deep-learning-from-scratch.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/oreilly-japan/deep-learning-from-scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(digits.data).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kot/miniconda2/envs/python3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import cross_validation as cv\n",
    "x_train, x_test, t_train, t_test = cv.train_test_split(digits.data, digits.target, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1437, 64)\n",
      "(1437,)\n",
      "(360, 64)\n",
      "(360,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(t_train.shape)\n",
    "print(x_test.shape)\n",
    "print(t_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def scale(X, eps = 0.001):\n",
    "    # scale the data points s.t the columns of the feature space\n",
    "    # (i.e the predictors) are within the range [0, 1]\n",
    "    return (X - np.min(X, axis = 0)) / (np.max(X, axis = 0) + eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_train2 = scale(x_train)\n",
    "x_test2 = scale(x_test)\n",
    "t_train2 = scale(t_train)\n",
    "t_test2 = scale(t_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kot/miniconda2/envs/python3/lib/python3.6/site-packages/matplotlib/font_manager.py:280: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  'Matplotlib is building the font cache using fc-list. '\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def img_show(img):\n",
    "    pil_img = Image.fromarray(np.uint8(img))\n",
    "    pil_img.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "img = x_train2[0]\n",
    "img = img.reshape(8,8)\n",
    "img_show(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shapeim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11712a350>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eBexJuYFB8nxrVsCzzZBJKcJhkpMrNM2QBsjEY7N7KueZcVAr/TPcR2/YxeA+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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116fd6d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(x_train[2].reshape((8, 8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1437, 64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def init_network():\n",
    "    with open(\"sample_weight.pkl\", 'rb') as f:\n",
    "        network = pickle.load(f)\n",
    "    return network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "__file__ = \".\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting train-images-idx3-ubyte.gz to NumPy Array ...\n",
      "Done\n",
      "Converting train-labels-idx1-ubyte.gz to NumPy Array ...\n",
      "Done\n",
      "Converting t10k-images-idx3-ubyte.gz to NumPy Array ...\n",
      "Done\n",
      "Converting t10k-labels-idx1-ubyte.gz to NumPy Array ...\n",
      "Done\n",
      "Creating pickle file ...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "#try:\n",
    "#    import urllib.request\n",
    "#except ImportError:\n",
    "#    raise ImportError('You should use Python 3.x')\n",
    "import os.path\n",
    "import gzip\n",
    "import pickle\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "url_base = 'http://yann.lecun.com/exdb/mnist/'\n",
    "key_file = {\n",
    "    'train_img':'train-images-idx3-ubyte.gz',\n",
    "    'train_label':'train-labels-idx1-ubyte.gz',\n",
    "    'test_img':'t10k-images-idx3-ubyte.gz',\n",
    "    'test_label':'t10k-labels-idx1-ubyte.gz'\n",
    "}\n",
    "\n",
    "dataset_dir = os.path.dirname(os.path.abspath(__file__))\n",
    "save_file = dataset_dir + \"/mnist.pkl\"\n",
    "\n",
    "train_num = 60000\n",
    "test_num = 10000\n",
    "img_dim = (1, 28, 28)\n",
    "img_size = 784\n",
    "\n",
    "\n",
    "def _download(file_name):\n",
    "    file_path = dataset_dir + \"/\" + file_name\n",
    "    \n",
    "    if os.path.exists(file_path):\n",
    "        return\n",
    "\n",
    "    print(\"Downloading \" + file_name + \" ... \")\n",
    "    urllib.request.urlretrieve(url_base + file_name, file_path)\n",
    "    print(\"Done\")\n",
    "    \n",
    "def download_mnist():\n",
    "    for v in key_file.values():\n",
    "       _download(v)\n",
    "        \n",
    "def _load_label(file_name):\n",
    "    file_path = dataset_dir + \"/\" + file_name\n",
    "    \n",
    "    print(\"Converting \" + file_name + \" to NumPy Array ...\")\n",
    "    with gzip.open(file_path, 'rb') as f:\n",
    "            labels = np.frombuffer(f.read(), np.uint8, offset=8)\n",
    "    print(\"Done\")\n",
    "    \n",
    "    return labels\n",
    "\n",
    "def _load_img(file_name):\n",
    "    file_path = dataset_dir + \"/\" + file_name\n",
    "    \n",
    "    print(\"Converting \" + file_name + \" to NumPy Array ...\")    \n",
    "    with gzip.open(file_path, 'rb') as f:\n",
    "            data = np.frombuffer(f.read(), np.uint8, offset=16)\n",
    "    data = data.reshape(-1, img_size)\n",
    "    print(\"Done\")\n",
    "    \n",
    "    return data\n",
    "    \n",
    "def _convert_numpy():\n",
    "    dataset = {}\n",
    "    dataset['train_img'] =  _load_img(key_file['train_img'])\n",
    "    dataset['train_label'] = _load_label(key_file['train_label'])    \n",
    "    dataset['test_img'] = _load_img(key_file['test_img'])\n",
    "    dataset['test_label'] = _load_label(key_file['test_label'])\n",
    "    \n",
    "    return dataset\n",
    "\n",
    "def init_mnist():\n",
    "    download_mnist()\n",
    "    dataset = _convert_numpy()\n",
    "    print(\"Creating pickle file ...\")\n",
    "    with open(save_file, 'wb') as f:\n",
    "        pickle.dump(dataset, f, -1)\n",
    "    print(\"Done!\")\n",
    "\n",
    "def _change_ont_hot_label(X):\n",
    "    T = np.zeros((X.size, 10))\n",
    "    for idx, row in enumerate(T):\n",
    "        row[X[idx]] = 1\n",
    "        \n",
    "    return T\n",
    "    \n",
    "\n",
    "def load_mnist(normalize=True, flatten=True, one_hot_label=False):\n",
    "    \"\"\"MNISTデータセットの読み込み\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    normalize : 画像のピクセル値を0.0~1.0に正規化する\n",
    "    one_hot_label : \n",
    "        one_hot_labelがTrueの場合、ラベルはone-hot配列として返す\n",
    "        one-hot配列とは、たとえば[0,0,1,0,0,0,0,0,0,0]のような配列\n",
    "    flatten : 画像を一次元配列に平にするかどうか \n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    (訓練画像, 訓練ラベル), (テスト画像, テストラベル)\n",
    "    \"\"\"\n",
    "    if not os.path.exists(save_file):\n",
    "        init_mnist()\n",
    "        \n",
    "    with open(save_file, 'rb') as f:\n",
    "        dataset = pickle.load(f)\n",
    "    \n",
    "    if normalize:\n",
    "        for key in ('train_img', 'test_img'):\n",
    "            dataset[key] = dataset[key].astype(np.float32)\n",
    "            dataset[key] /= 255.0\n",
    "            \n",
    "    if one_hot_label:\n",
    "        dataset['train_label'] = _change_ont_hot_label(dataset['train_label'])\n",
    "        dataset['test_label'] = _change_ont_hot_label(dataset['test_label'])    \n",
    "    \n",
    "    if not flatten:\n",
    "         for key in ('train_img', 'test_img'):\n",
    "            dataset[key] = dataset[key].reshape(-1, 1, 28, 28)\n",
    "\n",
    "    return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label']) \n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    init_mnist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(60000,)\n",
      "(10000, 784)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(t_train.shape)\n",
    "print(x_test.shape)\n",
    "print(t_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "(784,)\n",
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "\n",
    "\n",
    "def img_show(img):\n",
    "    pil_img = Image.fromarray(np.uint8(img))\n",
    "    pil_img.show()\n",
    "\n",
    "(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=False)\n",
    "\n",
    "img = x_train[0]\n",
    "label = t_train[0]\n",
    "print(label)  # 5\n",
    "\n",
    "print(img.shape)  # (784,)\n",
    "img = img.reshape(28, 28)  # 形状を元の画像サイズに変形\n",
    "print(img.shape)  # (28, 28)\n",
    "\n",
    "img_show(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# coding: utf-8\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def identity_function(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "def step_function(x):\n",
    "    return np.array(x > 0, dtype=np.int)\n",
    "\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))    \n",
    "\n",
    "\n",
    "def sigmoid_grad(x):\n",
    "    return (1.0 - sigmoid(x)) * sigmoid(x)\n",
    "    \n",
    "\n",
    "def relu(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "\n",
    "def relu_grad(x):\n",
    "    grad = np.zeros(x)\n",
    "    grad[x>=0] = 1\n",
    "    return grad\n",
    "    \n",
    "\n",
    "def softmax(x):\n",
    "    if x.ndim == 2:\n",
    "        x = x.T\n",
    "        x = x - np.max(x, axis=0)\n",
    "        y = np.exp(x) / np.sum(np.exp(x), axis=0)\n",
    "        return y.T \n",
    "\n",
    "    x = x - np.max(x) # オーバーフロー対策\n",
    "    return np.exp(x) / np.sum(np.exp(x))\n",
    "\n",
    "\n",
    "def mean_squared_error(y, t):\n",
    "    return 0.5 * np.sum((y-t)**2)\n",
    "\n",
    "\n",
    "def cross_entropy_error(y, t):\n",
    "    if y.ndim == 1:\n",
    "        t = t.reshape(1, t.size)\n",
    "        y = y.reshape(1, y.size)\n",
    "        \n",
    "    # 教師データがone-hot-vectorの場合、正解ラベルのインデックスに変換\n",
    "    if t.size == y.size:\n",
    "        t = t.argmax(axis=1)\n",
    "             \n",
    "    batch_size = y.shape[0]\n",
    "    return -np.sum(np.log(y[np.arange(batch_size), t])) / batch_size\n",
    "\n",
    "\n",
    "def softmax_loss(X, t):\n",
    "    y = softmax(X)\n",
    "    return cross_entropy_error(y, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:0.9352\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "#from common.functions import sigmoid, softmax\n",
    "\n",
    "def get_data():\n",
    "    (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False)\n",
    "    return x_test, t_test\n",
    "\n",
    "\n",
    "def init_network():\n",
    "    with open(\"sample_weight.pkl\", 'rb') as f:\n",
    "        network = pickle.load(f)\n",
    "    return network\n",
    "\n",
    "\n",
    "def predict(network, x):\n",
    "    W1, W2, W3 = network['W1'], network['W2'], network['W3']\n",
    "    b1, b2, b3 = network['b1'], network['b2'], network['b3']\n",
    "\n",
    "    a1 = np.dot(x, W1) + b1\n",
    "    z1 = sigmoid(a1)\n",
    "    a2 = np.dot(z1, W2) + b2\n",
    "    z2 = sigmoid(a2)\n",
    "    a3 = np.dot(z2, W3) + b3\n",
    "    y = softmax(a3)\n",
    "\n",
    "    return y\n",
    "\n",
    "\n",
    "x, t = get_data()\n",
    "network = init_network()\n",
    "accuracy_cnt = 0\n",
    "for i in range(len(x)):\n",
    "    y = predict(network, x[i])\n",
    "    p= np.argmax(y) # 最も確率の高い要素のインデックスを取得\n",
    "    if p == t[i]:\n",
    "        accuracy_cnt += 1\n",
    "\n",
    "print(\"Accuracy:\" + str(float(accuracy_cnt) / len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x, _ = get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "network = init_network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W1, W2, W3 = network['W1'], network['W2'], network['W3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 784)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784, 50)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 100)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:0.9352\n"
     ]
    }
   ],
   "source": [
    "x, t = get_data()\n",
    "network = init_network()\n",
    "batch_size = 100\n",
    "accuracy_cnt = 0\n",
    "for i in range(0, len(x), batch_size):\n",
    "    x_batch = x[i:i+batch_size]\n",
    "    y_batch = predict(network, x_batch)\n",
    "    p = np.argmax(y_batch, axis=1)\n",
    "    accuracy_cnt += np.sum(p == t[i:i+batch_size])\n",
    "print (\"Accuracy:\" + str(float(accuracy_cnt) / len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 1 0]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[0.1, 0.8, 0.1], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3], [0.8, 0.1, 0.1]])\n",
    "print (np.argmax(x, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True False  True]\n"
     ]
    }
   ],
   "source": [
    "y = np.array([1, 2, 1, 0])\n",
    "t = np.array([1, 2, 0, 0])\n",
    "print (y == t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y==t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(x_train, t_train), (x_test, t_test)= load_mnist(normalize=True, one_hot_label=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(t_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_size = x_train.shape[0]\n",
    "batch_size = 10\n",
    "batch_mask = np.random.choice(train_size, batch_size)\n",
    "x_batch = x_train[batch_mask]\n",
    "t_batch = t_train[batch_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14262, 57152, 12582, 53075, 17635, 26045, 43431, 58703, 29177, 48331])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#def cross_entropy_error(y, t):\n",
    "#    if y.ndim == 1:\n",
    "#        t = t.reshape(1, t.size)\n",
    "#        y = y.reshape(1, y.size)\n",
    "#    batch_size = y.shape[0]\n",
    "#    return -np.sum(t * np.log(y)) / batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gradient_descent(f, init_x, lr=0.01, step_num=100):\n",
    "    x = init_x\n",
    "    for i in range(step_num):\n",
    "        grad = numerical_gradient(f, x)\n",
    "        x -= lr * grad\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def numerical_gradient(f, x):\n",
    "    h = 1e-4\n",
    "    grad = np.zeros_like(x)\n",
    "    for idx in range(x.size):\n",
    "        tmp_val = x[idx]\n",
    "        x[idx] = tmp_val + h\n",
    "        fxh1 = f(x)\n",
    "        x[idx] = tmp_val - h\n",
    "        fxh2 = f(x)\n",
    "        grad[idx] = (fxh1 - fxh2) / (2*h)\n",
    "        x[idx] = tmp_val\n",
    "    return grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def function_2(x):\n",
    "    return x[0]**2 + x[1]**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -6.11110793e-10,   8.14814391e-10])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_x = np.array([-3.0, 4.0])\n",
    "gradient_descent(function_2, init_x=init_x, lr=0.1, step_num=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.58983747e+13,  -1.29524862e+12])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_x = np.array([-3.0, 4.0])\n",
    "gradient_descent(function_2, init_x=init_x, lr=10, step_num=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.99999994,  3.99999992])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_x = np.array([-3.0, 4.0])\n",
    "gradient_descent(function_2, init_x=init_x, lr=1e-10, step_num=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class simpleNet:\n",
    "    def __init__(self):\n",
    "        self.W = np.random.randn(2,3)\n",
    "\n",
    "    def predict(self, x):\n",
    "        return np.dot(x, self.W)\n",
    "\n",
    "    def loss(self, x, t):\n",
    "        z = self.predict(x)\n",
    "        y = softmax(z)\n",
    "        loss = cross_entropy_error(y, t)\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net = simpleNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.58865965  2.01375848  1.74139982]\n",
      " [-0.91928797  0.07140906 -1.07484407]]\n"
     ]
    }
   ],
   "source": [
    "print(net.W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.array([0.6, 0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = net.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.18055496  1.27252325  0.07748023]\n"
     ]
    }
   ],
   "source": [
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = np.array([0, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5234249725309608"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.loss(x, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(W):\n",
    "    return net.loss(x, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dW = #numerical_gradient(f, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.05177969  0.83503943]\n"
     ]
    }
   ],
   "source": [
    "#print(dW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def numerical_gradient(f, X):\n",
    "    if X.ndim == 1:\n",
    "        return _numerical_gradient_no_batch(f, X)\n",
    "    else:\n",
    "        grad = np.zeros_like(X)\n",
    "        \n",
    "        for idx, x in enumerate(X):\n",
    "            grad[idx] = _numerical_gradient_no_batch(f, x)\n",
    "        \n",
    "        return grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _numerical_gradient_no_batch(f, x):\n",
    "    h = 1e-4 # 0.0001\n",
    "    grad = np.zeros_like(x)\n",
    "    \n",
    "    for idx in range(x.size):\n",
    "        tmp_val = x[idx]\n",
    "        x[idx] = float(tmp_val) + h\n",
    "        fxh1 = f(x) # f(x+h)\n",
    "        \n",
    "        x[idx] = tmp_val - h \n",
    "        fxh2 = f(x) # f(x-h)\n",
    "        grad[idx] = (fxh1 - fxh2) / (2*h)\n",
    "        \n",
    "        x[idx] = tmp_val # 値を元に戻す\n",
    "        \n",
    "    return grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dW = numerical_gradient(f, net.W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.03716879  0.43205276 -0.46922155]\n",
      " [ 0.05575319  0.64807913 -0.70383232]]\n"
     ]
    }
   ],
   "source": [
    "print(dW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = lambda w: net.loss(x, t)\n",
    "dW = numerical_gradient(f, net.W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03716879,  0.43205276, -0.46922155],\n",
       "       [ 0.05575319,  0.64807913, -0.70383232]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class TwoLayerNet:\n",
    "\n",
    "    def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):\n",
    "        # 重みの初期化\n",
    "        self.params = {}\n",
    "        self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)\n",
    "        self.params['b1'] = np.zeros(hidden_size)\n",
    "        self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)\n",
    "        self.params['b2'] = np.zeros(output_size)\n",
    "\n",
    "    def predict(self, x):\n",
    "        W1, W2 = self.params['W1'], self.params['W2']\n",
    "        b1, b2 = self.params['b1'], self.params['b2']\n",
    "    \n",
    "        a1 = np.dot(x, W1) + b1\n",
    "        z1 = sigmoid(a1)\n",
    "        a2 = np.dot(z1, W2) + b2\n",
    "        y = softmax(a2)\n",
    "        \n",
    "        return y\n",
    "        \n",
    "    # x:入力データ, t:教師データ\n",
    "    def loss(self, x, t):\n",
    "        y = self.predict(x)\n",
    "        \n",
    "        return cross_entropy_error(y, t)\n",
    "    \n",
    "    def accuracy(self, x, t):\n",
    "        y = self.predict(x)\n",
    "        y = np.argmax(y, axis=1)\n",
    "        t = np.argmax(t, axis=1)\n",
    "        \n",
    "        accuracy = np.sum(y == t) / float(x.shape[0])\n",
    "        return accuracy\n",
    "        \n",
    "    # x:入力データ, t:教師データ\n",
    "    def numerical_gradient(self, x, t):\n",
    "        loss_W = lambda W: self.loss(x, t)\n",
    "        \n",
    "        grads = {}\n",
    "        grads['W1'] = numerical_gradient(loss_W, self.params['W1'])\n",
    "        grads['b1'] = numerical_gradient(loss_W, self.params['b1'])\n",
    "        grads['W2'] = numerical_gradient(loss_W, self.params['W2'])\n",
    "        grads['b2'] = numerical_gradient(loss_W, self.params['b2'])\n",
    "        \n",
    "        return grads\n",
    "        \n",
    "    def gradient(self, x, t):\n",
    "        W1, W2 = self.params['W1'], self.params['W2']\n",
    "        b1, b2 = self.params['b1'], self.params['b2']\n",
    "        grads = {}\n",
    "        \n",
    "        batch_num = x.shape[0]\n",
    "        \n",
    "        # forward\n",
    "        a1 = np.dot(x, W1) + b1\n",
    "        z1 = sigmoid(a1)\n",
    "        a2 = np.dot(z1, W2) + b2\n",
    "        y = softmax(a2)\n",
    "        \n",
    "        # backward\n",
    "        dy = (y - t) / batch_num\n",
    "        grads['W2'] = np.dot(z1.T, dy)\n",
    "        grads['b2'] = np.sum(dy, axis=0)\n",
    "        \n",
    "        da1 = np.dot(dy, W2.T)\n",
    "        dz1 = sigmoid_grad(a1) * da1\n",
    "        grads['W1'] = np.dot(x.T, dz1)\n",
    "        grads['b1'] = np.sum(dz1, axis=0)\n",
    "\n",
    "        return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net = TwoLayerNet(input_size=784, hidden_size=100, output_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784, 100)\n",
      "(100,)\n",
      "(100, 10)\n",
      "(10,)\n"
     ]
    }
   ],
   "source": [
    "print(net.params['W1'].shape)\n",
    "print(net.params['b1'].shape)\n",
    "print(net.params['W2'].shape)\n",
    "print(net.params['b2'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.random.rand(100, 784)\n",
    "y = net.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.random.rand(100, 784)\n",
    "t = np.random.rand(100, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Feb  1 15:40:30 JST 2017\n",
      "Wed Feb  1 15:43:16 JST 2017\n"
     ]
    }
   ],
   "source": [
    "!date\n",
    "grads = net.numerical_gradient(x, t)\n",
    "!date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'W1': array([[  2.67369611e-04,  -7.75889930e-05,   6.15602689e-04, ...,\n",
       "           1.73279897e-04,   4.78752291e-04,   6.94256137e-04],\n",
       "        [  3.84986976e-04,  -1.69142540e-04,   3.41504851e-04, ...,\n",
       "           2.58689967e-04,   2.58881654e-04,   2.32389707e-04],\n",
       "        [  4.54322020e-04,  -8.04876166e-05,   4.28592681e-04, ...,\n",
       "           2.46636551e-04,   4.09743581e-04,   3.52666802e-04],\n",
       "        ..., \n",
       "        [  4.59333496e-04,  -1.17816850e-04,   4.81431774e-04, ...,\n",
       "           2.26373862e-04,   5.49254056e-04,   4.90443135e-04],\n",
       "        [  2.42431257e-04,   7.76675613e-06,   5.13711722e-04, ...,\n",
       "           1.73029049e-04,   4.04340268e-04,   3.92859683e-04],\n",
       "        [  3.89662729e-04,  -2.30543939e-05,   4.59559706e-04, ...,\n",
       "           2.18205320e-04,   3.01693817e-04,   4.77104054e-04]]),\n",
       " 'W2': array([[-0.03198304,  0.02717815, -0.02981612,  0.00681504,  0.0089925 ,\n",
       "         -0.005349  , -0.01642694, -0.03256691,  0.04997319,  0.02318311],\n",
       "        [-0.03119389,  0.02721471, -0.03085734,  0.00642146,  0.00986938,\n",
       "         -0.00491048, -0.01702456, -0.03054009,  0.04743485,  0.02358596],\n",
       "        [-0.03267288,  0.0283553 , -0.03261903,  0.0062267 ,  0.01058678,\n",
       "         -0.00559903, -0.01814501, -0.03167549,  0.05122607,  0.02431658],\n",
       "        [-0.03357068,  0.02871875, -0.03182414,  0.00640361,  0.0103219 ,\n",
       "         -0.00616208, -0.01785473, -0.03269834,  0.05243036,  0.02423535],\n",
       "        [-0.03325657,  0.02865871, -0.03301966,  0.00677389,  0.00939585,\n",
       "         -0.00549831, -0.0179785 , -0.0309816 ,  0.05135571,  0.02455047],\n",
       "        [-0.03232786,  0.02749997, -0.03036128,  0.00619401,  0.01019825,\n",
       "         -0.00549758, -0.01586997, -0.03067399,  0.04855167,  0.0222868 ],\n",
       "        [-0.02967312,  0.02622947, -0.02904659,  0.00599259,  0.00847699,\n",
       "         -0.00524407, -0.01628509, -0.02841193,  0.04555139,  0.02241035],\n",
       "        [-0.03073476,  0.02635987, -0.02842785,  0.00611274,  0.00897285,\n",
       "         -0.00521932, -0.01578096, -0.03055737,  0.04699068,  0.02228413],\n",
       "        [-0.03259403,  0.02794242, -0.03159519,  0.00640731,  0.0100081 ,\n",
       "         -0.00553781, -0.01668065, -0.03168935,  0.05014844,  0.02359077],\n",
       "        [-0.03252071,  0.02855936, -0.03150123,  0.00638151,  0.00995984,\n",
       "         -0.00626738, -0.01823107, -0.03192518,  0.05142895,  0.02411591],\n",
       "        [-0.03294085,  0.02818082, -0.03252785,  0.00563558,  0.00974453,\n",
       "         -0.00514171, -0.0177371 , -0.03307581,  0.05261977,  0.02524261],\n",
       "        [-0.03217194,  0.02794838, -0.03126961,  0.0063377 ,  0.00959314,\n",
       "         -0.00601149, -0.01754752, -0.03098612,  0.0499801 ,  0.02412735],\n",
       "        [-0.030014  ,  0.02467216, -0.02836801,  0.00557356,  0.01004189,\n",
       "         -0.00572356, -0.01467843, -0.02894394,  0.04604153,  0.0213988 ],\n",
       "        [-0.03367155,  0.02895995, -0.03336724,  0.00682739,  0.00988598,\n",
       "         -0.00625143, -0.01778958, -0.03257559,  0.05276805,  0.02521403],\n",
       "        [-0.0309256 ,  0.02595567, -0.03020455,  0.00752918,  0.0084502 ,\n",
       "         -0.00503659, -0.01602513, -0.02924623,  0.046955  ,  0.02254805],\n",
       "        [-0.03244759,  0.028182  , -0.03089714,  0.00639037,  0.01003739,\n",
       "         -0.00567541, -0.01822319, -0.03269369,  0.05126547,  0.02406179],\n",
       "        [-0.0356662 ,  0.03022932, -0.03444256,  0.00682953,  0.01051575,\n",
       "         -0.00612012, -0.01910726, -0.03527303,  0.05681367,  0.02622091],\n",
       "        [-0.02852678,  0.0236224 , -0.02712294,  0.00473471,  0.00919544,\n",
       "         -0.00421963, -0.01460191, -0.0276146 ,  0.04331715,  0.02121616],\n",
       "        [-0.03022471,  0.02561573, -0.0298089 ,  0.00551717,  0.00949658,\n",
       "         -0.00563184, -0.01558725, -0.02807421,  0.04703132,  0.02166612],\n",
       "        [-0.03267307,  0.02831217, -0.03163069,  0.00636957,  0.00979272,\n",
       "         -0.00554511, -0.01640117, -0.03158512,  0.04999558,  0.02336512],\n",
       "        [-0.03301293,  0.02832135, -0.03233697,  0.00659926,  0.00998891,\n",
       "         -0.00725532, -0.01642633, -0.03249358,  0.05155703,  0.02505858],\n",
       "        [-0.03126952,  0.02665345, -0.02881072,  0.00606286,  0.00903231,\n",
       "         -0.00539506, -0.01620834, -0.02948131,  0.04793481,  0.02148154],\n",
       "        [-0.0334218 ,  0.02766113, -0.03181198,  0.00691299,  0.00920411,\n",
       "         -0.0050185 , -0.01749017, -0.03315036,  0.05145515,  0.02565941],\n",
       "        [-0.03244443,  0.02666704, -0.03239974,  0.0064261 ,  0.01042061,\n",
       "         -0.00478653, -0.0175079 , -0.03148418,  0.05127108,  0.02383793],\n",
       "        [-0.03558277,  0.02902961, -0.03426098,  0.00722922,  0.0118612 ,\n",
       "         -0.00580817, -0.01842815, -0.03536799,  0.05487755,  0.02645048],\n",
       "        [-0.03200738,  0.02730944, -0.02956318,  0.00587457,  0.01060228,\n",
       "         -0.00631517, -0.01598442, -0.03029297,  0.04731106,  0.02306577],\n",
       "        [-0.02980012,  0.0255988 , -0.02879604,  0.00645819,  0.00764886,\n",
       "         -0.00517377, -0.01669975, -0.02993129,  0.0481814 ,  0.02251371],\n",
       "        [-0.03227284,  0.02774468, -0.03127675,  0.00660656,  0.00887454,\n",
       "         -0.0052724 , -0.0169372 , -0.03123511,  0.05032571,  0.0234428 ],\n",
       "        [-0.03624249,  0.03032377, -0.03465814,  0.00684729,  0.01152002,\n",
       "         -0.00582574, -0.01861973, -0.03508448,  0.05568695,  0.02605255],\n",
       "        [-0.03057005,  0.02476645, -0.02885317,  0.00647178,  0.00951171,\n",
       "         -0.00546008, -0.01596552, -0.02918284,  0.04638251,  0.02289921],\n",
       "        [-0.03172336,  0.02660208, -0.02970977,  0.006237  ,  0.00919896,\n",
       "         -0.00521963, -0.01674724, -0.02952653,  0.04844093,  0.02244757],\n",
       "        [-0.03504131,  0.02867918, -0.03275866,  0.00704247,  0.0103246 ,\n",
       "         -0.00642769, -0.0178196 , -0.03226525,  0.05323468,  0.02503158],\n",
       "        [-0.02916934,  0.02394805, -0.02813669,  0.0053522 ,  0.00931758,\n",
       "         -0.00445221, -0.01530284, -0.02769686,  0.04516583,  0.02097428],\n",
       "        [-0.03464164,  0.030717  , -0.03375417,  0.00597023,  0.01072178,\n",
       "         -0.00590432, -0.01898756, -0.03413017,  0.05381267,  0.02619618],\n",
       "        [-0.03486024,  0.03060629, -0.03432934,  0.00639175,  0.01070439,\n",
       "         -0.00616784, -0.01852467, -0.03422899,  0.05469388,  0.02571477],\n",
       "        [-0.03538614,  0.0299507 , -0.03270079,  0.00675916,  0.01067077,\n",
       "         -0.00548131, -0.01843221, -0.0335557 ,  0.05278693,  0.02538859],\n",
       "        [-0.03230838,  0.02705587, -0.03136359,  0.00751807,  0.00955634,\n",
       "         -0.00522636, -0.01755897, -0.0331713 ,  0.05170664,  0.02379167],\n",
       "        [-0.03326106,  0.02856763, -0.03158246,  0.00631648,  0.01069272,\n",
       "         -0.00629406, -0.0171309 , -0.03329127,  0.05085762,  0.02512528],\n",
       "        [-0.03239345,  0.02723806, -0.03114829,  0.00625775,  0.0091459 ,\n",
       "         -0.00474821, -0.01626641, -0.03131074,  0.04934919,  0.0238762 ],\n",
       "        [-0.03368329,  0.02988329, -0.03245127,  0.00612585,  0.00994288,\n",
       "         -0.00637925, -0.01772364, -0.03310721,  0.05180858,  0.02558406],\n",
       "        [-0.02836455,  0.02459503, -0.02708566,  0.00583999,  0.00872248,\n",
       "         -0.00513982, -0.01471234, -0.02872357,  0.04395078,  0.02091765],\n",
       "        [-0.03286977,  0.02876312, -0.03215162,  0.00660443,  0.00964759,\n",
       "         -0.00513511, -0.01779803, -0.03295097,  0.05082242,  0.02506794],\n",
       "        [-0.03354837,  0.02899452, -0.03351285,  0.00706999,  0.0099633 ,\n",
       "         -0.00600172, -0.01829627, -0.03385407,  0.05331197,  0.02587351],\n",
       "        [-0.03198106,  0.02785144, -0.03112133,  0.0059883 ,  0.00967887,\n",
       "         -0.0056387 , -0.01676488, -0.03164117,  0.04967905,  0.02394948],\n",
       "        [-0.03225692,  0.02726237, -0.03188002,  0.00685312,  0.01070373,\n",
       "         -0.00560295, -0.01611813, -0.03215632,  0.05007988,  0.02311524],\n",
       "        [-0.03241688,  0.02812029, -0.03135574,  0.00548618,  0.00983476,\n",
       "         -0.00521725, -0.01655165, -0.03041096,  0.0495362 ,  0.02297505],\n",
       "        [-0.03549795,  0.02997884, -0.0341365 ,  0.00668125,  0.0109898 ,\n",
       "         -0.00634468, -0.01858077, -0.03388178,  0.05498474,  0.02580706],\n",
       "        [-0.0333684 ,  0.02838956, -0.03212935,  0.00664613,  0.01064295,\n",
       "         -0.00593044, -0.01698458, -0.03180736,  0.05064521,  0.02389629],\n",
       "        [-0.03332377,  0.02806536, -0.03194948,  0.00700422,  0.01063697,\n",
       "         -0.00575987, -0.01724805, -0.03318173,  0.05052466,  0.02523168],\n",
       "        [-0.03010449,  0.02509271, -0.0283036 ,  0.00543095,  0.00973582,\n",
       "         -0.00523325, -0.01575454, -0.02942076,  0.04668282,  0.02187435],\n",
       "        [-0.03340966,  0.02933937, -0.03271718,  0.00622493,  0.01087594,\n",
       "         -0.006915  , -0.0171087 , -0.03330728,  0.05251062,  0.02450696],\n",
       "        [-0.03314458,  0.02756864, -0.03136821,  0.0067043 ,  0.01050329,\n",
       "         -0.00576743, -0.01729282, -0.03131901,  0.04967041,  0.02444542],\n",
       "        [-0.02791689,  0.0236672 , -0.0269364 ,  0.00649901,  0.00876993,\n",
       "         -0.00482635, -0.01450332, -0.02729476,  0.04232563,  0.02021596],\n",
       "        [-0.03212791,  0.02760413, -0.03230463,  0.00700643,  0.0095329 ,\n",
       "         -0.00493933, -0.01729792, -0.03130945,  0.05007241,  0.02376336],\n",
       "        [-0.03375838,  0.02913737, -0.03275585,  0.00604866,  0.0097856 ,\n",
       "         -0.00481674, -0.01891366, -0.03191204,  0.05180573,  0.02537931],\n",
       "        [-0.03373562,  0.02829822, -0.03284443,  0.00696967,  0.01020142,\n",
       "         -0.00671042, -0.01698536, -0.03204993,  0.05255247,  0.02430396],\n",
       "        [-0.03125659,  0.02611411, -0.0296259 ,  0.00608051,  0.00961873,\n",
       "         -0.00584174, -0.01602322, -0.02986114,  0.04787214,  0.0229231 ],\n",
       "        [-0.03602021,  0.03000653, -0.0351347 ,  0.00660656,  0.01051215,\n",
       "         -0.00544103, -0.01828199, -0.03403504,  0.05506651,  0.02672122],\n",
       "        [-0.03371491,  0.02850074, -0.03322041,  0.00621146,  0.00978465,\n",
       "         -0.00481382, -0.01762117, -0.03269777,  0.05250961,  0.02506162],\n",
       "        [-0.03539243,  0.03019005, -0.03570016,  0.0077133 ,  0.01050243,\n",
       "         -0.00549932, -0.01942851, -0.03403616,  0.05470694,  0.02694387],\n",
       "        [-0.03538167,  0.0309353 , -0.03510876,  0.00680452,  0.01125513,\n",
       "         -0.00615746, -0.0188463 , -0.035316  ,  0.05550335,  0.02631189],\n",
       "        [-0.03442939,  0.02993247, -0.03237467,  0.00740703,  0.00931064,\n",
       "         -0.00612056, -0.01783796, -0.03388358,  0.05306398,  0.02493203],\n",
       "        [-0.02866022,  0.02402509, -0.02707872,  0.00610466,  0.00898028,\n",
       "         -0.00533369, -0.01397438, -0.02866188,  0.04400777,  0.02059111],\n",
       "        [-0.03368613,  0.0284892 , -0.03311794,  0.00667779,  0.00974075,\n",
       "         -0.00539205, -0.01695743, -0.03194102,  0.05107447,  0.02511235],\n",
       "        [-0.0338799 ,  0.02841247, -0.03331895,  0.00735235,  0.01048246,\n",
       "         -0.0062042 , -0.01682675, -0.03344237,  0.05236557,  0.02505931],\n",
       "        [-0.03140692,  0.02633876, -0.02869979,  0.00529764,  0.00918932,\n",
       "         -0.00515628, -0.01536006, -0.02930658,  0.04674755,  0.02235637],\n",
       "        [-0.03261945,  0.02703528, -0.03096781,  0.00640773,  0.00994492,\n",
       "         -0.0053254 , -0.01653287, -0.03065078,  0.04885715,  0.02385123],\n",
       "        [-0.03214316,  0.02701741, -0.03101326,  0.00687427,  0.00934358,\n",
       "         -0.00586959, -0.01600252, -0.03060755,  0.05002867,  0.02237214],\n",
       "        [-0.02992665,  0.02475511, -0.02713624,  0.00611988,  0.00935454,\n",
       "         -0.00529203, -0.01545367, -0.02855624,  0.04462555,  0.02150975],\n",
       "        [-0.02831092,  0.02448312, -0.02740556,  0.00572162,  0.00785943,\n",
       "         -0.00418882, -0.01441121, -0.02831959,  0.04371619,  0.02085574],\n",
       "        [-0.03007917,  0.02558904, -0.02931532,  0.00539514,  0.00951177,\n",
       "         -0.00513249, -0.01557556, -0.02884621,  0.04701886,  0.02143393],\n",
       "        [-0.03266611,  0.02787502, -0.03101623,  0.0063059 ,  0.00954711,\n",
       "         -0.0061714 , -0.01580115, -0.03134446,  0.05044341,  0.02282789],\n",
       "        [-0.03229678,  0.02842157, -0.03235689,  0.00683963,  0.00946099,\n",
       "         -0.00546106, -0.01703141, -0.03303495,  0.05186149,  0.02359742],\n",
       "        [-0.03382409,  0.02905957, -0.03254269,  0.00687754,  0.00947968,\n",
       "         -0.00622799, -0.01697716, -0.03223169,  0.05174077,  0.02464607],\n",
       "        [-0.03332122,  0.02801672, -0.03074191,  0.00684711,  0.00924434,\n",
       "         -0.00647087, -0.01756576, -0.03210407,  0.05086429,  0.02523136],\n",
       "        [-0.0312501 ,  0.02628983, -0.02918963,  0.00584961,  0.00898994,\n",
       "         -0.00452294, -0.01738409, -0.0302087 ,  0.04761856,  0.02380753],\n",
       "        [-0.02930271,  0.0255147 , -0.02848147,  0.0054396 ,  0.00924454,\n",
       "         -0.00386412, -0.01641887, -0.02857421,  0.04437237,  0.02207017],\n",
       "        [-0.03107337,  0.02567611, -0.02951677,  0.00595258,  0.00843881,\n",
       "         -0.00471157, -0.01603934, -0.02972317,  0.04815231,  0.02284441],\n",
       "        [-0.02988364,  0.02552669, -0.02867886,  0.00570943,  0.01020839,\n",
       "         -0.0051748 , -0.01607467, -0.02965776,  0.04594088,  0.02208434],\n",
       "        [-0.0318397 ,  0.0264761 , -0.03058091,  0.00625383,  0.01070792,\n",
       "         -0.00519583, -0.01592099, -0.03091565,  0.04792184,  0.02309338],\n",
       "        [-0.03352543,  0.02926421, -0.03307524,  0.0066982 ,  0.00950219,\n",
       "         -0.00528628, -0.01786752, -0.03377278,  0.05288171,  0.02518093],\n",
       "        [-0.03337192,  0.02834321, -0.03386094,  0.00690921,  0.01149573,\n",
       "         -0.00597221, -0.01834335, -0.03391577,  0.05288185,  0.02583419],\n",
       "        [-0.03290418,  0.02816869, -0.03097322,  0.00620668,  0.00916017,\n",
       "         -0.00734834, -0.01603954, -0.03090502,  0.0504636 ,  0.02417116],\n",
       "        [-0.03142334,  0.02780932, -0.03031838,  0.00628931,  0.00967489,\n",
       "         -0.00423896, -0.01776309, -0.0316775 ,  0.04816087,  0.02348687],\n",
       "        [-0.0323697 ,  0.02723762, -0.03103106,  0.00573535,  0.00962562,\n",
       "         -0.00522118, -0.01731385, -0.03092151,  0.05090904,  0.02334968],\n",
       "        [-0.03411187,  0.02953637, -0.03333079,  0.00684854,  0.01082413,\n",
       "         -0.00611057, -0.0182508 , -0.03304469,  0.0525314 ,  0.02510829],\n",
       "        [-0.03267471,  0.02769343, -0.03174318,  0.00760364,  0.00917385,\n",
       "         -0.00540552, -0.01778894, -0.03295473,  0.05167967,  0.02441648],\n",
       "        [-0.02682885,  0.02286263, -0.02654839,  0.00466919,  0.00889806,\n",
       "         -0.00363118, -0.0141405 , -0.02601186,  0.04038026,  0.02035065],\n",
       "        [-0.03298302,  0.02923848, -0.03259903,  0.00556848,  0.00982333,\n",
       "         -0.00540997, -0.01715113, -0.03269295,  0.05160448,  0.02460132],\n",
       "        [-0.02909083,  0.02469331, -0.02753569,  0.00556105,  0.00919983,\n",
       "         -0.00555487, -0.01537191, -0.02875262,  0.04507926,  0.02177248],\n",
       "        [-0.0327072 ,  0.02753722, -0.03175176,  0.00662967,  0.00976673,\n",
       "         -0.00505499, -0.01771411, -0.03233897,  0.05109376,  0.02453963],\n",
       "        [-0.03444812,  0.02893125, -0.03379944,  0.00578092,  0.01104381,\n",
       "         -0.00596671, -0.01655321, -0.03170244,  0.05169574,  0.02501821],\n",
       "        [-0.03281513,  0.02838266, -0.03203545,  0.00703576,  0.00983978,\n",
       "         -0.00571333, -0.01685194, -0.03148604,  0.0497263 ,  0.0239174 ],\n",
       "        [-0.03397787,  0.02919245, -0.03304477,  0.00609904,  0.01101417,\n",
       "         -0.00630255, -0.01813639, -0.03214453,  0.05285554,  0.02444491],\n",
       "        [-0.03551171,  0.02873089, -0.03234695,  0.00667004,  0.01161269,\n",
       "         -0.0066024 , -0.01799809, -0.03303289,  0.05289704,  0.02558137],\n",
       "        [-0.03345789,  0.02805731, -0.03216191,  0.0066332 ,  0.01110583,\n",
       "         -0.00668819, -0.01729537, -0.03171502,  0.05134273,  0.02417932],\n",
       "        [-0.03571531,  0.03177013, -0.03498455,  0.00626511,  0.01042538,\n",
       "         -0.00699867, -0.01937796, -0.03530985,  0.05753166,  0.02639405],\n",
       "        [-0.03071942,  0.02630471, -0.02964189,  0.00570243,  0.00995084,\n",
       "         -0.00487006, -0.0161814 , -0.03020009,  0.04778441,  0.02187048],\n",
       "        [-0.03365128,  0.02929494, -0.03307013,  0.00635135,  0.01120298,\n",
       "         -0.00555466, -0.01911942, -0.03370147,  0.05315707,  0.02509062],\n",
       "        [-0.03323815,  0.02909311, -0.03249018,  0.00619802,  0.01061225,\n",
       "         -0.00654409, -0.01836123, -0.03301659,  0.05221094,  0.02553593]]),\n",
       " 'b1': array([  9.72244178e-04,  -2.10516289e-04,   8.06499623e-04,\n",
       "         -4.54452058e-04,   1.03156506e-04,   5.78729837e-04,\n",
       "         -4.47715980e-04,  -1.31153719e-04,   7.02388725e-05,\n",
       "          1.83164666e-04,   5.85322102e-05,   1.85074382e-04,\n",
       "         -2.11243032e-04,   1.05337970e-03,   6.36353699e-04,\n",
       "          3.31976935e-04,   2.57807464e-05,  -7.88754750e-05,\n",
       "         -2.39597462e-04,  -5.87997890e-04,   1.20773258e-04,\n",
       "          1.45807508e-04,  -2.07594733e-04,   5.94327254e-06,\n",
       "          5.06617441e-04,  -4.48362734e-04,  -2.36052253e-04,\n",
       "          6.14401685e-05,   3.78585936e-04,   7.43117745e-04,\n",
       "          7.77693909e-05,   4.21173265e-04,   9.23945143e-04,\n",
       "          4.72733781e-04,   5.98088814e-04,  -7.56935989e-04,\n",
       "          5.16780592e-04,   7.64812924e-05,  -4.64091068e-04,\n",
       "          3.93122912e-04,   5.38132534e-04,   4.35375025e-04,\n",
       "          5.14997938e-04,  -3.97541839e-04,   5.27583035e-04,\n",
       "          2.77897207e-04,   3.31537384e-04,  -1.03570077e-04,\n",
       "          4.67731613e-04,   7.57756355e-04,   1.62951332e-04,\n",
       "          1.98281107e-04,   4.72577852e-04,   5.12691587e-04,\n",
       "          8.37634406e-05,  -4.05915230e-04,  -4.30050138e-04,\n",
       "          5.55485968e-04,   1.69313212e-04,   2.35962139e-04,\n",
       "          3.70075899e-04,  -1.70174008e-04,  -2.66616329e-04,\n",
       "         -2.39738664e-04,   1.20414425e-04,   1.15750600e-04,\n",
       "          4.94900121e-05,  -1.59238018e-04,   4.73938799e-04,\n",
       "          6.39885664e-04,  -1.28604221e-04,   1.08085398e-04,\n",
       "         -3.09770014e-04,   5.38120579e-04,   1.04430198e-04,\n",
       "          2.31152049e-04,  -2.39551134e-05,   5.03123765e-04,\n",
       "          1.29665523e-05,   7.30886125e-04,   4.54309479e-04,\n",
       "         -9.13621179e-06,   1.41973091e-04,   4.39805730e-04,\n",
       "          5.19062082e-04,   2.54709631e-04,   2.04762500e-04,\n",
       "          4.32227854e-05,   1.70664638e-05,   6.24581009e-05,\n",
       "          2.18631815e-04,  -2.65530637e-04,  -8.34974578e-05,\n",
       "          2.86758137e-04,   8.24308533e-05,  -1.96366283e-04,\n",
       "         -2.23455494e-04,   4.73181450e-04,   7.98598263e-04,\n",
       "          8.17088344e-04]),\n",
       " 'b2': array([-0.06582665,  0.05601371, -0.06356752,  0.01303664,  0.0200867 ,\n",
       "        -0.01138081, -0.03434326, -0.06420816,  0.10170829,  0.04848107])}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Feb  1 15:47:16 JST 2017\n",
      "train acc, test acc | 0.0993, 0.1032\n",
      "train acc, test acc | 0.789066666667, 0.796\n",
      "train acc, test acc | 0.875516666667, 0.8806\n",
      "train acc, test acc | 0.898766666667, 0.9007\n",
      "train acc, test acc | 0.905566666667, 0.9065\n",
      "train acc, test acc | 0.91295, 0.9158\n",
      "train acc, test acc | 0.9173, 0.9192\n",
      "train acc, test acc | 0.921083333333, 0.9225\n",
      "train acc, test acc | 0.92585, 0.9271\n",
      "train acc, test acc | 0.92865, 0.9295\n",
      "train acc, test acc | 0.932033333333, 0.9329\n",
      "train acc, test acc | 0.935066666667, 0.9348\n",
      "train acc, test acc | 0.937766666667, 0.937\n",
      "train acc, test acc | 0.9405, 0.9393\n",
      "train acc, test acc | 0.94185, 0.9411\n",
      "train acc, test acc | 0.943516666667, 0.9418\n",
      "train acc, test acc | 0.94555, 0.9423\n"
     ]
    },
    {
     "data": {
      "image/png": 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mk9TW1tLS0hJ0lG1OSUkJNTU1RKNb9+nKoiuFhqXzqGYtwzRGQaTHqq2tpU+f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f6efa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Feb  1 15:47:52 JST 2017\r\n"
     ]
    }
   ],
   "source": [
    "!date\n",
    "network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)\n",
    "\n",
    "iters_num = 10000  # 繰り返しの回数を適宜設定する\n",
    "train_size = x_train.shape[0]\n",
    "batch_size = 100\n",
    "learning_rate = 0.1\n",
    "\n",
    "train_loss_list = []\n",
    "train_acc_list = []\n",
    "test_acc_list = []\n",
    "\n",
    "iter_per_epoch = max(train_size / batch_size, 1)\n",
    "\n",
    "for i in range(iters_num):\n",
    "    batch_mask = np.random.choice(train_size, batch_size)\n",
    "    x_batch = x_train[batch_mask]\n",
    "    t_batch = t_train[batch_mask]\n",
    "    \n",
    "    # 勾配の計算\n",
    "    #grad = network.numerical_gradient(x_batch, t_batch)\n",
    "    grad = network.gradient(x_batch, t_batch)\n",
    "    \n",
    "    # パラメータの更新\n",
    "    for key in ('W1', 'b1', 'W2', 'b2'):\n",
    "        network.params[key] -= learning_rate * grad[key]\n",
    "    \n",
    "    loss = network.loss(x_batch, t_batch)\n",
    "    train_loss_list.append(loss)\n",
    "    \n",
    "    if i % iter_per_epoch == 0:\n",
    "        train_acc = network.accuracy(x_train, t_train)\n",
    "        test_acc = network.accuracy(x_test, t_test)\n",
    "        train_acc_list.append(train_acc)\n",
    "        test_acc_list.append(test_acc)\n",
    "        print(\"train acc, test acc | \" + str(train_acc) + \", \" + str(test_acc))\n",
    "\n",
    "# グラフの描画\n",
    "markers = {'train': 'o', 'test': 's'}\n",
    "x = np.arange(len(train_acc_list))\n",
    "plt.plot(x, train_acc_list, label='train acc')\n",
    "plt.plot(x, test_acc_list, label='test acc', linestyle='--')\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.ylim(0, 1.0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "!date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MulLayer:\n",
    "    def __init__(self):\n",
    "        self.x = None\n",
    "        self.y = None\n",
    "\n",
    "    def forward(self, x, y):\n",
    "        self.x = x\n",
    "        self.y = y                \n",
    "        out = x * y\n",
    "\n",
    "        return out\n",
    "\n",
    "    def backward(self, dout):\n",
    "        dx = dout * self.y\n",
    "        dy = dout * self.x\n",
    "\n",
    "        return dx, dy\n",
    "\n",
    "\n",
    "class AddLayer:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def forward(self, x, y):\n",
    "        out = x + y\n",
    "\n",
    "        return out\n",
    "\n",
    "    def backward(self, dout):\n",
    "        dx = dout * 1\n",
    "        dy = dout * 1\n",
    "\n",
    "        return dx, dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price: 220\n",
      "dApple: 2.2\n",
      "dApple_num: 110\n",
      "dTax: 200\n"
     ]
    }
   ],
   "source": [
    "apple = 100\n",
    "apple_num = 2\n",
    "tax = 1.1\n",
    "\n",
    "mul_apple_layer = MulLayer()\n",
    "mul_tax_layer = MulLayer()\n",
    "\n",
    "# forward\n",
    "apple_price = mul_apple_layer.forward(apple, apple_num)\n",
    "price = mul_tax_layer.forward(apple_price, tax)\n",
    "\n",
    "# backward\n",
    "dprice = 1\n",
    "dapple_price, dtax = mul_tax_layer.backward(dprice)\n",
    "dapple, dapple_num = mul_apple_layer.backward(dapple_price)\n",
    "\n",
    "print(\"price:\", int(price))\n",
    "print(\"dApple:\", dapple)\n",
    "print(\"dApple_num:\", int(dapple_num))\n",
    "print(\"dTax:\", dtax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price: 715\n",
      "dApple: 2.2\n",
      "dApple_num: 110\n",
      "dOrange: 3.3000000000000003\n",
      "dOrange_num: 165\n",
      "dTax: 650\n"
     ]
    }
   ],
   "source": [
    "apple = 100\n",
    "apple_num = 2\n",
    "orange = 150\n",
    "orange_num = 3\n",
    "tax = 1.1\n",
    "\n",
    "# layer\n",
    "mul_apple_layer = MulLayer()\n",
    "mul_orange_layer = MulLayer()\n",
    "add_apple_orange_layer = AddLayer()\n",
    "mul_tax_layer = MulLayer()\n",
    "\n",
    "# forward\n",
    "apple_price = mul_apple_layer.forward(apple, apple_num)  # (1)\n",
    "orange_price = mul_orange_layer.forward(orange, orange_num)  # (2)\n",
    "all_price = add_apple_orange_layer.forward(apple_price, orange_price)  # (3)\n",
    "price = mul_tax_layer.forward(all_price, tax)  # (4)\n",
    "\n",
    "# backward\n",
    "dprice = 1\n",
    "dall_price, dtax = mul_tax_layer.backward(dprice)  # (4)\n",
    "dapple_price, dorange_price = add_apple_orange_layer.backward(dall_price)  # (3)\n",
    "dorange, dorange_num = mul_orange_layer.backward(dorange_price)  # (2)\n",
    "dapple, dapple_num = mul_apple_layer.backward(dapple_price)  # (1)\n",
    "\n",
    "print(\"price:\", int(price))\n",
    "print(\"dApple:\", dapple)\n",
    "print(\"dApple_num:\", int(dapple_num))\n",
    "print(\"dOrange:\", dorange)\n",
    "print(\"dOrange_num:\", int(dorange_num))\n",
    "print(\"dTax:\", dtax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Relu:\n",
    "    def __init__(self):\n",
    "        self.mask = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        self.mask = (x <= 0)\n",
    "        out = x.copy()\n",
    "        out[self.mask] = 0\n",
    "\n",
    "        return out\n",
    "\n",
    "    def backward(self, dout):\n",
    "        dout[self.mask] = 0\n",
    "        dx = dout\n",
    "\n",
    "        return dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Sigmoid:\n",
    "    def __init__(self):\n",
    "        self.out = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = sigmoid(x)\n",
    "        self.out = out\n",
    "        return out\n",
    "\n",
    "    def backward(self, dout):\n",
    "        dx = dout * (1.0 - self.out) * self.out\n",
    "\n",
    "        return dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Affine:\n",
    "    def __init__(self, W, b):\n",
    "        self.W =W\n",
    "        self.b = b\n",
    "        \n",
    "        self.x = None\n",
    "        self.original_x_shape = None\n",
    "        # 重み・バイアスパラメータの微分\n",
    "        self.dW = None\n",
    "        self.db = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        # テンソル対応\n",
    "        self.original_x_shape = x.shape\n",
    "        x = x.reshape(x.shape[0], -1)\n",
    "        self.x = x\n",
    "\n",
    "        out = np.dot(self.x, self.W) + self.b\n",
    "\n",
    "        return out\n",
    "\n",
    "    def backward(self, dout):\n",
    "        dx = np.dot(dout, self.W.T)\n",
    "        self.dW = np.dot(self.x.T, dout)\n",
    "        self.db = np.sum(dout, axis=0)\n",
    "        \n",
    "        dx = dx.reshape(*self.original_x_shape)  # 入力データの形状に戻す（テンソル対応）\n",
    "        return dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SoftmaxWithLoss:\n",
    "    def __init__(self):\n",
    "        self.loss = None\n",
    "        self.y = None # softmaxの出力\n",
    "        self.t = None # 教師データ\n",
    "\n",
    "    def forward(self, x, t):\n",
    "        self.t = t\n",
    "        self.y = softmax(x)\n",
    "        self.loss = cross_entropy_error(self.y, self.t)\n",
    "        \n",
    "        return self.loss\n",
    "\n",
    "    def backward(self, dout=1):\n",
    "        batch_size = self.t.shape[0]\n",
    "        if self.t.size == self.y.size: # 教師データがone-hot-vectorの場合\n",
    "            dx = (self.y - self.t) / batch_size\n",
    "        else:\n",
    "            dx = self.y.copy()\n",
    "            dx[np.arange(batch_size), self.t] -= 1\n",
    "            dx = dx / batch_size\n",
    "        \n",
    "        return dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "class TwoLayerNet:\n",
    "\n",
    "    def __init__(self, input_size, hidden_size, output_size, weight_init_std = 0.01):\n",
    "        # 重みの初期化\n",
    "        self.params = {}\n",
    "        self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)\n",
    "        self.params['b1'] = np.zeros(hidden_size)\n",
    "        self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size) \n",
    "        self.params['b2'] = np.zeros(output_size)\n",
    "\n",
    "        # レイヤの生成\n",
    "        self.layers = OrderedDict()\n",
    "        self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])\n",
    "        self.layers['Relu1'] = Relu()\n",
    "        self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])\n",
    "\n",
    "        self.lastLayer = SoftmaxWithLoss()\n",
    "        \n",
    "    def predict(self, x):\n",
    "        for layer in self.layers.values():\n",
    "            x = layer.forward(x)\n",
    "        \n",
    "        return x\n",
    "        \n",
    "    # x:入力データ, t:教師データ\n",
    "    def loss(self, x, t):\n",
    "        y = self.predict(x)\n",
    "        return self.lastLayer.forward(y, t)\n",
    "    \n",
    "    def accuracy(self, x, t):\n",
    "        y = self.predict(x)\n",
    "        y = np.argmax(y, axis=1)\n",
    "        if t.ndim != 1 : t = np.argmax(t, axis=1)\n",
    "        \n",
    "        accuracy = np.sum(y == t) / float(x.shape[0])\n",
    "        return accuracy\n",
    "        \n",
    "    # x:入力データ, t:教師データ\n",
    "    def numerical_gradient(self, x, t):\n",
    "        loss_W = lambda W: self.loss(x, t)\n",
    "        \n",
    "        grads = {}\n",
    "        grads['W1'] = numerical_gradient(loss_W, self.params['W1'])\n",
    "        grads['b1'] = numerical_gradient(loss_W, self.params['b1'])\n",
    "        grads['W2'] = numerical_gradient(loss_W, self.params['W2'])\n",
    "        grads['b2'] = numerical_gradient(loss_W, self.params['b2'])\n",
    "        \n",
    "        return grads\n",
    "        \n",
    "    def gradient(self, x, t):\n",
    "        # forward\n",
    "        self.loss(x, t)\n",
    "\n",
    "        # backward\n",
    "        dout = 1\n",
    "        dout = self.lastLayer.backward(dout)\n",
    "        \n",
    "        layers = list(self.layers.values())\n",
    "        layers.reverse()\n",
    "        for layer in layers:\n",
    "            dout = layer.backward(dout)\n",
    "\n",
    "        # 設定\n",
    "        grads = {}\n",
    "        grads['W1'], grads['b1'] = self.layers['Affine1'].dW, self.layers['Affine1'].db\n",
    "        grads['W2'], grads['b2'] = self.layers['Affine2'].dW, self.layers['Affine2'].db\n",
    "\n",
    "        return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1:2.52951812473e-13\n",
      "b1:1.09754104026e-12\n",
      "W2:9.1445718356e-13\n",
      "b2:1.20348174482e-10\n"
     ]
    }
   ],
   "source": [
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)\n",
    "\n",
    "network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)\n",
    "\n",
    "x_batch = x_train[:3]\n",
    "t_batch = t_train[:3]\n",
    "\n",
    "grad_numerical = network.numerical_gradient(x_batch, t_batch)\n",
    "grad_backprop = network.gradient(x_batch, t_batch)\n",
    "\n",
    "for key in grad_numerical.keys():\n",
    "    diff = np.average( np.abs(grad_backprop[key] - grad_numerical[key]) )\n",
    "    print(key + \":\" + str(diff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Feb  1 16:27:22 JST 2017\n",
      "0.14105 0.1497\n",
      "0.902283333333 0.9043\n",
      "0.9222 0.9235\n",
      "0.935183333333 0.9334\n",
      "0.943066666667 0.94\n",
      "0.949916666667 0.9477\n",
      "0.955633333333 0.9539\n",
      "0.9594 0.957\n",
      "0.961283333333 0.9593\n",
      "0.963433333333 0.9583\n",
      "0.966233333333 0.9627\n",
      "0.9692 0.9653\n",
      "0.9713 0.9656\n",
      "0.972316666667 0.9667\n",
      "0.974016666667 0.9668\n",
      "0.97575 0.9678\n",
      "0.975566666667 0.9676\n",
      "Wed Feb  1 16:28:06 JST 2017\n"
     ]
    }
   ],
   "source": [
    "!date\n",
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)\n",
    "\n",
    "network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)\n",
    "\n",
    "iters_num = 10000\n",
    "train_size = x_train.shape[0]\n",
    "batch_size = 100\n",
    "learning_rate = 0.1\n",
    "\n",
    "train_loss_list = []\n",
    "train_acc_list = []\n",
    "test_acc_list = []\n",
    "\n",
    "iter_per_epoch = max(train_size / batch_size, 1)\n",
    "\n",
    "for i in range(iters_num):\n",
    "    batch_mask = np.random.choice(train_size, batch_size)\n",
    "    x_batch = x_train[batch_mask]\n",
    "    t_batch = t_train[batch_mask]\n",
    "    \n",
    "    # 勾配\n",
    "    #grad = network.numerical_gradient(x_batch, t_batch)\n",
    "    grad = network.gradient(x_batch, t_batch)\n",
    "    \n",
    "    # 更新\n",
    "    for key in ('W1', 'b1', 'W2', 'b2'):\n",
    "        network.params[key] -= learning_rate * grad[key]\n",
    "    \n",
    "    loss = network.loss(x_batch, t_batch)\n",
    "    train_loss_list.append(loss)\n",
    "    \n",
    "    if i % iter_per_epoch == 0:\n",
    "        train_acc = network.accuracy(x_train, t_train)\n",
    "        test_acc = network.accuracy(x_test, t_test)\n",
    "        train_acc_list.append(train_acc)\n",
    "        test_acc_list.append(test_acc)\n",
    "        print(train_acc, test_acc)\n",
    "!date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MGHNwulclIrJPSltRcPcocDXwIvAe8Et3X2ZmV5nZVcnZbgSqgDvNbLGZLUxX\nHoCPIyW8GD+OMdW6uY6IyM6k9X4K7j4PmNdn2t29Xn8FGLJrQ23V60y1TYwpnzlUqxQR2adk1U12\njln+H1Tl51Gc//Wgo4jIbnR1dVFbW0tHR0fQUfY5BQUFjB8/ntzc3AF9PquKQknnRrblHRt0DBHZ\ng9raWkpKSpg4cSJmFnScfYa7s3XrVmpra5k0aWCDfmbEJalDItpJWayBjqIxQScRkT3o6OigqqpK\nBaGfzIyqqqq92sPKnqKwLXE1bKxEHddE9gUqCAOzt7+3rCkKnVsSHddyKnTHNRGRXcmaorC+6GA+\nH/k2OeOPDjqKiGS4xsZG7rzzzgF99qyzzqKxsXGQEw2drCkKte25/D5+BNXVo4KOIiIZbndFIRqN\n7vaz8+bNo7y8PB2xhkTWXH3U0RVnVGk+48oLg44iIv3w3eeW8e76bYO6zMPHlnLTZ47YZfvcuXP5\n6KOPOProo5kxYwZnn3023/nOd6ioqOD999/nww8/5Pzzz2ft2rV0dHRw7bXXMmfOHAAmTpzIwoUL\naWlp4cwzz+Tkk0/mjTfeYNy4cTzzzDMUFm6/DXruuef4/ve/TyQSoaqqikceeYRRo0bR0tLCNddc\nw8KFCzEzbrrpJi688EJeeOEFvvWtbxGLxaiuruaVV14Z1N9N1hSFGYePYsbh2ksQkT275ZZbWLp0\nKYsXLwbgt7/9LYsWLWLp0qU9l3ref//9VFZW0t7ezrHHHsuFF15IVdX2oyUsX76cRx99lJ/+9Kd8\n9rOf5Ve/+hWXXHLJdvOcfPLJvPnmm5gZ9913H7feeis//OEP+d73vkdZWRlLliwBoKGhgbq6Oq64\n4gpef/11Jk2aRH19/aD/7FlTFERk37S7b/RD6bjjjtvu2v8f//jHPPXUUwCsXbuW5cuX71AUJk2a\nxNFHJ85jTps2jdWrV++w3NraWi6++GI2bNhAJBLpWcfLL7/MY4891jNfRUUFzz33HKeeemrPPJWV\nlYP6M0IWnVMQEdkbI0aM6Hn929/+lpdffpnf//73vP3220ydOnWnfQPy8/N7XofD4Z2ej7jmmmu4\n+uqrWbJkCffcc0/gvbhVFERE+igpKaG5uXmX7U1NTVRUVFBUVMT777/Pm2++OeB1NTU1MW5cov/U\nQw891DN9xowZ3HHHHT3vGxoaOP7443n99ddZtSpxG4B0HD5SURAR6aOqqoqTTjqJKVOmcN111+3Q\nPmvWLKLBiYFOAAAKuElEQVTRKIcddhhz587l+OOPH/C6br75Zi666CKmTZtGdXV1z/QbbriBhoYG\npkyZwlFHHcWrr75KTU0N9957LxdccAFHHXUUF1988YDXuyvm7oO+0HSaPn26L1yY1hG2RSRg7733\nHocddljQMfZZO/v9mdlb7j59T5/VnoKIiPRQURARkR4qCiIi0kNFQUREeqgoiIhIDxUFERHpoaIg\nItLH3gydDXD77bfT1tY2iImGjoqCiEgf2VwUNCCeiGS+B87ecdrBM+GkvxtY++W/3u3q+g6dfdtt\nt3Hbbbfxy1/+ks7OTmbPns13v/tdWltb+exnP0ttbS2xWIzvfOc7bNq0ifXr13P66adTXV3Nq6++\nut2y//mf/5nnnnuO9vZ2TjzxRO655x7MjBUrVnDVVVdRV1dHOBzm8ccfZ/Lkyfzrv/4rDz/8MKFQ\niDPPPJNbbrkl1d/agKgoiIj00Xfo7Pnz57N8+XL++Mc/4u6ce+65vP7669TV1TF27Fh+/etEkWlq\naqKsrIwf/ehHvPrqq9sNW9Ht6quv5sYbbwTg0ksv5fnnn+czn/kMX/ziF5k7dy6zZ8+mo6ODeDzO\nb37zG5555hn+8Ic/UFRUlJaxjvpSURCRzLeHb/Z73b4H8+fPZ/78+UydOhWAlpYWli9fzimnnMI3\nvvENrr/+es455xxOOeWUPS7r1Vdf5dZbb6WtrY36+nqOOOIITjvtNNatW8fs2bMBKCgoABLDZ19+\n+eUUFRUB6Rkquy8VBRGRPXB3vvnNb3LllVfu0LZo0SLmzZvHDTfcwBlnnNGzF7AzHR0dfPWrX2Xh\nwoVMmDCBm2++OfChsvvSiWYRkT76Dp09c+ZM7r//flpaWgBYt24dmzdvZv369RQVFXHJJZdw3XXX\nsWjRop1+vlt3AaiurqalpYUnnniiZ/7x48fz9NNPA9DZ2UlbWxszZszggQce6DlprcNHIiIB6D10\n9plnnsltt93Ge++9xwknnABAcXExDz/8MCtWrOC6664jFAqRm5vLXXfdBcCcOXOYNWsWY8eO3e5E\nc3l5OVdccQVTpkxh9OjRHHvssT1tv/jFL7jyyiu58cYbyc3N5fHHH2fWrFksXryY6dOnk5eXx1ln\nncUPfvCDtP7sGjpbRDKOhs7eOxo6W0REBoWKgoiI9FBREJGMtK8d2s4Ue/t7U1EQkYxTUFDA1q1b\nVRj6yd3ZunVrTz+HgdDVRyKSccaPH09tbS11dXVBR9nnFBQUMH78+AF/XkVBRDJObm4ukyZNCjpG\nVkrr4SMzm2VmH5jZCjObu5N2M7MfJ9vfMbNj0plHRER2L21FwczCwB3AmcDhwOfN7PA+s50JHJR8\nzAHuSlceERHZs3TuKRwHrHD3le4eAR4Dzuszz3nAzz3hTaDczMakMZOIiOxGOs8pjAPW9npfC3wq\nhXnGARt6z2Rmc0jsSQC0mNkHA8xUDWwZ4GfTKVNzQeZmU67+Ua7+GY659k9lpn3iRLO73wvcu7fL\nMbOFqXTzHmqZmgsyN5ty9Y9y9U8250rn4aN1wIRe78cnp/V3HhERGSLpLAoLgIPMbJKZ5QGfA57t\nM8+zwGXJq5COB5rcfUPfBYmIyNBI2+Ejd4+a2dXAi0AYuN/dl5nZVcn2u4F5wFnACqANuDxdeZL2\n+hBUmmRqLsjcbMrVP8rVP1mba58bOltERNJHYx+JiEgPFQUREemRNUVhT0NuBMHMJpjZq2b2rpkt\nM7Nrg87Um5mFzexPZvZ80Fm6mVm5mT1hZu+b2XtmdkLQmQDM7O+T/4ZLzexRMxv4MJV7l+N+M9ts\nZkt7Tas0s5fMbHnyuSJDct2W/Hd8x8yeMrPyTMjVq+0bZuZmVj3UuXaXzcyuSf7elpnZrYO93qwo\nCikOuRGEKPANdz8cOB74Wobk6nYt8F7QIfr4D+AFdz8UOIoMyGdm44C/A6a7+xQSF1Z8LqA4DwKz\n+kybC7zi7gcBryTfD7UH2THXS8AUdz8S+BD45lCHYue5MLMJwF8Ca4Y6UC8P0iebmZ1OYiSIo9z9\nCODfBnulWVEUSG3IjSHn7hvcfVHydTOJDdy4YFMlmNl44GzgvqCzdDOzMuBU4GcA7h5x98ZgU/XI\nAQrNLAcoAtYHEcLdXwfq+0w+D3go+foh4PwhDcXOc7n7fHePJt++SaKfUuC5kv4d+CcgsCtxdpHt\nb4Fb3L0zOc/mwV5vthSFXQ2nkTHMbCIwFfhDsEl63E7ijyIedJBeJgF1wAPJw1r3mdmIoEO5+zoS\n39jWkBiipcnd5webajujevX/2QiMCjLMLvwN8JugQwCY2XnAOnd/O+gsO3EwcIqZ/cHMXjOzYwd7\nBdlSFDKamRUDvwK+7u7bMiDPOcBmd38r6Cx95ADHAHe5+1SglWAOhWwneYz+PBJFaywwwswuCTbV\nznniGvSMug7dzL5N4lDqIxmQpQj4FnBj0Fl2IQeoJHG4+Trgl2Zmg7mCbCkKGTuchpnlkigIj7j7\nk0HnSToJONfMVpM41PbnZvZwsJGAxB5erbt37009QaJIBO0vgFXuXufuXcCTwIkBZ+ptU/fow8nn\nQT/kMFBm9tfAOcAXPTM6TU0mUdzfTv7/Hw8sMrPRgab6RC3wZHJk6T+S2JMf1BPh2VIUUhlyY8gl\nK/zPgPfc/UdB5+nm7t909/HuPpHE7+p/3D3wb77uvhFYa2aHJCedAbwbYKRua4Djzawo+W96Bhlw\nAryXZ4EvJV9/CXgmwCw9zGwWiUOU57p7W9B5ANx9ibuPdPeJyf//tcAxyf97meBp4HQAMzsYyGOQ\nR3PNiqKQPJnVPeTGe8Av3X1ZsKmAxDfyS0l8E1+cfJwVdKgMdw3wiJm9AxwN/CDgPCT3XJ4AFgFL\nSPxdBTJMgpk9CvweOMTMas3sy8AtwAwzW05ir+aWDMn1E6AEeCn5f//uDMmVEXaR7X7ggORlqo8B\nXxrsPSwNcyEiIj2yYk9BRERSo6IgIiI9VBRERKSHioKIiPRQURARkR4qCiJpZmanZdJIsyK7o6Ig\nIiI9VBREkszsEjP7Y7Ij1T3J+0m0mNm/J8euf8XMapLzHm1mb/a6F0BFcvqBZvaymb1tZovMbHJy\n8cW97gPxSPd4NWZ2iyXup/GOmQ36MMgi/aWiIAKY2WHAxcBJ7n40EAO+CIwAFibHrn8NuCn5kZ8D\n1yfvBbCk1/RHgDvc/SgS4x91j046Ffg6ift5HACcZGZVwGzgiORyvp/en1Jkz1QURBLOAKYBC8xs\ncfL9ASQGHPvv5DwPAycn7+tQ7u6vJac/BJxqZiXAOHd/CsDdO3qN6fNHd6919ziwGJgINAEdwM/M\n7AIgI8b/keymoiCSYMBD7n508nGIu9+8k/kGOi5MZ6/XMSAnOSbXcSTGTToHeGGAyxYZNCoKIgmv\nAH9lZiOh577G+5P4G/mr5DxfAH7n7k1Ag5mdkpx+KfBa8u55tWZ2fnIZ+cnx+XcqeR+NMnefB/w9\niduLigQqJ+gAIpnA3d81sxuA+WYWArqAr5G4kc9xybbNJM47QGII6ruTG/2VwOXJ6ZcC95jZPyeX\ncdFuVlsCPGNmBST2VP5hkH8skX7TKKkiu2FmLe5eHHQOkaGiw0ciItJDewoiItJDewoiItJDRUFE\nRHqoKIiISA8VBRER6aGiICIiPf4/VPA8nXZJ8EwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1391125c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Feb  1 16:28:15 JST 2017\r\n"
     ]
    }
   ],
   "source": [
    "# グラフの描画\n",
    "markers = {'train': 'o', 'test': 's'}\n",
    "x = np.arange(len(train_acc_list))\n",
    "plt.plot(x, train_acc_list, label='train acc')\n",
    "plt.plot(x, test_acc_list, label='test acc', linestyle='--')\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.ylim(0, 1.0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "!date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SGD:\n",
    "\n",
    "    \"\"\"確率的勾配降下法（Stochastic Gradient Descent）\"\"\"\n",
    "\n",
    "    def __init__(self, lr=0.01):\n",
    "        self.lr = lr\n",
    "        \n",
    "    def update(self, params, grads):\n",
    "        for key in params.keys():\n",
    "            params[key] -= self.lr * grads[key] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Momentum:\n",
    "\n",
    "    \"\"\"Momentum SGD\"\"\"\n",
    "\n",
    "    def __init__(self, lr=0.01, momentum=0.9):\n",
    "        self.lr = lr\n",
    "        self.momentum = momentum\n",
    "        self.v = None\n",
    "        \n",
    "    def update(self, params, grads):\n",
    "        if self.v is None:\n",
    "            self.v = {}\n",
    "            for key, val in params.items():                                \n",
    "                self.v[key] = np.zeros_like(val)\n",
    "                \n",
    "        for key in params.keys():\n",
    "            self.v[key] = self.momentum*self.v[key] - self.lr*grads[key] \n",
    "            params[key] += self.v[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class AdaGrad:\n",
    "\n",
    "    \"\"\"AdaGrad\"\"\"\n",
    "\n",
    "    def __init__(self, lr=0.01):\n",
    "        self.lr = lr\n",
    "        self.h = None\n",
    "        \n",
    "    def update(self, params, grads):\n",
    "        if self.h is None:\n",
    "            self.h = {}\n",
    "            for key, val in params.items():\n",
    "                self.h[key] = np.zeros_like(val)\n",
    "            \n",
    "        for key in params.keys():\n",
    "            self.h[key] += grads[key] * grads[key]\n",
    "            params[key] -= self.lr * grads[key] / (np.sqrt(self.h[key]) + 1e-7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Adam:\n",
    "\n",
    "    \"\"\"Adam (http://arxiv.org/abs/1412.6980v8)\"\"\"\n",
    "\n",
    "    def __init__(self, lr=0.001, beta1=0.9, beta2=0.999):\n",
    "        self.lr = lr\n",
    "        self.beta1 = beta1\n",
    "        self.beta2 = beta2\n",
    "        self.iter = 0\n",
    "        self.m = None\n",
    "        self.v = None\n",
    "        \n",
    "    def update(self, params, grads):\n",
    "        if self.m is None:\n",
    "            self.m, self.v = {}, {}\n",
    "            for key, val in params.items():\n",
    "                self.m[key] = np.zeros_like(val)\n",
    "                self.v[key] = np.zeros_like(val)\n",
    "        \n",
    "        self.iter += 1\n",
    "        lr_t  = self.lr * np.sqrt(1.0 - self.beta2**self.iter) / (1.0 - self.beta1**self.iter)         \n",
    "        \n",
    "        for key in params.keys():\n",
    "            #self.m[key] = self.beta1*self.m[key] + (1-self.beta1)*grads[key]\n",
    "            #self.v[key] = self.beta2*self.v[key] + (1-self.beta2)*(grads[key]**2)\n",
    "            self.m[key] += (1 - self.beta1) * (grads[key] - self.m[key])\n",
    "            self.v[key] += (1 - self.beta2) * (grads[key]**2 - self.v[key])\n",
    "            \n",
    "            params[key] -= lr_t * self.m[key] / (np.sqrt(self.v[key]) + 1e-7)\n",
    "            \n",
    "            #unbias_m += (1 - self.beta1) * (grads[key] - self.m[key]) # correct bias\n",
    "            #unbisa_b += (1 - self.beta2) * (grads[key]*grads[key] - self.v[key]) # correct bias\n",
    "            #params[key] += self.lr * unbias_m / (np.sqrt(unbisa_b) + 1e-7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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jIgwMJOnsHKStvZ+2tn5aWvtoaemlaU83AwMjzSJVVQXMnlXN/Hm1LF48mfKy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132768630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(x, y):\n",
    "    return x**2 / 20.0 + y**2\n",
    "\n",
    "\n",
    "def df(x, y):\n",
    "    return x / 10.0, 2.0*y\n",
    "\n",
    "init_pos = (-7.0, 2.0)\n",
    "params = {}\n",
    "params['x'], params['y'] = init_pos[0], init_pos[1]\n",
    "grads = {}\n",
    "grads['x'], grads['y'] = 0, 0\n",
    "\n",
    "\n",
    "optimizers = OrderedDict()\n",
    "optimizers[\"SGD\"] = SGD(lr=0.95)\n",
    "optimizers[\"Momentum\"] = Momentum(lr=0.1)\n",
    "optimizers[\"AdaGrad\"] = AdaGrad(lr=1.5)\n",
    "optimizers[\"Adam\"] = Adam(lr=0.3)\n",
    "\n",
    "idx = 1\n",
    "\n",
    "for key in optimizers:\n",
    "    optimizer = optimizers[key]\n",
    "    x_history = []\n",
    "    y_history = []\n",
    "    params['x'], params['y'] = init_pos[0], init_pos[1]\n",
    "    \n",
    "    for i in range(30):\n",
    "        x_history.append(params['x'])\n",
    "        y_history.append(params['y'])\n",
    "        \n",
    "        grads['x'], grads['y'] = df(params['x'], params['y'])\n",
    "        optimizer.update(params, grads)\n",
    "    \n",
    "\n",
    "    x = np.arange(-10, 10, 0.01)\n",
    "    y = np.arange(-5, 5, 0.01)\n",
    "    \n",
    "    X, Y = np.meshgrid(x, y) \n",
    "    Z = f(X, Y)\n",
    "    \n",
    "    # for simple contour line  \n",
    "    mask = Z > 7\n",
    "    Z[mask] = 0\n",
    "    \n",
    "    # plot \n",
    "    plt.subplot(2, 2, idx)\n",
    "    idx += 1\n",
    "    plt.plot(x_history, y_history, 'o-', color=\"red\")\n",
    "    plt.contour(X, Y, Z)\n",
    "    plt.ylim(-10, 10)\n",
    "    plt.xlim(-10, 10)\n",
    "    plt.plot(0, 0, '+')\n",
    "    #colorbar()\n",
    "    #spring()\n",
    "    plt.title(key)\n",
    "    plt.xlabel(\"x\")\n",
    "    plt.ylabel(\"y\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MultiLayerNet:\n",
    "    \"\"\"全結合による多層ニューラルネットワーク\n",
    "    Parameters\n",
    "    ----------\n",
    "    input_size : 入力サイズ（MNISTの場合は784）\n",
    "    hidden_size_list : 隠れ層のニューロンの数のリスト（e.g. [100, 100, 100]）\n",
    "    output_size : 出力サイズ（MNISTの場合は10）\n",
    "    activation : 'relu' or 'sigmoid'\n",
    "    weight_init_std : 重みの標準偏差を指定（e.g. 0.01）\n",
    "        'relu'または'he'を指定した場合は「Heの初期値」を設定\n",
    "        'sigmoid'または'xavier'を指定した場合は「Xavierの初期値」を設定\n",
    "    weight_decay_lambda : Weight Decay（L2ノルム）の強さ\n",
    "    \"\"\"\n",
    "    def __init__(self, input_size, hidden_size_list, output_size,\n",
    "                 activation='relu', weight_init_std='relu', weight_decay_lambda=0):\n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size\n",
    "        self.hidden_size_list = hidden_size_list\n",
    "        self.hidden_layer_num = len(hidden_size_list)\n",
    "        self.weight_decay_lambda = weight_decay_lambda\n",
    "        self.params = {}\n",
    "\n",
    "        # 重みの初期化\n",
    "        self.__init_weight(weight_init_std)\n",
    "\n",
    "        # レイヤの生成\n",
    "        activation_layer = {'sigmoid': Sigmoid, 'relu': Relu}\n",
    "        self.layers = OrderedDict()\n",
    "        for idx in range(1, self.hidden_layer_num+1):\n",
    "            self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)],\n",
    "                                                      self.params['b' + str(idx)])\n",
    "            self.layers['Activation_function' + str(idx)] = activation_layer[activation]()\n",
    "\n",
    "        idx = self.hidden_layer_num + 1\n",
    "        self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)],\n",
    "            self.params['b' + str(idx)])\n",
    "\n",
    "        self.last_layer = SoftmaxWithLoss()\n",
    "\n",
    "    def __init_weight(self, weight_init_std):\n",
    "        \"\"\"重みの初期値設定\n",
    "        Parameters\n",
    "        ----------\n",
    "        weight_init_std : 重みの標準偏差を指定（e.g. 0.01）\n",
    "            'relu'または'he'を指定した場合は「Heの初期値」を設定\n",
    "            'sigmoid'または'xavier'を指定した場合は「Xavierの初期値」を設定\n",
    "        \"\"\"\n",
    "        all_size_list = [self.input_size] + self.hidden_size_list + [self.output_size]\n",
    "        for idx in range(1, len(all_size_list)):\n",
    "            scale = weight_init_std\n",
    "            if str(weight_init_std).lower() in ('relu', 'he'):\n",
    "                scale = np.sqrt(2.0 / all_size_list[idx - 1])  # ReLUを使う場合に推奨される初期値\n",
    "            elif str(weight_init_std).lower() in ('sigmoid', 'xavier'):\n",
    "                scale = np.sqrt(1.0 / all_size_list[idx - 1])  # sigmoidを使う場合に推奨される初期値\n",
    "\n",
    "            self.params['W' + str(idx)] = scale * np.random.randn(all_size_list[idx-1], all_size_list[idx])\n",
    "            self.params['b' + str(idx)] = np.zeros(all_size_list[idx])\n",
    "\n",
    "    def predict(self, x):\n",
    "        for layer in self.layers.values():\n",
    "            x = layer.forward(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "    def loss(self, x, t):\n",
    "        \"\"\"損失関数を求める\n",
    "        Parameters\n",
    "        ----------\n",
    "        x : 入力データ\n",
    "        t : 教師ラベル\n",
    "        Returns\n",
    "        -------\n",
    "        損失関数の値\n",
    "        \"\"\"\n",
    "        y = self.predict(x)\n",
    "\n",
    "        weight_decay = 0\n",
    "        for idx in range(1, self.hidden_layer_num + 2):\n",
    "            W = self.params['W' + str(idx)]\n",
    "            weight_decay += 0.5 * self.weight_decay_lambda * np.sum(W ** 2)\n",
    "\n",
    "        return self.last_layer.forward(y, t) + weight_decay\n",
    "\n",
    "    def accuracy(self, x, t):\n",
    "        y = self.predict(x)\n",
    "        y = np.argmax(y, axis=1)\n",
    "        if t.ndim != 1 : t = np.argmax(t, axis=1)\n",
    "\n",
    "        accuracy = np.sum(y == t) / float(x.shape[0])\n",
    "        return accuracy\n",
    "\n",
    "    def numerical_gradient(self, x, t):\n",
    "        \"\"\"勾配を求める（数値微分）\n",
    "        Parameters\n",
    "        ----------\n",
    "        x : 入力データ\n",
    "        t : 教師ラベル\n",
    "        Returns\n",
    "        -------\n",
    "        各層の勾配を持ったディクショナリ変数\n",
    "            grads['W1']、grads['W2']、...は各層の重み\n",
    "            grads['b1']、grads['b2']、...は各層のバイアス\n",
    "        \"\"\"\n",
    "        loss_W = lambda W: self.loss(x, t)\n",
    "\n",
    "        grads = {}\n",
    "        for idx in range(1, self.hidden_layer_num+2):\n",
    "            grads['W' + str(idx)] = numerical_gradient(loss_W, self.params['W' + str(idx)])\n",
    "            grads['b' + str(idx)] = numerical_gradient(loss_W, self.params['b' + str(idx)])\n",
    "\n",
    "        return grads\n",
    "\n",
    "    def gradient(self, x, t):\n",
    "        \"\"\"勾配を求める（誤差逆伝搬法）\n",
    "        Parameters\n",
    "        ----------\n",
    "        x : 入力データ\n",
    "        t : 教師ラベル\n",
    "        Returns\n",
    "        -------\n",
    "        各層の勾配を持ったディクショナリ変数\n",
    "            grads['W1']、grads['W2']、...は各層の重み\n",
    "            grads['b1']、grads['b2']、...は各層のバイアス\n",
    "        \"\"\"\n",
    "        # forward\n",
    "        self.loss(x, t)\n",
    "\n",
    "        # backward\n",
    "        dout = 1\n",
    "        dout = self.last_layer.backward(dout)\n",
    "\n",
    "        layers = list(self.layers.values())\n",
    "        layers.reverse()\n",
    "        for layer in layers:\n",
    "            dout = layer.backward(dout)\n",
    "\n",
    "        # 設定\n",
    "        grads = {}\n",
    "        for idx in range(1, self.hidden_layer_num+2):\n",
    "            grads['W' + str(idx)] = self.layers['Affine' + str(idx)].dW + self.weight_decay_lambda * self.layers['Affine' + str(idx)].W\n",
    "            grads['b' + str(idx)] = self.layers['Affine' + str(idx)].db\n",
    "\n",
    "        return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def smooth_curve(x):\n",
    "    \"\"\"損失関数のグラフを滑らかにするために用いる\n",
    "    参考：http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html\n",
    "    \"\"\"\n",
    "    window_len = 11\n",
    "    s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]\n",
    "    w = np.kaiser(window_len, 2)\n",
    "    y = np.convolve(w/w.sum(), s, mode='valid')\n",
    "    return y[5:len(y)-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========iteration:0===========\n",
      "SGD:2.36758287946\n",
      "Momentum:2.35676778844\n",
      "AdaGrad:2.28180587728\n",
      "Adam:2.21227121869\n",
      "===========iteration:100===========\n",
      "SGD:1.38855685254\n",
      "Momentum:0.461870640267\n",
      "AdaGrad:0.211115915906\n",
      "Adam:0.315237686933\n",
      "===========iteration:200===========\n",
      "SGD:0.742795168146\n",
      "Momentum:0.357434278872\n",
      "AdaGrad:0.202908190167\n",
      "Adam:0.298717102536\n",
      "===========iteration:300===========\n",
      "SGD:0.510175628236\n",
      "Momentum:0.243544097013\n",
      "AdaGrad:0.115050226278\n",
      "Adam:0.130153141426\n",
      "===========iteration:400===========\n",
      "SGD:0.331651212545\n",
      "Momentum:0.168092310964\n",
      "AdaGrad:0.0878949825684\n",
      "Adam:0.139117592448\n",
      "===========iteration:500===========\n",
      "SGD:0.334500266159\n",
      "Momentum:0.141800168398\n",
      "AdaGrad:0.0582819956473\n",
      "Adam:0.0924050059469\n",
      "===========iteration:600===========\n",
      "SGD:0.374285241135\n",
      "Momentum:0.18735173572\n",
      "AdaGrad:0.0843068950526\n",
      "Adam:0.113304021182\n",
      "===========iteration:700===========\n",
      "SGD:0.392008858143\n",
      "Momentum:0.25524217641\n",
      "AdaGrad:0.0809886331434\n",
      "Adam:0.149813645512\n",
      "===========iteration:800===========\n",
      "SGD:0.359701837535\n",
      "Momentum:0.158389731597\n",
      "AdaGrad:0.0627873663215\n",
      "Adam:0.0977498236121\n",
      "===========iteration:900===========\n",
      "SGD:0.355107337708\n",
      "Momentum:0.145141113182\n",
      "AdaGrad:0.0792770906671\n",
      "Adam:0.0922878877236\n",
      "===========iteration:1000===========\n",
      "SGD:0.299876043291\n",
      "Momentum:0.101840715714\n",
      "AdaGrad:0.0507441246263\n",
      "Adam:0.0556904090103\n",
      "===========iteration:1100===========\n",
      "SGD:0.155368625734\n",
      "Momentum:0.10524641913\n",
      "AdaGrad:0.0575273175541\n",
      "Adam:0.0409443183873\n",
      "===========iteration:1200===========\n",
      "SGD:0.404946580285\n",
      "Momentum:0.0887576738966\n",
      "AdaGrad:0.0483943120411\n",
      "Adam:0.0516832823355\n",
      "===========iteration:1300===========\n",
      "SGD:0.161848101422\n",
      "Momentum:0.0572392158164\n",
      "AdaGrad:0.016754523541\n",
      "Adam:0.0439077479572\n",
      "===========iteration:1400===========\n",
      "SGD:0.306202349484\n",
      "Momentum:0.107992380205\n",
      "AdaGrad:0.0547485069623\n",
      "Adam:0.0654920911761\n",
      "===========iteration:1500===========\n",
      "SGD:0.259560793717\n",
      "Momentum:0.111217613058\n",
      "AdaGrad:0.0409710197983\n",
      "Adam:0.043987273782\n",
      "===========iteration:1600===========\n",
      "SGD:0.242831500432\n",
      "Momentum:0.0525726596907\n",
      "AdaGrad:0.0418354154041\n",
      "Adam:0.0456010996183\n",
      "===========iteration:1700===========\n",
      "SGD:0.352313053763\n",
      "Momentum:0.0850253266804\n",
      "AdaGrad:0.0285429751321\n",
      "Adam:0.0450659573381\n",
      "===========iteration:1800===========\n",
      "SGD:0.185902877271\n",
      "Momentum:0.0916553598803\n",
      "AdaGrad:0.0270357861851\n",
      "Adam:0.0126373417587\n",
      "===========iteration:1900===========\n",
      "SGD:0.149110393347\n",
      "Momentum:0.0417008407212\n",
      "AdaGrad:0.0286249824354\n",
      "Adam:0.0254952933551\n"
     ]
    },
    {
     "data": {
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8ae2f2Fka3H1zLG5ecnPQ9VmJWcw8bWaLx3744YdMmTKFQYMGkZSUxPr169m/fz+7du1i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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f892c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0:MNISTデータの読み込み==========\n",
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "train_size = x_train.shape[0]\n",
    "batch_size = 128\n",
    "max_iterations = 2000\n",
    "\n",
    "\n",
    "# 1:実験の設定==========\n",
    "optimizers = {}\n",
    "optimizers['SGD'] = SGD()\n",
    "optimizers['Momentum'] = Momentum()\n",
    "optimizers['AdaGrad'] = AdaGrad()\n",
    "optimizers['Adam'] = Adam()\n",
    "#optimizers['RMSprop'] = RMSprop()\n",
    "\n",
    "networks = {}\n",
    "train_loss = {}\n",
    "for key in optimizers.keys():\n",
    "    networks[key] = MultiLayerNet(\n",
    "        input_size=784, hidden_size_list=[100, 100, 100, 100],\n",
    "        output_size=10)\n",
    "    train_loss[key] = []    \n",
    "\n",
    "\n",
    "# 2:訓練の開始==========\n",
    "for i in range(max_iterations):\n",
    "    batch_mask = np.random.choice(train_size, batch_size)\n",
    "    x_batch = x_train[batch_mask]\n",
    "    t_batch = t_train[batch_mask]\n",
    "    \n",
    "    for key in optimizers.keys():\n",
    "        grads = networks[key].gradient(x_batch, t_batch)\n",
    "        optimizers[key].update(networks[key].params, grads)\n",
    "    \n",
    "        loss = networks[key].loss(x_batch, t_batch)\n",
    "        train_loss[key].append(loss)\n",
    "    \n",
    "    if i % 100 == 0:\n",
    "        print( \"===========\" + \"iteration:\" + str(i) + \"===========\")\n",
    "        for key in optimizers.keys():\n",
    "            loss = networks[key].loss(x_batch, t_batch)\n",
    "            print(key + \":\" + str(loss))\n",
    "\n",
    "\n",
    "# 3.グラフの描画==========\n",
    "markers = {\"SGD\": \"o\", \"Momentum\": \"x\", \"AdaGrad\": \"s\", \"Adam\": \"D\"}\n",
    "x = np.arange(max_iterations)\n",
    "for key in optimizers.keys():\n",
    "    plt.plot(x, smooth_curve(train_loss[key]), marker=markers[key], markevery=100, label=key)\n",
    "plt.xlabel(\"iterations\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.ylim(0, 1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1350e8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "def ReLU(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "\n",
    "def tanh(x):\n",
    "    return np.tanh(x)\n",
    "    \n",
    "input_data = np.random.randn(1000, 100)  # 1000個のデータ\n",
    "node_num = 100  # 各隠れ層のノード（ニューロン）の数\n",
    "hidden_layer_size = 5  # 隠れ層が5層\n",
    "activations = {}  # ここにアクティベーションの結果を格納する\n",
    "\n",
    "x = input_data\n",
    "\n",
    "for i in range(hidden_layer_size):\n",
    "    if i != 0:\n",
    "        x = activations[i-1]\n",
    "\n",
    "    # 初期値の値をいろいろ変えて実験しよう！\n",
    "    w = np.random.randn(node_num, node_num) * 1\n",
    "    # w = np.random.randn(node_num, node_num) * 0.01\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(1.0 / node_num)\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(2.0 / node_num)\n",
    "\n",
    "\n",
    "    a = np.dot(x, w)\n",
    "\n",
    "\n",
    "    # 活性化関数の種類も変えて実験しよう！\n",
    "    z = sigmoid(a)\n",
    "    # z = ReLU(a)\n",
    "    # z = tanh(a)\n",
    "\n",
    "    activations[i] = z\n",
    "\n",
    "# ヒストグラムを描画\n",
    "for i, a in activations.items():\n",
    "    plt.subplot(1, len(activations), i+1)\n",
    "    plt.title(str(i+1) + \"-layer\")\n",
    "    if i != 0: plt.yticks([], [])\n",
    "    # plt.xlim(0.1, 1)\n",
    "    # plt.ylim(0, 7000)\n",
    "    plt.hist(a.flatten(), 30, range=(0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1353e8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "def ReLU(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "\n",
    "def tanh(x):\n",
    "    return np.tanh(x)\n",
    "    \n",
    "input_data = np.random.randn(1000, 100)  # 1000個のデータ\n",
    "node_num = 100  # 各隠れ層のノード（ニューロン）の数\n",
    "hidden_layer_size = 5  # 隠れ層が5層\n",
    "activations = {}  # ここにアクティベーションの結果を格納する\n",
    "\n",
    "x = input_data\n",
    "\n",
    "for i in range(hidden_layer_size):\n",
    "    if i != 0:\n",
    "        x = activations[i-1]\n",
    "\n",
    "    # 初期値の値をいろいろ変えて実験しよう！\n",
    "    # w = np.random.randn(node_num, node_num) * 1\n",
    "    w = np.random.randn(node_num, node_num) * 0.01\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(1.0 / node_num)\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(2.0 / node_num)\n",
    "\n",
    "\n",
    "    a = np.dot(x, w)\n",
    "\n",
    "\n",
    "    # 活性化関数の種類も変えて実験しよう！\n",
    "    z = sigmoid(a)\n",
    "    # z = ReLU(a)\n",
    "    # z = tanh(a)\n",
    "\n",
    "    activations[i] = z\n",
    "\n",
    "# ヒストグラムを描画\n",
    "for i, a in activations.items():\n",
    "    plt.subplot(1, len(activations), i+1)\n",
    "    plt.title(str(i+1) + \"-layer\")\n",
    "    if i != 0: plt.yticks([], [])\n",
    "    # plt.xlim(0.1, 1)\n",
    "    # plt.ylim(0, 7000)\n",
    "    plt.hist(a.flatten(), 30, range=(0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x135168160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "def ReLU(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "\n",
    "def tanh(x):\n",
    "    return np.tanh(x)\n",
    "    \n",
    "input_data = np.random.randn(1000, 100)  # 1000個のデータ\n",
    "node_num = 100  # 各隠れ層のノード（ニューロン）の数\n",
    "hidden_layer_size = 5  # 隠れ層が5層\n",
    "activations = {}  # ここにアクティベーションの結果を格納する\n",
    "\n",
    "x = input_data\n",
    "\n",
    "for i in range(hidden_layer_size):\n",
    "    if i != 0:\n",
    "        x = activations[i-1]\n",
    "\n",
    "    # 初期値の値をいろいろ変えて実験しよう！\n",
    "    # w = np.random.randn(node_num, node_num) * 1\n",
    "    # w = np.random.randn(node_num, node_num) * 0.01\n",
    "    w = np.random.randn(node_num, node_num) * np.sqrt(1.0 / node_num)\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(2.0 / node_num)\n",
    "\n",
    "\n",
    "    a = np.dot(x, w)\n",
    "\n",
    "\n",
    "    # 活性化関数の種類も変えて実験しよう！\n",
    "    z = sigmoid(a)\n",
    "    # z = ReLU(a)\n",
    "    # z = tanh(a)\n",
    "\n",
    "    activations[i] = z\n",
    "\n",
    "# ヒストグラムを描画\n",
    "for i, a in activations.items():\n",
    "    plt.subplot(1, len(activations), i+1)\n",
    "    plt.title(str(i+1) + \"-layer\")\n",
    "    if i != 0: plt.yticks([], [])\n",
    "    # plt.xlim(0.1, 1)\n",
    "    # plt.ylim(0, 7000)\n",
    "    plt.hist(a.flatten(), 30, range=(0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x135260ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "def ReLU(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "\n",
    "def tanh(x):\n",
    "    return np.tanh(x)\n",
    "    \n",
    "input_data = np.random.randn(1000, 100)  # 1000個のデータ\n",
    "node_num = 100  # 各隠れ層のノード（ニューロン）の数\n",
    "hidden_layer_size = 5  # 隠れ層が5層\n",
    "activations = {}  # ここにアクティベーションの結果を格納する\n",
    "\n",
    "x = input_data\n",
    "\n",
    "for i in range(hidden_layer_size):\n",
    "    if i != 0:\n",
    "        x = activations[i-1]\n",
    "\n",
    "    # 初期値の値をいろいろ変えて実験しよう！\n",
    "    # w = np.random.randn(node_num, node_num) * 1\n",
    "    # w = np.random.randn(node_num, node_num) * 0.01\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(1.0 / node_num)\n",
    "    w = np.random.randn(node_num, node_num) * np.sqrt(2.0 / node_num)\n",
    "\n",
    "\n",
    "    a = np.dot(x, w)\n",
    "\n",
    "\n",
    "    # 活性化関数の種類も変えて実験しよう！\n",
    "    z = sigmoid(a)\n",
    "    # z = ReLU(a)\n",
    "    # z = tanh(a)\n",
    "\n",
    "    activations[i] = z\n",
    "\n",
    "# ヒストグラムを描画\n",
    "for i, a in activations.items():\n",
    "    plt.subplot(1, len(activations), i+1)\n",
    "    plt.title(str(i+1) + \"-layer\")\n",
    "    if i != 0: plt.yticks([], [])\n",
    "    # plt.xlim(0.1, 1)\n",
    "    # plt.ylim(0, 7000)\n",
    "    plt.hist(a.flatten(), 30, range=(0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========iteration:0===========\n",
      "std=0.01:2.30247970912\n",
      "Xavier:2.31785863418\n",
      "He:2.39733102841\n",
      "===========iteration:100===========\n",
      "std=0.01:2.30265410099\n",
      "Xavier:2.2260916212\n",
      "He:1.56083317506\n",
      "===========iteration:200===========\n",
      "std=0.01:2.30127066276\n",
      "Xavier:2.02348908912\n",
      "He:0.877107765076\n",
      "===========iteration:300===========\n",
      "std=0.01:2.30014016663\n",
      "Xavier:1.47061707486\n",
      "He:0.503355627607\n",
      "===========iteration:400===========\n",
      "std=0.01:2.30317732165\n",
      "Xavier:1.01448959406\n",
      "He:0.46532958735\n",
      "===========iteration:500===========\n",
      "std=0.01:2.2999843498\n",
      "Xavier:0.629062750472\n",
      "He:0.356675670914\n",
      "===========iteration:600===========\n",
      "std=0.01:2.30476419675\n",
      "Xavier:0.56507387768\n",
      "He:0.317460311184\n",
      "===========iteration:700===========\n",
      "std=0.01:2.30169904812\n",
      "Xavier:0.423849159431\n",
      "He:0.237480920752\n",
      "===========iteration:800===========\n",
      "std=0.01:2.30462699762\n",
      "Xavier:0.482986535346\n",
      "He:0.315248057758\n",
      "===========iteration:900===========\n",
      "std=0.01:2.2939850946\n",
      "Xavier:0.335377873382\n",
      "He:0.243736603279\n",
      "===========iteration:1000===========\n",
      "std=0.01:2.3019330985\n",
      "Xavier:0.331399077165\n",
      "He:0.251106529254\n",
      "===========iteration:1100===========\n",
      "std=0.01:2.30603321804\n",
      "Xavier:0.454444741848\n",
      "He:0.358556085083\n",
      "===========iteration:1200===========\n",
      "std=0.01:2.30795123028\n",
      "Xavier:0.242326469941\n",
      "He:0.168018775658\n",
      "===========iteration:1300===========\n",
      "std=0.01:2.29942535812\n",
      "Xavier:0.383945484494\n",
      "He:0.292905878093\n",
      "===========iteration:1400===========\n",
      "std=0.01:2.30093330509\n",
      "Xavier:0.339810814097\n",
      "He:0.274116232034\n",
      "===========iteration:1500===========\n",
      "std=0.01:2.30644293511\n",
      "Xavier:0.282457581235\n",
      "He:0.198203544087\n",
      "===========iteration:1600===========\n",
      "std=0.01:2.29968665162\n",
      "Xavier:0.305759304047\n",
      "He:0.192240097938\n",
      "===========iteration:1700===========\n",
      "std=0.01:2.30052529681\n",
      "Xavier:0.359125766982\n",
      "He:0.321194203252\n",
      "===========iteration:1800===========\n",
      "std=0.01:2.30342191874\n",
      "Xavier:0.318089330676\n",
      "He:0.218468892217\n",
      "===========iteration:1900===========\n",
      "std=0.01:2.29904458402\n",
      "Xavier:0.17868654387\n",
      "He:0.103663457598\n"
     ]
    },
    {
     "data": {
      "image/png": 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pJn766SeSk5PZtGkTFouFmJgYbDajyfiGG25g/vz5nDhxgnHjxpW7ntaa1157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\nYEJcYLzd0ZwItNBadwFeA76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WYGPNcWOOQkpOCg7t4EDGARZbzXwYHMTuv6+HGRmkPrKf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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1268bce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "train_size = x_train.shape[0]\n",
    "batch_size = 128\n",
    "max_iterations = 2000\n",
    "\n",
    "\n",
    "# 1:実験の設定==========\n",
    "weight_init_types = {'std=0.01': 0.01, 'Xavier': 'sigmoid', 'He': 'relu'}\n",
    "optimizer = SGD(lr=0.01)\n",
    "\n",
    "networks = {}\n",
    "train_loss = {}\n",
    "for key, weight_type in weight_init_types.items():\n",
    "    networks[key] = MultiLayerNet(input_size=784, hidden_size_list=[100, 100, 100, 100],\n",
    "                                  output_size=10, weight_init_std=weight_type)\n",
    "    train_loss[key] = []\n",
    "\n",
    "\n",
    "# 2:訓練の開始==========\n",
    "for i in range(max_iterations):\n",
    "    batch_mask = np.random.choice(train_size, batch_size)\n",
    "    x_batch = x_train[batch_mask]\n",
    "    t_batch = t_train[batch_mask]\n",
    "    \n",
    "    for key in weight_init_types.keys():\n",
    "        grads = networks[key].gradient(x_batch, t_batch)\n",
    "        optimizer.update(networks[key].params, grads)\n",
    "    \n",
    "        loss = networks[key].loss(x_batch, t_batch)\n",
    "        train_loss[key].append(loss)\n",
    "    \n",
    "    if i % 100 == 0:\n",
    "        print(\"===========\" + \"iteration:\" + str(i) + \"===========\")\n",
    "        for key in weight_init_types.keys():\n",
    "            loss = networks[key].loss(x_batch, t_batch)\n",
    "            print(key + \":\" + str(loss))\n",
    "\n",
    "\n",
    "# 3.グラフの描画==========\n",
    "markers = {'std=0.01': 'o', 'Xavier': 's', 'He': 'D'}\n",
    "x = np.arange(max_iterations)\n",
    "for key in weight_init_types.keys():\n",
    "    plt.plot(x, smooth_curve(train_loss[key]), marker=markers[key], markevery=100, label=key)\n",
    "plt.xlabel(\"iterations\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.ylim(0, 2.5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MultiLayerNetExtend:\n",
    "    \"\"\"拡張版の全結合による多層ニューラルネットワーク\n",
    "    \n",
    "    Weiht Decay、Dropout、Batch Normalizationの機能を持つ\n",
    "    Parameters\n",
    "    ----------\n",
    "    input_size : 入力サイズ（MNISTの場合は784）\n",
    "    hidden_size_list : 隠れ層のニューロンの数のリスト（e.g. [100, 100, 100]）\n",
    "    output_size : 出力サイズ（MNISTの場合は10）\n",
    "    activation : 'relu' or 'sigmoid'\n",
    "    weight_init_std : 重みの標準偏差を指定（e.g. 0.01）\n",
    "        'relu'または'he'を指定した場合は「Heの初期値」を設定\n",
    "        'sigmoid'または'xavier'を指定した場合は「Xavierの初期値」を設定\n",
    "    weight_decay_lambda : Weight Decay（L2ノルム）の強さ\n",
    "    use_dropout: Dropoutを使用するかどうか\n",
    "    dropout_ration : Dropoutの割り合い\n",
    "    use_batchNorm: Batch Normalizationを使用するかどうか\n",
    "    \"\"\"\n",
    "    def __init__(self, input_size, hidden_size_list, output_size,\n",
    "                 activation='relu', weight_init_std='relu', weight_decay_lambda=0, \n",
    "                 use_dropout = False, dropout_ration = 0.5, use_batchnorm=False):\n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size\n",
    "        self.hidden_size_list = hidden_size_list\n",
    "        self.hidden_layer_num = len(hidden_size_list)\n",
    "        self.use_dropout = use_dropout\n",
    "        self.weight_decay_lambda = weight_decay_lambda\n",
    "        self.use_batchnorm = use_batchnorm\n",
    "        self.params = {}\n",
    "\n",
    "        # 重みの初期化\n",
    "        self.__init_weight(weight_init_std)\n",
    "\n",
    "        # レイヤの生成\n",
    "        activation_layer = {'sigmoid': Sigmoid, 'relu': Relu}\n",
    "        self.layers = OrderedDict()\n",
    "        for idx in range(1, self.hidden_layer_num+1):\n",
    "            self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)],\n",
    "                                                      self.params['b' + str(idx)])\n",
    "            if self.use_batchnorm:\n",
    "                self.params['gamma' + str(idx)] = np.ones(hidden_size_list[idx-1])\n",
    "                self.params['beta' + str(idx)] = np.zeros(hidden_size_list[idx-1])\n",
    "                self.layers['BatchNorm' + str(idx)] = BatchNormalization(self.params['gamma' + str(idx)], self.params['beta' + str(idx)])\n",
    "                \n",
    "            self.layers['Activation_function' + str(idx)] = activation_layer[activation]()\n",
    "            \n",
    "            if self.use_dropout:\n",
    "                self.layers['Dropout' + str(idx)] = Dropout(dropout_ration)\n",
    "\n",
    "        idx = self.hidden_layer_num + 1\n",
    "        self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)], self.params['b' + str(idx)])\n",
    "\n",
    "        self.last_layer = SoftmaxWithLoss()\n",
    "\n",
    "    def __init_weight(self, weight_init_std):\n",
    "        \"\"\"重みの初期値設定\n",
    "        Parameters\n",
    "        ----------\n",
    "        weight_init_std : 重みの標準偏差を指定（e.g. 0.01）\n",
    "            'relu'または'he'を指定した場合は「Heの初期値」を設定\n",
    "            'sigmoid'または'xavier'を指定した場合は「Xavierの初期値」を設定\n",
    "        \"\"\"\n",
    "        all_size_list = [self.input_size] + self.hidden_size_list + [self.output_size]\n",
    "        for idx in range(1, len(all_size_list)):\n",
    "            scale = weight_init_std\n",
    "            if str(weight_init_std).lower() in ('relu', 'he'):\n",
    "                scale = np.sqrt(2.0 / all_size_list[idx - 1])  # ReLUを使う場合に推奨される初期値\n",
    "            elif str(weight_init_std).lower() in ('sigmoid', 'xavier'):\n",
    "                scale = np.sqrt(1.0 / all_size_list[idx - 1])  # sigmoidを使う場合に推奨される初期値\n",
    "            self.params['W' + str(idx)] = scale * np.random.randn(all_size_list[idx-1], all_size_list[idx])\n",
    "            self.params['b' + str(idx)] = np.zeros(all_size_list[idx])\n",
    "\n",
    "    def predict(self, x, train_flg=False):\n",
    "        for key, layer in self.layers.items():\n",
    "            if \"Dropout\" in key or \"BatchNorm\" in key:\n",
    "                x = layer.forward(x, train_flg)\n",
    "            else:\n",
    "                x = layer.forward(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "    def loss(self, x, t, train_flg=False):\n",
    "        \"\"\"損失関数を求める\n",
    "        引数のxは入力データ、tは教師ラベル\n",
    "        \"\"\"\n",
    "        y = self.predict(x, train_flg)\n",
    "\n",
    "        weight_decay = 0\n",
    "        for idx in range(1, self.hidden_layer_num + 2):\n",
    "            W = self.params['W' + str(idx)]\n",
    "            weight_decay += 0.5 * self.weight_decay_lambda * np.sum(W**2)\n",
    "\n",
    "        return self.last_layer.forward(y, t) + weight_decay\n",
    "\n",
    "    def accuracy(self, X, T):\n",
    "        Y = self.predict(X, train_flg=False)\n",
    "        Y = np.argmax(Y, axis=1)\n",
    "        if T.ndim != 1 : T = np.argmax(T, axis=1)\n",
    "\n",
    "        accuracy = np.sum(Y == T) / float(X.shape[0])\n",
    "        return accuracy\n",
    "\n",
    "    def numerical_gradient(self, X, T):\n",
    "        \"\"\"勾配を求める（数値微分）\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : 入力データ\n",
    "        T : 教師ラベル\n",
    "        Returns\n",
    "        -------\n",
    "        各層の勾配を持ったディクショナリ変数\n",
    "            grads['W1']、grads['W2']、...は各層の重み\n",
    "            grads['b1']、grads['b2']、...は各層のバイアス\n",
    "        \"\"\"\n",
    "        loss_W = lambda W: self.loss(X, T, train_flg=True)\n",
    "\n",
    "        grads = {}\n",
    "        for idx in range(1, self.hidden_layer_num+2):\n",
    "            grads['W' + str(idx)] = numerical_gradient(loss_W, self.params['W' + str(idx)])\n",
    "            grads['b' + str(idx)] = numerical_gradient(loss_W, self.params['b' + str(idx)])\n",
    "            \n",
    "            if self.use_batchnorm and idx != self.hidden_layer_num+1:\n",
    "                grads['gamma' + str(idx)] = numerical_gradient(loss_W, self.params['gamma' + str(idx)])\n",
    "                grads['beta' + str(idx)] = numerical_gradient(loss_W, self.params['beta' + str(idx)])\n",
    "\n",
    "        return grads\n",
    "        \n",
    "    def gradient(self, x, t):\n",
    "        # forward\n",
    "        self.loss(x, t, train_flg=True)\n",
    "\n",
    "        # backward\n",
    "        dout = 1\n",
    "        dout = self.last_layer.backward(dout)\n",
    "\n",
    "        layers = list(self.layers.values())\n",
    "        layers.reverse()\n",
    "        for layer in layers:\n",
    "            dout = layer.backward(dout)\n",
    "\n",
    "        # 設定\n",
    "        grads = {}\n",
    "        for idx in range(1, self.hidden_layer_num+2):\n",
    "            grads['W' + str(idx)] = self.layers['Affine' + str(idx)].dW + self.weight_decay_lambda * self.params['W' + str(idx)]\n",
    "            grads['b' + str(idx)] = self.layers['Affine' + str(idx)].db\n",
    "\n",
    "            if self.use_batchnorm and idx != self.hidden_layer_num+1:\n",
    "                grads['gamma' + str(idx)] = self.layers['BatchNorm' + str(idx)].dgamma\n",
    "                grads['beta' + str(idx)] = self.layers['BatchNorm' + str(idx)].dbeta\n",
    "\n",
    "        return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class BatchNormalization:\n",
    "    \"\"\"\n",
    "    http://arxiv.org/abs/1502.03167\n",
    "    \"\"\"\n",
    "    def __init__(self, gamma, beta, momentum=0.9, running_mean=None, running_var=None):\n",
    "        self.gamma = gamma\n",
    "        self.beta = beta\n",
    "        self.momentum = momentum\n",
    "        self.input_shape = None # Conv層の場合は4次元、全結合層の場合は2次元  \n",
    "\n",
    "        # テスト時に使用する平均と分散\n",
    "        self.running_mean = running_mean\n",
    "        self.running_var = running_var  \n",
    "        \n",
    "        # backward時に使用する中間データ\n",
    "        self.batch_size = None\n",
    "        self.xc = None\n",
    "        self.std = None\n",
    "        self.dgamma = None\n",
    "        self.dbeta = None\n",
    "\n",
    "    def forward(self, x, train_flg=True):\n",
    "        self.input_shape = x.shape\n",
    "        if x.ndim != 2:\n",
    "            N, C, H, W = x.shape\n",
    "            x = x.reshape(N, -1)\n",
    "\n",
    "        out = self.__forward(x, train_flg)\n",
    "        \n",
    "        return out.reshape(*self.input_shape)\n",
    "            \n",
    "    def __forward(self, x, train_flg):\n",
    "        if self.running_mean is None:\n",
    "            N, D = x.shape\n",
    "            self.running_mean = np.zeros(D)\n",
    "            self.running_var = np.zeros(D)\n",
    "                        \n",
    "        if train_flg:\n",
    "            mu = x.mean(axis=0)\n",
    "            xc = x - mu\n",
    "            var = np.mean(xc**2, axis=0)\n",
    "            std = np.sqrt(var + 10e-7)\n",
    "            xn = xc / std\n",
    "            \n",
    "            self.batch_size = x.shape[0]\n",
    "            self.xc = xc\n",
    "            self.xn = xn\n",
    "            self.std = std\n",
    "            self.running_mean = self.momentum * self.running_mean + (1-self.momentum) * mu\n",
    "            self.running_var = self.momentum * self.running_var + (1-self.momentum) * var            \n",
    "        else:\n",
    "            xc = x - self.running_mean\n",
    "            xn = xc / ((np.sqrt(self.running_var + 10e-7)))\n",
    "            \n",
    "        out = self.gamma * xn + self.beta \n",
    "        return out\n",
    "\n",
    "    def backward(self, dout):\n",
    "        if dout.ndim != 2:\n",
    "            N, C, H, W = dout.shape\n",
    "            dout = dout.reshape(N, -1)\n",
    "\n",
    "        dx = self.__backward(dout)\n",
    "\n",
    "        dx = dx.reshape(*self.input_shape)\n",
    "        return dx\n",
    "\n",
    "    def __backward(self, dout):\n",
    "        dbeta = dout.sum(axis=0)\n",
    "        dgamma = np.sum(self.xn * dout, axis=0)\n",
    "        dxn = self.gamma * dout\n",
    "        dxc = dxn / self.std\n",
    "        dstd = -np.sum((dxn * self.xc) / (self.std * self.std), axis=0)\n",
    "        dvar = 0.5 * dstd / self.std\n",
    "        dxc += (2.0 / self.batch_size) * self.xc * dvar\n",
    "        dmu = np.sum(dxc, axis=0)\n",
    "        dx = dxc - dmu / self.batch_size\n",
    "        \n",
    "        self.dgamma = dgamma\n",
    "        self.dbeta = dbeta\n",
    "        \n",
    "        return dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============== 1/16 ==============\n",
      "epoch:0 | 0.116 - 0.113\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".:56: RuntimeWarning: divide by zero encountered in log\n",
      ".:6: RuntimeWarning: invalid value encountered in less_equal\n",
      ".:91: RuntimeWarning: overflow encountered in square\n",
      ".:91: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:1 | 0.097 - 0.099\n",
      "epoch:2 | 0.097 - 0.126\n",
      "epoch:3 | 0.097 - 0.151\n",
      "epoch:4 | 0.097 - 0.17\n",
      "epoch:5 | 0.097 - 0.182\n",
      "epoch:6 | 0.097 - 0.198\n",
      "epoch:7 | 0.097 - 0.214\n",
      "epoch:8 | 0.097 - 0.236\n",
      "epoch:9 | 0.097 - 0.251\n",
      "epoch:10 | 0.097 - 0.274\n",
      "epoch:11 | 0.097 - 0.298\n",
      "epoch:12 | 0.097 - 0.31\n",
      "epoch:13 | 0.097 - 0.323\n",
      "epoch:14 | 0.097 - 0.345\n",
      "epoch:15 | 0.097 - 0.353\n",
      "epoch:16 | 0.097 - 0.36\n",
      "epoch:17 | 0.097 - 0.366\n",
      "epoch:18 | 0.097 - 0.386\n",
      "epoch:19 | 0.097 - 0.399\n",
      "============== 2/16 ==============\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kot/miniconda2/envs/python3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:0 | 0.099 - 0.11\n",
      "epoch:1 | 0.097 - 0.134\n",
      "epoch:2 | 0.097 - 0.169\n",
      "epoch:3 | 0.097 - 0.172\n",
      "epoch:4 | 0.097 - 0.197\n",
      "epoch:5 | 0.097 - 0.223\n",
      "epoch:6 | 0.097 - 0.237\n",
      "epoch:7 | 0.097 - 0.252\n",
      "epoch:8 | 0.097 - 0.269\n",
      "epoch:9 | 0.097 - 0.293\n",
      "epoch:10 | 0.097 - 0.317\n",
      "epoch:11 | 0.097 - 0.34\n",
      "epoch:12 | 0.097 - 0.357\n",
      "epoch:13 | 0.097 - 0.379\n",
      "epoch:14 | 0.097 - 0.398\n",
      "epoch:15 | 0.097 - 0.419\n",
      "epoch:16 | 0.097 - 0.449\n",
      "epoch:17 | 0.097 - 0.45\n",
      "epoch:18 | 0.097 - 0.46\n",
      "epoch:19 | 0.097 - 0.479\n",
      "============== 3/16 ==============\n",
      "epoch:0 | 0.114 - 0.093\n",
      "epoch:1 | 0.336 - 0.123\n",
      "epoch:2 | 0.496 - 0.147\n",
      "epoch:3 | 0.555 - 0.18\n",
      "epoch:4 | 0.653 - 0.225\n",
      "epoch:5 | 0.71 - 0.249\n",
      "epoch:6 | 0.77 - 0.287\n",
      "epoch:7 | 0.783 - 0.32\n",
      "epoch:8 | 0.824 - 0.363\n",
      "epoch:9 | 0.856 - 0.39\n",
      "epoch:10 | 0.875 - 0.426\n",
      "epoch:11 | 0.903 - 0.45\n",
      "epoch:12 | 0.921 - 0.476\n",
      "epoch:13 | 0.927 - 0.508\n",
      "epoch:14 | 0.937 - 0.528\n",
      "epoch:15 | 0.953 - 0.555\n",
      "epoch:16 | 0.967 - 0.578\n",
      "epoch:17 | 0.972 - 0.597\n",
      "epoch:18 | 0.979 - 0.604\n",
      "epoch:19 | 0.981 - 0.628\n",
      "============== 4/16 ==============\n",
      "epoch:0 | 0.117 - 0.095\n",
      "epoch:1 | 0.238 - 0.097\n",
      "epoch:2 | 0.375 - 0.16\n",
      "epoch:3 | 0.464 - 0.238\n",
      "epoch:4 | 0.569 - 0.305\n",
      "epoch:5 | 0.606 - 0.365\n",
      "epoch:6 | 0.665 - 0.434\n",
      "epoch:7 | 0.709 - 0.497\n",
      "epoch:8 | 0.728 - 0.542\n",
      "epoch:9 | 0.759 - 0.578\n",
      "epoch:10 | 0.779 - 0.61\n",
      "epoch:11 | 0.793 - 0.634\n",
      "epoch:12 | 0.818 - 0.663\n",
      "epoch:13 | 0.825 - 0.691\n",
      "epoch:14 | 0.843 - 0.716\n",
      "epoch:15 | 0.854 - 0.728\n",
      "epoch:16 | 0.861 - 0.747\n",
      "epoch:17 | 0.859 - 0.759\n",
      "epoch:18 | 0.865 - 0.786\n",
      "epoch:19 | 0.877 - 0.796\n",
      "============== 5/16 ==============\n",
      "epoch:0 | 0.077 - 0.113\n",
      "epoch:1 | 0.079 - 0.155\n",
      "epoch:2 | 0.095 - 0.32\n",
      "epoch:3 | 0.117 - 0.449\n",
      "epoch:4 | 0.138 - 0.529\n",
      "epoch:5 | 0.151 - 0.602\n",
      "epoch:6 | 0.165 - 0.657\n",
      "epoch:7 | 0.18 - 0.712\n",
      "epoch:8 | 0.196 - 0.743\n",
      "epoch:9 | 0.22 - 0.769\n",
      "epoch:10 | 0.238 - 0.789\n",
      "epoch:11 | 0.255 - 0.811\n",
      "epoch:12 | 0.273 - 0.825\n",
      "epoch:13 | 0.291 - 0.838\n",
      "epoch:14 | 0.301 - 0.857\n",
      "epoch:15 | 0.317 - 0.864\n",
      "epoch:16 | 0.329 - 0.872\n",
      "epoch:17 | 0.336 - 0.877\n",
      "epoch:18 | 0.357 - 0.886\n",
      "epoch:19 | 0.359 - 0.898\n",
      "============== 6/16 ==============\n",
      "epoch:0 | 0.133 - 0.089\n",
      "epoch:1 | 0.111 - 0.203\n",
      "epoch:2 | 0.118 - 0.374\n",
      "epoch:3 | 0.117 - 0.542\n",
      "epoch:4 | 0.117 - 0.684\n",
      "epoch:5 | 0.117 - 0.747\n",
      "epoch:6 | 0.118 - 0.79\n",
      "epoch:7 | 0.117 - 0.813\n",
      "epoch:8 | 0.104 - 0.839\n",
      "epoch:9 | 0.119 - 0.847\n",
      "epoch:10 | 0.1 - 0.865\n",
      "epoch:11 | 0.116 - 0.878\n",
      "epoch:12 | 0.118 - 0.892\n",
      "epoch:13 | 0.124 - 0.905\n",
      "epoch:14 | 0.115 - 0.919\n",
      "epoch:15 | 0.115 - 0.922\n",
      "epoch:16 | 0.118 - 0.929\n",
      "epoch:17 | 0.131 - 0.937\n",
      "epoch:18 | 0.12 - 0.941\n",
      "epoch:19 | 0.117 - 0.945\n",
      "============== 7/16 ==============\n",
      "epoch:0 | 0.099 - 0.113\n",
      "epoch:1 | 0.1 - 0.302\n",
      "epoch:2 | 0.116 - 0.566\n",
      "epoch:3 | 0.116 - 0.715\n",
      "epoch:4 | 0.116 - 0.778\n",
      "epoch:5 | 0.116 - 0.808\n",
      "epoch:6 | 0.116 - 0.838\n",
      "epoch:7 | 0.116 - 0.867\n",
      "epoch:8 | 0.116 - 0.884\n",
      "epoch:9 | 0.116 - 0.906\n",
      "epoch:10 | 0.116 - 0.925\n",
      "epoch:11 | 0.116 - 0.946\n",
      "epoch:12 | 0.116 - 0.953\n",
      "epoch:13 | 0.116 - 0.971\n",
      "epoch:14 | 0.116 - 0.978\n",
      "epoch:15 | 0.116 - 0.983\n",
      "epoch:16 | 0.116 - 0.985\n",
      "epoch:17 | 0.116 - 0.991\n",
      "epoch:18 | 0.116 - 0.994\n",
      "epoch:19 | 0.117 - 0.994\n",
      "============== 8/16 ==============\n",
      "epoch:0 | 0.117 - 0.122\n",
      "epoch:1 | 0.117 - 0.561\n",
      "epoch:2 | 0.099 - 0.755\n",
      "epoch:3 | 0.117 - 0.814\n",
      "epoch:4 | 0.099 - 0.849\n",
      "epoch:5 | 0.117 - 0.887\n",
      "epoch:6 | 0.117 - 0.903\n",
      "epoch:7 | 0.117 - 0.917\n",
      "epoch:8 | 0.117 - 0.946\n",
      "epoch:9 | 0.117 - 0.968\n",
      "epoch:10 | 0.117 - 0.977\n",
      "epoch:11 | 0.117 - 0.983\n",
      "epoch:12 | 0.117 - 0.989\n",
      "epoch:13 | 0.116 - 0.994\n",
      "epoch:14 | 0.116 - 0.996\n",
      "epoch:15 | 0.116 - 0.995\n",
      "epoch:16 | 0.116 - 0.998\n",
      "epoch:17 | 0.116 - 0.997\n",
      "epoch:18 | 0.116 - 0.999\n",
      "epoch:19 | 0.116 - 0.999\n",
      "============== 9/16 ==============\n",
      "epoch:0 | 0.094 - 0.129\n",
      "epoch:1 | 0.117 - 0.416\n",
      "epoch:2 | 0.117 - 0.658\n",
      "epoch:3 | 0.117 - 0.867\n",
      "epoch:4 | 0.117 - 0.921\n",
      "epoch:5 | 0.117 - 0.946\n",
      "epoch:6 | 0.117 - 0.965\n",
      "epoch:7 | 0.117 - 0.973\n",
      "epoch:8 | 0.117 - 0.986\n",
      "epoch:9 | 0.117 - 0.992\n",
      "epoch:10 | 0.117 - 0.995\n",
      "epoch:11 | 0.117 - 0.997\n",
      "epoch:12 | 0.117 - 0.996\n",
      "epoch:13 | 0.117 - 0.998\n",
      "epoch:14 | 0.117 - 0.998\n",
      "epoch:15 | 0.117 - 0.998\n",
      "epoch:16 | 0.117 - 0.999\n",
      "epoch:17 | 0.117 - 0.999\n",
      "epoch:18 | 0.117 - 0.999\n",
      "epoch:19 | 0.117 - 0.999\n",
      "============== 10/16 ==============\n",
      "epoch:0 | 0.117 - 0.156\n",
      "epoch:1 | 0.116 - 0.581\n",
      "epoch:2 | 0.117 - 0.682\n",
      "epoch:3 | 0.117 - 0.797\n",
      "epoch:4 | 0.117 - 0.868\n",
      "epoch:5 | 0.117 - 0.912\n",
      "epoch:6 | 0.117 - 0.845\n",
      "epoch:7 | 0.117 - 0.864\n",
      "epoch:8 | 0.117 - 0.882\n",
      "epoch:9 | 0.117 - 0.889\n",
      "epoch:10 | 0.117 - 0.902\n",
      "epoch:11 | 0.117 - 0.905\n",
      "epoch:12 | 0.117 - 0.936\n",
      "epoch:13 | 0.117 - 0.969\n",
      "epoch:14 | 0.117 - 0.984\n",
      "epoch:15 | 0.117 - 0.989\n",
      "epoch:16 | 0.117 - 0.989\n",
      "epoch:17 | 0.117 - 0.977\n",
      "epoch:18 | 0.117 - 0.995\n",
      "epoch:19 | 0.117 - 0.995\n",
      "============== 11/16 ==============\n",
      "epoch:0 | 0.097 - 0.13\n",
      "epoch:1 | 0.116 - 0.567\n",
      "epoch:2 | 0.116 - 0.778\n",
      "epoch:3 | 0.116 - 0.711\n",
      "epoch:4 | 0.116 - 0.837\n",
      "epoch:5 | 0.116 - 0.833\n",
      "epoch:6 | 0.116 - 0.881\n",
      "epoch:7 | 0.116 - 0.824\n",
      "epoch:8 | 0.116 - 0.877\n",
      "epoch:9 | 0.116 - 0.908\n",
      "epoch:10 | 0.116 - 0.921\n",
      "epoch:11 | 0.116 - 0.98\n",
      "epoch:12 | 0.116 - 0.989\n",
      "epoch:13 | 0.116 - 0.99\n",
      "epoch:14 | 0.116 - 0.99\n",
      "epoch:15 | 0.116 - 0.983\n",
      "epoch:16 | 0.116 - 0.992\n",
      "epoch:17 | 0.116 - 0.992\n",
      "epoch:18 | 0.116 - 0.994\n",
      "epoch:19 | 0.116 - 0.995\n",
      "============== 12/16 ==============\n",
      "epoch:0 | 0.094 - 0.122\n",
      "epoch:1 | 0.117 - 0.534\n",
      "epoch:2 | 0.117 - 0.632\n",
      "epoch:3 | 0.117 - 0.656\n",
      "epoch:4 | 0.117 - 0.691\n",
      "epoch:5 | 0.117 - 0.791\n",
      "epoch:6 | 0.117 - 0.766\n",
      "epoch:7 | 0.117 - 0.835\n",
      "epoch:8 | 0.117 - 0.834\n",
      "epoch:9 | 0.117 - 0.784\n",
      "epoch:10 | 0.117 - 0.875\n",
      "epoch:11 | 0.117 - 0.826\n",
      "epoch:12 | 0.117 - 0.873\n",
      "epoch:13 | 0.117 - 0.894\n",
      "epoch:14 | 0.117 - 0.897\n",
      "epoch:15 | 0.117 - 0.882\n",
      "epoch:16 | 0.117 - 0.895\n",
      "epoch:17 | 0.117 - 0.899\n",
      "epoch:18 | 0.117 - 0.901\n",
      "epoch:19 | 0.117 - 0.902\n",
      "============== 13/16 ==============\n",
      "epoch:0 | 0.087 - 0.156\n",
      "epoch:1 | 0.117 - 0.476\n",
      "epoch:2 | 0.117 - 0.505\n",
      "epoch:3 | 0.105 - 0.372\n",
      "epoch:4 | 0.105 - 0.57\n",
      "epoch:5 | 0.117 - 0.56\n",
      "epoch:6 | 0.117 - 0.57\n",
      "epoch:7 | 0.117 - 0.589\n",
      "epoch:8 | 0.116 - 0.607\n",
      "epoch:9 | 0.116 - 0.605\n",
      "epoch:10 | 0.116 - 0.597\n",
      "epoch:11 | 0.116 - 0.618\n",
      "epoch:12 | 0.116 - 0.676\n",
      "epoch:13 | 0.117 - 0.701\n",
      "epoch:14 | 0.116 - 0.692\n",
      "epoch:15 | 0.117 - 0.687\n",
      "epoch:16 | 0.117 - 0.704\n",
      "epoch:17 | 0.117 - 0.695\n",
      "epoch:18 | 0.117 - 0.691\n",
      "epoch:19 | 0.117 - 0.7\n",
      "============== 14/16 ==============\n",
      "epoch:0 | 0.116 - 0.14\n",
      "epoch:1 | 0.116 - 0.391\n",
      "epoch:2 | 0.116 - 0.433\n",
      "epoch:3 | 0.116 - 0.503\n",
      "epoch:4 | 0.116 - 0.564\n",
      "epoch:5 | 0.116 - 0.561\n",
      "epoch:6 | 0.116 - 0.542\n",
      "epoch:7 | 0.116 - 0.587\n",
      "epoch:8 | 0.116 - 0.594\n",
      "epoch:9 | 0.116 - 0.597\n",
      "epoch:10 | 0.116 - 0.599\n",
      "epoch:11 | 0.116 - 0.6\n",
      "epoch:12 | 0.116 - 0.608\n",
      "epoch:13 | 0.116 - 0.605\n",
      "epoch:14 | 0.116 - 0.608\n",
      "epoch:15 | 0.116 - 0.603\n",
      "epoch:16 | 0.116 - 0.601\n",
      "epoch:17 | 0.116 - 0.609\n",
      "epoch:18 | 0.116 - 0.612\n",
      "epoch:19 | 0.116 - 0.61\n",
      "============== 15/16 ==============\n",
      "epoch:0 | 0.116 - 0.097\n",
      "epoch:1 | 0.116 - 0.317\n",
      "epoch:2 | 0.116 - 0.325\n",
      "epoch:3 | 0.116 - 0.324\n",
      "epoch:4 | 0.116 - 0.325\n",
      "epoch:5 | 0.116 - 0.358\n",
      "epoch:6 | 0.116 - 0.408\n",
      "epoch:7 | 0.116 - 0.417\n",
      "epoch:8 | 0.116 - 0.424\n",
      "epoch:9 | 0.116 - 0.422\n",
      "epoch:10 | 0.116 - 0.401\n",
      "epoch:11 | 0.116 - 0.432\n",
      "epoch:12 | 0.116 - 0.429\n",
      "epoch:13 | 0.116 - 0.374\n",
      "epoch:14 | 0.116 - 0.426\n",
      "epoch:15 | 0.116 - 0.432\n",
      "epoch:16 | 0.116 - 0.432\n",
      "epoch:17 | 0.116 - 0.432\n",
      "epoch:18 | 0.116 - 0.429\n",
      "epoch:19 | 0.116 - 0.428\n",
      "============== 16/16 ==============\n",
      "epoch:0 | 0.097 - 0.097\n",
      "epoch:1 | 0.116 - 0.159\n",
      "epoch:2 | 0.117 - 0.412\n",
      "epoch:3 | 0.116 - 0.401\n",
      "epoch:4 | 0.116 - 0.413\n",
      "epoch:5 | 0.116 - 0.417\n",
      "epoch:6 | 0.116 - 0.414\n",
      "epoch:7 | 0.116 - 0.416\n",
      "epoch:8 | 0.116 - 0.428\n",
      "epoch:9 | 0.116 - 0.432\n",
      "epoch:10 | 0.116 - 0.432\n",
      "epoch:11 | 0.116 - 0.432\n",
      "epoch:12 | 0.116 - 0.519\n",
      "epoch:13 | 0.116 - 0.519\n",
      "epoch:14 | 0.116 - 0.517\n",
      "epoch:15 | 0.116 - 0.526\n",
      "epoch:16 | 0.116 - 0.523\n",
      "epoch:17 | 0.116 - 0.527\n",
      "epoch:18 | 0.116 - 0.53\n",
      "epoch:19 | 0.117 - 0.529\n"
     ]
    },
    {
     "data": {
      "image/png": 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aguuczevjiPpGJxmFlezILWdZeiFL0wsprdY9mVB/H+LCA4gLD6BPl1BiQ/3p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KoCO1HVyN8Amw2/FCrXngTwF1QmcNb4LarEtRr14f5IlTlFqIzliPFxfQ40q+L/rCHraVTNLdbr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\nIGgDpNFoNJoG4f8B0+eoYfy/i3EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109787390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "# 学習データを削減\n",
    "x_train = x_train[:1000]\n",
    "t_train = t_train[:1000]\n",
    "\n",
    "max_epochs = 20\n",
    "train_size = x_train.shape[0]\n",
    "batch_size = 100\n",
    "learning_rate = 0.01\n",
    "\n",
    "\n",
    "def __train(weight_init_std):\n",
    "    bn_network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100, 100], output_size=10, \n",
    "                                    weight_init_std=weight_init_std, use_batchnorm=True)\n",
    "    network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100, 100], output_size=10,\n",
    "                                weight_init_std=weight_init_std)\n",
    "    optimizer = SGD(lr=learning_rate)\n",
    "    \n",
    "    train_acc_list = []\n",
    "    bn_train_acc_list = []\n",
    "    \n",
    "    iter_per_epoch = max(train_size / batch_size, 1)\n",
    "    epoch_cnt = 0\n",
    "    \n",
    "    for i in range(1000000000):\n",
    "        batch_mask = np.random.choice(train_size, batch_size)\n",
    "        x_batch = x_train[batch_mask]\n",
    "        t_batch = t_train[batch_mask]\n",
    "    \n",
    "        for _network in (bn_network, network):\n",
    "            grads = _network.gradient(x_batch, t_batch)\n",
    "            optimizer.update(_network.params, grads)\n",
    "    \n",
    "        if i % iter_per_epoch == 0:\n",
    "            train_acc = network.accuracy(x_train, t_train)\n",
    "            bn_train_acc = bn_network.accuracy(x_train, t_train)\n",
    "            train_acc_list.append(train_acc)\n",
    "            bn_train_acc_list.append(bn_train_acc)\n",
    "    \n",
    "            print(\"epoch:\" + str(epoch_cnt) + \" | \" + str(train_acc) + \" - \" + str(bn_train_acc))\n",
    "    \n",
    "            epoch_cnt += 1\n",
    "            if epoch_cnt >= max_epochs:\n",
    "                break\n",
    "                \n",
    "    return train_acc_list, bn_train_acc_list\n",
    "\n",
    "\n",
    "# 3.グラフの描画==========\n",
    "weight_scale_list = np.logspace(0, -4, num=16)\n",
    "x = np.arange(max_epochs)\n",
    "\n",
    "for i, w in enumerate(weight_scale_list):\n",
    "    print( \"============== \" + str(i+1) + \"/16\" + \" ==============\")\n",
    "    train_acc_list, bn_train_acc_list = __train(w)\n",
    "    \n",
    "    plt.subplot(4,4,i+1)\n",
    "    plt.title(\"W:\" + str(w))\n",
    "    if i == 15:\n",
    "        plt.plot(x, bn_train_acc_list, label='Batch Normalization', markevery=2)\n",
    "        plt.plot(x, train_acc_list, linestyle = \"--\", label='Normal(without BatchNorm)', markevery=2)\n",
    "    else:\n",
    "        plt.plot(x, bn_train_acc_list, markevery=2)\n",
    "        plt.plot(x, train_acc_list, linestyle=\"--\", markevery=2)\n",
    "\n",
    "    plt.ylim(0, 1.0)\n",
    "    if i % 4:\n",
    "        plt.yticks([])\n",
    "    else:\n",
    "        plt.ylabel(\"accuracy\")\n",
    "    if i < 12:\n",
    "        plt.xticks([])\n",
    "    else:\n",
    "        plt.xlabel(\"epochs\")\n",
    "    plt.legend(loc='lower right')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:0, train acc:0.106666666667, test acc:0.112\n",
      "epoch:1, train acc:0.113333333333, test acc:0.1232\n",
      "epoch:2, train acc:0.15, test acc:0.1383\n",
      "epoch:3, train acc:0.163333333333, test acc:0.1517\n",
      "epoch:4, train acc:0.193333333333, test acc:0.166\n",
      "epoch:5, train acc:0.236666666667, test acc:0.1995\n",
      "epoch:6, train acc:0.263333333333, test acc:0.2215\n",
      "epoch:7, train acc:0.313333333333, test acc:0.2465\n",
      "epoch:8, train acc:0.35, test acc:0.2623\n",
      "epoch:9, train acc:0.39, test acc:0.2796\n",
      "epoch:10, train acc:0.413333333333, test acc:0.2973\n",
      "epoch:11, train acc:0.416666666667, test acc:0.3035\n",
      "epoch:12, train acc:0.426666666667, test acc:0.3084\n",
      "epoch:13, train acc:0.463333333333, test acc:0.3176\n",
      "epoch:14, train acc:0.493333333333, test acc:0.3334\n",
      "epoch:15, train acc:0.493333333333, test acc:0.3368\n",
      "epoch:16, train acc:0.493333333333, test acc:0.343\n",
      "epoch:17, train acc:0.5, test acc:0.3451\n",
      "epoch:18, train acc:0.53, test acc:0.3601\n",
      "epoch:19, train acc:0.526666666667, test acc:0.3712\n",
      "epoch:20, train acc:0.52, test acc:0.3679\n",
      "epoch:21, train acc:0.543333333333, test acc:0.3823\n",
      "epoch:22, train acc:0.54, test acc:0.3897\n",
      "epoch:23, train acc:0.553333333333, test acc:0.3967\n",
      "epoch:24, train acc:0.556666666667, test acc:0.4065\n",
      "epoch:25, train acc:0.563333333333, test acc:0.4194\n",
      "epoch:26, train acc:0.576666666667, test acc:0.4337\n",
      "epoch:27, train acc:0.583333333333, test acc:0.4293\n",
      "epoch:28, train acc:0.586666666667, test acc:0.4332\n",
      "epoch:29, train acc:0.573333333333, test acc:0.4335\n",
      "epoch:30, train acc:0.603333333333, test acc:0.4406\n",
      "epoch:31, train acc:0.603333333333, test acc:0.4386\n",
      "epoch:32, train acc:0.6, test acc:0.4418\n",
      "epoch:33, train acc:0.6, test acc:0.4485\n",
      "epoch:34, train acc:0.593333333333, test acc:0.4542\n",
      "epoch:35, train acc:0.626666666667, test acc:0.4725\n",
      "epoch:36, train acc:0.67, test acc:0.5008\n",
      "epoch:37, train acc:0.663333333333, test acc:0.5165\n",
      "epoch:38, train acc:0.656666666667, test acc:0.5096\n",
      "epoch:39, train acc:0.65, test acc:0.5134\n",
      "epoch:40, train acc:0.653333333333, test acc:0.5166\n",
      "epoch:41, train acc:0.69, test acc:0.5334\n",
      "epoch:42, train acc:0.7, test acc:0.5327\n",
      "epoch:43, train acc:0.703333333333, test acc:0.5421\n",
      "epoch:44, train acc:0.703333333333, test acc:0.5277\n",
      "epoch:45, train acc:0.7, test acc:0.5426\n",
      "epoch:46, train acc:0.706666666667, test acc:0.5558\n",
      "epoch:47, train acc:0.69, test acc:0.555\n",
      "epoch:48, train acc:0.71, test acc:0.5631\n",
      "epoch:49, train acc:0.716666666667, test acc:0.561\n",
      "epoch:50, train acc:0.723333333333, test acc:0.5544\n",
      "epoch:51, train acc:0.74, test acc:0.5658\n",
      "epoch:52, train acc:0.753333333333, test acc:0.5852\n",
      "epoch:53, train acc:0.76, test acc:0.5933\n",
      "epoch:54, train acc:0.73, test acc:0.5916\n",
      "epoch:55, train acc:0.736666666667, test acc:0.5902\n",
      "epoch:56, train acc:0.74, test acc:0.5681\n",
      "epoch:57, train acc:0.736666666667, test acc:0.5757\n",
      "epoch:58, train acc:0.753333333333, test acc:0.5897\n",
      "epoch:59, train acc:0.763333333333, test acc:0.588\n",
      "epoch:60, train acc:0.763333333333, test acc:0.6019\n",
      "epoch:61, train acc:0.753333333333, test acc:0.5983\n",
      "epoch:62, train acc:0.746666666667, test acc:0.5884\n",
      "epoch:63, train acc:0.77, test acc:0.611\n",
      "epoch:64, train acc:0.77, test acc:0.6102\n",
      "epoch:65, train acc:0.763333333333, test acc:0.6084\n",
      "epoch:66, train acc:0.773333333333, test acc:0.6055\n",
      "epoch:67, train acc:0.766666666667, test acc:0.6016\n",
      "epoch:68, train acc:0.756666666667, test acc:0.5974\n",
      "epoch:69, train acc:0.786666666667, test acc:0.6124\n",
      "epoch:70, train acc:0.793333333333, test acc:0.616\n",
      "epoch:71, train acc:0.783333333333, test acc:0.6179\n",
      "epoch:72, train acc:0.793333333333, test acc:0.6107\n",
      "epoch:73, train acc:0.81, test acc:0.64\n",
      "epoch:74, train acc:0.793333333333, test acc:0.6308\n",
      "epoch:75, train acc:0.793333333333, test acc:0.6415\n",
      "epoch:76, train acc:0.786666666667, test acc:0.651\n",
      "epoch:77, train acc:0.816666666667, test acc:0.6498\n",
      "epoch:78, train acc:0.796666666667, test acc:0.6312\n",
      "epoch:79, train acc:0.79, test acc:0.6216\n",
      "epoch:80, train acc:0.78, test acc:0.6201\n",
      "epoch:81, train acc:0.813333333333, test acc:0.6437\n",
      "epoch:82, train acc:0.796666666667, test acc:0.6366\n",
      "epoch:83, train acc:0.81, test acc:0.6619\n",
      "epoch:84, train acc:0.826666666667, test acc:0.6603\n",
      "epoch:85, train acc:0.836666666667, test acc:0.6599\n",
      "epoch:86, train acc:0.823333333333, test acc:0.6513\n",
      "epoch:87, train acc:0.813333333333, test acc:0.6485\n",
      "epoch:88, train acc:0.816666666667, test acc:0.6616\n",
      "epoch:89, train acc:0.84, test acc:0.663\n",
      "epoch:90, train acc:0.846666666667, test acc:0.6699\n",
      "epoch:91, train acc:0.833333333333, test acc:0.6716\n",
      "epoch:92, train acc:0.813333333333, test acc:0.6567\n",
      "epoch:93, train acc:0.82, test acc:0.6609\n",
      "epoch:94, train acc:0.84, test acc:0.679\n",
      "epoch:95, train acc:0.823333333333, test acc:0.6676\n",
      "epoch:96, train acc:0.843333333333, test acc:0.684\n",
      "epoch:97, train acc:0.856666666667, test acc:0.6867\n",
      "epoch:98, train acc:0.85, test acc:0.688\n",
      "epoch:99, train acc:0.826666666667, test acc:0.6773\n",
      "epoch:100, train acc:0.833333333333, test acc:0.6796\n",
      "epoch:101, train acc:0.84, test acc:0.6829\n",
      "epoch:102, train acc:0.846666666667, test acc:0.6797\n",
      "epoch:103, train acc:0.84, test acc:0.6783\n",
      "epoch:104, train acc:0.85, test acc:0.6846\n",
      "epoch:105, train acc:0.826666666667, test acc:0.6719\n",
      "epoch:106, train acc:0.843333333333, test acc:0.6875\n",
      "epoch:107, train acc:0.853333333333, test acc:0.6845\n",
      "epoch:108, train acc:0.856666666667, test acc:0.6875\n",
      "epoch:109, train acc:0.86, test acc:0.6922\n",
      "epoch:110, train acc:0.843333333333, test acc:0.6956\n",
      "epoch:111, train acc:0.86, test acc:0.6957\n",
      "epoch:112, train acc:0.843333333333, test acc:0.7025\n",
      "epoch:113, train acc:0.863333333333, test acc:0.71\n",
      "epoch:114, train acc:0.86, test acc:0.6901\n",
      "epoch:115, train acc:0.853333333333, test acc:0.6846\n",
      "epoch:116, train acc:0.853333333333, test acc:0.69\n",
      "epoch:117, train acc:0.863333333333, test acc:0.6928\n",
      "epoch:118, train acc:0.856666666667, test acc:0.6812\n",
      "epoch:119, train acc:0.85, test acc:0.6987\n",
      "epoch:120, train acc:0.86, test acc:0.6972\n",
      "epoch:121, train acc:0.856666666667, test acc:0.6938\n",
      "epoch:122, train acc:0.87, test acc:0.7056\n",
      "epoch:123, train acc:0.87, test acc:0.7052\n",
      "epoch:124, train acc:0.856666666667, test acc:0.7109\n",
      "epoch:125, train acc:0.86, test acc:0.6864\n",
      "epoch:126, train acc:0.86, test acc:0.6993\n",
      "epoch:127, train acc:0.856666666667, test acc:0.7031\n",
      "epoch:128, train acc:0.856666666667, test acc:0.6939\n",
      "epoch:129, train acc:0.866666666667, test acc:0.7051\n",
      "epoch:130, train acc:0.86, test acc:0.7166\n",
      "epoch:131, train acc:0.863333333333, test acc:0.6946\n",
      "epoch:132, train acc:0.85, test acc:0.7039\n",
      "epoch:133, train acc:0.846666666667, test acc:0.7078\n",
      "epoch:134, train acc:0.85, test acc:0.7144\n",
      "epoch:135, train acc:0.863333333333, test acc:0.7085\n",
      "epoch:136, train acc:0.866666666667, test acc:0.7034\n",
      "epoch:137, train acc:0.87, test acc:0.7016\n",
      "epoch:138, train acc:0.876666666667, test acc:0.7134\n",
      "epoch:139, train acc:0.866666666667, test acc:0.7087\n",
      "epoch:140, train acc:0.86, test acc:0.7056\n",
      "epoch:141, train acc:0.856666666667, test acc:0.7134\n",
      "epoch:142, train acc:0.876666666667, test acc:0.7079\n",
      "epoch:143, train acc:0.886666666667, test acc:0.7114\n",
      "epoch:144, train acc:0.846666666667, test acc:0.7071\n",
      "epoch:145, train acc:0.866666666667, test acc:0.7211\n",
      "epoch:146, train acc:0.863333333333, test acc:0.715\n",
      "epoch:147, train acc:0.866666666667, test acc:0.712\n",
      "epoch:148, train acc:0.86, test acc:0.7155\n",
      "epoch:149, train acc:0.85, test acc:0.721\n",
      "epoch:150, train acc:0.87, test acc:0.7121\n",
      "epoch:151, train acc:0.866666666667, test acc:0.7234\n",
      "epoch:152, train acc:0.89, test acc:0.7236\n",
      "epoch:153, train acc:0.88, test acc:0.7244\n",
      "epoch:154, train acc:0.86, test acc:0.7216\n",
      "epoch:155, train acc:0.836666666667, test acc:0.7133\n",
      "epoch:156, train acc:0.856666666667, test acc:0.7174\n",
      "epoch:157, train acc:0.87, test acc:0.7129\n",
      "epoch:158, train acc:0.863333333333, test acc:0.7077\n",
      "epoch:159, train acc:0.866666666667, test acc:0.7092\n",
      "epoch:160, train acc:0.88, test acc:0.7194\n",
      "epoch:161, train acc:0.86, test acc:0.7162\n",
      "epoch:162, train acc:0.856666666667, test acc:0.6953\n",
      "epoch:163, train acc:0.863333333333, test acc:0.7132\n",
      "epoch:164, train acc:0.876666666667, test acc:0.7096\n",
      "epoch:165, train acc:0.87, test acc:0.7134\n",
      "epoch:166, train acc:0.853333333333, test acc:0.7106\n",
      "epoch:167, train acc:0.86, test acc:0.7119\n",
      "epoch:168, train acc:0.88, test acc:0.7107\n",
      "epoch:169, train acc:0.843333333333, test acc:0.7174\n",
      "epoch:170, train acc:0.873333333333, test acc:0.7188\n",
      "epoch:171, train acc:0.876666666667, test acc:0.7214\n",
      "epoch:172, train acc:0.866666666667, test acc:0.7121\n",
      "epoch:173, train acc:0.866666666667, test acc:0.718\n",
      "epoch:174, train acc:0.886666666667, test acc:0.7193\n",
      "epoch:175, train acc:0.873333333333, test acc:0.7247\n",
      "epoch:176, train acc:0.863333333333, test acc:0.7228\n",
      "epoch:177, train acc:0.876666666667, test acc:0.71\n",
      "epoch:178, train acc:0.89, test acc:0.7136\n",
      "epoch:179, train acc:0.866666666667, test acc:0.7158\n",
      "epoch:180, train acc:0.886666666667, test acc:0.7179\n",
      "epoch:181, train acc:0.863333333333, test acc:0.7185\n",
      "epoch:182, train acc:0.87, test acc:0.7129\n",
      "epoch:183, train acc:0.863333333333, test acc:0.7216\n",
      "epoch:184, train acc:0.88, test acc:0.7206\n",
      "epoch:185, train acc:0.886666666667, test acc:0.7309\n",
      "epoch:186, train acc:0.893333333333, test acc:0.7189\n",
      "epoch:187, train acc:0.883333333333, test acc:0.7161\n",
      "epoch:188, train acc:0.866666666667, test acc:0.725\n",
      "epoch:189, train acc:0.873333333333, test acc:0.7204\n",
      "epoch:190, train acc:0.866666666667, test acc:0.7208\n",
      "epoch:191, train acc:0.88, test acc:0.7211\n",
      "epoch:192, train acc:0.88, test acc:0.7156\n",
      "epoch:193, train acc:0.883333333333, test acc:0.7214\n",
      "epoch:194, train acc:0.873333333333, test acc:0.7292\n",
      "epoch:195, train acc:0.89, test acc:0.7234\n",
      "epoch:196, train acc:0.886666666667, test acc:0.7268\n",
      "epoch:197, train acc:0.886666666667, test acc:0.7214\n",
      "epoch:198, train acc:0.883333333333, test acc:0.7242\n",
      "epoch:199, train acc:0.876666666667, test acc:0.721\n",
      "epoch:200, train acc:0.87, test acc:0.7165\n"
     ]
    },
    {
     "data": {
      "image/png": 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ROPdm+HBK3bENud6ot771Q7jwT9A+1njWsH+pUSvGXuMm2yv2iF7wwFrjobMQ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\nkoIQQggrSQpCCCGsJCkIIYSw+n8LSzCJv1eArQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x139112940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "# 過学習を再現するために、学習データを削減\n",
    "x_train = x_train[:300]\n",
    "t_train = t_train[:300]\n",
    "\n",
    "# weight decay（荷重減衰）の設定 =======================\n",
    "#weight_decay_lambda = 0 # weight decayを使用しない場合\n",
    "weight_decay_lambda = 0.1\n",
    "# ====================================================\n",
    "\n",
    "network = MultiLayerNet(input_size=784, hidden_size_list=[100, 100, 100, 100, 100, 100], output_size=10,\n",
    "                        weight_decay_lambda=weight_decay_lambda)\n",
    "optimizer = SGD(lr=0.01)\n",
    "\n",
    "max_epochs = 201\n",
    "train_size = x_train.shape[0]\n",
    "batch_size = 100\n",
    "\n",
    "train_loss_list = []\n",
    "train_acc_list = []\n",
    "test_acc_list = []\n",
    "\n",
    "iter_per_epoch = max(train_size / batch_size, 1)\n",
    "epoch_cnt = 0\n",
    "\n",
    "for i in range(1000000000):\n",
    "    batch_mask = np.random.choice(train_size, batch_size)\n",
    "    x_batch = x_train[batch_mask]\n",
    "    t_batch = t_train[batch_mask]\n",
    "\n",
    "    grads = network.gradient(x_batch, t_batch)\n",
    "    optimizer.update(network.params, grads)\n",
    "\n",
    "    if i % iter_per_epoch == 0:\n",
    "        train_acc = network.accuracy(x_train, t_train)\n",
    "        test_acc = network.accuracy(x_test, t_test)\n",
    "        train_acc_list.append(train_acc)\n",
    "        test_acc_list.append(test_acc)\n",
    "\n",
    "        print(\"epoch:\" + str(epoch_cnt) + \", train acc:\" + str(train_acc) + \", test acc:\" + str(test_acc))\n",
    "\n",
    "        epoch_cnt += 1\n",
    "        if epoch_cnt >= max_epochs:\n",
    "            break\n",
    "\n",
    "\n",
    "# 3.グラフの描画==========\n",
    "markers = {'train': 'o', 'test': 's'}\n",
    "x = np.arange(max_epochs)\n",
    "plt.plot(x, train_acc_list, marker='o', label='train', markevery=10)\n",
    "plt.plot(x, test_acc_list, marker='s', label='test', markevery=10)\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.ylim(0, 1.0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:0, train acc:0.0833333333333, test acc:0.1107\n",
      "epoch:1, train acc:0.103333333333, test acc:0.1234\n",
      "epoch:2, train acc:0.126666666667, test acc:0.1395\n",
      "epoch:3, train acc:0.163333333333, test acc:0.1542\n",
      "epoch:4, train acc:0.22, test acc:0.1746\n",
      "epoch:5, train acc:0.24, test acc:0.1998\n",
      "epoch:6, train acc:0.276666666667, test acc:0.2196\n",
      "epoch:7, train acc:0.296666666667, test acc:0.2362\n",
      "epoch:8, train acc:0.326666666667, test acc:0.2536\n",
      "epoch:9, train acc:0.373333333333, test acc:0.2771\n",
      "epoch:10, train acc:0.37, test acc:0.2845\n",
      "epoch:11, train acc:0.37, test acc:0.303\n",
      "epoch:12, train acc:0.4, test acc:0.3285\n",
      "epoch:13, train acc:0.453333333333, test acc:0.341\n",
      "epoch:14, train acc:0.47, test acc:0.3554\n",
      "epoch:15, train acc:0.5, test acc:0.3704\n",
      "epoch:16, train acc:0.513333333333, test acc:0.3856\n",
      "epoch:17, train acc:0.536666666667, test acc:0.4005\n",
      "epoch:18, train acc:0.56, test acc:0.407\n",
      "epoch:19, train acc:0.58, test acc:0.4176\n",
      "epoch:20, train acc:0.566666666667, test acc:0.4143\n",
      "epoch:21, train acc:0.553333333333, test acc:0.4161\n",
      "epoch:22, train acc:0.593333333333, test acc:0.4325\n",
      "epoch:23, train acc:0.583333333333, test acc:0.4383\n",
      "epoch:24, train acc:0.583333333333, test acc:0.4475\n",
      "epoch:25, train acc:0.61, test acc:0.4485\n",
      "epoch:26, train acc:0.633333333333, test acc:0.4598\n",
      "epoch:27, train acc:0.646666666667, test acc:0.4768\n",
      "epoch:28, train acc:0.68, test acc:0.4813\n",
      "epoch:29, train acc:0.663333333333, test acc:0.4908\n",
      "epoch:30, train acc:0.68, test acc:0.4926\n",
      "epoch:31, train acc:0.676666666667, test acc:0.4929\n",
      "epoch:32, train acc:0.7, test acc:0.503\n",
      "epoch:33, train acc:0.696666666667, test acc:0.5116\n",
      "epoch:34, train acc:0.7, test acc:0.5141\n",
      "epoch:35, train acc:0.693333333333, test acc:0.5026\n",
      "epoch:36, train acc:0.73, test acc:0.5204\n",
      "epoch:37, train acc:0.72, test acc:0.529\n",
      "epoch:38, train acc:0.746666666667, test acc:0.5226\n",
      "epoch:39, train acc:0.723333333333, test acc:0.5358\n",
      "epoch:40, train acc:0.76, test acc:0.5372\n",
      "epoch:41, train acc:0.77, test acc:0.5668\n",
      "epoch:42, train acc:0.766666666667, test acc:0.5584\n",
      "epoch:43, train acc:0.76, test acc:0.5628\n",
      "epoch:44, train acc:0.78, test acc:0.5625\n",
      "epoch:45, train acc:0.79, test acc:0.5857\n",
      "epoch:46, train acc:0.786666666667, test acc:0.574\n",
      "epoch:47, train acc:0.803333333333, test acc:0.5899\n",
      "epoch:48, train acc:0.81, test acc:0.5975\n",
      "epoch:49, train acc:0.806666666667, test acc:0.5863\n",
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      "epoch:136, train acc:0.883333333333, test acc:0.6928\n",
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      "epoch:139, train acc:0.9, test acc:0.7027\n",
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      "epoch:160, train acc:0.906666666667, test acc:0.7171\n",
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      "epoch:173, train acc:0.903333333333, test acc:0.7148\n",
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      "epoch:175, train acc:0.903333333333, test acc:0.7184\n",
      "epoch:176, train acc:0.903333333333, test acc:0.7204\n",
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      "epoch:179, train acc:0.903333333333, test acc:0.7194\n",
      "epoch:180, train acc:0.923333333333, test acc:0.7168\n",
      "epoch:181, train acc:0.923333333333, test acc:0.7216\n",
      "epoch:182, train acc:0.906666666667, test acc:0.7221\n",
      "epoch:183, train acc:0.906666666667, test acc:0.7187\n",
      "epoch:184, train acc:0.903333333333, test acc:0.7199\n",
      "epoch:185, train acc:0.893333333333, test acc:0.7112\n",
      "epoch:186, train acc:0.886666666667, test acc:0.7073\n",
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      "epoch:188, train acc:0.906666666667, test acc:0.7165\n",
      "epoch:189, train acc:0.886666666667, test acc:0.7103\n",
      "epoch:190, train acc:0.896666666667, test acc:0.712\n",
      "epoch:191, train acc:0.9, test acc:0.7185\n",
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      "epoch:193, train acc:0.906666666667, test acc:0.7144\n",
      "epoch:194, train acc:0.89, test acc:0.7088\n",
      "epoch:195, train acc:0.903333333333, test acc:0.7187\n",
      "epoch:196, train acc:0.926666666667, test acc:0.722\n",
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      "epoch:198, train acc:0.91, test acc:0.7232\n",
      "epoch:199, train acc:0.906666666667, test acc:0.7255\n",
      "epoch:200, train acc:0.916666666667, test acc:0.7238\n"
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4wJEQ4oALRKTaGDOvsXF5S8r+AgYmRBIVFlj3BbsdfnwOEk6FflN8\nE5xSSjWTJ8tyrgH6i0hvrGRwLXB97RWMMb2PPRaRN4EvWmNCmLc+kzmLUskqtEbhntG384krpS2C\n3B0w49/WVYJSSrVBLTDbvHPGmGrgXmARsB34yBizVUTuEpE2M3HA3LUZPDp3E5mFZRis27jX7Ctg\n3vp6Fz0/PAtRPWDIDF+EqZRSLcKjBfyNMQuBhfWWvexi3V94Mpam+v2CLZRX2essq7IZ5ixK5bKR\njn7zA6sgfRVMf1rnRFBKtWkeu1JoL0oqbE6XH2tKAuD7ZyC0M4y80UtRKaWUZ2hSaIAxBle9A4nR\njvsUsjdb/Qmn3w1B4V6LTSmlPEGTQgMKSqswQKB/3dQQGujPrKmOe/d++AcERcLY270foFJKtTBN\nCg3Yk1sMwK3je5EUHYoASdGhPDVjqNWfkLcbtn4GY26DUCf3LSilVBujvaIN2HPYqnR6/bie/O+F\nJ5Rtgh+fBf8gOKNDT1etlGpH9EqhAXsPlxDoLyTHOKlzVJQJG963ahxFaNkKpVT7oEmhAXtyi+nR\nOYwAZ/Mm/PAPMHYYf5/3A1NKKQ/RpNCAvYdL6NMl4sQXcndCyhsw6hcQ09PrcSmllKdoUnDBZjfs\nyyulT5yTYabf/MEafjr5Me8HppRSHqRJwYXMgjIqq+30rp8U8vfAzq9g/K8hootvglNKKQ/RpODC\n+vQCAIYm15s3Yfcy699TtcaRUqr90aTgQsq+AiKCAzila6e6L+xeDtE9oHMf3wSmlFIepEnBhTX7\n8hnZIxp/v1p3M9uqYO930PccLY+tlGqXNCk4UVRWReqho4zuWW/ehIwUqDhiJQWllGqHNCk4sf5A\nAcbAmF71SlfsXgriB73P8k1gSinlYZoUnEjZV4C/nzCiR/TxhXY7bP4Yek3UOkdKqXZLax85sWJn\nLsOSowgLCoA5/aEk5/iLBfvgiSgIj4dZaT6LUSmlPEGvFOrZnVvM5swiLhzazVpQOyHU5mq5Ukq1\nYZoU6lmwIQsRuHh4oq9DUUopr9OkUIsxhs83ZnFGn1gSOoX4OhyllPI6TQq1pB46yp7DJXqVoJTq\nsDQp1LIpvQiAcb0d9yfYqnwYjVJKeZ8mhVq2ZhURHuRPr1hHEbwDK12vHK4T6yil2h8dklrL1qwj\nDOrWCb9jpS12LrKm23x4LwQ7mVdBKaXaGb1ScLDbDdsPHmFIYq0CeKlfQa8zNSEopToMTQoO+/NL\nKam0MfhYUsjZDvm7YcA03wamlFJepEnBYWuW1ck8JNExf0LKf6ymI503QSnVgWhScNiadYQAP6F/\nQgRUlsDG92HwpRAe5+vQlFLKazQpOKw/UED/hEiCA/ytwncVR2DM7b4OSymlvEqTApBztJzVe/OZ\nMigejIE1r0P8EOg+ztehKaWUV2lSAL7cdBC7gUtHJELmWsjeBGN+qbOrKaU6HE0KwPwNWQzu1ol+\n8ZGw5jUIioBh1/g6LKWU8roOnxTS80vZkF7IJSMSoTQftsy1EkJwpK9DU0opr+vwSeHYUNSJ/eJg\n61ywVcCoW3wclVJK+YZHk4KITBORVBHZJSKPOnn9BhHZJCKbReQnERnuyXicySgoAyA5JhQ2fwJd\nToGuw7wdhlJKtQoeSwoi4g+8AEwHBgPXicjgeqvtBSYZY4YCfwRe9VQ8rmQUlBERHEBUxUGrAN7Q\nq7SDWSnVYXnySmEssMsYs8cYUwl8AFxaewVjzE/GmALH01VAsgfjcSqzsIyk6FBky6fWgqFXejsE\npZRqNTyZFJKA9FrPMxzLXLkN+MrZCyIyU0RSRCQlNze3BUO0rhSSY0Jh+wJIHgMxvVr0/ZVSqi1p\nFR3NInI2VlJ4xNnrxphXjTGjjTGju3Tp0qL7zigopX9EOWRtgP7nt+h7K6VUW+PJ+RQyge61nic7\nltUhIsOA14Dpxpg8D8ZzgqKyKo6WVzPGvhEw0Pdcb+5eKaVaHU9eKawB+otIbxEJAq4FFtReQUR6\nAHOBm4wxOz0Yi1OZjpFHA4tXQ2gMJI7wdghKKdWqeOxKwRhTLSL3AosAf+ANY8xWEbnL8frLwB+A\nWOBFsUb8VBtjRnsqpvoyCkoBQ0LuT9DnbPDz99aulVKqVfLodJzGmIXAwnrLXq71+HbAZ6VIMwvL\nGCz7CSzLhX7adKSUUh16juaMgjIuDlyDET+k/1Rfh6OU8qCqqioyMjIoLy/3dSgeFRISQnJyMoGB\ngU3avmMnhfwSfum/Euk9CSJadlSTUqp1ycjIIDIykl69eiHt9AZVYwx5eXlkZGTQu3fvJr1HqxiS\n6iuBhzaRZLLh1Ct8HYpSysPKy8uJjY1ttwkBQESIjY1t1tVQh00K5VU2RhxZik0CYNBFvg5HKeUF\n7TkhHNPcz9hhk0Ja+iEu9/uO3G6TreGoSimlOm5SqFzzJrFyFMb/2tehKKVaoXnrM5kwexm9H/2S\nCbOXMW/9CffeNkphYSEvvvhio7e74IILKCwsbNa+G6NjJoXqSvrteoM1ZjDxg8/ydTRKqVZm3vpM\nHpu7mczCMgzW8PXH5m5uVmJwlRSqq6sb3G7hwoVER0c3eb+N1TFHH23+mKiqXBbH3s8Yv/bfxqiU\nquvJz7eyLeuIy9fXHyik0mavs6ysysbDn2zi/dUHnG4zOLETj188xOV7Pvroo+zevZsRI0YQGBhI\nSEgIMTEx7Nixg507d3LZZZeRnp5OeXk5999/PzNnzgSgV69epKSkUFxczPTp05k4cSI//fQTSUlJ\nzJ8/n9DQ0CYcAdc63pWCMZifX2KnSaa619m+jkYp1QrVTwgnW+6O2bNn07dvXzZs2MCcOXNYt24d\nzz33HDt3WhV+3njjDdauXUtKSgrPP/88eXknloJLS0vjnnvuYevWrURHR/Ppp582OR5XOt6Vwv4f\nkezNvFF9O6OTvHdJppRqPRr6Rg8wYfYyMgvLTlieFB3Kh3ee0SIxjB07ts69BM8//zyfffYZAOnp\n6aSlpREbG1tnm969ezNihFWjbdSoUezbt69FYqmt410prHyRisAo5tkmMDw5ytfRKKVaoVlTBxIa\nWLcWWmigP7OmDmyxfYSHh9c8/vbbb1myZAkrV65k48aNjBw50um9BsHBwTWP/f39T9of0RTt/0ph\nTn8oyamzKBj4MeQ3dI7f55OQlFKt22UjrfnA5ixKJauwjMToUGZNHVizvCkiIyM5evSo09eKioqI\niYkhLCyMHTt2sGrVqibvp7naf1KolxCOiaVQ52JWSrl02cikZiWB+mJjY5kwYQKnnnoqoaGhJCQk\n1Lw2bdo0Xn75ZQYNGsTAgQM5/fTTW2y/jdX+k4JSSrUS7733ntPlwcHBfPWV09mIa/oN4uLi2LJl\nS83yhx56qMXjg47Yp6CUUsolTQpKKaVqaFJQSilVo/0nhfD4xi1XSqkOrN13NM+b8i2Pzd1MWZWt\nZllooD9PTRnKZT6MSymlWqN2f6UwZ1FqnYQAVg2TOYtSfRSRUkq1Xu3+SiHLya3qDS1XSilnN70C\nVrPzrLQmvWVhYSHvvfced999d6O3ffbZZ5k5cyZhYWFN2ndjtPsrhcRo5xUEXS1XSilXN726XO6G\nps6nAFZSKC0tbfK+G6PdXynMmjrQaZ9CS9YwUUq1MV89Ctmbm7btfy50vrzrUJg+2+VmtUtnn3fe\necTHx/PRRx9RUVHB5ZdfzpNPPklJSQlXX301GRkZ2Gw2fv/733Po0CGysrI4++yziYuLY/ny5U2L\n203tPil4ooaJUko11uzZs9myZQsbNmxg8eLFfPLJJ6xevRpjDJdccgnfffcdubm5JCYm8uWXXwJW\nTaSoqCieeeYZli9fTlxcnMfjbPdJAVq+holSqo1r4Bs9AE80UEH51i+bvfvFixezePFiRo4cCUBx\ncTFpaWmceeaZPPjggzzyyCNcdNFFnHnmmc3eV2N1iKSglFKtiTGGxx57jDvvvPOE19atW8fChQv5\n3e9+x7nnnssf/vAHr8bW7jualVKq0Txw02vt0tlTp07ljTfeoLi4GIDMzExycnLIysoiLCyMG2+8\nkVmzZrFu3boTtvU0vVJQSqn6mjjstCG1S2dPnz6d66+/njPOsGZxi4iI4J133mHXrl3MmjULPz8/\nAgMDeemllwCYOXMm06ZNIzEx0eMdzWKM8egOWtro0aNNSkqKr8NQSrUx27dvZ9CgQb4OwyucfVYR\nWWuMGX2ybbX5SCmlVA1NCkoppWpoUlBKdRhtrbm8KZr7GTUpKKU6hJCQEPLy8tp1YjDGkJeXR0hI\nSJPfQ0cfKaU6hOTkZDIyMsjNzfV1KB4VEhJCcnJyk7fXpKCU6hACAwPp3bu3r8No9TzafCQi00Qk\nVUR2icijTl4XEXne8fomETnNk/EopZRqmMeSgoj4Ay8A04HBwHUiMrjeatOB/o6fmcBLnopHKaXU\nyXnySmEssMsYs8cYUwl8AFxab51LgbeNZRUQLSLdPBiTUkqpBniyTyEJSK/1PAMY58Y6ScDB2iuJ\nyEysKwmAYhFp6lyaccDhJm7rSa01Lmi9sWlcjaNxNU57jKunOyu1iY5mY8yrwKvNfR8RSXHnNm9v\na61xQeuNTeNqHI2rcTpyXJ5sPsoEutd6nuxY1th1lFJKeYknk8IaoL+I9BaRIOBaYEG9dRYANztG\nIZ0OFBljDtZ/I6WUUt7hseYjY0y1iNwLLAL8gTeMMVtF5C7H6y8DC4ELgF1AKXCrp+JxaHYTlIe0\n1rig9camcTWOxtU4HTauNlc6WymllOdo7SOllFI1NCkopZSq0WGSwslKbngxju4islxEtonIVhG5\n37H8CRHJFJENjp8LfBDbPhHZ7Nh/imNZZxH5RkTSHP/GeDmmgbWOyQYROSIiD/jieInIGyKSIyJb\nai1zeXxE5DHH31uqiEz1clxzRGSHo3zMZyIS7VjeS0TKah23l70cl8vfm4+P14e1YtonIhscy715\nvFydG7z7N2aMafc/WB3du4E+QBCwERjso1i6Aac5HkcCO7HKgDwBPOTj47QPiKu37GngUcfjR4G/\n+vj3mI11E47XjxdwFnAasOVkx8fxO90IBAO9HX9//l6M63wgwPH4r7Xi6lV7PR8cL6e/N18fr3qv\n/x34gw+Ol6tzg1f/xjrKlYI7JTe8whhz0BizzvH4KLAd6y7u1upS4C3H47eAy3wYy7nAbmPMfl/s\n3BjzHZBfb7Gr43Mp8IExpsIYsxdrhN1Yb8VljFlsjKl2PF2FdQ+QV7k4Xq749HgdIyICXA2874l9\nN6SBc4NX/8Y6SlJwVU7Dp0SkFzAS+Nmx6D7H5f4b3m6mcTDAEhFZ6ygtApBgjt87kg0k+CCuY66l\n7n9WXx8vcH18WtPf3C+Br2o97+1oClkhImf6IB5nv7fWcrzOBA4ZY9JqLfP68ap3bvDq31hHSQqt\njohEAJ8CDxhjjmBViO0DjMCq/fR3H4Q10RgzAqt67T0iclbtF411zeqTMcxi3QB5CfCxY1FrOF51\n+PL4uCIivwWqgXcdiw4CPRy/5/8B3hORTl4MqdX93uq5jrpfPLx+vJycG2p442+soySFVlVOQ0QC\nsX7p7xpj5gIYYw4ZY2zGGDvwbzx06dwQY0ym498c4DNHDIfEUbnW8W+Ot+NymA6sM8YccsTo8+Pl\n4Or4+PxvTkR+AVwE3OA4meBoashzPF6L1Q49wFsxNfB7aw3HKwCYAXx4bJm3j5ezcwNe/hvrKEnB\nnZIbXuFos3wd2G6MeabW8tolwy8HttTf1sNxhYtI5LHHWB2VW7CO0y2O1W4B5nszrlrqfIPz9fGq\nxdXxWQBcKyLBItIba86Q1d4KSkSmAQ8DlxhjSmst7yLWXCeISB9HXHu8GJer35tPj5fDFGCHMSbj\n2AJvHi9X5wa8/TfmjV711vCDVU5jJ1am/60P45iIdfm3Cdjg+LkA+C+w2bF8AdDNy3H1wRrJsBHY\neuwYAbHAUiANWAJ09sExCwfygKhay7x+vLCS0kGgCqv99raGjg/wW8ffWyow3ctx7cJqbz72N/ay\nY90rHL/fDcA64GIvx+Xy9+bL4+VY/iZwV711vXm8XJ0bvPo3pmUulFJK1egozUdKKaXcoElBKaVU\nDU0KSimlamhSUEopVUOTglJKqRqaFJTyMBGZLCJf+DoOpdyhSUEppVQNTQpKOYjIjSKy2lH87BUR\n8ReRYhH5h6O+/VIR6eJYd4SIrJLj8xXEOJb3E5ElIrJRRNaJSF/H20eIyCdizXHwruPuVURktqN+\n/iYR+ZuPPrpSNTQpKAWIyCDgGmCCsYqf2YAbsO6mTjHGDAFWAI87NnkbeMQYMwzrDt1jy98FXjDG\nDAfGY905C1bFywewauD3ASaISCxWqYchjvf5k2c/pVInp0lBKcu5wChgjWPWrXOxTt52jhdIeweY\nKCJRQLQxZoVj+VvAWY7aUUnGmM8AjDHl5njdodXGmAxjFYLbgDV5SxFQDrwuIjOAmhpFSvmKJgWl\nLAK8ZYwZ4fgZaIx5wsl6Ta0LU1HrsQ1rVrRqrCqhn2BVM/26ie+tVIvRpKCUZSlwpYjEQ828uD2x\n/o9c6VjneuAHY0wRUFBrwpWbgBXGmi0rQ0Quc7xHsIiEudqho25+lDFmIfAbYLgnPphSjRHg6wCU\nag2MMdtE5HfAYhHxw6qgeQ9QAox1vJaD1e8AVgnjlx0n/T3ArY7lNwGviMj/Od7jqgZ2GwnMF5EQ\nrCuV/2nhj6VUo2mVVKUaICLFxpgIX8ehlLdo85FSSqkaeqWglFKqhl4pKKWUqqFJQSmlVA1NCkop\npWpoUlBKKVVDk4JSSqka/w/POEll06ja2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ab4668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "# 過学習を再現するために、学習データを削減\n",
    "x_train = x_train[:300]\n",
    "t_train = t_train[:300]\n",
    "\n",
    "# weight decay（荷重減衰）の設定 =======================\n",
    "#weight_decay_lambda = 0 # weight decayを使用しない場合\n",
    "weight_decay_lambda = 0.1\n",
    "# ====================================================\n",
    "\n",
    "network = MultiLayerNet(input_size=784, hidden_size_list=[100, 100, 100, 100, 100, 100], output_size=10,\n",
    "                        weight_decay_lambda=weight_decay_lambda)\n",
    "optimizer = SGD(lr=0.01)\n",
    "\n",
    "max_epochs = 201\n",
    "train_size = x_train.shape[0]\n",
    "batch_size = 100\n",
    "\n",
    "train_loss_list = []\n",
    "train_acc_list = []\n",
    "test_acc_list = []\n",
    "\n",
    "iter_per_epoch = max(train_size / batch_size, 1)\n",
    "epoch_cnt = 0\n",
    "\n",
    "for i in range(1000000000):\n",
    "    batch_mask = np.random.choice(train_size, batch_size)\n",
    "    x_batch = x_train[batch_mask]\n",
    "    t_batch = t_train[batch_mask]\n",
    "\n",
    "    grads = network.gradient(x_batch, t_batch)\n",
    "    optimizer.update(network.params, grads)\n",
    "\n",
    "    if i % iter_per_epoch == 0:\n",
    "        train_acc = network.accuracy(x_train, t_train)\n",
    "        test_acc = network.accuracy(x_test, t_test)\n",
    "        train_acc_list.append(train_acc)\n",
    "        test_acc_list.append(test_acc)\n",
    "\n",
    "        print(\"epoch:\" + str(epoch_cnt) + \", train acc:\" + str(train_acc) + \", test acc:\" + str(test_acc))\n",
    "\n",
    "        epoch_cnt += 1\n",
    "        if epoch_cnt >= max_epochs:\n",
    "            break\n",
    "\n",
    "\n",
    "# 3.グラフの描画==========\n",
    "markers = {'train': 'o', 'test': 's'}\n",
    "x = np.arange(max_epochs)\n",
    "plt.plot(x, train_acc_list, marker='o', label='train', markevery=10)\n",
    "plt.plot(x, test_acc_list, marker='s', label='test', markevery=10)\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.ylim(0, 1.0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Dropout:\n",
    "    \"\"\"\n",
    "    http://arxiv.org/abs/1207.0580\n",
    "    \"\"\"\n",
    "    def __init__(self, dropout_ratio=0.5):\n",
    "        self.dropout_ratio = dropout_ratio\n",
    "        self.mask = None\n",
    "\n",
    "    def forward(self, x, train_flg=True):\n",
    "        if train_flg:\n",
    "            self.mask = np.random.rand(*x.shape) > self.dropout_ratio\n",
    "            return x * self.mask\n",
    "        else:\n",
    "            return x * (1.0 - self.dropout_ratio)\n",
    "\n",
    "    def backward(self, dout):\n",
    "        return dout * self.mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Trainer:\n",
    "    \"\"\"ニューラルネットの訓練を行うクラス\n",
    "    \"\"\"\n",
    "    def __init__(self, network, x_train, t_train, x_test, t_test,\n",
    "                 epochs=20, mini_batch_size=100,\n",
    "                 optimizer='SGD', optimizer_param={'lr':0.01}, \n",
    "                 evaluate_sample_num_per_epoch=None, verbose=True):\n",
    "        self.network = network\n",
    "        self.verbose = verbose\n",
    "        self.x_train = x_train\n",
    "        self.t_train = t_train\n",
    "        self.x_test = x_test\n",
    "        self.t_test = t_test\n",
    "        self.epochs = epochs\n",
    "        self.batch_size = mini_batch_size\n",
    "        self.evaluate_sample_num_per_epoch = evaluate_sample_num_per_epoch\n",
    "\n",
    "        # optimzer\n",
    "        optimizer_class_dict = {'sgd':SGD, 'momentum':Momentum, 'nesterov':Nesterov,\n",
    "                                'adagrad':AdaGrad, 'rmsprpo':RMSprop, 'adam':Adam}\n",
    "        self.optimizer = optimizer_class_dict[optimizer.lower()](**optimizer_param)\n",
    "        \n",
    "        self.train_size = x_train.shape[0]\n",
    "        self.iter_per_epoch = max(self.train_size / mini_batch_size, 1)\n",
    "        self.max_iter = int(epochs * self.iter_per_epoch)\n",
    "        self.current_iter = 0\n",
    "        self.current_epoch = 0\n",
    "        \n",
    "        self.train_loss_list = []\n",
    "        self.train_acc_list = []\n",
    "        self.test_acc_list = []\n",
    "\n",
    "    def train_step(self):\n",
    "        batch_mask = np.random.choice(self.train_size, self.batch_size)\n",
    "        x_batch = self.x_train[batch_mask]\n",
    "        t_batch = self.t_train[batch_mask]\n",
    "        \n",
    "        grads = self.network.gradient(x_batch, t_batch)\n",
    "        self.optimizer.update(self.network.params, grads)\n",
    "        \n",
    "        loss = self.network.loss(x_batch, t_batch)\n",
    "        self.train_loss_list.append(loss)\n",
    "        if self.verbose: print(\"train loss:\" + str(loss))\n",
    "        \n",
    "        if self.current_iter % self.iter_per_epoch == 0:\n",
    "            self.current_epoch += 1\n",
    "            \n",
    "            x_train_sample, t_train_sample = self.x_train, self.t_train\n",
    "            x_test_sample, t_test_sample = self.x_test, self.t_test\n",
    "            if not self.evaluate_sample_num_per_epoch is None:\n",
    "                t = self.evaluate_sample_num_per_epoch\n",
    "                x_train_sample, t_train_sample = self.x_train[:t], self.t_train[:t]\n",
    "                x_test_sample, t_test_sample = self.x_test[:t], self.t_test[:t]\n",
    "                \n",
    "            train_acc = self.network.accuracy(x_train_sample, t_train_sample)\n",
    "            test_acc = self.network.accuracy(x_test_sample, t_test_sample)\n",
    "            self.train_acc_list.append(train_acc)\n",
    "            self.test_acc_list.append(test_acc)\n",
    "\n",
    "            if self.verbose: print(\"=== epoch:\" + str(self.current_epoch) + \", train acc:\" + str(train_acc) + \", test acc:\" + str(test_acc) + \" ===\")\n",
    "        self.current_iter += 1\n",
    "\n",
    "    def train(self):\n",
    "        for i in range(self.max_iter):\n",
    "            self.train_step()\n",
    "\n",
    "        test_acc = self.network.accuracy(self.x_test, self.t_test)\n",
    "\n",
    "        if self.verbose:\n",
    "            print(\"=============== Final Test Accuracy ===============\")\n",
    "            print(\"test acc:\" + str(test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Nesterov:\n",
    "\n",
    "    \"\"\"Nesterov's Accelerated Gradient (http://arxiv.org/abs/1212.0901)\"\"\"\n",
    "\n",
    "    def __init__(self, lr=0.01, momentum=0.9):\n",
    "        self.lr = lr\n",
    "        self.momentum = momentum\n",
    "        self.v = None\n",
    "        \n",
    "    def update(self, params, grads):\n",
    "        if self.v is None:\n",
    "            self.v = {}\n",
    "            for key, val in params.items():\n",
    "                self.v[key] = np.zeros_like(val)\n",
    "            \n",
    "        for key in params.keys():\n",
    "            self.v[key] *= self.momentum\n",
    "            self.v[key] -= self.lr * grads[key]\n",
    "            params[key] += self.momentum * self.momentum * self.v[key]\n",
    "            params[key] -= (1 + self.momentum) * self.lr * grads[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class RMSprop:\n",
    "\n",
    "    \"\"\"RMSprop\"\"\"\n",
    "\n",
    "    def __init__(self, lr=0.01, decay_rate = 0.99):\n",
    "        self.lr = lr\n",
    "        self.decay_rate = decay_rate\n",
    "        self.h = None\n",
    "        \n",
    "    def update(self, params, grads):\n",
    "        if self.h is None:\n",
    "            self.h = {}\n",
    "            for key, val in params.items():\n",
    "                self.h[key] = np.zeros_like(val)\n",
    "            \n",
    "        for key in params.keys():\n",
    "            self.h[key] *= self.decay_rate\n",
    "            self.h[key] += (1 - self.decay_rate) * grads[key] * grads[key]\n",
    "            params[key] -= self.lr * grads[key] / (np.sqrt(self.h[key]) + 1e-7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "=== epoch:1, train acc:0.0466666666667, test acc:0.082 ===\n",
      "train loss:2.32410263111\n",
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      "train loss:2.30768186918\n",
      "=== epoch:2, train acc:0.0433333333333, test acc:0.0809 ===\n",
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      "=== epoch:3, train acc:0.04, test acc:0.0781 ===\n",
      "train loss:2.30110057525\n",
      "train loss:2.29054599374\n",
      "train loss:2.31063401819\n",
      "=== epoch:4, train acc:0.04, test acc:0.0751 ===\n",
      "train loss:2.30010960583\n",
      "train loss:2.31345330727\n",
      "train loss:2.30922172308\n",
      "=== epoch:5, train acc:0.0366666666667, test acc:0.0739 ===\n",
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      "train loss:2.30333256562\n",
      "=== epoch:6, train acc:0.0366666666667, test acc:0.0734 ===\n",
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      "=== epoch:7, train acc:0.0333333333333, test acc:0.0741 ===\n",
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      "=== epoch:8, train acc:0.03, test acc:0.075 ===\n",
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      "=== epoch:9, train acc:0.0333333333333, test acc:0.0759 ===\n",
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      "=== epoch:10, train acc:0.04, test acc:0.0749 ===\n",
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      "=== epoch:15, train acc:0.0666666666667, test acc:0.0812 ===\n",
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      "=== epoch:89, train acc:0.333333333333, test acc:0.2759 ===\n",
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      "train loss:1.19074464714\n",
      "=== epoch:301, train acc:0.646666666667, test acc:0.54 ===\n",
      "train loss:1.29322324211\n",
      "train loss:1.23080112617\n",
      "=============== Final Test Accuracy ===============\n",
      "test acc:0.5402\n"
     ]
    },
    {
     "data": {
      "image/png": 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sji8ih0w1hWbm31+s49OVOxj+90/ZfaCUJANf0PzWdR5mWnYQvqjhZv+rJd8+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\ngJKCiIgEKCmIiEjA/wO0u0DpQ0qwCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x135433320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "# 過学習を再現するために、学習データを削減\n",
    "x_train = x_train[:300]\n",
    "t_train = t_train[:300]\n",
    "\n",
    "# Dropuoutの有無、割り合いの設定 ========================\n",
    "use_dropout = True  # Dropoutなしのときの場合はFalseに\n",
    "dropout_ratio = 0.2\n",
    "# ====================================================\n",
    "\n",
    "network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100, 100, 100],\n",
    "                              output_size=10, use_dropout=use_dropout, dropout_ration=dropout_ratio)\n",
    "trainer = Trainer(network, x_train, t_train, x_test, t_test,\n",
    "                  epochs=301, mini_batch_size=100,\n",
    "                  optimizer='sgd', optimizer_param={'lr': 0.01}, verbose=True)\n",
    "trainer.train()\n",
    "\n",
    "train_acc_list, test_acc_list = trainer.train_acc_list, trainer.test_acc_list\n",
    "\n",
    "# グラフの描画==========\n",
    "markers = {'train': 'o', 'test': 's'}\n",
    "x = np.arange(len(train_acc_list))\n",
    "plt.plot(x, train_acc_list, marker='o', label='train', markevery=10)\n",
    "plt.plot(x, test_acc_list, marker='s', label='test', markevery=10)\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.ylim(0, 1.0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def shuffle_dataset(x, t):\n",
    "    \"\"\"データセットのシャッフルを行う\n",
    "    Parameters\n",
    "    ----------\n",
    "    x : 訓練データ\n",
    "    t : 教師データ\n",
    "    Returns\n",
    "    -------\n",
    "    x, t : シャッフルを行った訓練データと教師データ\n",
    "    \"\"\"\n",
    "    permutation = np.random.permutation(x.shape[0])\n",
    "    x = x[permutation,:] if x.ndim == 2 else x[permutation,:,:,:]\n",
    "    t = t[permutation]\n",
    "\n",
    "    return x, t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".:11: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      ".:12: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      ".:13: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      ".:14: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val acc:0.14 | lr:1.799831042641291e-05, weight decay:1.83778494041255e-07\n",
      "val acc:0.05 | lr:0.0002347318914508403, weight decay:6.152274981702408e-08\n",
      "val acc:0.72 | lr:0.006096954087946476, weight decay:2.8837250105419758e-08\n",
      "val acc:0.15 | lr:0.000537954978468719, weight decay:5.878394641264319e-06\n",
      "val acc:0.12 | lr:0.00016448980891706314, weight decay:1.314304106861244e-06\n",
      "val acc:0.1 | lr:8.049231738261445e-05, weight decay:2.4262099041145846e-06\n",
      "val acc:0.11 | lr:1.3502871720517409e-06, weight decay:1.2036817506870037e-06\n",
      "val acc:0.19 | lr:0.0005901490903505406, weight decay:1.752333287592728e-05\n",
      "val acc:0.17 | lr:0.00010492606760134446, weight decay:3.5827537896359874e-08\n",
      "val acc:0.27 | lr:0.0006082215111986305, weight decay:1.2330059021007899e-08\n",
      "val acc:0.1 | lr:2.0585419491007794e-06, weight decay:1.3271515844256667e-08\n",
      "val acc:0.77 | lr:0.007314328741051734, weight decay:6.221610323334567e-05\n",
      "val acc:0.34 | lr:0.0014885112166808552, weight decay:2.1864035487015072e-07\n",
      "val acc:0.12 | lr:2.0416981064014347e-06, weight decay:6.031239870556507e-05\n",
      "val acc:0.1 | lr:2.7307582038505023e-05, weight decay:3.1848671075102033e-05\n",
      "val acc:0.15 | lr:2.035276709669739e-06, weight decay:4.33801261677729e-06\n",
      "val acc:0.69 | lr:0.007978656136557872, weight decay:2.1181845167084804e-08\n",
      "val acc:0.36 | lr:0.0022067061737045318, weight decay:5.432257356005885e-08\n",
      "val acc:0.07 | lr:3.853895365113746e-05, weight decay:5.107802983714721e-05\n",
      "val acc:0.77 | lr:0.005546405062300536, weight decay:2.244491953842928e-07\n",
      "val acc:0.2 | lr:0.00024467696966955693, weight decay:1.1809479844342444e-08\n",
      "val acc:0.13 | lr:2.973069492874515e-06, weight decay:2.3182482689189374e-06\n",
      "val acc:0.23 | lr:0.001958960507923658, weight decay:2.5427429360304095e-05\n",
      "val acc:0.18 | lr:0.0009367897210582247, weight decay:1.198279219319932e-07\n",
      "val acc:0.07 | lr:1.4047887365937155e-05, weight decay:5.577247901016212e-06\n",
      "val acc:0.14 | lr:0.00023218023232259645, weight decay:1.285380488762258e-08\n",
      "val acc:0.16 | lr:0.0012854534763825018, weight decay:6.22892974898882e-05\n",
      "val acc:0.07 | lr:3.7693088063227846e-05, weight decay:3.4697176622460025e-07\n",
      "val acc:0.12 | lr:9.254775261159934e-05, weight decay:4.195261536260933e-07\n",
      "val acc:0.11 | lr:0.00033554516651217094, weight decay:1.5655903081586805e-06\n",
      "val acc:0.15 | lr:0.0005684466072664272, weight decay:8.191101644551462e-06\n",
      "val acc:0.14 | lr:0.0001918173312108617, weight decay:4.220879161487208e-08\n",
      "val acc:0.18 | lr:0.0012728129972591367, weight decay:3.0940193198532466e-06\n",
      "val acc:0.56 | lr:0.005435330538585565, weight decay:1.11889668740693e-08\n",
      "val acc:0.03 | lr:1.8702383864207209e-06, weight decay:2.691985935193073e-05\n",
      "val acc:0.18 | lr:0.00012912367358681347, weight decay:2.230314066915655e-06\n",
      "val acc:0.18 | lr:0.0011710826048938314, weight decay:3.707969136914042e-05\n",
      "val acc:0.76 | lr:0.007954156922587819, weight decay:8.113842039183665e-05\n",
      "val acc:0.1 | lr:2.2189263098489247e-05, weight decay:1.266444417960917e-06\n",
      "val acc:0.11 | lr:3.95013497639435e-06, weight decay:1.3889503617062258e-07\n",
      "val acc:0.07 | lr:2.84964816157627e-06, weight decay:7.873261365055332e-07\n",
      "val acc:0.07 | lr:6.791751737541861e-05, weight decay:1.1666232402530862e-07\n",
      "val acc:0.12 | lr:4.7605110774414986e-05, weight decay:4.701289026463789e-07\n",
      "val acc:0.11 | lr:1.613959255760313e-05, weight decay:1.4474311342819413e-07\n",
      "val acc:0.08 | lr:1.9084937013001158e-05, weight decay:1.1580359946842445e-08\n",
      "val acc:0.06 | lr:1.1309654576294414e-06, weight decay:8.046637985249008e-07\n",
      "val acc:0.03 | lr:0.00016809676629749238, weight decay:6.684320372588282e-08\n",
      "val acc:0.15 | lr:2.6284254582504244e-06, weight decay:1.7712046750623615e-05\n",
      "val acc:0.16 | lr:2.5240862936759203e-06, weight decay:1.7453400948168694e-07\n",
      "val acc:0.1 | lr:1.1158786610524581e-05, weight decay:4.1154975866773e-08\n",
      "val acc:0.12 | lr:1.547685797530406e-05, weight decay:1.569480965439299e-07\n",
      "val acc:0.55 | lr:0.004131630902309629, weight decay:1.0886928474624086e-08\n",
      "val acc:0.59 | lr:0.0034377009625794937, weight decay:3.711910367116388e-06\n",
      "val acc:0.13 | lr:0.0008904922751777309, weight decay:1.1743775191245708e-08\n",
      "val acc:0.08 | lr:2.158803706372628e-05, weight decay:3.1611583940055314e-07\n",
      "val acc:0.26 | lr:0.0026125619375359215, weight decay:2.6524829769885797e-07\n",
      "val acc:0.39 | lr:0.0013423928947151845, weight decay:2.279218062293639e-05\n",
      "val acc:0.07 | lr:1.9002323147660616e-06, weight decay:7.325596990703465e-08\n",
      "val acc:0.11 | lr:1.0648049458192806e-05, weight decay:1.0503886281971934e-08\n",
      "val acc:0.05 | lr:1.155051836331536e-05, weight decay:3.724438766693503e-08\n",
      "val acc:0.1 | lr:3.6502819162719045e-05, weight decay:4.580456535431928e-06\n",
      "val acc:0.1 | lr:0.0004027230904781737, weight decay:4.624566296444368e-07\n",
      "val acc:0.1 | lr:0.00023972576360574787, weight decay:1.0888731070724182e-06\n",
      "val acc:0.09 | lr:4.561877303150583e-05, weight decay:1.074280722217715e-06\n",
      "val acc:0.1 | lr:0.00016611550855609631, weight decay:4.874471865684263e-08\n",
      "val acc:0.28 | lr:0.002022949093719039, weight decay:8.610289954328187e-07\n",
      "val acc:0.07 | lr:3.1568345661005386e-05, weight decay:4.881558365923912e-08\n",
      "val acc:0.15 | lr:0.00031082988319757415, weight decay:9.305666819798751e-05\n",
      "val acc:0.13 | lr:0.0005578967857041792, weight decay:1.0455604777430316e-06\n",
      "val acc:0.07 | lr:2.3210352685796004e-06, weight decay:2.316184047929858e-05\n",
      "val acc:0.04 | lr:5.397767615145739e-06, weight decay:2.9720550443668605e-05\n",
      "val acc:0.15 | lr:0.00028073761943585627, weight decay:2.3303993788433552e-07\n",
      "val acc:0.08 | lr:6.500794855230586e-06, weight decay:8.163747423298429e-08\n",
      "val acc:0.13 | lr:6.716395506786275e-05, weight decay:4.2812435460540633e-07\n",
      "val acc:0.05 | lr:8.612898100000524e-05, weight decay:1.385570360005581e-06\n",
      "val acc:0.17 | lr:0.00014666983480335065, weight decay:1.8597783261658382e-06\n",
      "val acc:0.33 | lr:0.002340700358489932, weight decay:3.826747082331894e-05\n",
      "val acc:0.15 | lr:0.00043884273977923234, weight decay:5.3010740583566995e-05\n",
      "val acc:0.35 | lr:0.0017426894072650968, weight decay:2.0552950835899585e-06\n",
      "val acc:0.04 | lr:3.4007550973010675e-06, weight decay:1.2293547098157056e-06\n",
      "val acc:0.1 | lr:2.0928485346802797e-05, weight decay:4.046823185041274e-07\n",
      "val acc:0.3 | lr:0.0011420214429134596, weight decay:9.834080999472783e-07\n",
      "val acc:0.28 | lr:0.0020373091497788015, weight decay:1.8208579179541355e-05\n",
      "val acc:0.09 | lr:1.2662784890230906e-05, weight decay:1.1954731974341795e-05\n",
      "val acc:0.12 | lr:1.0774019168468301e-05, weight decay:4.231135927240067e-05\n",
      "val acc:0.83 | lr:0.007955421023163349, weight decay:1.0179821040366434e-06\n",
      "val acc:0.1 | lr:1.8461463359567466e-05, weight decay:5.11546180963856e-06\n",
      "val acc:0.11 | lr:1.025610398022961e-06, weight decay:1.1519228022943636e-08\n",
      "val acc:0.09 | lr:4.355868861135961e-06, weight decay:7.402958626649398e-05\n",
      "val acc:0.11 | lr:1.7976552837758237e-05, weight decay:3.121220660884959e-05\n",
      "val acc:0.15 | lr:0.0008823477486653762, weight decay:4.279730409066619e-07\n",
      "val acc:0.08 | lr:0.00019115989393658422, weight decay:4.911706483065088e-08\n",
      "val acc:0.17 | lr:8.349245371612928e-06, weight decay:5.556885007143987e-07\n",
      "val acc:0.21 | lr:0.0009229338974106432, weight decay:4.2219636978150777e-07\n",
      "val acc:0.12 | lr:1.9085241976115366e-06, weight decay:5.939819864892863e-06\n",
      "val acc:0.1 | lr:2.7219447274445154e-05, weight decay:2.947521095934162e-06\n",
      "val acc:0.13 | lr:0.00031152967951795114, weight decay:3.653708844484683e-06\n",
      "val acc:0.14 | lr:2.30721778848321e-05, weight decay:1.3037807375881878e-05\n",
      "val acc:0.05 | lr:3.101814696159778e-06, weight decay:4.2643538930905157e-07\n",
      "val acc:0.04 | lr:1.788154005514171e-06, weight decay:1.1283063237907293e-07\n",
      "=========== Hyper-Parameter Optimization Result ===========\n",
      "Best-1(val acc:0.83) | lr:0.007955421023163349, weight decay:1.0179821040366434e-06\n",
      "Best-2(val acc:0.77) | lr:0.007314328741051734, weight decay:6.221610323334567e-05\n",
      "Best-3(val acc:0.77) | lr:0.005546405062300536, weight decay:2.244491953842928e-07\n",
      "Best-4(val acc:0.76) | lr:0.007954156922587819, weight decay:8.113842039183665e-05\n",
      "Best-5(val acc:0.72) | lr:0.006096954087946476, weight decay:2.8837250105419758e-08\n",
      "Best-6(val acc:0.69) | lr:0.007978656136557872, weight decay:2.1181845167084804e-08\n",
      "Best-7(val acc:0.59) | lr:0.0034377009625794937, weight decay:3.711910367116388e-06\n",
      "Best-8(val acc:0.56) | lr:0.005435330538585565, weight decay:1.11889668740693e-08\n",
      "Best-9(val acc:0.55) | lr:0.004131630902309629, weight decay:1.0886928474624086e-08\n",
      "Best-10(val acc:0.39) | lr:0.0013423928947151845, weight decay:2.279218062293639e-05\n",
      "Best-11(val acc:0.36) | lr:0.0022067061737045318, weight decay:5.432257356005885e-08\n",
      "Best-12(val acc:0.35) | lr:0.0017426894072650968, weight decay:2.0552950835899585e-06\n",
      "Best-13(val acc:0.34) | lr:0.0014885112166808552, weight decay:2.1864035487015072e-07\n",
      "Best-14(val acc:0.33) | lr:0.002340700358489932, weight decay:3.826747082331894e-05\n",
      "Best-15(val acc:0.3) | lr:0.0011420214429134596, weight decay:9.834080999472783e-07\n",
      "Best-16(val acc:0.28) | lr:0.002022949093719039, weight decay:8.610289954328187e-07\n",
      "Best-17(val acc:0.28) | lr:0.0020373091497788015, weight decay:1.8208579179541355e-05\n",
      "Best-18(val acc:0.27) | lr:0.0006082215111986305, weight decay:1.2330059021007899e-08\n",
      "Best-19(val acc:0.26) | lr:0.0026125619375359215, weight decay:2.6524829769885797e-07\n",
      "Best-20(val acc:0.23) | lr:0.001958960507923658, weight decay:2.5427429360304095e-05\n"
     ]
    },
    {
     "data": {
      "image/png": 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mup2roc2qN/wdHcezhW9S5uhPg6lrlflPTQ1NhRKC8WmNpuV9EDIErJ3YGJXC\n4p3niErK54Ox4dzd1tfQltYJF7JL+H5/AjoJVhadeXZCL5MSdqhn4p6WX8b3+89TqXWlM/9j5oCW\nzOzb1HRa9K/Bjwfi2RNzgbnD2+HRrW5T5td3pJSUbl6AqyhEjlttmsIOsPtD5PY3WdnhJ946pMNM\nCGwszEjJLyPQzY6372llMsKeVVTOGxtO4WpWzC/tT5Eddj++LndGPpiapF6Je9yqeaww34H24T8A\n6n1KzdpkfXQqB+KzGRjmReWml9lrtwPn0L8MbVa9Y1tsBp/mdsKtdSidfE0wVw5AfhJyx/844tib\nuTvK6dPMA0tzM8o0Ol4Z2ZJ++gmYTQGtTjL0452kF5SzLDiSgKjPCeg6xtBmGYT6Ie6acrR7PqV7\n8mIiHXvQPsAJzE1wFKGez7ef5e0/YuhtdpQGh1fygFkC2c0mIFwCDW1avUCrk8Sk5OJ7YTWv7Q4E\nt5a0vbenoc0yGHLn+2i1OmZljOQ/A0KYZcJvu+ZmgldHtqRJA0GT72dB0/7g1cLQZhkEw4t7ZSks\nHoB5+jG2attiNuQLkxZ2AJ2U7HR+Cb/yM5Q4BpHe4QO8ekxRRhmaKJtPpLFwWxwAxQV5PFyymBYW\n2/DSvMysB+833clZjv2MPLyUlZpePDK8F1O6BRnaIoMzsIU37HwfSrKh11xDm2MwDC/u29+E9GN8\n6Pwcy4s6sC/UhCZLuAaP924CpX3AaRJ2nadjZ2FtaJMMjo2lOd3NjtOs/BiDKn/G2qKMLW6TeHr4\nA7QPNM3G5bT8Mn5cd4BuumDWe05leZdAQ5tUPzizBba9ASGDwK+Doa0xGAav7oyNasMrTOWjjHDm\nDA69aj/2wMBAbG1tcXBwwMXFhaFDh5KYmHiVo1WfwMBAtm7desP9tm7dSrt27bC3t8fX15dVq1bd\nVrnVQQiBGPI/6D4briHshvJJixYtcHBwuPSxsLBg+PDht1VudegZ4sEcx02MzF+OdUhfmLKRATMX\n/kvYDeWTnJwcxo4di5ubG+7u7kycOJGCgoLr/qYmsDQXnA0cz8qwhSwY1/2acXVD+SU5OZmRI0fi\n6uqKr68vixYtuu7+NYZHKISNgNFfXlplKB+sWrWKrl27YmdnR+/eva/YfvToUSIiIrCzsyMiIoKj\nR4/elk1VqZa4CyEGCSFihRBxQogr3nOEwsf67dFCiHbVNaBlaChlrSfzzYMduK+93zX3W7t2LUVF\nRaSmpuLInaTCAAAgAElEQVTl5cWsWbOqW8Qtc/LkSSZMmMDrr79Ofn4+UVFRRERE1Hq51cUQPjlx\n4gRFRUUUFRVRWFiIn58f9957b62XC8C4FTDzEIz7AQK6XjVMZQifzJ8/n9zcXOLj4zl79izp6em8\n/PLLtV6um4M1n4xvy7tj29PU8/rjHQzhl0mTJhEUFER6ejrr16/nhRdeYNu2bTf+4e3SwE+ZF/ay\nnlOG8IGrqyuzZ89m7twrw0MVFRWMHDmSSZMmkZubywMPPMDIkSOpqKiomcKllNf9oEzQcRZoDFgB\nUUDYZfsMATYCAugM7L/RcSMiImR1CQgIkFu2bLm0vH79ehkcHCyllLKsrEw+/fTT0s/PT3p6esrH\nHntMlpSUSCmlzMzMlEOHDpXOzs7SxcVFdu/eXWq1Wjlp0iQphJA2NjbS3t5evv3221ctd/z48XL+\n/PnVtvNqAIfkDXwh7yCfVGX79u3SwcFBFhUVVdtuKW/OJ/Im/GIonwwaNEh++umnl5YXLlwoBw4c\nWC98IqVh/FJYWCgBmZ6efmndo48+KidNmlRrfrmeTwx9v3z11VeyV69e/1q3adMm2bBhQ6nT6S6t\n8/Pzkxs3bqwRn1Sn5t4RiJNSnpNSVgA/AiMv22ck8J2+7H1AAyFErQTPS0pKWLlyJZ07dwZg7ty5\nnD59mqNHjxIXF0dycjILFiwA4L333sPX15fMzEzS09N54403EEKwbNky/P39Lz3J58yZc9Wy9u3b\nB0CrVq3w8fFh0qRJ5OTk1MZp3RZ16ZOqLF26lHvuuQd7+/qXB7sufTJjxgzWrVtHbm4uubm5/PLL\nLwwePLjOzvVmqCu/yKtMxCal5Pjx47V7gtXAUPfL5Zw4cYLWrVv/q2dTeHg4J06cqJkTvZH6o0yh\nt7jK8v3Awsv2WQd0r7L8J9D+ese92ZqHvb29dHZ2lhYWFtLHx0dGR0dLnU4n7ezsZFxc3KV99+zZ\nIwMDA6WUUr744otyxIgR8syZM1c9ZtUn+dWwtLSUAQEBMjY2VhYWFsrRo0fLCRMmVNtuKWu35m4I\nn1ykuLhYOjo6ym3btlXb5ovcjE/kTdbcDeGT5ORk2a9fPymEkEII2b9/f1leXl4tmy9SWz65eA6G\n8Eu3bt3kzJkzZWlpqTx8+LB0cXGRISEh1bZbypqtuRvyfrlazX3BggVy7Nix/1o3YcIE+dJLL133\nWNX1iZBXecJWRQgxBhgkpXxEv3w/0ElKObPKPuuAt6SUu/TLfwLPSSkPXXasqcBU/WIzILaaz6BW\nwHmgUL/cAAgETuq3aS83GziC0qbQEHDRr88E0q5xTH/g4qipVP1+bYB0/TKAHRAC3EyrR4CU0qM6\nOwohMoGEah7XUD65iCvQCDhWTXurUm2fwE35xVA+aYYyd3CSfr0vSk+0c9Ww+SK15RMwnF+s9Ovt\ngXKgGLAFTlfTbqi5+8fQ94u7fltVzfMEnIC4Kuua6o+Xfo3zgOr65EbqD3QBNlVZfh54/rJ9vgDG\nV1mOBXyq83SpzkfvwP6XrcsE7gNKgEbVOEZLIAPop1+Ov/yYV/nNTuC/VZbbAbk1dV53ok+q/HYL\nsMDQfqgPPgGKgPAqy22AIkP7w9B+ucoxfgDeNEUfAI8A2y9bNxClQiCqrLuAUpm+7XOuTsz9IBAs\nhAgSQlgB44A1l+2zBpis7zXTGciXUqZefqCaQF/GSJQn6QngK+ADIYSnfnsjIcRd+u/DhBBNhRLU\nykd5Ouv0h0pHaSS+Ht8CU4QQjYUQdsBclBBUvaKOfYIQwhfoAyyt8ZOpIerYJweBR4QQtkIIW5S3\n0+gaP6kaoC79IoRoLoRwFEJYCSEmoYjZ+7VyYjdBHfvAXAhhg/ImZyaEsBFCXByluV1/vCeEENZC\niCcACdRMnpFqPnWGoLxKnQXm6ddNA6bpvwvgU/32Y9wg3n6LT91SlBpSIXAcmKjfZgO8gfIKXACc\nAp7Qb3tK/9tilCfki1WOORLlKZkHPHOdsl9BecJnAssAF0PUPOqZT54HdhraB/XFJ0AQsBbIBnKA\nP4BgQ/ujHvhltv6+KQZ2UcO6cIf44EEUwa76WVJle1vgsN62SKBtTZ3zDWPuKioqKip3HgYfoaqi\noqKiUvPcUNyFEN8IITKEEFftoHo7o1NVVFRUVGqH6tTclwCDrrN9MBCs/0wFPr99s1RUVFRUbocb\niruUcgdKI9G1qLPRqSoqKioq1aMmUv42AqqmV0vSr7uiK2TVQUz29vYRoaGhNVB8/ebw4cNZspqD\nMNzd3WVgYGAtW2R4bsYnYBp+UX1yddT750qq65M6zecupfwS+BKgffv28tChQzf4xZ2PEKK6owgJ\nDAxE9cmVmIJfVJ9cHfX+uZLq+qQmesskA1Vz9frq16moqKioGIiaEPc6G52qoqKiolI9bhiWEUKs\nAHoD7kKIJOAlwBJASrkI2IAygjUOJUfDlNoyVkVFRUWletxQ3KWU42+wXQIzasyiO53KUrC0NbQV\nKioqJo7hJ8i+kzmzFfIvgFMjqCiGg4uhKANmGX+jjoqKSv1GFfebIe8CXNgHzYaAtQMk7IZdVZLc\n2XtA96cMZ5+KioqKHlXcb4ROC7Eb4O93IE2fxbX7U9D/Zej5DHR4GArTQJiBdyswt7ze0VRUVFTq\nBFXcb8Q3gyDpALiHwF1vKgLu10nZZmWvfJx9DWujioqKymWo4n45WXGw4WkYtQgcvaFhW+jyOIQO\nB3PVXSoqKncGqlpdJPYPiPoBTm9SersUpoCTDwx5x9CWqaioqNw0qrgDRK2E36eBgzc0HwEDFoCT\nD2n5ZRw8n8OQVj6YmwkAUvJK+c+qo9zVwpsHuway92w2WinZcTqT1UdTaObtyFeT22NjaW7gk1JR\nUTFlTFfcywoACTbOkHoUArrBhJVKDB3YeSaTKd8eRKOTZBSWY2kuSMot5efDSeQUV3AgPofl+xI4\nm1l86ZA9gt3ZeSaLJXvOM61XEwOdmIqKioqpiXtxttJ1sWk/+G0a9HkBIh6Ega+BTgMW1pd2/XDr\nGbydbfB2suHVdScBEAJ6hXjwRL9gXvz9OELAe/eGY2Gu1OpHtmnEw0sOsvCvOO5p54uHo/XVrDAt\nClIh/Tg4NQSvFoa2RqW+U1ECVnaGtsIoMC1x/30anNkMexeCmSW4NVXWm5lToRNk5JYAsO9cDocT\ncnl5eBidGrsx44dInugbzPDwhpfCM+uf6HHVIl4Y2pwN0ak4WJuWa68g/SRseh7ObVeWOz6mtl+o\nXJu8C/DXa3ByDTx5VOnMoHJbGL8CSQmaMqWRdNgHkHgAtr4EnaZBYHeyisr5ft8Flu1LIKuo/NLP\nmno6cF8HP+ysLPjr6d7VLq6JhwOz+gXXwoncQcRthR8nKQO9+syHwO7QwO/GvzMlNOVQkAKuQYa2\nxDAkHgTnRsob3dEVsP4/IHXKm7SUhrbOKDB+cY9eBafWwNjlSn90Z19ocTfvbIrl2Nf72R+fQ4VG\nR+9mHtzVwhtzM4G7gxU9gz2wMFfnD78lijLAKwzGrQBHL0NbUz+QEpIOQtyfELUCygvB2hFmHABL\nG0NbV3dICZFLYd1/oO1EGPEJlGQrbV7DPlArATWIcYt7SY5SS3f0Vi4qoYRUTmcU8dn2swS62XFv\nhC9TugXS1NPRwMbeoWgrQeh7BhWmUGTjzWG7ATgNvIs2Dq4IQErJ2uhUhrT0Np0HppSKkPt3UkT8\n73dg+xvKtsa9lZ5ZLUaZjrBnxkLsRjj2k9IG07Q/DHxd2db5ceg607D2GSHGK+4nV8Mvjyjic+8S\nMDMjr6SCOT9Hk19aiYWZ4JfpXXFzUBs9b4mUI7BmFqQdB2snAGRlMW9bzWFZXksAfF1s8XS0xsPR\nmk0n0im7pzX3dTDympmmAmLWwt7PIPkQ9H8Fus+G8LFKaLDNBLB350RKPq72VpjMZMPHfoId/1MG\nBY74BMLH/5Oqw8xEHvh1jHGKe0UxbHwOPJrBiIXQsA1SSp75KYqtpzIAGBjmpQr77WDnBjod9HwW\nijNJLyxj0VlX9pb58/nEdhSWafgrJoPE3BI2nUhneu8mjIkwsjQNUiohBXt3ZfmLXpAZo7TxuDaG\noe9B28nKNpdA6PYEAJmF5Yz6dDdSwvTeTXh6YDPD2F/b6HRQmKrE1js8Cu0fUmLs1yEmrYBzmcUM\naWUyj71ao1riLoQYBHwEmAOLpZRvXba9N7AaiNev+lVKuaAG7bw5SvOUXDB95kHDNgCsiUph66kM\nnhsUSqVWp1481aEkB87vUsSqJEcZA1CQDD3nQAN/mL4bhOC5n6NZGZ1Iowa2LJnagWAvJcR1Xwc/\npJRkFVUYT7fQwjQlG2jMBkjYo7QpTN2ubPNtrzQeB/VSwg7XqJH+fiSZSq1kXAc/mno61JnpdUZp\nntLOdehbyEuA6Xtv2Pai00k2n0zn2Z+icLazpF9zT6wt1IGAt0N1ZmIyBz4FBgBJwEEhxBop5cnL\ndt0ppRxWCzZWn5Icpa+6cyN4YM2l1UXlGt7eGEOLhk481rMxZvrujCpXQaPvMWRhreSn3/b6v7db\n2ELTARR7hLPhWCo5xRWsPJTI/Z0DmDs4FPvLuoAKIe5sYS9IVdJQAPw2XUlRAWDnDkE9FRG/2J4z\n9L2rHqJCoyM1v5Tv9iawPjqV4nINbf0b8NY9revoJOoIrQZ2vAN7FkJlMbg2gX4vgYPnFbvqdJKf\nDydxLquYP46nkpRbikYnaeblyLdTOqjCXgNUp+beEYiTUp4DEEL8CIwELhd3w6LTwU8PKH8fXIcE\nDsTn8GdMBjtOZ5JeWM6H49qqwn4tijKUdoo9HyP7vUxF81FYt3+YIp+u/HVBw1t/Z9A3yBpHzyD6\na32Z9f7fpOSXAdDQ2YZ5Q5sbT8qF8iKI36FkAz2wGAa+Cu2nQPNh4B6siHrDtmB2/fOVUvLZ9rN8\nsOU0Gp3E3EzQI9idEykFPNzdCLtAntkMf78NLe6GrrOgYbtLnRiqUq7R8uxP0ayJSgGgfYALg1v5\nEOrtyOCWPlhZqDH4mqA64t4ISKyynAR0usp+XYUQ0UAy8IyU8sTlOwghpgJTAfz9/W/e2mshJex8\nV7khR3wCQvDNrnheXXcSK3Mz3BysWDy5PR2DXGuuTGNAp4PTG+HgYuTZbQgkhS4tmLsxi92//UnP\nYA/WROUD0Nq3IZtTy8iJS+LzXYlYW5ix/OFOFJZVEuBmbxzCrtPC4W9h+1tQnAlmFhDQFYIHKttD\nhyqf61Cu0ZKaV0Z0cj5f7zxHVFI+d7XwomOQG0NaeePjbIRTMKYcUR52oUPgsZ3gc+03krJKLVO+\nPcjec9nMGdSM6b2aIK7yAFC5fWqqQTUS8JdSFgkhhgC/A1eM5JFSfgl8CdC+ffuaGamgqYB1T8HR\n5dDyHmh7P1lF5Xyw5TQ9Qzz4YlIEtlZGIDy1gRBoNjxHSWkpP8jR/EEXotN8aOLhiKudZE1UCuM7\n+tEpyI1hrX2wMDcjPquY536OZmJnf7oHuxv6DG6PxAOw+UWwbQDjfwQERH6ntNfcs1jJ26+fD1dK\nJceQl5PSdTG9oAwnG0s+3x6Ho40lHYNc2RabwfJ9CWQVVQAQ5G7PG3e3YnxHP+MUMG0lrJ4J0T/C\nzEPKW811hB3gpdUn2Hsum/fvC2d0OyNrYK9nVEfck4Gq/dd89esuIaUsqPJ9gxDiMyGEu5Qyq2bM\nvA7r9cLe6znoNZe80kqmLTtMWaWW/w4LU4W9Kid+h03zwMqekilbmb/+HFFZs0nCkwEtfQk0EzQW\ngtfubkmlVhKbVnjF206Quz2rpnUx0AnUEBXFsP5pZTCRUyMozVHCB0LA/b+DrculcEKlVkdUYh6r\nj6awbF8CYyJ8SckrZc/ZbKwtzCjX6P516D7NPBjcyoeGzrZ0beJmvGFArQZWz4Dolcq95xJ4w5/8\nGpnEykOJzOjTRBX2OqA64n4QCBZCBKGI+jhgQtUdhBDeQLqUUgohOgJmQHZNG/svdFol5tltNgT2\nVPoRA8/8FE10Uj4fjmtjnD0RboWCFPjrdeUh6NMGrUcYzyzeyB9pDjzcvStTugXRsMGV4QKjDWP9\nOlWZOrH7U9DjaWWQkZ4c6cBhfXdZKSVL955nd5xyKbf1b8DPh5PwdrLhiX7BxKYV0L+5F618nUnM\nKaWJhz2NPUzgmks6BJvnw4W90He+0h32BsRlFDLvt+N0DHLlqf4hdWCkyg3FXUqpEULMBDahdIX8\nRkp5QggxTb99ETAGmC6E0AClwDgpazFBxPa3lVFuY75RXgXdlQjQ7rgstp5KZ86gZgxrff3+tCbF\nZ12U4e7dn4LeL7BkXzIbDpzko3FtGNmmkaGtq32khJj1SrxcCGX57i+h9b3/2q2grJJRn+7mQk7J\npXUWZoKXh4fRIciVMB8nTqcX0djDHsvLRtqGejvVyanUCxL3K/35R38Fre+74e4lFRqmL4/E3tqc\nT8a3NZ1RyrdJYk4JS/ecZ+eZLHRSsuzhTng7V39Ec7Vi7lLKDcCGy9YtqvJ9IbCw2qXeDqc3KcO4\nwyewPz6X05nKjejvZs/834/h62LLQ92MsCfCzaCtVGLHEQ8qbzd9XoDggeTb+rJqTyIf/3mGHsHu\njAg3gQdgxiklj8mFPTD2e6XHy/gfrthtz9ks3t4YQ0peKZ9PbIefq5J21t3B+l83VDNvE0xTodNB\nfqJSQfBuCRFTlE81UvNKKZn/+3HiMotY9lCnS20WKv/mQHwOsWkF6KQyl0Ra8nm8i2Mowg6vJj1w\nsDa/lFq8utxZI1QzY5XGU4/m7Al7kYlfH/xXAjkLM8HKx7oYR8+NWyVhL/zxHKRGkW3hxVZNa1r6\njeOnXUn8dOhPiiu0dApy5Y27WxlnI19VTq1VQjCWdjDswyt6usRnFTPj+0gcbSw4eD4HLycb3rsv\nnMHqADeF/CQlDe/xX0FbDlYOMOvwNdPx6nSSbbEZxKQVcuh8Dil5Zfi72bHlZDpP9gu+8xvgb4Ej\nF3LZdy4HybUDGRkF5coEP+ZruNf8bwableNKARaWGio8W2P10NO3VPadI+6JB5FLhoKVHSsC3+DN\nFcdo4uHAdw91xMJMsD02Exd7KyICXAxtqeHY/jZsfwPp7MvJbh8zZYMNGYXHALA0Fwxr3ZCHuwfR\nspGzgQ2tA47/Ar88Co3aKRlBqwhSWaWW6KR8Hlt2CID0AkH/5l58OK4NdlZ3zi1Rqxz5XqlIgZIP\nx7sl+He9qrAn55Uy9btDnEi51K8CdwdrGrnYcvB8DtN6NeEJE0mDXaHRsS46hW92x3M8ueCyrZJA\nkYYbBXQxO4mjKKGFOM/sypmMCG/OLAdXLPLCsXRww8zOBZqPwMrm1sN9d8yVvPVEEs6aIF6zmkPU\nAUsGhLkxf2jzSw2BRp+Q6kbEbYXtbxDfcBhPFE7m2J8afF3MWDQpgozCMu5q4W1ar8TCXJlxa8w3\nYO1IVlE5CdnFLNmTwIZjqWh1kgA3O5ZO6UiAmxJeMPo3mZshoKvSJtHrOSXVxDXIKa7gvkV7KSir\nZEafJoT5ONMn1AMbC3Pj7Sl0Db7fn8CHW8+QWVhOiIctL3Y2x8cOunXqgrWdA2bHVmG15p9auDS3\nQrqFsGdoE6wC2gJta9Seei/uUkre33KaT7ZbEe77HoVlGt65p4kq5qDk8KgoQjo14pczkmY0ZfS5\n+2jqY8e79wYxPNzHtIZxn1oLqdFKG0OLUdBiFBqtjhd/jWbFAWUcnqO1Bfd3DiDQzY4RbRrham9l\nYKPrESlHle6hg95SJhEZ+el1d9fpJP9ZdZTMwnJ+nt6F1r4N6sjQ+sfmE2nM++04/fzNeKpTLi1i\nFyCOxigb7fSZQVuOAE2RkjzNvwvCTkmJXVtXYL0X953LXsXm9BnGRzzFq6PbqC3toKQK+PkhuLAX\nXbNhPG/+NCsPldOv6Yd81yeMzo1dTasWqq1UsoAe+hoaRVDU8QlKpRW2VubM+iGSbbGZTO4SQDt/\nF/qHealTIF6OpgJ2vqeM8nbwhm5PXjd7Y1xGIYVlGracTGd7bCavjmppcsIupWR/fA6rDiWSU1DE\nznMFtGrkzOLSRxC7UpXxE8M/AltXsNGHQa3soeOjdWZjvb3Kd5/J5Ny697g//3OcG/Sk9ejWCFXY\nlQE4y++B7DgqOj7Oq+ebs/J8Ik/0DeapASGmJepSwqFv4Oj3kHwYusxE9pnHlG+OEnkhDztLc4or\nNLx+d0smdgowtLX1k80vKn3+s+Og1X0w+G2wu/r4Bp1O8tYfMXy549yldcNa+zCpUw2mEqmHlFZo\nibyQy+n0Qv6KyUCjlWQVlZOXkcS7Nl/TzCyJxV1+ZVqfYETiu4r/GrYz+EQs9VLcL2SXsPv7Bczh\nO445dKPp9BUI83ppap2i0+rIXvE47mnH+MDzVX46EkZGYTlvjm7J+I7GfYNdlbwL8OcrYO0MoxZB\nm/Gsi0rh4Plc+oV64mxnyeQugbTxM61a5RVoKuD8Dkg7pkxcbmWn1CpBmSzetwMMfA2aDb7mIQrL\nKnnht+OsjUphYid/+od5YWVuRscg435LTC8o48FvD3IqVWkcbe5pRxfzU3TU7aen8x5sdUWITo8x\nv2djsLJWutrWE+qlYr65PpqX5TrK/LrR6qF1Jj9TS4VGx6YTaaw9mszkuDMs093DkowQOgY581aX\nAHqFeBjaxDolOimPF38/TkGZBjfzjxjVuQ2B9o7E7orn/c2xhPk48eXk9pibWIMeOh3oKqEs/580\nu7F/KA/ADH0SVzs36PDIP795Kfeqh0rKLWHP2Wyik/LYE5dNekEZxRVa5g4O5bGejY1a0AGQkvKC\nDBYv/o6h+THMHXAPPi17EFIZC4ufV1Jf+3VQZtpq1M7Q1l6Veifu8VnFJMYcwsWmFKseT5i8sAP8\nb/1Rfth7jhJhS+uu75KvdeCXLkGXJsUwBU6nF7LrTBYpeaX8sv8M91rvI73xGBJyLZm/+p/s0819\nnPh2SgfTEfaiDEiOVNobzu+CSv3o2nnpSljg9B9QVqBMNdmk7z/x38vQaHVsi80kMaeECq2Oz7ef\nJb+0EisLM3qFeNCliRvjOvjTytcEutFmnaF41VTsMyKZB0oylXI38BoCMkKZ+L1x72oN4jIk9U7c\nP9p6mtOiCQXTo3F3M71BDxeRUnIoIZfIE7EMPPwkUxposZ2+HRdn0xH0qhy5kMuCdSexFFp+df2c\nlsX7ED3vQePTha93xePtbENEgAs+zramI+yg5FBfPUOZPKTtJHDwUgRc6hOaDftA+Vulpl1aoeXH\ngxcoLNNQXK5hXXQqmYXlVGj/SYLW1NOB7x/pRICbHY42lnV5RnWLpgJiN6DLvYCuy0w0OknB949i\nlxPDJ+YTCY3oxYBBo5X0z6D4MXSIYW2uJvVG3Ms1WpZvO4b9se+Y1vMx3D2unL3FVNhwLJXVf+2k\na+YqJprvxNJMS1n/z3E2UWEHGNW2EYNa+GB75EustuyFIe+Cb3ssgMd6NTG0eYaj6QAlk2WjCLja\ngJcqop5TXMGSPefZHptBdFL+pfXdm7ozrLUPbf1dlJ5WCBxsLIzrIanVwMV2u4S9cOJXtMlHICsO\n8/JcyrCm77oGpOFGhBiBp99TvPngYBrY3bldZeuFuJdVannt48+YmP8lD1gmI8LrrrtQfeTU8cO8\nlfcMjlZl0Gwo5n2fx9rDtDPpWVuYY12WDTvfUeYorRo3NmUcvS7NT1pYVkleSSVeTjZYWZhRqdWR\npp8tC2DOz9Hsi8/Gzd6aRZPaMSBMGW1qVCJ+kbwLoCkn5Y930SZH4aLJJOuenzD3DMXhXCQOh5dz\npNKfeF04G3WdcAjrz0QfZXR7K98O9Az2uOMHYRlc3BNzSvh69SbmFSxA5+CF+dBvEN5hhjbLoEwb\n1h27jX0Rfeddynhp8ui0sHQ4VJbCoDevOn2bqVGu0fL48kjuauFN5IVcfjuSTLlGh7uDFZ0au3Ew\nPoeMwvJ//eZ/Y1pzb3vjHgAYHx9H0NIIANylOZGiOSe1gby/dD+xMhVz/NDyFb1CPOnf3JPXQj3x\ndanf8fNbQkppkE9ERIQsKdfIu//7hTzz31BZ+qqflPkpUkopAwICpI2NjbS3t5cNGjSQQ4YMkRcu\nXJC3Q0BAgNyyZct191m5cqXs0qWLtLW1lb169bpi+6OPPipDQkKkEEJ+++231SoXOCRvwifXs7++\n+SQ2NlaOGDFCuru7SxcXFzlw4EAZExNzw3Jvxieyql9iNkqZHHnFOdQ3v2RmZsquXbtKV1dX6eTk\nJDt37ix37dp13ePdik/iMgplr3f+kgHPrZMh8zbIub9EyxX7E6SDm480s7CSFta20s7RSbbt1ld+\ntnaf3Hk685b8IWXN3D8XWbp0qQTkV199dcNyb+X+GfbOWvnCC7Plcy/8R/536QZZWqGRvn7+0tLa\nWlrb2kl7RyfZoUc/eS7+fDXO/NrUhE8AaWdnJ+3t7aW9vb18+OGHb1hudX1Sra4oQohBQohYIUSc\nEGLuVbYLIcTH+u3RQohq9Q2ytTLnmX5BBDmCzcTl/8wyD6xdu5aioiJSU1Px8vJi1qxZ1TnkbeHq\n6srs2bOZ+//2zjw8qups4L8zS/Z9B7ICAQQEZF8FFWUTcKsV3NDyqbXuqKC2tXaxUrWtVRTRqlWr\nUtEisoiKUnYh7FtCAoSQkITsySSZTGbmfH+cAUKAZICBJMP5Pc99Mvecc899z5t73/ve9yx31ilN\nBKB37968+eab9O3bMkOfWptOysvLmTRpEhkZGRQWFjJw4EAmT5584YTpOlZ9q7MRrU0vQUFBvPvu\nuxQWFlJeXs7MmTOZOHEidrvdozJ0ig7ihxmj+OKXQ1j/zDX8+abLuW1gIpFBPixftoR6aw0lRwvp\n0yWJ5e/++YKvytjc/QNQVlbGiy++SI8ePS6YHE9NHsSIKU8z/dEXeOGucfiZjRgNgqWLF2Otqab4\naK7dWwgAACAASURBVCE9Oyfy+GOPXjAZjuGOTrZv347FYsFisfDuu+967uTNWX/UBzr2Ax1RyyBs\nB7o3KjMeWAYIYDDwU3P1nuSl1tc1+URcsmSJTE1NlVJKabVa5YwZM2RCQoKMiYmR999/v6ypqZFS\nKo9pwoQJMjQ0VIaHh8vhw4dLh8Mh77jjDimEOO7hzZ49u8kn4zvvvNOk5zFs2LAW8dxbs06klLKk\npEQCsri42GM6kW1cLw6HQy5atEgCsrCw8JLXyf333y/nzJkjR44cecE897akE0BmZmY2q4dGx3jM\ncx8IZEkpD0gpbcBnQGP3bDLwoevcG4AwIYT7i2KbztwjXVNTw/z58xk8eDAAs2bNYt++fWzbto2s\nrCzy8vL4/e9/D8Crr75KfHw8RUVFFBYW8uKLLyKE4KOPPiIxMfG4h/f000+7LVprpLXqZNWqVcTF\nxREZGXnedZ0LrU0vvXr1ws/Pj0mTJjF9+nRiYi7+CLDWpJONGzeSlpbGAw884LH2nQutSScAV155\nJXFxcdx0001kZ2d7ookACPUgaKKAELcAY6WU0137dwKDpJQPNSizGHhJSrnGtb8CmCmlTGtU133A\nfa7drkDGGU57OaqzV6KmENiBTNQn/K4A9gDHeooCUW8VO4H2gD+Q2yC/YZ3ZQFWTDVZEAZFNyNcV\nKMa978QmSSndmkIqhCgCDp0hu7XrxAxc5jpPaTN1ua0TaPN6EUC4629T18uloJPLgBygGnUPlaDu\no6bw9vsnCKUPA9ABCAZ2N1OXezppzrVHfR/13Qb7dwJvNCqzGBjeYH8F0N+dV4cznDMbGC1PhIVu\nQhmMRNQ/p7zBVgFYXGWDgVeBA65t1unqdO3PBSyu7dlG558OrGxCvjXAtHNtn7fpBIhG3RzPXUyd\ntHa9NCq3F+h9qeoEeBj1/eVj+yuB6fo6OamM0XXs5R5psxtKGQIsb7D/DPBMozJvA1Ma7GcA7Tzx\nj2iQVgTcCtQAHdyooydwFLjGtX+wcZ1NHNuqjXtr0gnKK92KenO7aPpo7Xo5Tbks4MZLVSfAQqAM\nKHBtNpcRfcOdOr1RJ6cpc8y49/JEm92JuW8CUoUQKUIIH+A2YFGjMouAu1yjZgYDFVLKfDfqbhZX\nnZNRRmQ38A7wNyFEjCu/gxBijOv39UKIzkKtalQBOIBjc6oLUa9aTZ3LKITwQ72+GYQQfkIIc4N8\nH1e+AMyu/Iu++E1r0YkQIgRYDqyVUp55OMBFohXpZbAQYrjrevEXQswEYoGfPN7oZmgtOgGmocIy\nfVxbGvACqOVbLiatRSdCiB5CiD6uMkHAX4E81Fve+ePmU2c8sA81auY5V9oDwAOu3wKY48rfyXmE\nZBo8ZWtRT7EqYBdwuyvPD3gR9YpU6VLEI668x13HVqNiZL9pUOdkVLyvHHjyDOedhnpFa7h90CB/\n5WnyR11Ez6NV6QS427VfzYnXUQuQeDF00or1MhI1qqwK9er/P+DKS1knpym7kosflmlVOgGuRkU5\nqlFvBAuBVE+1udkOVY1Go9G0PfR6uhqNRuOFNGvchRDvCSGOCiF2nSFfiHOYnarRaDSaC4c7nvsH\nwNgm8scBqa7tPuCt8xdLo9FoNOdDs8ZdSrmKpielnN/sVI1Go9F4HE8s+dsBONxgP9eVdspQyIYz\nVAMDA/t169bNA6dv3WzevLlYujnDLioqSiYnJ19giVqes9EJXBp60To5Pfr+ORV3dXJR13OXUs4D\n5gH0799fpqWlNXNE20cIcabp0KeQnJyM1smpXAp60To5Pfr+ORV3deKJ0TJ5QMPV/+NdaRqNRqNp\nITxh3C/Y7FSNRqPRnBvNhmWEEJ8Co4AoIUQu8DxqBUCklHOBpagZrFmoNRruuVDCajQajcY9mjXu\nUsopzeRL4Fcek0ij0Wg0542eoarRaDReiDbuGo1G44Vo467RaDReiDbuGo1G44Vo467RaDReiDbu\nGo1G44Vo467RaDReiDbuGo1G44Vo467RaDReiDbuGo1G44Vo467RaDReiDbuGo1G44Vo467RaDRe\niDbuGo1G44W4ZdyFEGOFEBlCiCwhxKzT5I8SQlQIIba5tt96XlSNRqPRuIs7H+swAnOAa1Efv94k\nhFgkpdzTqOhqKeX1F0BGjTcgJQjR0lJoNJcM7njuA4EsKeUBKaUN+AyYfGHF0ngNlfnw+T2w8s8t\nLYlGc0nhjnHvABxusJ/rSmvMUCHEDiHEMiFEj9NVJIS4TwiRJoRIKyoqOgdxNW0CpxNsNer3gR8h\nYymY/FpWJo3mEqPZsIybbAESpZQWIcR4YCGQ2riQlHIeMA+gf//+0kPn1rQmqgrhqwchMAZufAt6\nT4GUKyE0vqUl02guKdzx3POAhAb78a6040gpK6WUFtfvpYBZCBHlMSlbK7YayN8B69+EqgKVtvw5\nOLypZeW6kFiKYMUfYP4dqu0Au76EeaPglS7w955wcBXE91N5QmjDrnEPuw2+eUY5CJrzxh3PfROQ\nKoRIQRn124CpDQsIIeKAQimlFEIMRD00SjwtbKtASijLhj0LYe0/oLZUpXceDcFxcNkkqKtsURE9\nTnkObP4XFKVD1gpw2CCyE4QlqvzKIyCMkHot+IbAgOkqX6NpCinVteUXAv7hsPVD2PAmtO8LvX7W\n0tK1eZo17lJKuxDiIWA5YATek1LuFkI84MqfC9wC/FIIYQdqgduklN4TdrHXga0aAiLgyFZ45yqV\n3nEU9JsGcb1OGLPEQS0k5AXEVgNr/6488D5TYNAvIbrLifwhv4KhD7WcfJq2Q02pco7Wz4Hs1WAp\nhEmvQ9+7IKIT3PSuNuwewq2YuyvUsrRR2twGv98A3vCsaK2A2nL4aS5sehe6jlMXYWxPGP8KdLra\nu73TI1sh83sY+RTEdIOnDyoP63ToIY5nh9MJhkto/qCtGnwCoewQvN4XnHbwDYUuYyBhoOqTAeh0\nVcvK2dpIXwoRHdX9dw54qkPVu5AStv0bvv011JZB6nVwucubMPnAwP87qbilzk6NzU6Qr4kvtuTx\n/Z5C3ps2AKOhjRm96mLY+7UKOR1YCQFR6s0kKPrMht0NpJSs319Cv+RwfE1Gj4nb6nE6YOvHcHQv\n1BRD9hr1FhTfH+78sqWlu/Ac2abav/u/MG2JMlI3vQN2K3QZq96ENSco3K1Cn+U5kPkdHFoLPW6C\nn71/TtVp4346/jdbjctOHArjZkO7Xmcsaq13cNu89ew5Ukmgj4mqOju940MpqqojLrSVD/+TEqQT\nDEZ1Ay64V+1HdIJRz8DgX4Jf6FlXa613sL/IQqfoIL7efoR/rjlIekEVr/6sNzf38+LOVVs1HP4J\nctNg+ONgNKvQQ/pS8A2C5OEQEIUMimHr/iO8uyGfQSmR3D00uaUlPz9qy8Doo7xzgDoLLJsJ2z4G\nkz90m3DCkPe86YzVVFrr2ZZTjr+PkQHJl4jhPza5z+mEBb+Aor0qPfoyGP0CDH7wnKvWxv0YUoK1\nAvzDoO/dqmNw0APHX5/tDifrD5RQZbVTZ3ewbGcBRypqqbLaOVRSw81945FIpgxMpH9SOKI1hyrs\nNlfn1VswYgb0mQrxA2D4E9DjBhV6Okv5DxZXsze/Einh7VX72ZFbga/JQJ3dSdfYYP5ycy8m9Gp3\ngRrUghTsgqVPQV2V8rjqKpAI/lncgw2WGMZ0e4bA1N8f12dlbT2fbjrM9qVbCfU30y+pDRqxeis4\n69UosfxtkPmtumbu/5/Kf2solB9SD7jhj7vlINgdTm6du570giqSIwNY+ZSXhmgsR6H0AOSsh33L\noTIPHlijdPSzD1TIKrgdBEaedJjd4cRkPLtQnjbuoGKBXz8Cjnq4ezGEtIMhD1JWbWPlvqMcKbfy\n6cYccstqjx8SFeRDr/gwYoPhvis7cvugpBZsgJtICXu+gu9/B2UHoUN/CIpVeaHxcM1vzrrKzYfK\nmPNjFj+kHz2e5mc28PDVnSmstDKpdweGdY5s3Q+7s8Fhh7w05ZV36Ac+AVCVD+HJyOiuzK8fzks7\ng6lMg+jgCr7fe/SUKlKiAvnD5B7c3C+eAJ82eAsufxa2fKgMUURHNToqZeSJ/GGPQlgSpI4+5dBa\nm4N1+4vp3j6ETdll/GfTYWJD/Aj2M5FeUMULk3rQLyn8IjbmIpL2Pix+7MR+u96qv8FSpIz7aWLr\neeW1fLgum6+2HWH5Y1cSGmB2+3Rt8MryHI56G/uXvUby9r/icEo+CvoFzlX7yTxaw6rMIipq6rE5\nnAAMTI7gufGX0TE6CCEgKTKg7cWPFz8Om9+HmO5w+wI1fNNNo1tWbeOb3QUUVlpZvCOf8pp6erQP\nYU1WMeEBZh4bncp13eMwGgTRwb5EBPpc4MZcRKRUs2y3/hty1qkwxGWT4OcfKeP28BasDsmM/2xn\nyc58ftYvnkeuSaV9mD8Hiy24LiEAjAboGBWEoS31x9RVwZInod/dkDRUjY4SBuh1q+oQdSGlZP2B\nEmwhExnRKRojsL/IwvtrD/LdnkIcTkl1nYPaesfxYxIi/NmSU0aNzcGortHcNSTJOxyB8hzIWKYc\nx+6T1Si6ruOVI9ChnzLswXGnPfRgcTU/pB9la04Zy3ap+TNje8ZRbbNr4346HE6JU0osVjuLdxyh\n9uh+Rm97jC4yh1WOy3nV90EstGf/N/sI8DFyXfdYYkP8uL5Xe2JDfYkJbuXx84bYbWra/+6FcGgN\n/OJ7CI6FuJ4w8R9wxR04MeBwSuwOJ8t3F3CoRC0XkBjpT2yIH2nZZWQUVvFj+lEcTkm9w4nTNbh1\nQHI4fRLC+HZ3AUM7RfLm7X0J9nP/omtz/PcB2PEZhHTA2nEMaw39yfHrRuLeQv72/T4yCy04paTe\nIXlu/GVMH5Fy3EB1jgluYeHPk8Mb4YtfQEWuMkpJQ9UosQmvnFTsm10F/O27fWQUVgFgNgoMQlBn\nd+JjNHBtj1jC/M2YjQaGdY5i95EKerYP5epuMVjtDspq6mkX4tf2DXtNKax+FTbOU/NBzIHKGUgc\npO7Bq55t8vCiqjpufXs9RVV1BPuZ+MXwFO4akkR8eMBZi+LVxr3SWo/DIflubyG//WoX1voTLlQ4\nlVwT5ENa/zn0HnErX/qbMQiotNrxMxvanlcOsP9HWPsa5G2Bugo13Kzz1SBdntKA6eSW1fDB0gzm\npx2mympvsroQPxOT+3QgxN+Er9HA2J7tSIwMIMhXXTb2my7HaBBt/4ZsSMEu2LcMDm/Efv3rmELj\nOBh1JQeS4vja93qWbit2vc1Vwuo0QvxM3D00GSFgSMdIRnWNaekWnDsFOyGkg+r8LMpQoZdN7yoP\n855lkDj4pOK1NgfWegc78yp48N+b6RwTxMu39CLAx8SOvHIAIgJ8uKlvPNHBvicde2332OO/A3xM\nbTM8VZShRrdkr4HxL6u0z25X8fQ+U+HKpyAixe3qSix1TP8wjSprPV/9ahiXtQvBx3TuQ2bboEab\nxu5wkl9h5c2VWXy68cR6Z4OSw5katZ9OJT9iGD+bbh2iMIjbTglLhPq3MQ/UbgNrOZWmcL7LdDAo\nP5/8gOHkdbqWHsMncbjSzooVR/ExlRET7Mer32YggXE94+gWp7zK3glhDO2kVov46UAJRZY6ruse\nh5/Z0KThPtsOnlaNww475sOSJ8BupdSnPTP+sYCQ1GF8tS0Mk2EQwX5l3DYwgWlDk4kO9mXZzgIG\npESQEhXY0tKfG4565RDUV8Omf0L2auzTluGMH4zI+gHzT2+riXo3zoVAdX1Y6uzM+TGLQyXVfL/n\n6PGwZUpUIF/8cujxNziv7DwHNaExfYmaSZvrWmYkMPqEcb/6OTXbNva0ayeelkprPdnF1Tz62TaO\nlNfy+pQr6J0Qdt6itnnjXmKpo7CyDolkdWYx/1qXTX6FFYA7BifSOTqIBGsGVx36I4Zda1UHouEw\nGKJbWPLzR1qKKJp7PeV2H26seZZqm5OooD9hsEPR1jrklg0ABPuaqHc6sdY7Gd45ir/c0ov2Yf6n\nrXNoZ+9fEugU6q0wb6TywuIH8E3PV3lgYS4BPkZqth3h3mEpPHFdl+NvLMe4dUDCGSpsA5QfhvfH\nQYVygGRgNHN97+XTj4qpkt8jHYkM7/Q1GHzhqxxC/PO5vlc7Pt5wiG92FRAX4setA+LpHB2EEILr\nesR6b2hOShViMfmqt5mlT0J4Cox9SY0yi2swVDp5+PGf1noHB4qqT6mu2FLHkh35VNvs1NocrM5U\nb4NhAWY++b9BHhtB1eLG/VefbKG6runwwJmw2Z1sPFiK3XlipYOhnSJ5+OpUUqICGZISBj/8Edb8\nFQIi1czSvnepf1IbRkrJ/owdhH11N8E1efzd9wmu69GOe4elcHm8GnaWU1LDpuxSQvzNjOoajbXe\nwU8HSrmyS/R5vep5BU6nGsJXlg09bgSzn3qNjuiIJWUMv35lFX0Swnj37v5kHbUwuGNks1W2OULj\nVadwyggIjOa9DF9mf5fD0E6RhAWY8TEa2JFXAdQBcLSyjk9+ygFg5thu/HKUl87OdjpUeOrgKjWi\nrPKI8tAH/1KFWbrfoEItHa9S80Nc1Duc/PW7fezNV+tKSQk7csspq6k/7WmC/UxEB/sigNsGJtAr\nPowhnSLpcAan61xoceNeXmPD0kzstynuGpLMwJRwQJASFUjX2CCoLoKgSHUTpy9W49av++N5zbJs\nSarr7KQdKuNwaQ0/7tjPhKJ/Msb2HTbMvNXhJf70f/eeEj5JjAwgMfJEJ4zZaGB0gzjnpYa13sGW\nvVkkHZxP+6xPEVVHANiZb6EoYQz2sJ+zYvdR0pauo9hSxz/v7k9UkC9RQW3bEQBUyKlwJ5Tsh+2f\nqc7Q8GTqRv+BZ7/cRUZhJbvyCrm6WwzvTRtw2iosdXY27C8h0NfE4I5tcGz+mSjOVKEp32C1bpKU\nau0o6cThH4HVHE5l7EjyZRfK09VqlfkVqSxbmYa1waif0mobB4qr6dE+BJNrJNTQTlGM6RmHT6Pw\npY9JMLRTFH7mC9uv1+LG/d/TBzdfyB2O7oWtr8OeRVCRA88VKo/s7sWql7oN8mP6UV75NoOc0prj\nnZ9dov0Z6fyJwvajKR8yi4d79PSuDs1zQErJpuwy8itqT0o32SoxOWrYURnINxt2sEKqZSOyggfy\nZcAUviuLJXNFBJAGgL/ZSJ+EMKYMTPRIzLMl2VdYRXpeKVFFG+iV8Q+CSnepDL9QbIX72FgcxMbs\nUr7YksvgjhE8eV2XJmfKBvmavMM5cDph3zfKMy89AJnLVXrXCcq4G03sH/0uH2b58XGGwOGUcBTY\nC8euE4BO0YEnzUBvH+bPw9d05sYrWs8M7BY37ufMsWm7xZnw45/U5BxhVIsPDX0IcIVq2qhht9TZ\neWrBDvx9DFzfPYJfii+xDXqITgntEY49RJq8aBy5G3y9/QgvLUs/NUNK6hySYksdz5v+RZIopJ0o\nJVjUEEMZ3zv78ab9MW5MDSM7+EE+rOrPe/v8iAz0YdbN3egSe2KoYnJUYJvqULfU2flq24lPK9Tb\nnazemUldSS5rqmLxxcZm30eoxYe3/B8m+fJhHDQk8Nnn+ZRW/wTANd1i+OcZvHWvwVGvDHl4ipp8\ntvM/anaoXxj1I2ayXAxn3m5ByUs/YHc6KaxUk6ruHZbA2J7tjnvix/AzG+kSG9T6nSopZYts/fr1\nk1JKKSuOSLnkyZO2pJgQ6efrIwMDA2VYaIgc3ztW5rw4UMo5g6Wc3VHKF+Ol3PpvdXxumpQvJUv5\nzbNSVpfIM5GUlCS/++67M+ZLKeX8+fPlkCFDpL+/vxw5cuQp+Xa7XT733HOyXbt2MigoSPbp00eW\nlZU1WSeQdrY6WZtZJKfMXS1ve2a2PPLV76R8Y5BMChXSz9esdBIWJsePHy9zcnKaPHdznK9OVq1a\nJQMDA0/aALlgwYIm6zwbnUiXXjbsL5YL33xWbn9lglzz97vk8tcflrteHis7hPtKk9lX+voHyDB/\nkxzbPUymvzJOVn5yryxdOEse3rVGHq20Hj93Za1NvvxNuswutlwwvUgp5YoVK+QVV1whg4ODZUpK\ninz77bc9rpPDpdUyaeZimTRzsfzZrJfl289NkdXPx8j4MLM0+/jKgIBAGRISLPsNGyWveWGBTJq5\nWCbPWiyn/2uT/Hp7nvzTkj0yt6ymSbk8qZNFixbJHj16yMDAQDlkyBC5e/fuZs97LvfPgtXb5eH/\nPC2di2dI+e1vpXx9gOv+UTbFLzBYhqYOkN0e/0h2eW6pTJq5WI77+yr5xPxt8on52+T7aw7IKmu9\nR3QyY8YM2blzZxkUFCS7du0q//Wvf52Uv3XrVtm3b1/p7+8v+/btK7du3eoxnbS8526tgJ2fn5xm\nq+brvz7B6Af/gjV3Fw/+/Foe/iKXhU+OgoRBqkM03DV+tF0feDITjOfflIiICB577DHS09P54Ycf\nTsl//vnnWbduHevXrycxMZHdu3fj5+f5yU0L0g7xVv7PCfWxqA8YRnSCoBi+XvAxo0ePxmq18uCD\nD/Lwww+zcOFCj5+/IU3pZMSIEVgsluP7K1euZOLEiYwdO9bjcgzqGAld/CC9CKp2qOsmoiMmsy/L\nFn7E6PGTjutl5upSFi78JwCNJ7IH+5l5ckzX85anKb3U19dz44038pe//IX77ruPtLQ0rrrqKgYN\nGkTv3r3P+9zHiAvxY8u0EAI3vobvge+QwgjdrscY+C1LPz/5Wind8hFpH8/HbDQcfzu5vld7j8kC\nTeskMzOT22+/naVLlzJ48GBefvllJk2aRHp6OiaT58yQwyn5YO0BJta8Q5UhgEBqqTZHYTWH8uQz\nvyUvdjg/7snDb+P7yHXvc8evX2f0ZbEM7hhxQTzxwMBAvv76a7p06cKmTZsYO3YsnTt3ZujQodhs\nNiZPnsxjjz3Ggw8+yNtvv83kyZPJzMzEx8cDb+buPAGAsUAGkAXMOk2+AP7hyt8B9G2uzuOeuxtP\nxCVLlsjU1FQppZRWq1XOmDFDJiQkyJiYGHn//ffLmhrlfRQVFckJEybI0NBQGR4eLocPHy4dDoe8\n4447pBBC+vn5ycDAQDl79uwmn4zvvPPOKZ5HaWmpDAwMlFlZWU0e2xjOwfMoqrJK2+rXpNy9UMo6\nS6vVSWOmTZsmp02b5lGdyDNdK3ablLJ16qWgoEACsrq6+nha//795SeffOJ5nez6UspXukr540ut\n+lp5/fXX5bhx447vOxwO6efnJ7///vsm6zqX+6e2rl7O35gtx/59lUyZtUimzFokjSExMubnf5RJ\nMxfL99ccuOg6OcbEiRPlK6+8IqWUcvny5bJ9+/bS6XQez09ISJDLli3ziE6aHRMnhDACc4BxQHdg\nihCie6Ni41AfxE4F7gPeOr9HzglqamqYP38+gwerjtdZs2axb98+tm3bRlZWFnl5efz+978H4NVX\nXyU+Pp6ioiIKCwt58cUXEULw0UcfkZiYyNdff43FYuHpp58+azl27tyJyWRiwYIFxMXF0aVLF+bM\nmeOpZp5EVJAv5uGPqDUpfE6dINNadNKQ6upqFixYwN13331e9biN8dTYeGvRS2xsLFOmTOH999/H\n4XCwfv16Dh06xPDhw5s/+GzpcSPMSIdRM9vMtQInnMpdu3add12N8fMxceuAJJY9OoIDf57IgT9P\nJD7cn5duupzXbuvDz/rEtIhOamtr2bRpEz16qAlOu3fvplevXie9MfTu3Zvdu3d7RA/uvA8NBLKk\nlAcAhBCfAZOBPQ3KTAY+dD1VNgghwoQQ7aSU+ecq2A033IDJZKK6upro6GiWL1+OlJJ58+axY8cO\nIiLUcKxnn32WqVOn8uc//xmz2Ux+fj6HDh2ic+fOjBgx4lxPfwq5ublUVFSwb98+Dh48SGZmJtdc\ncw1dunTh2muv9dh5mqK16aQhX375JVFRUYwcObL5wh6mNeplypQpTJ8+nUcffRSAt956i4SEizfp\nqbXpZPTo0cycOZOVK1cydOhQZs+ejc1mo6amxmPnaI6H753aojp54IEH6N27N2PGjAHAYrEQGnry\ncsghISFUVVWdeyMbIJQ9bqKAELcAY6WU0137dwKDpJQPNSizGHhJSrnGtb8CmCmlTGtU130ozx6g\nKyrUczouB7KBY60MA5JRD5TLAUej8gLYivowd3tOhFqLgIIz1JkIHJudkt+gHECUK6+hfGFAJ2An\nYHOlHbtbD3NmkqSUbk2HFUIUAYfOkN0addKQLoAFOHKG/Ia4rRNok3rxAy4D9gOVgC/qrfYwUHGG\ndni7TnDV2x4wAyVAiOvY0jO0A7zn/okHglE6ObbIVQxKB1kNynV21Vd4hnaAuzppLm6D+vj1uw32\n7wTeaFRmMTC8wf4KoL87caEznDMbGN0orQi4FagBOrhRR0/UCNVrXPsHG9fZxLHTgZWN0jqhxlcm\nNkj7B/C3c21nW9dJg7wEwA50uhi6aO16cd0zWxul/b3xfXMp6eQ0ZcJQzkA3b9cJ8AKwC4hslH4d\nkIvLyXal5aCc6fNuszvz0PM44aGCegLlnUOZc0IoJqOenLuBd4C/CSFiXPkdhBBjXL+vF0J0FiqI\nVYF6Gh97ShYCHZs5l1EI4YcKVxmEEH5CCDOAlHI/sBp4TgjhK4S4DLgN9WC7qLQWnTTgTmCdS0ct\nRivSy1agsxDiapdMnYDrUYMNLiqtSCcIIfq5ykQD84BFUsrTTF64sFxknTwDTEU9BEoaZa901feI\ny6Y8gnIgTx2qdy648dQxAQeAFMAH2A70aFRmArAM9SozGNjogadsLerJXoV66t3uyvMDXnTJVIma\nO/aIK+9x17HVqCfibxrUORn1VCwHnjzDeae5lNtw+6BBfgfgG5dcB4D7L4bX0Zp14iqTDvziYumi\nLegF5RHucsmUC8wGDJe4Tta45CkF3gYCL4HrRKIW6LE02J5tkH8FsNkl2xbgCk+1udmYO4AQYEGn\nLQAAEmVJREFUYjzqtdIIvCel/JMQ4gEAKeVc11PtDdSQyRrgHtko3q7RaDSai4dbxl2j0Wg0bQt3\nxrm/J4Q4KoQ47YBUV/zqH0KILCHEDiFEX8+LqdFoNJqzwZ0O1Q9Q4ZYzccEmMGk0Go3m3GjWuEsp\nV9H0ONTjE5iklBuAMCGEl35jS6PRaNoGnvgkTwdOnsST60rTaDQaTQtxUVeFbDhDNTAwsF+3bt0u\n5ulbhM2bNxdLN2fYRUVFyeTk5AssUctzNjqBS0MvWienR98/p+KuTjxh3N2ewCSlnIeavED//v1l\nWpr3j5YUQpxpOvQpJCcno3VyKpeCXrROTo++f07FXZ14IiyzCLjLNWpmMFAhz2PBMI1Go9GcP816\n7kKIT4FRQJQQIhd4HrXwD1LKucBSYDxq8Zsa4J4LJaxGo9Fo3KNZ4y6lnNJMvgR+5TGJNBqNRnPe\neCIso9FoNJpWhjbuGo1G44Vo467RaDReiDbuGo1G44Vo467RaDReiDbuGo1G44Vo467RaDReiDbu\nGo1G44Vo467RaDReiDbuGo1G44Vo467RaDReiDbuGo1G44Vo467RaDReiDbuGo1G44Vo467RaDRe\niFvGXQgxVgiRIYTIEkLMOk3+KCFEhRBim2v7redF1Wg0Go27uPMlJiMwB7gWyAU2CSEWSSn3NCq6\nWkp5/QWQUaPRXCrUlEJAREtL4RW447kPBLKklAeklDbgM2DyhRVLo9FcUmz+AN4cAq92hZL9LS2N\nV+COce8AHG6wn+tKa8xQIcQOIcQyIUSP01UkhLhPCJEmhEgrKio6B3E1Go1XICVkLIPM79V+t+sh\noiMMfhB8glpWNi+h2bCMm2wBEqWUFiHEeGAhkNq4kJRyHjAPoH///tJD59ZoNG2FogxY/hzYLJCz\nXhn11NEQGAW3/bulpfMq3PHc84CEBvvxrrTjSCkrpZQW1++lgFkIEeUxKTUaTdvE6Tzx+8D/4N1r\nIS8NLEfhqufglvdaTjYvxx3PfROQKoRIQRn124CpDQsIIeKAQimlFEIMRD00SjwtrEajaSM46mHx\n47Dt33Dj29DrVghPhpQRMOZP6rfmgtKscZdS2oUQDwHLASPwnpRytxDiAVf+XOAW4JdCCDtQC9wm\npdRhF43mUsNWrWLpa/4Ghbugzx1gMKq88CQdermIuBVzd4ValjZKm9vg9xvAG54VTaPRtDkWPQy7\nvlCe+c8/hssmtrRElyye6lDVaE6mphR2fg7F+2D8KyBES0ukuVDUlEJtGUR2gqt/DX3vguQrwaAn\nwLck2rhrPIfTAfuWw56v1Kt5XQWEJsDwJyD0dKNnNW2a8sPwv5dgx3+g4yi4/XM1nDGiY0tLpkEb\nd8+yeyHUVUHfO1takouL3QYmH9jyL9WJFhClhreNmAGxp53yoGnLSAkb3oIVvwfphL53Q+8pLS2V\n93E0HfYshLBE6DO1+fKN0Mb9fLDXwYGVcHAVHN0L+1dQ0XkyeXGT6RoXjNHghaEIawUc2QqWItj0\nLpQdhPgBqqOs7zQI6QCdrgGjurSklOwvqsZmd9Ih3J9Qf3PLyq85f3Z+DsufgS5jVcgtLKH5YzTu\nUZYNG9+BXV9C1RFAQP97tHG/kEgpyTh8FMPBHymPvwa7FBi/eYpBxV9Sj5k8nyQ2iPE8v+tG6nat\nZtcLYwjy9TL1Op3wwQQo2Kn2QxMg9TqIuxyACquDDPMAdq4/zNqsYpxScqS8ln2FFgDm3tGXsT3b\ntZT0F4+6KhWDBvCPAN8gnMUHKP/2JeyWYrItRoz11VQaw8gZ+iJ3D01uUXHPmssmwriXYcB0j8fV\ni6rqyC2r4YrEcI/W22ZYNhMyv4VuE6DjDOg2EYJjz6kqL7M+nsVqrWXVqh8p2L2K5Opt9K/fQoCo\nY1zdn9krk7jC1J8+Eb3YYe6DXZiJC/XjL5e3w9dkxNfURjuTnE7I26xeBwt2qqngFTnqZk4aArd+\nBKUH1IzCqK5g9iOnpIa3/7uTL7bkYq1Xk1ZSY4II8DESEejDH27oSXSQL1ckhrVw484Ca6XqDPYN\ngeguUG/FsWEuxdk7sRkDMNeVEVy2h9zUO8lLnYpvdT69//cL/GoLMNVbjlfzz5AHWeY/kYjyXfzB\nupwyGUyCoZY6YyC1RkG1zd6CjTwLsr5XYZgb3lKhtkH3efwUGQVVTP9wE/V2yf+eHoWvyejxc7Qa\n7DY4vEFdYxnfwPV/VeGXETPguj9C1CkT/M+aS9a4SynZfKiMvLIaAqtziC1chf3wFmyWEj72m0qm\nKZUeFSt5Vb4KQIkxmsOJk6jvMpHfxAxAGn3oFjeayCDfFm7JWeJ0QsVhqC5Sfwt2Ki+z2wToPBoK\ndsA/R4PBDHE9wXIUe1A7Nh8ooqAsD/ABuoEFKCyhympn9jfp1Nmd3NCnPeMvb0e7UH+6xgW3cENP\npbrOzudph1m47QjWegehzgr8pJVCYyxCOnmh+g/EOguJcpbgTy0GJP/xvZn3/KcR7Kzk88rnMcgQ\nwqingkDWORP59/oaVq5NI44SfmcOJ192okBGUIpqf72hG75mA/Z2V7Cq5xoSIgJITgjDz2wkGRje\nohpxg7Js+OGPsHOBMjj2Oo9Uu25/McUWGwOSwwkP8OE3C3exYEsukYE+vDdtgPca9norbHgT1r0O\ntaUqLTQRW/4env+hnK05ViDftZ3MB/cMJC7Uz+1TXVLGvdbm4Lu9hRyttPLFljwsBZl85vNHOgg1\nmfaIjMTmG8Fgvxxsob3xjxlJRlwKXfpdRWRoPJEtLP85UbIfNs5TcfHLb1EjWF7rdSLfYAK/MBVi\n6TxaeWU3zqO0w1VsLHBQ75A899+dVO61A9tOe4rUmCDemzaAhIiAi9OmJqh3OPnD4j38sKeACMrx\nlTaOGOIAiKvJoKsjkzuDy+lNBh2tu9kUdDXvRT6LkE5irZVU+LQnyzyAakMwh307c9inE0nmAIT0\nY2bM11zVJ5V2of4AxACPuzbFJBoGncIDfEiMbHmdnDNZ38OX94PdCkMfglHPgo977UkvqGTzIRWa\n2n+0mhXphdgdEh+TgaTIAFZmqIUDTQZBiL+Z0mob913Zkf8b0ZHo4DbmMJ2JuirY/yNs+VBN4Jrw\nKhU1dYSueIEjUUPZd9nPKQvpRrkphv98m8fe/MNc1TUanzO89ZuMZ9eH59XG/ZOfcvjb9/uw2Z2E\nyiqk3YqPo5oOohhH9DAeuuEqQrJWU9R+ALXxwwiN7057fzPJwO0tLfz5sPY1dWPWlEFJlkoTRmXc\nzQEw6Q0VVgnpAFGpWJxmnvzPdtb/8C0AcSHtsNTtIq+8FoDeCWHMGtuNmJDT33QJ4QFnvCAvCJaj\nsGM+5G2mwrc9d+aM51BJDe/J39JZ5vAE8FtRhwk7B/178kbKmwDM3D+XmLpssJlVP0GXWQzqMoZB\n7a9wVbzp4rWhLbD/R/ALganfNBsmcDgl3+4u4P212aQXVFJpPRFuMhkEo7pGE+rvQ0l1Hav2FXHP\nsGRu7hvP0p35FFbWMaZHLNf1iLvQLbowSKkGGvi7wo7r58Du/0JuGiAhMIa1AVfz2J++p7zGRpzz\nbxzOjVXr61IClJAcGcDcO/oxtqfndNDixr3W5jhtuo/JcNJoE0udnY83HKK6zn78uOV7CmgX6s9d\nQ5L4PC2XNVnFJ9XhcEpmxG5lgvNHOlalqdaawBEYg+GRpxBGMwx6n9YXQDhL6ixqVmC/u9V+/nbl\nNYQlQIe+ZHX/FUuyDdi/zXAdMBBcqqp3HGLJziMcKbdyS994/H2MbMkpo7TGyV9u7kWRpY57h6Xg\n79Oyr8l2hxO55jWM6V8hCncinHbqghPZXJVHjmEUN/RpT2nhKNLri4gL9SMsLhpCOpAS2ZlXO/VW\nlRx8Xc2cDOlwYkq85mQKdoF0QLveMHImjP4dGM88wqnKWs/8TYf5YF02uWW1JET4M7lPBxIjAhjb\nMw5fkwF/HyPBfifqsNY78DMr/ffsEHqBG3QeWCuhKl8tqWA5Cl3HqvTPboeqfGRNCVQVIO02aswR\nvD1ATeK/ac8iAuorWRU4la/Kk9lYdhm2jQYGpgTSNzGeCZcPI7ahoyQgKtAXg4dH17W4cR/y0grK\na+pPSQ/1NzOuZ9zxi2DDgRLSC6o41n4hBINSIjhaUsrHn62hs6GAtzr6E0QtQbajrE78FcGRcdxR\nuQ7DvnIYOQtC2oEwYOx4VZMXbJtASjUMc/8K2PYJ1JRA4mCI7go3vs3O/Br+uzWPjMJK1q5T3vuZ\nrp0+CWHMvqkXQzufWMjT6ZQev9jOhy+25FLw3Q76Cxvb5Hi+cFzJAWt7EiL8+eKegXSKDgJearqS\nlCsviqxtDinh0DrY/qnaYnvAff9TXvtp2F9k4cstuRytrGPZrgIsdXYGJIfz6wndubZ7bLNDgI/d\n060CKaE4Uw3dDUtSD/29X6u1cfK2ACeWyCp+NJtDlZLD9YPoWvYV+y3tOeLsQR1m8usj+exHdZ/N\n4WFAkBARwNhhcfQxGogJ8WXqwERMxov3htvixv3Ra1KpsztPSd9+uJxluwqQUhJHMSPM6fz9yji6\nxQWrJ+oVt4N/OI60DzAu/qM6KBdAQFAMvQY+CzHJwJ/UKnRtFbsNsr6DrBVqCFqnq6Asm/r50zAX\nbMUhjOwNGsyPKbdzaKUV2I5TSpbuzMcpIS7Ej6fGdGXqwETCA33cPm1rMuwAveLDKLv21+xErV53\nK2A2Gpjcpz1Rba1TuyWpyIXsNWqMun8Y5PwES2eojnVzgFo6YNQzIAQ2u5MlO4+wLqvkuIkrrLSy\nOrMYk0EQ5Gdi9GUx/GJ4Ry6Pb8UeeGOcTjU/Y8uHalRYWbZKf3gLFQGJ1OcfIqjeTs2AJ6gP78SB\nMgefpjtYMns1Doz4GFMJ8Z/FmCviSI4MZEzXaLrEBvNiizbqVFrcuN/TwwjLf3NqxqC74Y7rIGcD\nvHcr1AMbG+T7BkG/aRiThsDUzyG2uxq2ZvZv8175oZJqfNb/jcCCTQTlr8fgqMNhDqRIhrLb1p3P\n1x3koSNlfOq4l6/klYT6hro610+ssjwoJZJXb+3tNYbvsnYhXNbu9J7kpUqJpY4vP3qdnxe9flK6\n1RjInLg/UeiTyICqFfyseA4AZurxd1YD8HzCBxT6JDK64r+MttZSNWI2xSmTWJFZhXF9OT6mSj5c\nn01hZR2RgT7HvW1fs4HHR3fh9sGJrfbaSi+opL7Wgn/pXkw1hQD4Vhykuv0QamL6Upm5luH/m4oD\nI3v9+7Et+iYcmNiy8ADLDu7DZk8GZkLOiTpTY4J4elw8sSF+jO4e2ybmsLS8hHbb6b+ZWFuu/kZ2\nhjEvQserTrwmCgMEu8YlRHdVmxfx8vIMnsl4j1Jp4nPn1axzdmeltQ+OtUZYm0ZUkC/fjficQTFB\nPJwccVbDozTeg90p2WUJIc405HiaAIKkhaNlFRw0VuNvDyXGlW/HxFFDDFtMfThcEYpDVDO37kqe\nLB8O3wlgKwahAhFSwojUKF66uRcjU6Nb3ZtcU7zw4TI+rTl1HP5v6qfxkaOWWEq5NeAeNgeOpMQQ\nCTZXARvc0i+OgckRJ61zFxPsx+COEYg2tvhdyxv3qM7w4Poz5wdGwZBfXTx5WgEPjOxERu/VSIOJ\nZCCZE19H8TEZGJgS4b3jgDVuExvix2tP3QecashGHf91JXD/GeuQUrI9t4ISixq/3q1dCFJK6h2S\nlKhAD0t8cXjkhuFk7ZpBTXAKtUGJSAT1vmGM8o9lFBDgY2Jgyh3euTxIA9wy7kKIscBrqHDnu1LK\nlxrlC1f+eKAGmCal3OJhWS8ZenYIhdY8ikDjNQgh6JPQhmYOu8GQLh2gy29bWowWp9muWyGEEZgD\njAO6A1OEEN0bFRuH+iB2KsqNeMvDcmo0Go3mLHBnXM5AIEtKeUBKaQM+AyY3KjMZ+FAqNgBhQohL\nYIUojUajaZ24E5bpABxusJ8LDHKjTAcaLZAghGgYILQIITLwfpLcLbh58+ZiIcShCylMK8FtncAl\noxetk9Oj759TcUsnF7VDVUo5D5h3Mc/ZlpBSRre0DK0RrZdT0To5Fa2Tk3EnLJMHNFyNP96VdrZl\nNBqNRnORcMe4bwJShRApQggf4DZgUaMyi4C7hGIwUCGlPHXNSo1Go9FcFJoNy0gp7UKIh4DlqKGQ\n70kpdwshHnDlzwWWooZBZqGGQt5z4UTWaDQaTXMIKWXzpTQajUbTpmij34LTaDQaTVNo467RaDRe\niDbuGo1G44Vo467RaDReiDbuGo1G44Vo467RaDReiDbuGo1G44X8P5Ida2x5qyA2AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117906160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "# 高速化のため訓練データの削減\n",
    "x_train = x_train[:500]\n",
    "t_train = t_train[:500]\n",
    "\n",
    "# 検証データの分離\n",
    "validation_rate = 0.20\n",
    "validation_num = x_train.shape[0] * validation_rate\n",
    "x_train, t_train = shuffle_dataset(x_train, t_train)\n",
    "x_val = x_train[:validation_num]\n",
    "t_val = t_train[:validation_num]\n",
    "x_train = x_train[validation_num:]\n",
    "t_train = t_train[validation_num:]\n",
    "\n",
    "\n",
    "def __train(lr, weight_decay, epocs=50):\n",
    "    network = MultiLayerNet(input_size=784, hidden_size_list=[100, 100, 100, 100, 100, 100],\n",
    "                            output_size=10, weight_decay_lambda=weight_decay)\n",
    "    trainer = Trainer(network, x_train, t_train, x_val, t_val,\n",
    "                      epochs=epocs, mini_batch_size=100,\n",
    "                      optimizer='sgd', optimizer_param={'lr': lr}, verbose=False)\n",
    "    trainer.train()\n",
    "\n",
    "    return trainer.test_acc_list, trainer.train_acc_list\n",
    "\n",
    "\n",
    "# ハイパーパラメータのランダム探索======================================\n",
    "optimization_trial = 100\n",
    "results_val = {}\n",
    "results_train = {}\n",
    "for _ in range(optimization_trial):\n",
    "    # 探索したハイパーパラメータの範囲を指定===============\n",
    "    weight_decay = 10 ** np.random.uniform(-8, -4)\n",
    "    lr = 10 ** np.random.uniform(-6, -2)\n",
    "    # ================================================\n",
    "\n",
    "    val_acc_list, train_acc_list = __train(lr, weight_decay)\n",
    "    print(\"val acc:\" + str(val_acc_list[-1]) + \" | lr:\" + str(lr) + \", weight decay:\" + str(weight_decay))\n",
    "    key = \"lr:\" + str(lr) + \", weight decay:\" + str(weight_decay)\n",
    "    results_val[key] = val_acc_list\n",
    "    results_train[key] = train_acc_list\n",
    "\n",
    "# グラフの描画========================================================\n",
    "print(\"=========== Hyper-Parameter Optimization Result ===========\")\n",
    "graph_draw_num = 20\n",
    "col_num = 5\n",
    "row_num = int(np.ceil(graph_draw_num / col_num))\n",
    "i = 0\n",
    "\n",
    "for key, val_acc_list in sorted(results_val.items(), key=lambda x:x[1][-1], reverse=True):\n",
    "    print(\"Best-\" + str(i+1) + \"(val acc:\" + str(val_acc_list[-1]) + \") | \" + key)\n",
    "\n",
    "    plt.subplot(row_num, col_num, i+1)\n",
    "    plt.title(\"Best-\" + str(i+1))\n",
    "    plt.ylim(0.0, 1.0)\n",
    "    if i % 5: plt.yticks([])\n",
    "    plt.xticks([])\n",
    "    x = np.arange(len(val_acc_list))\n",
    "    plt.plot(x, val_acc_list)\n",
    "    plt.plot(x, results_train[key], \"--\")\n",
    "    i += 1\n",
    "\n",
    "    if i >= graph_draw_num:\n",
    "        break\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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