss: 0.0190 - accuracy: 0.0000e+00\n",
      "Epoch 597/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0292 - accuracy: 0.0000e+00\n",
      "Epoch 598/600\n",
      "100/100 [==============================] - 0s 60us/step - loss: 0.0175 - accuracy: 0.0000e+00\n",
      "Epoch 599/600\n",
      "100/100 [==============================] - 0s 75us/step - loss: 0.0288 - accuracy: 0.0000e+00\n",
      "Epoch 600/600\n",
      "100/100 [==============================] - 0s 70us/step - loss: 0.0249 - accuracy: 0.0000e+00\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'coordinates_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-e33fcc1e2b92>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# Evaluate your model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Final lost value:\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcoordinates_df\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mR\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m# Plot the twenty minute orbit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'coordinates_df' is not defined"
     ]
    }
   ],
   "source": [
    "# Fit your model on your data for 30 epochs\n",
    "#X_train,y_train,X_test,y_test=train_test_split(coordinates_df,R,test_size=.3,random_state=42)\n",
    "\n",
    "#print(R)\n",
    "#print(y_train)\n",
    "\n",
    "history=model.fit(x,y, epochs = 600)\n",
    "#history=model.fit(X_train,y_train,  epochs = 600)\n",
    "\n",
    "# Evaluate your model \n",
    "print(\"Final lost value:\",model.evaluate(coordinates_df,R))\n",
    "\n",
    "# Plot the twenty minute orbit \n",
    "#plot_orbit(twenty_min_orbit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(pred_coordinates)\n",
    "# Predict the twenty minutes orbit\n",
    "pred_y = model.predict(x)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,3)) #fig size same as before\n",
    "ax = plt.gca() #you first need to get the axis handle\n",
    "ax.set_aspect(1.5) #sets the height to width ratio to 1.5. \n",
    "ax.plot(x,pred_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}