{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kerasを使った関数近似のサンプルコード\n",
    "- sin関数に従って作ったランダムデータから,sin関数をニューラルネットで学習します."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学習に使うsin関数に従ったランダムデータの作成\n",
    "- 学習データ 800件, テストデータ 200件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X = np.random.random(1000) * 10 - 5\n",
    "Y = np.sin(X)\n",
    "\n",
    "training_num = 800\n",
    "idx = np.arange(1000)\n",
    "np.random.shuffle(idx)\n",
    "training_idx = idx[:training_num]\n",
    "test_idx = idx[training_num:]\n",
    "\n",
    "trainingX, testX = X[training_idx], X[test_idx]\n",
    "trainingY, testY = np.sin(trainingX), np.sin(testX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sin関数を学習するためのニューラルネットワークの形状を定義"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "dense_4 (Dense)                  (None, 30)            60          dense_input_2[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "activation_3 (Activation)        (None, 30)            0           dense_4[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_5 (Dense)                  (None, 30)            930         activation_3[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "activation_4 (Activation)        (None, 30)            0           dense_5[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_6 (Dense)                  (None, 1)             31          activation_4[0][0]               \n",
      "====================================================================================================\n",
      "Total params: 1021\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(30, input_shape=(1,)))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "model.add(Dense(30))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "model.add(Dense(1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学習データを使って,sin関数を学習\n",
    "- ミニバッチ確率的勾配降下法を**100iteration**回して,どの程度学習できるか試してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 720 samples, validate on 80 samples\n",
      "Epoch 1/100\n",
      "0s - loss: 1.0418 - val_loss: 0.7496\n",
      "Epoch 2/100\n",
      "0s - loss: 0.5749 - val_loss: 0.6715\n",
      "Epoch 3/100\n",
      "0s - loss: 0.5439 - val_loss: 0.6627\n",
      "Epoch 4/100\n",
      "0s - loss: 0.5405 - val_loss: 0.6615\n",
      "Epoch 5/100\n",
      "0s - loss: 0.5395 - val_loss: 0.6593\n",
      "Epoch 6/100\n",
      "0s - loss: 0.5387 - val_loss: 0.6575\n",
      "Epoch 7/100\n",
      "0s - loss: 0.5387 - val_loss: 0.6570\n",
      "Epoch 8/100\n",
      "0s - loss: 0.5374 - val_loss: 0.6584\n",
      "Epoch 9/100\n",
      "0s - loss: 0.5363 - val_loss: 0.6572\n",
      "Epoch 10/100\n",
      "0s - loss: 0.5352 - val_loss: 0.6563\n",
      "Epoch 11/100\n",
      "0s - loss: 0.5346 - val_loss: 0.6561\n",
      "Epoch 12/100\n",
      "0s - loss: 0.5337 - val_loss: 0.6550\n",
      "Epoch 13/100\n",
      "0s - loss: 0.5336 - val_loss: 0.6540\n",
      "Epoch 14/100\n",
      "0s - loss: 0.5333 - val_loss: 0.6532\n",
      "Epoch 15/100\n",
      "0s - loss: 0.5320 - val_loss: 0.6530\n",
      "Epoch 16/100\n",
      "0s - loss: 0.5310 - val_loss: 0.6531\n",
      "Epoch 17/100\n",
      "0s - loss: 0.5305 - val_loss: 0.6533\n",
      "Epoch 18/100\n",
      "0s - loss: 0.5301 - val_loss: 0.6512\n",
      "Epoch 19/100\n",
      "0s - loss: 0.5297 - val_loss: 0.6515\n",
      "Epoch 20/100\n",
      "0s - loss: 0.5292 - val_loss: 0.6508\n",
      "Epoch 21/100\n",
      "0s - loss: 0.5286 - val_loss: 0.6516\n",
      "Epoch 22/100\n",
      "0s - loss: 0.5281 - val_loss: 0.6517\n",
      "Epoch 23/100\n",
      "0s - loss: 0.5294 - val_loss: 0.6501\n",
      "Epoch 24/100\n",
      "0s - loss: 0.5277 - val_loss: 0.6493\n",
      "Epoch 25/100\n",
      "0s - loss: 0.5277 - val_loss: 0.6505\n",
      "Epoch 26/100\n",
      "0s - loss: 0.5265 - val_loss: 0.6489\n",
      "Epoch 27/100\n",
      "0s - loss: 0.5258 - val_loss: 0.6500\n",
      "Epoch 28/100\n",
      "0s - loss: 0.5256 - val_loss: 0.6491\n",
      "Epoch 29/100\n",
      "0s - loss: 0.5249 - val_loss: 0.6497\n",
      "Epoch 30/100\n",
      "0s - loss: 0.5247 - val_loss: 0.6485\n",
      "Epoch 31/100\n",
      "0s - loss: 0.5246 - val_loss: 0.6474\n",
      "Epoch 32/100\n",
      "0s - loss: 0.5246 - val_loss: 0.6474\n",
      "Epoch 33/100\n",
      "0s - loss: 0.5240 - val_loss: 0.6476\n",
      "Epoch 34/100\n",
      "0s - loss: 0.5238 - val_loss: 0.6471\n",
      "Epoch 35/100\n",
      "0s - loss: 0.5232 - val_loss: 0.6468\n",
      "Epoch 36/100\n",
      "0s - loss: 0.5233 - val_loss: 0.6455\n",
      "Epoch 37/100\n",
      "0s - loss: 0.5224 - val_loss: 0.6458\n",
      "Epoch 38/100\n",
      "0s - loss: 0.5227 - val_loss: 0.6460\n",
      "Epoch 39/100\n",
      "0s - loss: 0.5232 - val_loss: 0.6444\n",
      "Epoch 40/100\n",
      "0s - loss: 0.5225 - val_loss: 0.6447\n",
      "Epoch 41/100\n",
      "0s - loss: 0.5219 - val_loss: 0.6463\n",
      "Epoch 42/100\n",
      "0s - loss: 0.5212 - val_loss: 0.6481\n",
      "Epoch 43/100\n",
      "0s - loss: 0.5213 - val_loss: 0.6470\n",
      "Epoch 44/100\n",
      "0s - loss: 0.5204 - val_loss: 0.6451\n",
      "Epoch 45/100\n",
      "0s - loss: 0.5201 - val_loss: 0.6450\n",
      "Epoch 46/100\n",
      "0s - loss: 0.5200 - val_loss: 0.6437\n",
      "Epoch 47/100\n",
      "0s - loss: 0.5201 - val_loss: 0.6441\n",
      "Epoch 48/100\n",
      "0s - loss: 0.5205 - val_loss: 0.6444\n",
      "Epoch 49/100\n",
      "0s - loss: 0.5199 - val_loss: 0.6437\n",
      "Epoch 50/100\n",
      "0s - loss: 0.5190 - val_loss: 0.6452\n",
      "Epoch 51/100\n",
      "0s - loss: 0.5191 - val_loss: 0.6444\n",
      "Epoch 52/100\n",
      "0s - loss: 0.5203 - val_loss: 0.6457\n",
      "Epoch 53/100\n",
      "0s - loss: 0.5189 - val_loss: 0.6425\n",
      "Epoch 54/100\n",
      "0s - loss: 0.5196 - val_loss: 0.6428\n",
      "Epoch 55/100\n",
      "0s - loss: 0.5189 - val_loss: 0.6437\n",
      "Epoch 56/100\n",
      "0s - loss: 0.5184 - val_loss: 0.6431\n",
      "Epoch 57/100\n",
      "0s - loss: 0.5198 - val_loss: 0.6425\n",
      "Epoch 58/100\n",
      "0s - loss: 0.5180 - val_loss: 0.6425\n",
      "Epoch 59/100\n",
      "0s - loss: 0.5178 - val_loss: 0.6429\n",
      "Epoch 60/100\n",
      "0s - loss: 0.5177 - val_loss: 0.6426\n",
      "Epoch 61/100\n",
      "0s - loss: 0.5174 - val_loss: 0.6427\n",
      "Epoch 62/100\n",
      "0s - loss: 0.5172 - val_loss: 0.6436\n",
      "Epoch 63/100\n",
      "0s - loss: 0.5171 - val_loss: 0.6432\n",
      "Epoch 64/100\n",
      "0s - loss: 0.5169 - val_loss: 0.6415\n",
      "Epoch 65/100\n",
      "0s - loss: 0.5176 - val_loss: 0.6416\n",
      "Epoch 66/100\n",
      "0s - loss: 0.5172 - val_loss: 0.6426\n",
      "Epoch 67/100\n",
      "0s - loss: 0.5165 - val_loss: 0.6426\n",
      "Epoch 68/100\n",
      "0s - loss: 0.5167 - val_loss: 0.6417\n",
      "Epoch 69/100\n",
      "0s - loss: 0.5165 - val_loss: 0.6419\n",
      "Epoch 70/100\n",
      "0s - loss: 0.5168 - val_loss: 0.6409\n",
      "Epoch 71/100\n",
      "0s - loss: 0.5167 - val_loss: 0.6410\n",
      "Epoch 72/100\n",
      "0s - loss: 0.5161 - val_loss: 0.6433\n",
      "Epoch 73/100\n",
      "0s - loss: 0.5162 - val_loss: 0.6414\n",
      "Epoch 74/100\n",
      "0s - loss: 0.5157 - val_loss: 0.6419\n",
      "Epoch 75/100\n",
      "0s - loss: 0.5161 - val_loss: 0.6423\n",
      "Epoch 76/100\n",
      "0s - loss: 0.5155 - val_loss: 0.6428\n",
      "Epoch 77/100\n",
      "0s - loss: 0.5158 - val_loss: 0.6442\n",
      "Epoch 78/100\n",
      "0s - loss: 0.5163 - val_loss: 0.6426\n",
      "Epoch 79/100\n",
      "0s - loss: 0.5156 - val_loss: 0.6429\n",
      "Epoch 80/100\n",
      "0s - loss: 0.5152 - val_loss: 0.6409\n",
      "Epoch 81/100\n",
      "0s - loss: 0.5153 - val_loss: 0.6421\n",
      "Epoch 82/100\n",
      "0s - loss: 0.5153 - val_loss: 0.6437\n",
      "Epoch 83/100\n",
      "0s - loss: 0.5156 - val_loss: 0.6414\n",
      "Epoch 84/100\n",
      "0s - loss: 0.5149 - val_loss: 0.6414\n",
      "Epoch 85/100\n",
      "0s - loss: 0.5151 - val_loss: 0.6427\n",
      "Epoch 86/100\n",
      "0s - loss: 0.5157 - val_loss: 0.6410\n",
      "Epoch 87/100\n",
      "0s - loss: 0.5154 - val_loss: 0.6404\n",
      "Epoch 88/100\n",
      "0s - loss: 0.5158 - val_loss: 0.6413\n",
      "Epoch 89/100\n",
      "0s - loss: 0.5144 - val_loss: 0.6414\n",
      "Epoch 90/100\n",
      "0s - loss: 0.5155 - val_loss: 0.6413\n",
      "Epoch 91/100\n",
      "0s - loss: 0.5164 - val_loss: 0.6416\n",
      "Epoch 92/100\n",
      "0s - loss: 0.5149 - val_loss: 0.6403\n",
      "Epoch 93/100\n",
      "0s - loss: 0.5149 - val_loss: 0.6399\n",
      "Epoch 94/100\n",
      "0s - loss: 0.5147 - val_loss: 0.6397\n",
      "Epoch 95/100\n",
      "0s - loss: 0.5143 - val_loss: 0.6427\n",
      "Epoch 96/100\n",
      "0s - loss: 0.5150 - val_loss: 0.6420\n",
      "Epoch 97/100\n",
      "0s - loss: 0.5141 - val_loss: 0.6414\n",
      "Epoch 98/100\n",
      "0s - loss: 0.5148 - val_loss: 0.6427\n",
      "Epoch 99/100\n",
      "0s - loss: 0.5139 - val_loss: 0.6424\n",
      "Epoch 100/100\n",
      "0s - loss: 0.5149 - val_loss: 0.6416\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
    "history = model.fit(trainingX, trainingY, batch_size=128, nb_epoch=100, validation_split=0.1, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ニューラルネットワークがsin関数をどの程度学習できたか確認してみる\n",
    "- 左図 : 学習中の予測誤差の変化をプロットしたもの (x軸: iteration, y軸: 予測誤差)\n",
    "- 右図 : ニューラルネットが学習した関数の形状 (赤: 正しいsin関数, 青: ニューラルネットの学習した関数)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10eb04910>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8lVy8dYUjbq4fgHgcfuRHzL8CZqGUxzPPuFy9CpmMWQ0bixkLftTbzungMAfE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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ea172d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictY = model.predict(testX)\n",
    "plt.subplot(121)\n",
    "plt.plot(np.arange(len(history.history[\"loss\"])), history.history[\"loss\"],  color=\"r\", alpha=0.3, label=\"loss\")\n",
    "plt.plot(np.arange(len(history.history[\"val_loss\"])), history.history[\"val_loss\"], color=\"b\", alpha=0.3, label=\"val_loss\")\n",
    "plt.subplot(122)\n",
    "plt.plot(testX, predictY, \"bo\", alpha=0.3)\n",
    "plt.plot(testX, testY, \"ro\", alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学習データを使って,sin関数を学習\n",
    "- ミニバッチ確率的勾配降下法を**さらに1000 iteration**回して,どの程度学習できるか試してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 720 samples, validate on 80 samples\n",
      "Epoch 1/1000\n",
      "0s - loss: 0.5141 - val_loss: 0.6403\n",
      "Epoch 2/1000\n",
      "0s - loss: 0.5139 - val_loss: 0.6405\n",
      "Epoch 3/1000\n",
      "0s - loss: 0.5139 - val_loss: 0.6416\n",
      "Epoch 4/1000\n",
      "0s - loss: 0.5140 - val_loss: 0.6401\n",
      "Epoch 5/1000\n",
      "0s - loss: 0.5141 - val_loss: 0.6396\n",
      "Epoch 6/1000\n",
      "0s - loss: 0.5146 - val_loss: 0.6402\n",
      "Epoch 7/1000\n",
      "0s - loss: 0.5139 - val_loss: 0.6419\n",
      "Epoch 8/1000\n",
      "0s - loss: 0.5135 - val_loss: 0.6415\n",
      "Epoch 9/1000\n",
      "0s - loss: 0.5140 - val_loss: 0.6414\n",
      "Epoch 10/1000\n",
      "0s - loss: 0.5136 - val_loss: 0.6425\n",
      "Epoch 11/1000\n",
      "0s - loss: 0.5137 - val_loss: 0.6428\n",
      "Epoch 12/1000\n",
      "0s - loss: 0.5136 - val_loss: 0.6417\n",
      "Epoch 13/1000\n",
      "0s - loss: 0.5141 - val_loss: 0.6407\n",
      "Epoch 14/1000\n",
      "0s - loss: 0.5132 - val_loss: 0.6402\n",
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      "0s - loss: 0.0830 - val_loss: 0.1067\n",
      "Epoch 890/1000\n",
      "0s - loss: 0.0825 - val_loss: 0.1056\n",
      "Epoch 891/1000\n",
      "0s - loss: 0.0819 - val_loss: 0.1043\n",
      "Epoch 892/1000\n",
      "0s - loss: 0.0814 - val_loss: 0.1034\n",
      "Epoch 893/1000\n",
      "0s - loss: 0.0805 - val_loss: 0.1028\n",
      "Epoch 894/1000\n",
      "0s - loss: 0.0797 - val_loss: 0.1018\n",
      "Epoch 895/1000\n",
      "0s - loss: 0.0790 - val_loss: 0.1009\n",
      "Epoch 896/1000\n",
      "0s - loss: 0.0785 - val_loss: 0.1007\n",
      "Epoch 897/1000\n",
      "0s - loss: 0.0779 - val_loss: 0.0995\n",
      "Epoch 898/1000\n",
      "0s - loss: 0.0771 - val_loss: 0.0987\n",
      "Epoch 899/1000\n",
      "0s - loss: 0.0764 - val_loss: 0.0980\n",
      "Epoch 900/1000\n",
      "0s - loss: 0.0761 - val_loss: 0.0974\n",
      "Epoch 901/1000\n",
      "0s - loss: 0.0752 - val_loss: 0.0961\n",
      "Epoch 902/1000\n",
      "0s - loss: 0.0746 - val_loss: 0.0953\n",
      "Epoch 903/1000\n",
      "0s - loss: 0.0740 - val_loss: 0.0945\n",
      "Epoch 904/1000\n",
      "0s - loss: 0.0732 - val_loss: 0.0937\n",
      "Epoch 905/1000\n",
      "0s - loss: 0.0729 - val_loss: 0.0929\n",
      "Epoch 906/1000\n",
      "0s - loss: 0.0721 - val_loss: 0.0921\n",
      "Epoch 907/1000\n",
      "0s - loss: 0.0716 - val_loss: 0.0914\n",
      "Epoch 908/1000\n",
      "0s - loss: 0.0709 - val_loss: 0.0909\n",
      "Epoch 909/1000\n",
      "0s - loss: 0.0704 - val_loss: 0.0900\n",
      "Epoch 910/1000\n",
      "0s - loss: 0.0698 - val_loss: 0.0889\n",
      "Epoch 911/1000\n",
      "0s - loss: 0.0692 - val_loss: 0.0884\n",
      "Epoch 912/1000\n",
      "0s - loss: 0.0688 - val_loss: 0.0875\n",
      "Epoch 913/1000\n",
      "0s - loss: 0.0681 - val_loss: 0.0868\n",
      "Epoch 914/1000\n",
      "0s - loss: 0.0675 - val_loss: 0.0864\n",
      "Epoch 915/1000\n",
      "0s - loss: 0.0670 - val_loss: 0.0854\n",
      "Epoch 916/1000\n",
      "0s - loss: 0.0665 - val_loss: 0.0849\n",
      "Epoch 917/1000\n",
      "0s - loss: 0.0658 - val_loss: 0.0843\n",
      "Epoch 918/1000\n",
      "0s - loss: 0.0653 - val_loss: 0.0832\n",
      "Epoch 919/1000\n",
      "0s - loss: 0.0648 - val_loss: 0.0825\n",
      "Epoch 920/1000\n",
      "0s - loss: 0.0642 - val_loss: 0.0820\n",
      "Epoch 921/1000\n",
      "0s - loss: 0.0638 - val_loss: 0.0815\n",
      "Epoch 922/1000\n",
      "0s - loss: 0.0634 - val_loss: 0.0807\n",
      "Epoch 923/1000\n",
      "0s - loss: 0.0626 - val_loss: 0.0798\n",
      "Epoch 924/1000\n",
      "0s - loss: 0.0622 - val_loss: 0.0792\n",
      "Epoch 925/1000\n",
      "0s - loss: 0.0622 - val_loss: 0.0786\n",
      "Epoch 926/1000\n",
      "0s - loss: 0.0613 - val_loss: 0.0781\n",
      "Epoch 927/1000\n",
      "0s - loss: 0.0607 - val_loss: 0.0779\n",
      "Epoch 928/1000\n",
      "0s - loss: 0.0603 - val_loss: 0.0769\n",
      "Epoch 929/1000\n",
      "0s - loss: 0.0597 - val_loss: 0.0760\n",
      "Epoch 930/1000\n",
      "0s - loss: 0.0593 - val_loss: 0.0754\n",
      "Epoch 931/1000\n",
      "0s - loss: 0.0588 - val_loss: 0.0753\n",
      "Epoch 932/1000\n",
      "0s - loss: 0.0583 - val_loss: 0.0742\n",
      "Epoch 933/1000\n",
      "0s - loss: 0.0578 - val_loss: 0.0736\n",
      "Epoch 934/1000\n",
      "0s - loss: 0.0574 - val_loss: 0.0732\n",
      "Epoch 935/1000\n",
      "0s - loss: 0.0571 - val_loss: 0.0724\n",
      "Epoch 936/1000\n",
      "0s - loss: 0.0565 - val_loss: 0.0719\n",
      "Epoch 937/1000\n",
      "0s - loss: 0.0561 - val_loss: 0.0712\n",
      "Epoch 938/1000\n",
      "0s - loss: 0.0557 - val_loss: 0.0709\n",
      "Epoch 939/1000\n",
      "0s - loss: 0.0553 - val_loss: 0.0703\n",
      "Epoch 940/1000\n",
      "0s - loss: 0.0547 - val_loss: 0.0701\n",
      "Epoch 941/1000\n",
      "0s - loss: 0.0544 - val_loss: 0.0692\n",
      "Epoch 942/1000\n",
      "0s - loss: 0.0539 - val_loss: 0.0684\n",
      "Epoch 943/1000\n",
      "0s - loss: 0.0535 - val_loss: 0.0679\n",
      "Epoch 944/1000\n",
      "0s - loss: 0.0531 - val_loss: 0.0675\n",
      "Epoch 945/1000\n",
      "0s - loss: 0.0527 - val_loss: 0.0668\n",
      "Epoch 946/1000\n",
      "0s - loss: 0.0523 - val_loss: 0.0663\n",
      "Epoch 947/1000\n",
      "0s - loss: 0.0518 - val_loss: 0.0661\n",
      "Epoch 948/1000\n",
      "0s - loss: 0.0515 - val_loss: 0.0655\n",
      "Epoch 949/1000\n",
      "0s - loss: 0.0512 - val_loss: 0.0648\n",
      "Epoch 950/1000\n",
      "0s - loss: 0.0507 - val_loss: 0.0643\n",
      "Epoch 951/1000\n",
      "0s - loss: 0.0503 - val_loss: 0.0637\n",
      "Epoch 952/1000\n",
      "0s - loss: 0.0499 - val_loss: 0.0632\n",
      "Epoch 953/1000\n",
      "0s - loss: 0.0495 - val_loss: 0.0627\n",
      "Epoch 954/1000\n",
      "0s - loss: 0.0493 - val_loss: 0.0623\n",
      "Epoch 955/1000\n",
      "0s - loss: 0.0489 - val_loss: 0.0622\n",
      "Epoch 956/1000\n",
      "0s - loss: 0.0487 - val_loss: 0.0614\n",
      "Epoch 957/1000\n",
      "0s - loss: 0.0482 - val_loss: 0.0608\n",
      "Epoch 958/1000\n",
      "0s - loss: 0.0480 - val_loss: 0.0604\n",
      "Epoch 959/1000\n",
      "0s - loss: 0.0474 - val_loss: 0.0599\n",
      "Epoch 960/1000\n",
      "0s - loss: 0.0471 - val_loss: 0.0596\n",
      "Epoch 961/1000\n",
      "0s - loss: 0.0468 - val_loss: 0.0591\n",
      "Epoch 962/1000\n",
      "0s - loss: 0.0465 - val_loss: 0.0588\n",
      "Epoch 963/1000\n",
      "0s - loss: 0.0461 - val_loss: 0.0581\n",
      "Epoch 964/1000\n",
      "0s - loss: 0.0457 - val_loss: 0.0577\n",
      "Epoch 965/1000\n",
      "0s - loss: 0.0455 - val_loss: 0.0572\n",
      "Epoch 966/1000\n",
      "0s - loss: 0.0451 - val_loss: 0.0568\n",
      "Epoch 967/1000\n",
      "0s - loss: 0.0448 - val_loss: 0.0564\n",
      "Epoch 968/1000\n",
      "0s - loss: 0.0446 - val_loss: 0.0559\n",
      "Epoch 969/1000\n",
      "0s - loss: 0.0442 - val_loss: 0.0556\n",
      "Epoch 970/1000\n",
      "0s - loss: 0.0439 - val_loss: 0.0553\n",
      "Epoch 971/1000\n",
      "0s - loss: 0.0436 - val_loss: 0.0548\n",
      "Epoch 972/1000\n",
      "0s - loss: 0.0433 - val_loss: 0.0544\n",
      "Epoch 973/1000\n",
      "0s - loss: 0.0431 - val_loss: 0.0539\n",
      "Epoch 974/1000\n",
      "0s - loss: 0.0427 - val_loss: 0.0535\n",
      "Epoch 975/1000\n",
      "0s - loss: 0.0424 - val_loss: 0.0533\n",
      "Epoch 976/1000\n",
      "0s - loss: 0.0421 - val_loss: 0.0530\n",
      "Epoch 977/1000\n",
      "0s - loss: 0.0419 - val_loss: 0.0526\n",
      "Epoch 978/1000\n",
      "0s - loss: 0.0416 - val_loss: 0.0521\n",
      "Epoch 979/1000\n",
      "0s - loss: 0.0413 - val_loss: 0.0517\n",
      "Epoch 980/1000\n",
      "0s - loss: 0.0410 - val_loss: 0.0513\n",
      "Epoch 981/1000\n",
      "0s - loss: 0.0408 - val_loss: 0.0510\n",
      "Epoch 982/1000\n",
      "0s - loss: 0.0405 - val_loss: 0.0506\n",
      "Epoch 983/1000\n",
      "0s - loss: 0.0402 - val_loss: 0.0504\n",
      "Epoch 984/1000\n",
      "0s - loss: 0.0400 - val_loss: 0.0499\n",
      "Epoch 985/1000\n",
      "0s - loss: 0.0398 - val_loss: 0.0496\n",
      "Epoch 986/1000\n",
      "0s - loss: 0.0394 - val_loss: 0.0492\n",
      "Epoch 987/1000\n",
      "0s - loss: 0.0393 - val_loss: 0.0489\n",
      "Epoch 988/1000\n",
      "0s - loss: 0.0390 - val_loss: 0.0486\n",
      "Epoch 989/1000\n",
      "0s - loss: 0.0388 - val_loss: 0.0484\n",
      "Epoch 990/1000\n",
      "0s - loss: 0.0385 - val_loss: 0.0479\n",
      "Epoch 991/1000\n",
      "0s - loss: 0.0383 - val_loss: 0.0476\n",
      "Epoch 992/1000\n",
      "0s - loss: 0.0381 - val_loss: 0.0472\n",
      "Epoch 993/1000\n",
      "0s - loss: 0.0379 - val_loss: 0.0470\n",
      "Epoch 994/1000\n",
      "0s - loss: 0.0376 - val_loss: 0.0466\n",
      "Epoch 995/1000\n",
      "0s - loss: 0.0374 - val_loss: 0.0463\n",
      "Epoch 996/1000\n",
      "0s - loss: 0.0372 - val_loss: 0.0461\n",
      "Epoch 997/1000\n",
      "0s - loss: 0.0369 - val_loss: 0.0458\n",
      "Epoch 998/1000\n",
      "0s - loss: 0.0368 - val_loss: 0.0454\n",
      "Epoch 999/1000\n",
      "0s - loss: 0.0365 - val_loss: 0.0453\n",
      "Epoch 1000/1000\n",
      "0s - loss: 0.0363 - val_loss: 0.0448\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(trainingX, trainingY, batch_size=128, nb_epoch=1000, validation_split=0.1, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ニューラルネットワークがsin関数をどの程度学習できたか確認してみる\n",
    "- 左図 : 学習中の予測誤差の変化をプロットしたもの (x軸: iteration, y軸: 予測誤差)\n",
    "- 右図 : ニューラルネットが学習した関数の形状 (赤: 正しいsin関数, 青: ニューラルネットの学習した関数)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10ed8ddd0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7pmnuGvr11Oi6+ud2IwF5ZJd6b5YFPkH2i0Fcffd23E0sLqIPDqyJI9lHjyIr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\nxbKDsEbdYrFYdhD/H7jwoh+ZWD2SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb04890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictY = model.predict(testX)\n",
    "plt.subplot(121)\n",
    "plt.plot(np.arange(len(history.history[\"loss\"])), history.history[\"loss\"],  color=\"r\", alpha=0.3, label=\"loss\")\n",
    "plt.plot(np.arange(len(history.history[\"val_loss\"])), history.history[\"val_loss\"], color=\"b\", alpha=0.3, label=\"val_loss\")\n",
    "plt.subplot(122)\n",
    "plt.plot(testX, predictY, \"bo\", alpha=0.3)\n",
    "plt.plot(testX, testY, \"ro\", alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# まとめ\n",
    "- ニューラルネットでsin関数を学習した\n",
    "  - plateauと呼ばれる，学習の停滞を確認できた\n",
    "    - 400 iteration辺りまで，validation errorが0.65とほとんど学習が進んでいなかった\n",
    "    - 400 iterationすぎてから,急劇に validation errorが下がり始め,学習が進み始めた"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}