Preliminary_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "from keras.models import Sequential,Model\n",
    "from keras import losses\n",
    "from keras.layers import Dense,Activation,LSTM\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from keras.utils.conv_utils import convert_kernel\n",
    "\n",
    "from random import seed\n",
    "from random import choice\n",
    "\n",
    "%matplotlib inline \n",
    "#to plot inline in jupyter notebook  \n",
    "import numpy as np #for array related \n",
    "import scipy.io as spio #to load data \n",
    "import cmath #for complex operations\n",
    "import matplotlib.pyplot as plt #for plotting graphs\n",
    "import math # for log \n",
    "from scipy.signal import hann, welch #for spectral analysis\n",
    "import keras.backend as K\n",
    "import tensorflow as tf\n",
    "import tensorflow.contrib.eager as tfe\n",
    "from tensorflow.python.eager.context import context, EAGER_MODE, GRAPH_MODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(x):\n",
    "    train_len = 8000\n",
    "    \n",
    "    y_train = Non_Linearity(x[0:train_len])\n",
    "    x_train = x[0:train_len]\n",
    "    \n",
    "    x_test = Non_Linearity(x[len(ip)-2000:])\n",
    "    y_test = x[len(ip)-2000:]\n",
    "    \n",
    "    return (x_train,y_train,x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/75\n",
      "2000/2000 [==============================] - 0s 173us/step - loss: 0.1313\n",
      "Epoch 2/75\n",
      "2000/2000 [==============================] - 0s 99us/step - loss: 0.0082\n",
      "Epoch 3/75\n",
      "2000/2000 [==============================] - 0s 121us/step - loss: 0.0011\n",
      "Epoch 4/75\n",
      "2000/2000 [==============================] - 0s 95us/step - loss: 1.6153e-04\n",
      "Epoch 5/75\n",
      "2000/2000 [==============================] - 0s 124us/step - loss: 2.5969e-05\n",
      "Epoch 6/75\n",
      "2000/2000 [==============================] - 0s 90us/step - loss: 3.9284e-06\n",
      "Epoch 7/75\n",
      "2000/2000 [==============================] - 0s 116us/step - loss: 3.5445e-07\n",
      "Epoch 8/75\n",
      "2000/2000 [==============================] - 0s 132us/step - loss: 6.8083e-08\n",
      "Epoch 9/75\n",
      "2000/2000 [==============================] - 0s 107us/step - loss: 7.8584e-09\n",
      "Epoch 10/75\n",
      "2000/2000 [==============================] - 0s 96us/step - loss: 7.6550e-10\n",
      "Epoch 11/75\n",
      "2000/2000 [==============================] - 0s 106us/step - loss: 1.0022e-10\n",
      "Epoch 12/75\n",
      "2000/2000 [==============================] - 0s 88us/step - loss: 1.2209e-11\n",
      "Epoch 13/75\n",
      "2000/2000 [==============================] - 0s 102us/step - loss: 1.6576e-12\n",
      "Epoch 14/75\n",
      "2000/2000 [==============================] - 0s 84us/step - loss: 2.2042e-13\n",
      "Epoch 15/75\n",
      "2000/2000 [==============================] - 0s 96us/step - loss: 2.9910e-14\n",
      "Epoch 16/75\n",
      "2000/2000 [==============================] - 0s 87us/step - loss: 5.0075e-15\n",
      "Epoch 17/75\n",
      "2000/2000 [==============================] - 0s 86us/step - loss: 3.2223e-15\n",
      "Epoch 18/75\n",
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      "Epoch 19/75\n",
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      "Epoch 20/75\n",
      "2000/2000 [==============================] - 0s 75us/step - loss: 3.3555e-15\n",
      "Epoch 21/75\n",
      "2000/2000 [==============================] - 0s 73us/step - loss: 3.7819e-15\n",
      "Epoch 22/75\n",
      "2000/2000 [==============================] - 0s 67us/step - loss: 6.4144e-15\n",
      "Epoch 23/75\n",
      "2000/2000 [==============================] - 0s 80us/step - loss: 3.6842e-15\n",
      "Epoch 24/75\n",
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      "Epoch 25/75\n",
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      "Epoch 26/75\n",
      "2000/2000 [==============================] - 0s 66us/step - loss: 4.0661e-15\n",
      "Epoch 27/75\n",
      "2000/2000 [==============================] - 0s 85us/step - loss: 4.7997e-15\n",
      "Epoch 28/75\n",
      "2000/2000 [==============================] - 0s 70us/step - loss: 3.6646e-15\n",
      "Epoch 29/75\n",
      "2000/2000 [==============================] - 0s 92us/step - loss: 5.6541e-15\n",
      "Epoch 30/75\n",
      "2000/2000 [==============================] - 0s 77us/step - loss: 6.4109e-15\n",
      "Epoch 31/75\n",
      "2000/2000 [==============================] - 0s 61us/step - loss: 1.2330e-14\n",
      "Epoch 32/75\n",
      "2000/2000 [==============================] - 0s 68us/step - loss: 6.8514e-15\n",
      "Epoch 33/75\n",
      "2000/2000 [==============================] - 0s 63us/step - loss: 3.9471e-15\n",
      "Epoch 34/75\n",
      "2000/2000 [==============================] - 0s 53us/step - loss: 8.8445e-15\n",
      "Epoch 35/75\n",
      "2000/2000 [==============================] - 0s 56us/step - loss: 5.9881e-15\n",
      "Epoch 36/75\n",
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      "Epoch 37/75\n",
      "2000/2000 [==============================] - 0s 62us/step - loss: 4.9933e-15\n",
      "Epoch 38/75\n",
      "2000/2000 [==============================] - 0s 61us/step - loss: 6.4677e-15\n",
      "Epoch 39/75\n",
      "2000/2000 [==============================] - 0s 57us/step - loss: 4.5777e-15\n",
      "Epoch 40/75\n",
      "2000/2000 [==============================] - 0s 57us/step - loss: 1.1791e-14\n",
      "Epoch 41/75\n",
      "2000/2000 [==============================] - 0s 52us/step - loss: 7.9474e-15\n",
      "Epoch 42/75\n",
      "2000/2000 [==============================] - 0s 61us/step - loss: 1.4374e-14\n",
      "Epoch 43/75\n",
      "2000/2000 [==============================] - 0s 59us/step - loss: 6.3185e-15\n",
      "Epoch 44/75\n",
      "2000/2000 [==============================] - 0s 59us/step - loss: 4.0110e-15\n",
      "Epoch 45/75\n",
      "2000/2000 [==============================] - 0s 62us/step - loss: 5.6044e-15\n",
      "Epoch 46/75\n",
      "2000/2000 [==============================] - 0s 53us/step - loss: 5.0733e-15\n",
      "Epoch 47/75\n",
      "2000/2000 [==============================] - 0s 51us/step - loss: 5.0182e-15\n",
      "Epoch 48/75\n",
      "2000/2000 [==============================] - 0s 46us/step - loss: 3.3875e-15\n",
      "Epoch 49/75\n",
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      "Epoch 50/75\n",
      "2000/2000 [==============================] - 0s 52us/step - loss: 5.7163e-15\n",
      "Epoch 51/75\n",
      "2000/2000 [==============================] - 0s 50us/step - loss: 3.7659e-15\n",
      "Epoch 52/75\n",
      "2000/2000 [==============================] - 0s 61us/step - loss: 1.0457e-14\n",
      "Epoch 53/75\n",
      "2000/2000 [==============================] - ETA: 0s - loss: 1.4302e-1 - 0s 61us/step - loss: 1.3296e-14\n",
      "Epoch 54/75\n",
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      "Epoch 55/75\n",
      "2000/2000 [==============================] - 0s 61us/step - loss: 7.1427e-15\n",
      "Epoch 56/75\n",
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      "Epoch 57/75\n",
      "2000/2000 [==============================] - 0s 51us/step - loss: 6.2368e-15\n",
      "Epoch 58/75\n",
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      "Epoch 59/75\n",
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      "Epoch 60/75\n",
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      "Epoch 61/75\n",
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      "Epoch 62/75\n",
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      "Epoch 63/75\n",
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      "Epoch 64/75\n",
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      "Epoch 65/75\n",
      "2000/2000 [==============================] - 0s 55us/step - loss: 7.3417e-15\n",
      "Epoch 66/75\n",
      "2000/2000 [==============================] - 0s 49us/step - loss: 7.2564e-15\n",
      "Epoch 67/75\n",
      "2000/2000 [==============================] - 0s 64us/step - loss: 1.5808e-14\n",
      "Epoch 68/75\n",
      "2000/2000 [==============================] - 0s 70us/step - loss: 8.7290e-15\n",
      "Epoch 69/75\n",
      "2000/2000 [==============================] - 0s 65us/step - loss: 5.1035e-15\n",
      "Epoch 70/75\n",
      "2000/2000 [==============================] - 0s 56us/step - loss: 8.0309e-15\n",
      "Epoch 71/75\n",
      "2000/2000 [==============================] - 0s 56us/step - loss: 1.3197e-14\n",
      "Epoch 72/75\n",
      "2000/2000 [==============================] - 0s 47us/step - loss: 1.8707e-14\n",
      "Epoch 73/75\n",
      "2000/2000 [==============================] - 0s 53us/step - loss: 1.7224e-14\n",
      "Epoch 74/75\n",
      "2000/2000 [==============================] - 0s 42us/step - loss: 3.6506e-14\n",
      "Epoch 75/75\n",
      "2000/2000 [==============================] - 0s 60us/step - loss: 8.4004e-15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x2048283ce08>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Convert2Feature(input):\n",
    "\toutput = [(np.real(i),np.imag(i)) for i in input]\n",
    "\treturn np.array(output).reshape(-1,2)\n",
    "\n",
    "def Convert2Complex(input):\n",
    "\treturn np.array([t[0]+1j*t[1] for t in np.array(input)])\n",
    "\n",
    "def Non_Linearity(input):\n",
    "\toutput = input-0.1*input*(np.abs(input)**2)\n",
    "\treturn output\n",
    "\n",
    "\n",
    "def Generate_Data():\n",
    "\tx_train=np.array([np.random.choice([-1,1]) for i in range(2000) ])\n",
    "\t#x_train=x_train.reshape(-1,2)\n",
    "\ty_train=Non_Linearity(x_train)\n",
    "\t\n",
    "\tx_test=np.array([np.random.choice([-1,1]) for i in range(2000) ])\n",
    "\t#x_test=x_test.reshape(-1,2)\n",
    "\ty_test=Non_Linearity(x_test)\n",
    "\n",
    "\treturn (x_train,y_train,x_test,y_test)\n",
    "\n",
    "x_train,y_train,x_test,y_test = Generate_Data()\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(units=2000, input_dim=1))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(units=100))\n",
    "model.add(Activation('tanh'))\n",
    "model.add(Dense(units=10))\n",
    "model.add(Activation('linear'))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "ans = model.compile(loss='mean_squared_error',\n",
    "              optimizer='adam')\n",
    "model.fit(y_train, x_train, epochs=75, batch_size=100, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2880x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = model.predict(x_test, batch_size=1)\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(x_test, Non_Linearity(classes),c='r',label = 'Compensated PA Output(w)',linewidth = 4)\n",
    "plt.plot(x_test,x_test,c='b',label = 'Data Input(x)')\n",
    "plt.plot(x_test,Non_Linearity(x_test),c='k',label = 'PA Output(y)')\n",
    "plt.xlabel('Data Input')\n",
    "plt.legend()\n",
    "\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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}