02_1_simple-RNN-diffrent-sequence-length.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">به نام خدا</div></center>\n",
    "<img src=\"./logo.png\" alt=\"class.vision\" style=\"width: 200px;\"/>\n",
    "<h1><center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">تغییر در طول بازه‌های زمانی ورودی شبکه</div></center></h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, SimpleRNN\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "t = np.arange(0,1500)\n",
    "x = np.sin(0.02*t)+ np.random.rand(1500) * 2\n",
    "\n",
    "train,test = x[0:1000], x[1000:]\n",
    "\n",
    "# convert into dataset data and label\n",
    "def convertToDataset(data, step):\n",
    "    #data = np.append(data,np.repeat(data[-1,],step))\n",
    "    X, Y =[], []\n",
    "    for i in range(len(data)-step):\n",
    "        d=i+step  \n",
    "        X.append(data[i:d,])\n",
    "        Y.append(data[d,])\n",
    "    return np.array(X), np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_step = 10\n",
    "test_step = 20\n",
    "\n",
    "trainX,trainY =convertToDataset(train,train_step)\n",
    "testX,testY =convertToDataset(test,test_step)\n",
    "\n",
    "trainX = np.reshape(trainX, (trainX.shape[0],  trainX.shape[1], 1))\n",
    "testX = np.reshape(testX, (testX.shape[0],testX.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(990, 10, 1)\n",
      "(480, 20, 1)\n"
     ]
    }
   ],
   "source": [
    "print(trainX.shape)\n",
    "print(testX.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">معماری شبکه و compile آن</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(SimpleRNN(units=64, input_shape=(None, 1),  activation=\"tanh\"))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='rmsprop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "simple_rnn_1 (SimpleRNN)     (None, 64)                4224      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,289\n",
      "Trainable params: 4,289\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'simple_rnn_1_input:0' shape=(?, ?, 1) dtype=float32>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">آموزش مدل</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "حالا با این پیاده سازی به نظر شما میتوانیم در طول آموزش هم طول timestep متفاوت داشته باشیم؟<br>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 4s - loss: 0.5067\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.4110\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.4095\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.3995\n",
      "Epoch 5/100\n",
      " - 1s - loss: 0.3934\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.3865\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.3920\n",
      "Epoch 8/100\n",
      " - 1s - loss: 0.3851\n",
      "Epoch 9/100\n",
      " - 1s - loss: 0.3858\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.3869\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.3804\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.3806\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.3736\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.3740\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.3662\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.3708\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.3668\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.3632\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.3697\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.3612\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.3619\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.3577\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.3577\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.3534\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.3537\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.3556\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.3499\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.3484\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.3455\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.3415\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.3393\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.3332\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.3361\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.3387\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.3236\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.3313\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.3276\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.3225\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.3239\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.3161\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.3075\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.3118\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.3085\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.3020\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.2969\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.2989\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.2919\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.2893\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.2869\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.2843\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.2818\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.2788\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.2698\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.2686\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.2606\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.2595\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.2569\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.2531\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.2535\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.2413\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.2381\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.2340\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.2337\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.2308\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.2165\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.2228\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.2148\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.2090\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.2065\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.2043\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.1964\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.1957\n",
      "Epoch 73/100\n",
      " - 1s - loss: 0.1896\n",
      "Epoch 74/100\n",
      " - 1s - loss: 0.1830\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.1827\n",
      "Epoch 76/100\n",
      " - 1s - loss: 0.1808\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.1748\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.1662\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.1674\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.1636\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.1580\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.1580\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.1476\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.1502\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.1431\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.1377\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.1369\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.1321\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.1286\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.1256\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.1229\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.1206\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.1167\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.1145\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.1071\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.1078\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.1038\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.1023\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0970\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0994\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\"> ارزیابی مدل</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07633800416281729\n"
     ]
    }
   ],
   "source": [
    "trainScore = model.evaluate(trainX, trainY, verbose=0)\n",
    "print(trainScore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\"> رسم سری اصلی و پیش بینی برای داده های آموزشی و تست </div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainPredict = model.predict(trainX)\n",
    "testPredict= model.predict(testX)\n",
    "predicted=np.concatenate((trainPredict,testPredict),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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"
    }
   ],
   "source": [
    "plt.plot(x)\n",
    "plt.plot(predicted)\n",
    "plt.axvline(len(trainX), c=\"r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\"> دوره پیشرفته یادگیری عمیق<br>علیرضا اخوان پور<br>  آبان و آذر 1399<br>\n",
    "</div>\n",
    "<a href=\"http://class.vision\">Class.Vision</a> - <a href=\"http://AkhavanPour.ir\">AkhavanPour.ir</a> - <a href=\"https://github.com/Alireza-Akhavan/\">GitHub</a>\n",
    "\n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}