notebooks/ch06.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ch06/optimizer_compare_naive.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys, os\n",
    "sys.path.append(os.pardir)  # 親ディレクトリのファイルをインポートするための設定\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import OrderedDict\n",
    "from common.optimizer import *\n",
    "\n",
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "# ch06/optimizer_compare_mnist.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========iteration:0===========\n",
      "SGD:2.408242718249455\n",
      "Momentum:2.4068394349139055\n",
      "AdaGrad:2.399048853500868\n",
      "Adam:2.1944214827730084\n",
      "===========iteration:100===========\n",
      "SGD:1.4838006036312186\n",
      "Momentum:0.42186019265352653\n",
      "AdaGrad:0.1908289162678628\n",
      "Adam:0.3378542776491954\n",
      "===========iteration:200===========\n",
      "SGD:0.6389040136786772\n",
      "Momentum:0.16794397652275167\n",
      "AdaGrad:0.08875720456184207\n",
      "Adam:0.12348264009510228\n",
      "===========iteration:300===========\n",
      "SGD:0.46480378235552966\n",
      "Momentum:0.14420780552466841\n",
      "AdaGrad:0.07464379771654434\n",
      "Adam:0.10528838920196336\n",
      "===========iteration:400===========\n",
      "SGD:0.39548868057941255\n",
      "Momentum:0.13572647486759665\n",
      "AdaGrad:0.0697300630156496\n",
      "Adam:0.1405630630811324\n",
      "===========iteration:500===========\n",
      "SGD:0.28667939371789586\n",
      "Momentum:0.12319715144353018\n",
      "AdaGrad:0.04046594711361991\n",
      "Adam:0.07378898604506837\n",
      "===========iteration:600===========\n",
      "SGD:0.4203787432917502\n",
      "Momentum:0.21723350800065033\n",
      "AdaGrad:0.11933290725927105\n",
      "Adam:0.14664824336555854\n",
      "===========iteration:700===========\n",
      "SGD:0.42863850044500007\n",
      "Momentum:0.20009240372246626\n",
      "AdaGrad:0.11970742168590243\n",
      "Adam:0.13967806430721877\n",
      "===========iteration:800===========\n",
      "SGD:0.3915109361065571\n",
      "Momentum:0.10323704894217445\n",
      "AdaGrad:0.05088538767729365\n",
      "Adam:0.07558018332606653\n",
      "===========iteration:900===========\n",
      "SGD:0.23459267648641485\n",
      "Momentum:0.06335814220380202\n",
      "AdaGrad:0.019046470069010515\n",
      "Adam:0.04403565735528974\n",
      "===========iteration:1000===========\n",
      "SGD:0.18104781617672527\n",
      "Momentum:0.04430297808879973\n",
      "AdaGrad:0.02048050412458766\n",
      "Adam:0.026183928702842415\n",
      "===========iteration:1100===========\n",
      "SGD:0.21099037163055678\n",
      "Momentum:0.0797077905798077\n",
      "AdaGrad:0.022282712932068266\n",
      "Adam:0.044275855491497436\n",
      "===========iteration:1200===========\n",
      "SGD:0.34508382286376155\n",
      "Momentum:0.13305598824629167\n",
      "AdaGrad:0.04725019424990363\n",
      "Adam:0.0644705074347057\n",
      "===========iteration:1300===========\n",
      "SGD:0.16513184838885026\n",
      "Momentum:0.07516918271367126\n",
      "AdaGrad:0.026788681222553026\n",
      "Adam:0.06267274556112155\n",
      "===========iteration:1400===========\n",
      "SGD:0.43583312126779966\n",
      "Momentum:0.2262067214897179\n",
      "AdaGrad:0.06212248074215315\n",
      "Adam:0.10569854570251946\n",
      "===========iteration:1500===========\n",
      "SGD:0.18167619464491278\n",
      "Momentum:0.11680919783422977\n",
      "AdaGrad:0.04729464192344421\n",
      "Adam:0.05039766650537868\n",
      "===========iteration:1600===========\n",
      "SGD:0.31215857087563703\n",
      "Momentum:0.11483831333949618\n",
      "AdaGrad:0.04182234328922706\n",
      "Adam:0.050503175499379764\n",
      "===========iteration:1700===========\n",
      "SGD:0.27291607983052535\n",
      "Momentum:0.06180286048109937\n",
      "AdaGrad:0.033157015644196944\n",
      "Adam:0.031447574485801474\n",
      "===========iteration:1800===========\n",
      "SGD:0.2396810301947451\n",
      "Momentum:0.04416182199806418\n",
      "AdaGrad:0.04471766721705878\n",
      "Adam:0.010261639304473418\n",
      "===========iteration:1900===========\n",
      "SGD:0.14991115071397773\n",
      "Momentum:0.04998606313077592\n",
      "AdaGrad:0.01634098375415749\n",
      "Adam:0.031891773034629325\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append(os.pardir)  # 親ディレクトリのファイルをインポートするための設定\n",
    "import matplotlib.pyplot as plt\n",
    "from dataset.mnist import load_mnist\n",
    "from common.util import smooth_curve\n",
    "from common.multi_layer_net import MultiLayerNet\n",
    "from common.optimizer import *\n",
    "\n",
    "\n",
    "# 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()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ch06/weight_init_activation_histogram.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "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()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ch06/weight_init_compare.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========iteration:0===========\n",
      "std=0.01:2.3025157956274507\n",
      "Xavier:2.3020734893031785\n",
      "He:2.4544075041920133\n",
      "===========iteration:100===========\n",
      "std=0.01:2.302178072880435\n",
      "Xavier:2.2321235770179886\n",
      "He:1.5613527786505133\n",
      "===========iteration:200===========\n",
      "std=0.01:2.303043613078236\n",
      "Xavier:2.093645801413508\n",
      "He:0.842903460666329\n",
      "===========iteration:300===========\n",
      "std=0.01:2.3004026014519234\n",
      "Xavier:1.9030425166471616\n",
      "He:0.532314643590287\n",
      "===========iteration:400===========\n",
      "std=0.01:2.3034116818307107\n",
      "Xavier:1.56656187320357\n",
      "He:0.5057243829319312\n",
      "===========iteration:500===========\n",
      "std=0.01:2.301104689616488\n",
      "Xavier:1.06381099382007\n",
      "He:0.3409061411273618\n",
      "===========iteration:600===========\n",
      "std=0.01:2.3008604448121255\n",
      "Xavier:0.7205375193206949\n",
      "He:0.36962253123804845\n",
      "===========iteration:700===========\n",
      "std=0.01:2.299281852690279\n",
      "Xavier:0.5682012162189156\n",
      "He:0.23922734532605736\n",
      "===========iteration:800===========\n",
      "std=0.01:2.300877042213036\n",
      "Xavier:0.4956058039394971\n",
      "He:0.34717193249557177\n",
      "===========iteration:900===========\n",
      "std=0.01:2.3001882419519166\n",
      "Xavier:0.4594295883635798\n",
      "He:0.27103628528917806\n",
      "===========iteration:1000===========\n",
      "std=0.01:2.3016996984501894\n",
      "Xavier:0.35554694828959393\n",
      "He:0.20503593688770466\n",
      "===========iteration:1100===========\n",
      "std=0.01:2.301986296505439\n",
      "Xavier:0.35148351952933043\n",
      "He:0.19678273612669414\n",
      "===========iteration:1200===========\n",
      "std=0.01:2.3018558582216544\n",
      "Xavier:0.28625712460757885\n",
      "He:0.24837893136211936\n",
      "===========iteration:1300===========\n",
      "std=0.01:2.302925005426953\n",
      "Xavier:0.4217661813273085\n",
      "He:0.37196938150073644\n",
      "===========iteration:1400===========\n",
      "std=0.01:2.2978512186117315\n",
      "Xavier:0.30184341751436855\n",
      "He:0.26110116085328117\n",
      "===========iteration:1500===========\n",
      "std=0.01:2.2987644707512027\n",
      "Xavier:0.2648922031151903\n",
      "He:0.19659482699956082\n",
      "===========iteration:1600===========\n",
      "std=0.01:2.3086202541372414\n",
      "Xavier:0.2557195699721114\n",
      "He:0.18013130420301487\n",
      "===========iteration:1700===========\n",
      "std=0.01:2.299934772086039\n",
      "Xavier:0.3887508636321957\n",
      "He:0.2779525888762716\n",
      "===========iteration:1800===========\n",
      "std=0.01:2.302657326960535\n",
      "Xavier:0.22841618202850045\n",
      "He:0.16583472025998403\n",
      "===========iteration:1900===========\n",
      "std=0.01:2.304983037427144\n",
      "Xavier:0.37690140419997054\n",
      "He:0.28639987649711807\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(os.pardir)  # 親ディレクトリのファイルをインポートするための設定\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataset.mnist import load_mnist\n",
    "from common.util import smooth_curve\n",
    "from common.multi_layer_net import MultiLayerNet\n",
    "from common.optimizer import SGD\n",
    "\n",
    "\n",
    "# 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",
    "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": "markdown",
   "metadata": {},
   "source": [
    "# ch06/batch_norm_gradient_check.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1:0.0\n",
      "b1:0.0\n",
      "gamma1:0.0\n",
      "beta1:0.0\n",
      "W2:0.0\n",
      "b2:0.0\n",
      "gamma2:0.0\n",
      "beta2:0.05629097639991976\n",
      "W3:0.0\n",
      "b3:1.7990357854824612e-07\n"
     ]
    }
   ],
   "source": [
    "import sys, os\n",
    "sys.path.append(os.pardir)  # 親ディレクトリのファイルをインポートするための設定\n",
    "import numpy as np\n",
    "from dataset.mnist import load_mnist\n",
    "from common.multi_layer_net_extend import MultiLayerNetExtend\n",
    "\n",
    "# データの読み込み\n",
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)\n",
    "\n",
    "network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100], output_size=10,\n",
    "                              use_batchnorm=True)\n",
    "\n",
    "x_batch = x_train[:1]\n",
    "t_batch = t_train[:1]\n",
    "\n",
    "grad_backprop = network.gradient(x_batch, t_batch)\n",
    "grad_numerical = network.numerical_gradient(x_batch, t_batch)\n",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "# ch06/batch_norm_test.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============== 1/16 ==============\n",
      "epoch:0 | 0.093 - 0.102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/studio-lab-user/deep-learning-from-scratch/notebooks/../common/multi_layer_net_extend.py:101: RuntimeWarning: overflow encountered in square\n",
      "  weight_decay += 0.5 * self.weight_decay_lambda * np.sum(W**2)\n",
      "/home/studio-lab-user/deep-learning-from-scratch/notebooks/../common/multi_layer_net_extend.py:101: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  weight_decay += 0.5 * self.weight_decay_lambda * np.sum(W**2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:1 | 0.097 - 0.124\n",
      "epoch:2 | 0.097 - 0.147\n",
      "epoch:3 | 0.097 - 0.173\n",
      "epoch:4 | 0.097 - 0.182\n",
      "epoch:5 | 0.097 - 0.207\n",
      "epoch:6 | 0.097 - 0.228\n",
      "epoch:7 | 0.097 - 0.258\n",
      "epoch:8 | 0.097 - 0.272\n",
      "epoch:9 | 0.097 - 0.289\n",
      "epoch:10 | 0.097 - 0.295\n",
      "epoch:11 | 0.097 - 0.317\n",
      "epoch:12 | 0.097 - 0.333\n",
      "epoch:13 | 0.097 - 0.337\n",
      "epoch:14 | 0.097 - 0.356\n",
      "epoch:15 | 0.097 - 0.363\n",
      "epoch:16 | 0.097 - 0.384\n",
      "epoch:17 | 0.097 - 0.395\n",
      "epoch:18 | 0.097 - 0.403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.097 - 0.419\n",
      "============== 2/16 ==============\n",
      "epoch:0 | 0.087 - 0.092\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/studio-lab-user/deep-learning-from-scratch/notebooks/../common/functions.py:32: RuntimeWarning: invalid value encountered in subtract\n",
      "  x = x - np.max(x, axis=-1, keepdims=True)   # オーバーフロー対策\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:1 | 0.097 - 0.121\n",
      "epoch:2 | 0.097 - 0.166\n",
      "epoch:3 | 0.097 - 0.199\n",
      "epoch:4 | 0.097 - 0.228\n",
      "epoch:5 | 0.097 - 0.262\n",
      "epoch:6 | 0.097 - 0.297\n",
      "epoch:7 | 0.097 - 0.307\n",
      "epoch:8 | 0.097 - 0.326\n",
      "epoch:9 | 0.097 - 0.348\n",
      "epoch:10 | 0.097 - 0.368\n",
      "epoch:11 | 0.097 - 0.378\n",
      "epoch:12 | 0.097 - 0.398\n",
      "epoch:13 | 0.097 - 0.424\n",
      "epoch:14 | 0.097 - 0.418\n",
      "epoch:15 | 0.097 - 0.44\n",
      "epoch:16 | 0.097 - 0.45\n",
      "epoch:17 | 0.097 - 0.468\n",
      "epoch:18 | 0.097 - 0.485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.097 - 0.498\n",
      "============== 3/16 ==============\n",
      "epoch:0 | 0.138 - 0.108\n",
      "epoch:1 | 0.385 - 0.095\n",
      "epoch:2 | 0.504 - 0.129\n",
      "epoch:3 | 0.591 - 0.147\n",
      "epoch:4 | 0.647 - 0.192\n",
      "epoch:5 | 0.727 - 0.229\n",
      "epoch:6 | 0.761 - 0.274\n",
      "epoch:7 | 0.799 - 0.314\n",
      "epoch:8 | 0.84 - 0.359\n",
      "epoch:9 | 0.883 - 0.4\n",
      "epoch:10 | 0.908 - 0.418\n",
      "epoch:11 | 0.931 - 0.451\n",
      "epoch:12 | 0.942 - 0.462\n",
      "epoch:13 | 0.946 - 0.495\n",
      "epoch:14 | 0.952 - 0.525\n",
      "epoch:15 | 0.964 - 0.546\n",
      "epoch:16 | 0.966 - 0.574\n",
      "epoch:17 | 0.972 - 0.593\n",
      "epoch:18 | 0.978 - 0.614\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.979 - 0.63\n",
      "============== 4/16 ==============\n",
      "epoch:0 | 0.126 - 0.074\n",
      "epoch:1 | 0.233 - 0.086\n",
      "epoch:2 | 0.352 - 0.148\n",
      "epoch:3 | 0.441 - 0.207\n",
      "epoch:4 | 0.508 - 0.282\n",
      "epoch:5 | 0.574 - 0.359\n",
      "epoch:6 | 0.617 - 0.409\n",
      "epoch:7 | 0.652 - 0.46\n",
      "epoch:8 | 0.68 - 0.506\n",
      "epoch:9 | 0.694 - 0.553\n",
      "epoch:10 | 0.715 - 0.584\n",
      "epoch:11 | 0.742 - 0.621\n",
      "epoch:12 | 0.752 - 0.646\n",
      "epoch:13 | 0.765 - 0.672\n",
      "epoch:14 | 0.777 - 0.69\n",
      "epoch:15 | 0.785 - 0.7\n",
      "epoch:16 | 0.793 - 0.728\n",
      "epoch:17 | 0.803 - 0.736\n",
      "epoch:18 | 0.818 - 0.752\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.823 - 0.763\n",
      "============== 5/16 ==============\n",
      "epoch:0 | 0.13 - 0.098\n",
      "epoch:1 | 0.14 - 0.155\n",
      "epoch:2 | 0.145 - 0.309\n",
      "epoch:3 | 0.147 - 0.461\n",
      "epoch:4 | 0.16 - 0.546\n",
      "epoch:5 | 0.172 - 0.608\n",
      "epoch:6 | 0.182 - 0.657\n",
      "epoch:7 | 0.186 - 0.689\n",
      "epoch:8 | 0.205 - 0.724\n",
      "epoch:9 | 0.203 - 0.75\n",
      "epoch:10 | 0.212 - 0.769\n",
      "epoch:11 | 0.207 - 0.787\n",
      "epoch:12 | 0.214 - 0.797\n",
      "epoch:13 | 0.22 - 0.813\n",
      "epoch:14 | 0.215 - 0.834\n",
      "epoch:15 | 0.217 - 0.844\n",
      "epoch:16 | 0.216 - 0.863\n",
      "epoch:17 | 0.223 - 0.867\n",
      "epoch:18 | 0.229 - 0.88\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.235 - 0.89\n",
      "============== 6/16 ==============\n",
      "epoch:0 | 0.072 - 0.113\n",
      "epoch:1 | 0.121 - 0.161\n",
      "epoch:2 | 0.116 - 0.406\n",
      "epoch:3 | 0.116 - 0.595\n",
      "epoch:4 | 0.116 - 0.707\n",
      "epoch:5 | 0.116 - 0.753\n",
      "epoch:6 | 0.116 - 0.791\n",
      "epoch:7 | 0.116 - 0.807\n",
      "epoch:8 | 0.116 - 0.828\n",
      "epoch:9 | 0.116 - 0.848\n",
      "epoch:10 | 0.116 - 0.86\n",
      "epoch:11 | 0.116 - 0.882\n",
      "epoch:12 | 0.116 - 0.888\n",
      "epoch:13 | 0.116 - 0.9\n",
      "epoch:14 | 0.116 - 0.91\n",
      "epoch:15 | 0.116 - 0.919\n",
      "epoch:16 | 0.116 - 0.937\n",
      "epoch:17 | 0.116 - 0.945\n",
      "epoch:18 | 0.116 - 0.952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.116 - 0.95\n",
      "============== 7/16 ==============\n",
      "epoch:0 | 0.087 - 0.081\n",
      "epoch:1 | 0.117 - 0.167\n",
      "epoch:2 | 0.117 - 0.501\n",
      "epoch:3 | 0.117 - 0.662\n",
      "epoch:4 | 0.116 - 0.737\n",
      "epoch:5 | 0.116 - 0.759\n",
      "epoch:6 | 0.116 - 0.819\n",
      "epoch:7 | 0.116 - 0.841\n",
      "epoch:8 | 0.116 - 0.873\n",
      "epoch:9 | 0.116 - 0.902\n",
      "epoch:10 | 0.116 - 0.918\n",
      "epoch:11 | 0.116 - 0.931\n",
      "epoch:12 | 0.117 - 0.945\n",
      "epoch:13 | 0.117 - 0.954\n",
      "epoch:14 | 0.117 - 0.964\n",
      "epoch:15 | 0.117 - 0.967\n",
      "epoch:16 | 0.116 - 0.976\n",
      "epoch:17 | 0.116 - 0.983\n",
      "epoch:18 | 0.116 - 0.983\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.116 - 0.99\n",
      "============== 8/16 ==============\n",
      "epoch:0 | 0.094 - 0.093\n",
      "epoch:1 | 0.116 - 0.376\n",
      "epoch:2 | 0.117 - 0.706\n",
      "epoch:3 | 0.117 - 0.782\n",
      "epoch:4 | 0.117 - 0.852\n",
      "epoch:5 | 0.117 - 0.897\n",
      "epoch:6 | 0.117 - 0.918\n",
      "epoch:7 | 0.117 - 0.935\n",
      "epoch:8 | 0.117 - 0.961\n",
      "epoch:9 | 0.117 - 0.97\n",
      "epoch:10 | 0.117 - 0.977\n",
      "epoch:11 | 0.117 - 0.986\n",
      "epoch:12 | 0.117 - 0.992\n",
      "epoch:13 | 0.117 - 0.993\n",
      "epoch:14 | 0.117 - 0.995\n",
      "epoch:15 | 0.116 - 0.998\n",
      "epoch:16 | 0.117 - 0.999\n",
      "epoch:17 | 0.117 - 1.0\n",
      "epoch:18 | 0.117 - 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.116 - 1.0\n",
      "============== 9/16 ==============\n",
      "epoch:0 | 0.094 - 0.171\n",
      "epoch:1 | 0.105 - 0.575\n",
      "epoch:2 | 0.116 - 0.674\n",
      "epoch:3 | 0.116 - 0.759\n",
      "epoch:4 | 0.117 - 0.81\n",
      "epoch:5 | 0.117 - 0.832\n",
      "epoch:6 | 0.117 - 0.899\n",
      "epoch:7 | 0.116 - 0.951\n",
      "epoch:8 | 0.116 - 0.974\n",
      "epoch:9 | 0.116 - 0.988\n",
      "epoch:10 | 0.116 - 0.992\n",
      "epoch:11 | 0.116 - 0.996\n",
      "epoch:12 | 0.116 - 0.997\n",
      "epoch:13 | 0.116 - 0.999\n",
      "epoch:14 | 0.116 - 1.0\n",
      "epoch:15 | 0.116 - 1.0\n",
      "epoch:16 | 0.116 - 1.0\n",
      "epoch:17 | 0.116 - 1.0\n",
      "epoch:18 | 0.116 - 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.116 - 1.0\n",
      "============== 10/16 ==============\n",
      "epoch:0 | 0.092 - 0.127\n",
      "epoch:1 | 0.1 - 0.574\n",
      "epoch:2 | 0.116 - 0.702\n",
      "epoch:3 | 0.117 - 0.744\n",
      "epoch:4 | 0.117 - 0.813\n",
      "epoch:5 | 0.117 - 0.857\n",
      "epoch:6 | 0.117 - 0.882\n",
      "epoch:7 | 0.117 - 0.935\n",
      "epoch:8 | 0.117 - 0.964\n",
      "epoch:9 | 0.117 - 0.975\n",
      "epoch:10 | 0.117 - 0.987\n",
      "epoch:11 | 0.117 - 0.964\n",
      "epoch:12 | 0.117 - 0.993\n",
      "epoch:13 | 0.117 - 0.995\n",
      "epoch:14 | 0.117 - 0.997\n",
      "epoch:15 | 0.117 - 0.994\n",
      "epoch:16 | 0.117 - 0.995\n",
      "epoch:17 | 0.117 - 0.998\n",
      "epoch:18 | 0.117 - 0.998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.117 - 0.998\n",
      "============== 11/16 ==============\n",
      "epoch:0 | 0.097 - 0.192\n",
      "epoch:1 | 0.105 - 0.606\n",
      "epoch:2 | 0.116 - 0.684\n",
      "epoch:3 | 0.116 - 0.619\n",
      "epoch:4 | 0.116 - 0.738\n",
      "epoch:5 | 0.116 - 0.82\n",
      "epoch:6 | 0.116 - 0.894\n",
      "epoch:7 | 0.116 - 0.951\n",
      "epoch:8 | 0.116 - 0.968\n",
      "epoch:9 | 0.117 - 0.975\n",
      "epoch:10 | 0.117 - 0.988\n",
      "epoch:11 | 0.116 - 0.986\n",
      "epoch:12 | 0.117 - 0.993\n",
      "epoch:13 | 0.117 - 0.994\n",
      "epoch:14 | 0.117 - 0.991\n",
      "epoch:15 | 0.117 - 0.971\n",
      "epoch:16 | 0.117 - 0.994\n",
      "epoch:17 | 0.117 - 0.997\n",
      "epoch:18 | 0.117 - 0.995\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.116 - 0.999\n",
      "============== 12/16 ==============\n",
      "epoch:0 | 0.116 - 0.176\n",
      "epoch:1 | 0.117 - 0.374\n",
      "epoch:2 | 0.116 - 0.649\n",
      "epoch:3 | 0.117 - 0.756\n",
      "epoch:4 | 0.117 - 0.757\n",
      "epoch:5 | 0.117 - 0.764\n",
      "epoch:6 | 0.117 - 0.813\n",
      "epoch:7 | 0.117 - 0.811\n",
      "epoch:8 | 0.117 - 0.862\n",
      "epoch:9 | 0.117 - 0.838\n",
      "epoch:10 | 0.117 - 0.846\n",
      "epoch:11 | 0.117 - 0.863\n",
      "epoch:12 | 0.117 - 0.891\n",
      "epoch:13 | 0.117 - 0.89\n",
      "epoch:14 | 0.117 - 0.89\n",
      "epoch:15 | 0.117 - 0.897\n",
      "epoch:16 | 0.117 - 0.897\n",
      "epoch:17 | 0.117 - 0.888\n",
      "epoch:18 | 0.117 - 0.898\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.117 - 0.831\n",
      "============== 13/16 ==============\n",
      "epoch:0 | 0.117 - 0.106\n",
      "epoch:1 | 0.116 - 0.422\n",
      "epoch:2 | 0.116 - 0.511\n",
      "epoch:3 | 0.116 - 0.59\n",
      "epoch:4 | 0.116 - 0.648\n",
      "epoch:5 | 0.116 - 0.574\n",
      "epoch:6 | 0.116 - 0.65\n",
      "epoch:7 | 0.116 - 0.652\n",
      "epoch:8 | 0.116 - 0.669\n",
      "epoch:9 | 0.116 - 0.693\n",
      "epoch:10 | 0.116 - 0.67\n",
      "epoch:11 | 0.116 - 0.701\n",
      "epoch:12 | 0.116 - 0.7\n",
      "epoch:13 | 0.116 - 0.69\n",
      "epoch:14 | 0.116 - 0.685\n",
      "epoch:15 | 0.116 - 0.701\n",
      "epoch:16 | 0.116 - 0.702\n",
      "epoch:17 | 0.116 - 0.704\n",
      "epoch:18 | 0.116 - 0.706\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.116 - 0.804\n",
      "============== 14/16 ==============\n",
      "epoch:0 | 0.094 - 0.197\n",
      "epoch:1 | 0.116 - 0.282\n",
      "epoch:2 | 0.116 - 0.406\n",
      "epoch:3 | 0.116 - 0.559\n",
      "epoch:4 | 0.116 - 0.546\n",
      "epoch:5 | 0.116 - 0.572\n",
      "epoch:6 | 0.116 - 0.584\n",
      "epoch:7 | 0.116 - 0.585\n",
      "epoch:8 | 0.116 - 0.586\n",
      "epoch:9 | 0.116 - 0.591\n",
      "epoch:10 | 0.116 - 0.594\n",
      "epoch:11 | 0.116 - 0.589\n",
      "epoch:12 | 0.116 - 0.594\n",
      "epoch:13 | 0.116 - 0.594\n",
      "epoch:14 | 0.117 - 0.594\n",
      "epoch:15 | 0.117 - 0.594\n",
      "epoch:16 | 0.117 - 0.593\n",
      "epoch:17 | 0.117 - 0.594\n",
      "epoch:18 | 0.116 - 0.595\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.116 - 0.595\n",
      "============== 15/16 ==============\n",
      "epoch:0 | 0.117 - 0.142\n",
      "epoch:1 | 0.117 - 0.346\n",
      "epoch:2 | 0.116 - 0.484\n",
      "epoch:3 | 0.116 - 0.41\n",
      "epoch:4 | 0.116 - 0.493\n",
      "epoch:5 | 0.116 - 0.388\n",
      "epoch:6 | 0.116 - 0.528\n",
      "epoch:7 | 0.116 - 0.589\n",
      "epoch:8 | 0.116 - 0.596\n",
      "epoch:9 | 0.116 - 0.604\n",
      "epoch:10 | 0.116 - 0.594\n",
      "epoch:11 | 0.116 - 0.606\n",
      "epoch:12 | 0.116 - 0.596\n",
      "epoch:13 | 0.117 - 0.609\n",
      "epoch:14 | 0.117 - 0.612\n",
      "epoch:15 | 0.116 - 0.614\n",
      "epoch:16 | 0.116 - 0.614\n",
      "epoch:17 | 0.117 - 0.614\n",
      "epoch:18 | 0.117 - 0.62\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:19 | 0.117 - 0.618\n",
      "============== 16/16 ==============\n",
      "epoch:0 | 0.087 - 0.136\n",
      "epoch:1 | 0.117 - 0.219\n",
      "epoch:2 | 0.117 - 0.32\n",
      "epoch:3 | 0.117 - 0.357\n",
      "epoch:4 | 0.117 - 0.37\n",
      "epoch:5 | 0.117 - 0.392\n",
      "epoch:6 | 0.117 - 0.4\n",
      "epoch:7 | 0.117 - 0.412\n",
      "epoch:8 | 0.117 - 0.42\n",
      "epoch:9 | 0.117 - 0.398\n",
      "epoch:10 | 0.117 - 0.437\n",
      "epoch:11 | 0.117 - 0.44\n",
      "epoch:12 | 0.117 - 0.445\n",
      "epoch:13 | 0.117 - 0.441\n",
      "epoch:14 | 0.117 - 0.452\n",
      "epoch:15 | 0.117 - 0.426\n",
      "epoch:16 | 0.117 - 0.432\n",
      "epoch:17 | 0.117 - 0.492\n",
      "epoch:18 | 0.117 - 0.499\n",
      "epoch:19 | 0.116 - 0.523\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys, os\n",
    "sys.path.append(os.pardir)  # 親ディレクトリのファイルをインポートするための設定\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataset.mnist import load_mnist\n",
    "from common.multi_layer_net_extend import MultiLayerNetExtend\n",
    "from common.optimizer import SGD, Adam\n",
    "\n",
    "(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": "markdown",
   "metadata": {},
   "source": [
    "# ch06/overfit_weight_decay.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:0, train acc:0.11666666666666667, test acc:0.1019\n",
      "epoch:1, train acc:0.13333333333333333, test acc:0.1154\n",
      "epoch:2, train acc:0.15, test acc:0.1339\n",
      "epoch:3, train acc:0.16666666666666666, test acc:0.1544\n",
      "epoch:4, train acc:0.19666666666666666, test acc:0.1714\n",
      "epoch:5, train acc:0.20333333333333334, test acc:0.1888\n",
      "epoch:6, train acc:0.24666666666666667, test acc:0.2075\n",
      "epoch:7, train acc:0.26666666666666666, test acc:0.2218\n",
      "epoch:8, train acc:0.2733333333333333, test acc:0.2377\n",
      "epoch:9, train acc:0.28, test acc:0.2503\n",
      "epoch:10, train acc:0.30333333333333334, test acc:0.2638\n",
      "epoch:11, train acc:0.32666666666666666, test acc:0.2733\n",
      "epoch:12, train acc:0.36, test acc:0.2859\n",
      "epoch:13, train acc:0.36, test acc:0.2926\n",
      "epoch:14, train acc:0.4, test acc:0.3058\n",
      "epoch:15, train acc:0.4, test acc:0.3183\n",
      "epoch:16, train acc:0.41, test acc:0.3231\n",
      "epoch:17, train acc:0.42, test acc:0.3265\n",
      "epoch:18, train acc:0.43333333333333335, test acc:0.3319\n",
      "epoch:19, train acc:0.45, test acc:0.3426\n",
      "epoch:20, train acc:0.4666666666666667, test acc:0.3515\n",
      "epoch:21, train acc:0.4666666666666667, test acc:0.3619\n",
      "epoch:22, train acc:0.5033333333333333, test acc:0.3728\n",
      "epoch:23, train acc:0.52, test acc:0.3761\n",
      "epoch:24, train acc:0.5233333333333333, test acc:0.3937\n",
      "epoch:25, train acc:0.55, test acc:0.4012\n",
      "epoch:26, train acc:0.56, test acc:0.4219\n",
      "epoch:27, train acc:0.5833333333333334, test acc:0.4321\n",
      "epoch:28, train acc:0.56, test acc:0.4225\n",
      "epoch:29, train acc:0.5833333333333334, test acc:0.4408\n",
      "epoch:30, train acc:0.5733333333333334, test acc:0.4374\n",
      "epoch:31, train acc:0.5833333333333334, test acc:0.4473\n",
      "epoch:32, train acc:0.59, test acc:0.451\n",
      "epoch:33, train acc:0.59, test acc:0.4553\n",
      "epoch:34, train acc:0.6066666666666667, test acc:0.4766\n",
      "epoch:35, train acc:0.5966666666666667, test acc:0.4671\n",
      "epoch:36, train acc:0.5933333333333334, test acc:0.4794\n",
      "epoch:37, train acc:0.6066666666666667, test acc:0.4853\n",
      "epoch:38, train acc:0.63, test acc:0.4978\n",
      "epoch:39, train acc:0.6333333333333333, test acc:0.5033\n",
      "epoch:40, train acc:0.6366666666666667, test acc:0.5096\n",
      "epoch:41, train acc:0.6366666666666667, test acc:0.5079\n",
      "epoch:42, train acc:0.6366666666666667, test acc:0.5144\n",
      "epoch:43, train acc:0.6266666666666667, test acc:0.5146\n",
      "epoch:44, train acc:0.6366666666666667, test acc:0.5181\n",
      "epoch:45, train acc:0.6633333333333333, test acc:0.5273\n",
      "epoch:46, train acc:0.6633333333333333, test acc:0.529\n",
      "epoch:47, train acc:0.6566666666666666, test acc:0.5283\n",
      "epoch:48, train acc:0.6633333333333333, test acc:0.5398\n",
      "epoch:49, train acc:0.6633333333333333, test acc:0.5393\n",
      "epoch:50, train acc:0.67, test acc:0.5487\n",
      "epoch:51, train acc:0.7, test acc:0.5544\n",
      "epoch:52, train acc:0.69, test acc:0.5569\n",
      "epoch:53, train acc:0.6933333333333334, test acc:0.5636\n",
      "epoch:54, train acc:0.69, test acc:0.5627\n",
      "epoch:55, train acc:0.6933333333333334, test acc:0.5616\n",
      "epoch:56, train acc:0.7, test acc:0.5706\n",
      "epoch:57, train acc:0.7133333333333334, test acc:0.5715\n",
      "epoch:58, train acc:0.72, test acc:0.5826\n",
      "epoch:59, train acc:0.7266666666666667, test acc:0.5775\n",
      "epoch:60, train acc:0.75, test acc:0.589\n",
      "epoch:61, train acc:0.71, test acc:0.5858\n",
      "epoch:62, train acc:0.72, test acc:0.5842\n",
      "epoch:63, train acc:0.7166666666666667, test acc:0.5816\n",
      "epoch:64, train acc:0.73, test acc:0.5929\n",
      "epoch:65, train acc:0.7366666666666667, test acc:0.5977\n",
      "epoch:66, train acc:0.76, test acc:0.6046\n",
      "epoch:67, train acc:0.73, test acc:0.6009\n",
      "epoch:68, train acc:0.74, test acc:0.6053\n",
      "epoch:69, train acc:0.7533333333333333, test acc:0.6057\n",
      "epoch:70, train acc:0.7466666666666667, test acc:0.6131\n",
      "epoch:71, train acc:0.7733333333333333, test acc:0.619\n",
      "epoch:72, train acc:0.7433333333333333, test acc:0.6145\n",
      "epoch:73, train acc:0.7666666666666667, test acc:0.6214\n",
      "epoch:74, train acc:0.7866666666666666, test acc:0.6283\n",
      "epoch:75, train acc:0.76, test acc:0.6163\n",
      "epoch:76, train acc:0.8066666666666666, test acc:0.6366\n",
      "epoch:77, train acc:0.7966666666666666, test acc:0.6338\n",
      "epoch:78, train acc:0.8133333333333334, test acc:0.6309\n",
      "epoch:79, train acc:0.8166666666666667, test acc:0.6385\n",
      "epoch:80, train acc:0.8033333333333333, test acc:0.6221\n",
      "epoch:81, train acc:0.8333333333333334, test acc:0.6526\n",
      "epoch:82, train acc:0.82, test acc:0.6452\n",
      "epoch:83, train acc:0.8266666666666667, test acc:0.6453\n",
      "epoch:84, train acc:0.8366666666666667, test acc:0.6581\n",
      "epoch:85, train acc:0.8133333333333334, test acc:0.6506\n",
      "epoch:86, train acc:0.8266666666666667, test acc:0.6569\n",
      "epoch:87, train acc:0.8333333333333334, test acc:0.6569\n",
      "epoch:88, train acc:0.83, test acc:0.6613\n",
      "epoch:89, train acc:0.82, test acc:0.6548\n",
      "epoch:90, train acc:0.82, test acc:0.6581\n",
      "epoch:91, train acc:0.8233333333333334, test acc:0.6639\n",
      "epoch:92, train acc:0.84, test acc:0.6668\n",
      "epoch:93, train acc:0.85, test acc:0.6766\n",
      "epoch:94, train acc:0.83, test acc:0.6671\n",
      "epoch:95, train acc:0.8366666666666667, test acc:0.6759\n",
      "epoch:96, train acc:0.8366666666666667, test acc:0.6738\n",
      "epoch:97, train acc:0.87, test acc:0.6856\n",
      "epoch:98, train acc:0.8566666666666667, test acc:0.6888\n",
      "epoch:99, train acc:0.8533333333333334, test acc:0.6792\n",
      "epoch:100, train acc:0.8533333333333334, test acc:0.6851\n",
      "epoch:101, train acc:0.85, test acc:0.6841\n",
      "epoch:102, train acc:0.8566666666666667, test acc:0.686\n",
      "epoch:103, train acc:0.86, test acc:0.6816\n",
      "epoch:104, train acc:0.8466666666666667, test acc:0.6828\n",
      "epoch:105, train acc:0.85, test acc:0.6855\n",
      "epoch:106, train acc:0.8533333333333334, test acc:0.6911\n",
      "epoch:107, train acc:0.8566666666666667, test acc:0.6953\n",
      "epoch:108, train acc:0.85, test acc:0.6881\n",
      "epoch:109, train acc:0.8433333333333334, test acc:0.6686\n",
      "epoch:110, train acc:0.85, test acc:0.6739\n",
      "epoch:111, train acc:0.8566666666666667, test acc:0.6897\n",
      "epoch:112, train acc:0.8466666666666667, test acc:0.6881\n",
      "epoch:113, train acc:0.8633333333333333, test acc:0.6924\n",
      "epoch:114, train acc:0.8566666666666667, test acc:0.6871\n",
      "epoch:115, train acc:0.8766666666666667, test acc:0.699\n",
      "epoch:116, train acc:0.8666666666666667, test acc:0.703\n",
      "epoch:117, train acc:0.8566666666666667, test acc:0.6972\n",
      "epoch:118, train acc:0.8766666666666667, test acc:0.7029\n",
      "epoch:119, train acc:0.8666666666666667, test acc:0.6935\n",
      "epoch:120, train acc:0.8766666666666667, test acc:0.7004\n",
      "epoch:121, train acc:0.87, test acc:0.7003\n",
      "epoch:122, train acc:0.8666666666666667, test acc:0.6994\n",
      "epoch:123, train acc:0.8766666666666667, test acc:0.7111\n",
      "epoch:124, train acc:0.87, test acc:0.6948\n",
      "epoch:125, train acc:0.87, test acc:0.6984\n",
      "epoch:126, train acc:0.8766666666666667, test acc:0.7029\n",
      "epoch:127, train acc:0.88, test acc:0.7005\n",
      "epoch:128, train acc:0.8933333333333333, test acc:0.7065\n",
      "epoch:129, train acc:0.8933333333333333, test acc:0.7067\n",
      "epoch:130, train acc:0.88, test acc:0.7133\n",
      "epoch:131, train acc:0.87, test acc:0.7007\n",
      "epoch:132, train acc:0.8666666666666667, test acc:0.6992\n",
      "epoch:133, train acc:0.88, test acc:0.6999\n",
      "epoch:134, train acc:0.8733333333333333, test acc:0.6934\n",
      "epoch:135, train acc:0.8766666666666667, test acc:0.7067\n",
      "epoch:136, train acc:0.8933333333333333, test acc:0.7112\n",
      "epoch:137, train acc:0.89, test acc:0.7084\n",
      "epoch:138, train acc:0.89, test acc:0.709\n",
      "epoch:139, train acc:0.8866666666666667, test acc:0.7003\n",
      "epoch:140, train acc:0.8766666666666667, test acc:0.7069\n",
      "epoch:141, train acc:0.8833333333333333, test acc:0.7048\n",
      "epoch:142, train acc:0.8866666666666667, test acc:0.7075\n",
      "epoch:143, train acc:0.8833333333333333, test acc:0.7079\n",
      "epoch:144, train acc:0.8933333333333333, test acc:0.718\n",
      "epoch:145, train acc:0.8933333333333333, test acc:0.718\n",
      "epoch:146, train acc:0.88, test acc:0.7067\n",
      "epoch:147, train acc:0.8933333333333333, test acc:0.7192\n",
      "epoch:148, train acc:0.89, test acc:0.7121\n",
      "epoch:149, train acc:0.8933333333333333, test acc:0.7154\n",
      "epoch:150, train acc:0.8933333333333333, test acc:0.7175\n",
      "epoch:151, train acc:0.8966666666666666, test acc:0.7077\n",
      "epoch:152, train acc:0.8833333333333333, test acc:0.7061\n",
      "epoch:153, train acc:0.87, test acc:0.6993\n",
      "epoch:154, train acc:0.88, test acc:0.7079\n",
      "epoch:155, train acc:0.8966666666666666, test acc:0.7135\n",
      "epoch:156, train acc:0.8866666666666667, test acc:0.7136\n",
      "epoch:157, train acc:0.9, test acc:0.7129\n",
      "epoch:158, train acc:0.8866666666666667, test acc:0.7088\n",
      "epoch:159, train acc:0.89, test acc:0.7184\n",
      "epoch:160, train acc:0.8933333333333333, test acc:0.7105\n",
      "epoch:161, train acc:0.9, test acc:0.7189\n",
      "epoch:162, train acc:0.8966666666666666, test acc:0.7115\n",
      "epoch:163, train acc:0.9, test acc:0.721\n",
      "epoch:164, train acc:0.9, test acc:0.7151\n",
      "epoch:165, train acc:0.89, test acc:0.7188\n",
      "epoch:166, train acc:0.9066666666666666, test acc:0.7234\n",
      "epoch:167, train acc:0.9066666666666666, test acc:0.7186\n",
      "epoch:168, train acc:0.9066666666666666, test acc:0.7252\n",
      "epoch:169, train acc:0.9066666666666666, test acc:0.7216\n",
      "epoch:170, train acc:0.9033333333333333, test acc:0.7107\n",
      "epoch:171, train acc:0.91, test acc:0.7215\n",
      "epoch:172, train acc:0.9033333333333333, test acc:0.7139\n",
      "epoch:173, train acc:0.8966666666666666, test acc:0.7098\n",
      "epoch:174, train acc:0.9, test acc:0.7237\n",
      "epoch:175, train acc:0.9066666666666666, test acc:0.723\n",
      "epoch:176, train acc:0.9033333333333333, test acc:0.7194\n",
      "epoch:177, train acc:0.91, test acc:0.713\n",
      "epoch:178, train acc:0.9133333333333333, test acc:0.7131\n",
      "epoch:179, train acc:0.9066666666666666, test acc:0.7093\n",
      "epoch:180, train acc:0.92, test acc:0.7167\n",
      "epoch:181, train acc:0.9166666666666666, test acc:0.7166\n",
      "epoch:182, train acc:0.91, test acc:0.7113\n",
      "epoch:183, train acc:0.92, test acc:0.7243\n",
      "epoch:184, train acc:0.91, test acc:0.7207\n",
      "epoch:185, train acc:0.91, test acc:0.7157\n",
      "epoch:186, train acc:0.9233333333333333, test acc:0.7219\n",
      "epoch:187, train acc:0.93, test acc:0.7244\n",
      "epoch:188, train acc:0.9066666666666666, test acc:0.7233\n",
      "epoch:189, train acc:0.9133333333333333, test acc:0.7158\n",
      "epoch:190, train acc:0.92, test acc:0.7225\n",
      "epoch:191, train acc:0.9066666666666666, test acc:0.7159\n",
      "epoch:192, train acc:0.9066666666666666, test acc:0.7171\n",
      "epoch:193, train acc:0.91, test acc:0.7124\n",
      "epoch:194, train acc:0.9133333333333333, test acc:0.71\n",
      "epoch:195, train acc:0.9033333333333333, test acc:0.7119\n",
      "epoch:196, train acc:0.92, test acc:0.7204\n",
      "epoch:197, train acc:0.9266666666666666, test acc:0.7233\n",
      "epoch:198, train acc:0.92, test acc:0.715\n",
      "epoch:199, train acc:0.9166666666666666, test acc:0.7189\n",
      "epoch:200, train acc:0.9266666666666666, test acc:0.7207\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(os.pardir)  # 親ディレクトリのファイルをインポートするための設定\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataset.mnist import load_mnist\n",
    "from common.multi_layer_net import MultiLayerNet\n",
    "from common.optimizer import SGD\n",
    "\n",
    "(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": "markdown",
   "metadata": {},
   "source": [
    "# ch06/overfit_dropout.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss:2.306975946889085\n",
      "=== epoch:1, train acc:0.10333333333333333, test acc:0.1049 ===\n",
      "train loss:2.309044746734719\n",
      "train loss:2.300197138914193\n",
      "train loss:2.2899364208250144\n",
      "=== epoch:2, train acc:0.10333333333333333, test acc:0.1078 ===\n",
      "train loss:2.299351697213905\n",
      "train loss:2.298579211918337\n",
      "train loss:2.30033972809789\n",
      "=== epoch:3, train acc:0.10333333333333333, test acc:0.1084 ===\n",
      "train loss:2.298407722243274\n",
      "train loss:2.290574548807106\n",
      "train loss:2.2910548846749506\n",
      "=== epoch:4, train acc:0.11333333333333333, test acc:0.1092 ===\n",
      "train loss:2.300846571325947\n",
      "train loss:2.291865408690855\n",
      "train loss:2.290360587823036\n",
      "=== epoch:5, train acc:0.11666666666666667, test acc:0.1106 ===\n",
      "train loss:2.2935854359351695\n",
      "train loss:2.2860466184842587\n",
      "train loss:2.28443588064665\n",
      "=== epoch:6, train acc:0.11333333333333333, test acc:0.1116 ===\n",
      "train loss:2.2806688119504157\n",
      "train loss:2.289689215478857\n",
      "train loss:2.288062519098229\n",
      "=== epoch:7, train acc:0.11, test acc:0.1132 ===\n",
      "train loss:2.2997597326484582\n",
      "train loss:2.293960038462788\n",
      "train loss:2.2962785576423546\n",
      "=== epoch:8, train acc:0.12333333333333334, test acc:0.1151 ===\n",
      "train loss:2.3016057682614446\n",
      "train loss:2.2891207472883432\n",
      "train loss:2.293077883892882\n",
      "=== epoch:9, train acc:0.13, test acc:0.1165 ===\n",
      "train loss:2.2837024428643558\n",
      "train loss:2.283104450165207\n",
      "train loss:2.296227749178443\n",
      "=== epoch:10, train acc:0.13, test acc:0.1195 ===\n",
      "train loss:2.2823934018312344\n",
      "train loss:2.297337577384607\n",
      "train loss:2.2761268293851287\n",
      "=== epoch:11, train acc:0.13333333333333333, test acc:0.1215 ===\n",
      "train loss:2.289489515439413\n",
      "train loss:2.2843581955024295\n",
      "train loss:2.281907216022086\n",
      "=== epoch:12, train acc:0.13666666666666666, test acc:0.1255 ===\n",
      "train loss:2.2870273342481506\n",
      "train loss:2.2778838158651906\n",
      "train loss:2.2712467140491293\n",
      "=== epoch:13, train acc:0.13333333333333333, test acc:0.1273 ===\n",
      "train loss:2.2705069651233303\n",
      "train loss:2.280030507185659\n",
      "train loss:2.281818190438288\n",
      "=== epoch:14, train acc:0.15, test acc:0.1301 ===\n",
      "train loss:2.283118525611744\n",
      "train loss:2.279424654121827\n",
      "train loss:2.2680640026206773\n",
      "=== epoch:15, train acc:0.15, test acc:0.1318 ===\n",
      "train loss:2.281561882309629\n",
      "train loss:2.286785707294033\n",
      "train loss:2.2642210625895403\n",
      "=== epoch:16, train acc:0.14666666666666667, test acc:0.1358 ===\n",
      "train loss:2.2762069333605135\n",
      "train loss:2.2815003248727033\n",
      "train loss:2.2763327912572753\n",
      "=== epoch:17, train acc:0.15666666666666668, test acc:0.1409 ===\n",
      "train loss:2.270337243587788\n",
      "train loss:2.2738767121005856\n",
      "train loss:2.282176561609498\n",
      "=== epoch:18, train acc:0.15666666666666668, test acc:0.1459 ===\n",
      "train loss:2.270373824110397\n",
      "train loss:2.277922212855415\n",
      "train loss:2.271734155406358\n",
      "=== epoch:19, train acc:0.16333333333333333, test acc:0.1496 ===\n",
      "train loss:2.278230385227485\n",
      "train loss:2.274433845940685\n",
      "train loss:2.27056882259031\n",
      "=== epoch:20, train acc:0.16, test acc:0.1557 ===\n",
      "train loss:2.280450537658955\n",
      "train loss:2.2721093704818913\n",
      "train loss:2.2706191417828263\n",
      "=== epoch:21, train acc:0.16666666666666666, test acc:0.1592 ===\n",
      "train loss:2.276154345239888\n",
      "train loss:2.2689874080612755\n",
      "train loss:2.2771162367644253\n",
      "=== epoch:22, train acc:0.16666666666666666, test acc:0.164 ===\n",
      "train loss:2.267437376344244\n",
      "train loss:2.2602728739579616\n",
      "train loss:2.2901501594202625\n",
      "=== epoch:23, train acc:0.17, test acc:0.1678 ===\n",
      "train loss:2.263614854340955\n",
      "train loss:2.2604084328702374\n",
      "train loss:2.270209157559474\n",
      "=== epoch:24, train acc:0.16666666666666666, test acc:0.1692 ===\n",
      "train loss:2.260170281383398\n",
      "train loss:2.259418243352805\n",
      "train loss:2.256095417477822\n",
      "=== epoch:25, train acc:0.18333333333333332, test acc:0.1707 ===\n",
      "train loss:2.2694912129414555\n",
      "train loss:2.270330980071956\n",
      "train loss:2.2606662061793696\n",
      "=== epoch:26, train acc:0.21, test acc:0.1783 ===\n",
      "train loss:2.269843242782169\n",
      "train loss:2.260657040444838\n",
      "train loss:2.2621188219204424\n",
      "=== epoch:27, train acc:0.2, test acc:0.1833 ===\n",
      "train loss:2.26067560664325\n",
      "train loss:2.267021319188731\n",
      "train loss:2.263387103067806\n",
      "=== epoch:28, train acc:0.21, test acc:0.1878 ===\n",
      "train loss:2.258819199738674\n",
      "train loss:2.2661658282656996\n",
      "train loss:2.2744914707290267\n",
      "=== epoch:29, train acc:0.21666666666666667, test acc:0.1909 ===\n",
      "train loss:2.2567009413464296\n",
      "train loss:2.268322727117065\n",
      "train loss:2.2626033140682584\n",
      "=== epoch:30, train acc:0.23, test acc:0.1917 ===\n",
      "train loss:2.256583739149273\n",
      "train loss:2.261182439960741\n",
      "train loss:2.2512332810640534\n",
      "=== epoch:31, train acc:0.23, test acc:0.1895 ===\n",
      "train loss:2.2641900906455588\n",
      "train loss:2.2718897148990487\n",
      "train loss:2.252125558075326\n",
      "=== epoch:32, train acc:0.23, test acc:0.1934 ===\n",
      "train loss:2.2658337119719887\n",
      "train loss:2.258535484142778\n",
      "train loss:2.260886647908109\n",
      "=== epoch:33, train acc:0.23333333333333334, test acc:0.1939 ===\n",
      "train loss:2.2656067963394104\n",
      "train loss:2.2578331627947676\n",
      "train loss:2.2549891714495067\n",
      "=== epoch:34, train acc:0.23666666666666666, test acc:0.1971 ===\n",
      "train loss:2.2556081739445597\n",
      "train loss:2.264837771235288\n",
      "train loss:2.2620911752708857\n",
      "=== epoch:35, train acc:0.24666666666666667, test acc:0.2023 ===\n",
      "train loss:2.2452513443188264\n",
      "train loss:2.267594545780028\n",
      "train loss:2.262284491662893\n",
      "=== epoch:36, train acc:0.24666666666666667, test acc:0.2033 ===\n",
      "train loss:2.2529061216988255\n",
      "train loss:2.2600651256387954\n",
      "train loss:2.2546854110541785\n",
      "=== epoch:37, train acc:0.24666666666666667, test acc:0.2053 ===\n",
      "train loss:2.2557607244673936\n",
      "train loss:2.2438664854082977\n",
      "train loss:2.2513048925990553\n",
      "=== epoch:38, train acc:0.24666666666666667, test acc:0.2049 ===\n",
      "train loss:2.252614742427819\n",
      "train loss:2.254733126114017\n",
      "train loss:2.254513853621485\n",
      "=== epoch:39, train acc:0.24333333333333335, test acc:0.2023 ===\n",
      "train loss:2.251614279575521\n",
      "train loss:2.2602675110585784\n",
      "train loss:2.254226884804384\n",
      "=== epoch:40, train acc:0.25666666666666665, test acc:0.2041 ===\n",
      "train loss:2.2543743936416143\n",
      "train loss:2.2287664529028106\n",
      "train loss:2.245764345182028\n",
      "=== epoch:41, train acc:0.26666666666666666, test acc:0.2062 ===\n",
      "train loss:2.239671346997371\n",
      "train loss:2.2677143576806547\n",
      "train loss:2.230461583849819\n",
      "=== epoch:42, train acc:0.26, test acc:0.2036 ===\n",
      "train loss:2.252296050095919\n",
      "train loss:2.252990071889017\n",
      "train loss:2.267681836147949\n",
      "=== epoch:43, train acc:0.25666666666666665, test acc:0.2051 ===\n",
      "train loss:2.2421847400380646\n",
      "train loss:2.2551411892020017\n",
      "train loss:2.232610001415765\n",
      "=== epoch:44, train acc:0.2633333333333333, test acc:0.2101 ===\n",
      "train loss:2.2502639357467737\n",
      "train loss:2.242021983432611\n",
      "train loss:2.2433283166602513\n",
      "=== epoch:45, train acc:0.2733333333333333, test acc:0.214 ===\n",
      "train loss:2.2592877915665754\n",
      "train loss:2.247252066180725\n",
      "train loss:2.2493195651992655\n",
      "=== epoch:46, train acc:0.2733333333333333, test acc:0.2186 ===\n",
      "train loss:2.2551692947044213\n",
      "train loss:2.248553563251894\n",
      "train loss:2.254827706940689\n",
      "=== epoch:47, train acc:0.27666666666666667, test acc:0.2218 ===\n",
      "train loss:2.259614158937188\n",
      "train loss:2.2410715347800663\n",
      "train loss:2.2621564873497677\n",
      "=== epoch:48, train acc:0.2833333333333333, test acc:0.2295 ===\n",
      "train loss:2.2383266840213447\n",
      "train loss:2.248458471268658\n",
      "train loss:2.2423860782959837\n",
      "=== epoch:49, train acc:0.28, test acc:0.2283 ===\n",
      "train loss:2.24519805971186\n",
      "train loss:2.255207574403741\n",
      "train loss:2.240437166349051\n",
      "=== epoch:50, train acc:0.29, test acc:0.2364 ===\n",
      "train loss:2.2395416674589956\n",
      "train loss:2.2251572617996254\n",
      "train loss:2.2317575041780815\n",
      "=== epoch:51, train acc:0.30333333333333334, test acc:0.2405 ===\n",
      "train loss:2.2374850219584075\n",
      "train loss:2.242415701505303\n",
      "train loss:2.233109936722285\n",
      "=== epoch:52, train acc:0.30666666666666664, test acc:0.2497 ===\n",
      "train loss:2.2419303441088134\n",
      "train loss:2.233835868753863\n",
      "train loss:2.2230581518087136\n",
      "=== epoch:53, train acc:0.32, test acc:0.2502 ===\n",
      "train loss:2.2289864163441218\n",
      "train loss:2.2335218021565293\n",
      "train loss:2.23135672771954\n",
      "=== epoch:54, train acc:0.3233333333333333, test acc:0.2516 ===\n",
      "train loss:2.2165571494656273\n",
      "train loss:2.2420159852056147\n",
      "train loss:2.211385740924624\n",
      "=== epoch:55, train acc:0.32, test acc:0.2523 ===\n",
      "train loss:2.2357223839071407\n",
      "train loss:2.2232123274892244\n",
      "train loss:2.2376003474711847\n",
      "=== epoch:56, train acc:0.32666666666666666, test acc:0.2587 ===\n",
      "train loss:2.227395397072194\n",
      "train loss:2.225975282328216\n",
      "train loss:2.2262030901163463\n",
      "=== epoch:57, train acc:0.3333333333333333, test acc:0.262 ===\n",
      "train loss:2.2341974888382925\n",
      "train loss:2.2369963911740953\n",
      "train loss:2.230124992740441\n",
      "=== epoch:58, train acc:0.33666666666666667, test acc:0.2655 ===\n",
      "train loss:2.227276809935038\n",
      "train loss:2.2065295186556266\n",
      "train loss:2.2179974625157675\n",
      "=== epoch:59, train acc:0.3466666666666667, test acc:0.2696 ===\n",
      "train loss:2.230103608338899\n",
      "train loss:2.2180426491941123\n",
      "train loss:2.236315293979962\n",
      "=== epoch:60, train acc:0.3566666666666667, test acc:0.2749 ===\n",
      "train loss:2.217764740376142\n",
      "train loss:2.222775794516294\n",
      "train loss:2.237155869611543\n",
      "=== epoch:61, train acc:0.3466666666666667, test acc:0.2782 ===\n",
      "train loss:2.220306901571902\n",
      "train loss:2.212515961264379\n",
      "train loss:2.2464853106228246\n",
      "=== epoch:62, train acc:0.3466666666666667, test acc:0.2798 ===\n",
      "train loss:2.220273236308964\n",
      "train loss:2.213498533687184\n",
      "train loss:2.21974520864083\n",
      "=== epoch:63, train acc:0.35333333333333333, test acc:0.2792 ===\n",
      "train loss:2.2236832315614063\n",
      "train loss:2.227275792992562\n",
      "train loss:2.234266760002367\n",
      "=== epoch:64, train acc:0.35333333333333333, test acc:0.2834 ===\n",
      "train loss:2.2105830878239265\n",
      "train loss:2.233480446683819\n",
      "train loss:2.2220289654727248\n",
      "=== epoch:65, train acc:0.36333333333333334, test acc:0.2877 ===\n",
      "train loss:2.2279776932215665\n",
      "train loss:2.228535262735526\n",
      "train loss:2.2160356343702383\n",
      "=== epoch:66, train acc:0.38, test acc:0.2968 ===\n",
      "train loss:2.228190953614025\n",
      "train loss:2.216578607973356\n",
      "train loss:2.217185117297706\n",
      "=== epoch:67, train acc:0.38333333333333336, test acc:0.3006 ===\n",
      "train loss:2.210712862720454\n",
      "train loss:2.195199967111954\n",
      "train loss:2.2257372007534655\n",
      "=== epoch:68, train acc:0.38, test acc:0.2992 ===\n",
      "train loss:2.1902629458056206\n",
      "train loss:2.1944473994836904\n",
      "train loss:2.212720915334319\n",
      "=== epoch:69, train acc:0.38333333333333336, test acc:0.3025 ===\n",
      "train loss:2.2065761676497138\n",
      "train loss:2.2285601757297813\n",
      "train loss:2.214600051319356\n",
      "=== epoch:70, train acc:0.37666666666666665, test acc:0.3025 ===\n",
      "train loss:2.196187762466001\n",
      "train loss:2.2036441805220552\n",
      "train loss:2.231994498069516\n",
      "=== epoch:71, train acc:0.38, test acc:0.3041 ===\n",
      "train loss:2.205077207204769\n",
      "train loss:2.212010329244357\n",
      "train loss:2.218659564032508\n",
      "=== epoch:72, train acc:0.39, test acc:0.3063 ===\n",
      "train loss:2.2059587177215367\n",
      "train loss:2.2017814135792197\n",
      "train loss:2.2233181013835854\n",
      "=== epoch:73, train acc:0.39666666666666667, test acc:0.3086 ===\n",
      "train loss:2.208266469080981\n",
      "train loss:2.2271696991784244\n",
      "train loss:2.217635131014215\n",
      "=== epoch:74, train acc:0.39666666666666667, test acc:0.315 ===\n",
      "train loss:2.1879191541283047\n",
      "train loss:2.216058027418126\n",
      "train loss:2.194127705330161\n",
      "=== epoch:75, train acc:0.39666666666666667, test acc:0.3147 ===\n",
      "train loss:2.183178847648176\n",
      "train loss:2.2089999164207463\n",
      "train loss:2.235466822391836\n",
      "=== epoch:76, train acc:0.39666666666666667, test acc:0.3159 ===\n",
      "train loss:2.20518556024596\n",
      "train loss:2.1884383476079865\n",
      "train loss:2.1911150212072967\n",
      "=== epoch:77, train acc:0.4033333333333333, test acc:0.3179 ===\n",
      "train loss:2.213597627439107\n",
      "train loss:2.221771932623602\n",
      "train loss:2.198374169173094\n",
      "=== epoch:78, train acc:0.39666666666666667, test acc:0.3185 ===\n",
      "train loss:2.2122488338522945\n",
      "train loss:2.2079088316517246\n",
      "train loss:2.2235294823252376\n",
      "=== epoch:79, train acc:0.39666666666666667, test acc:0.3255 ===\n",
      "train loss:2.1882153118020957\n",
      "train loss:2.175545912300624\n",
      "train loss:2.2066848528038623\n",
      "=== epoch:80, train acc:0.39666666666666667, test acc:0.3273 ===\n",
      "train loss:2.1891153379826123\n",
      "train loss:2.1834844516015695\n",
      "train loss:2.213396484950631\n",
      "=== epoch:81, train acc:0.4033333333333333, test acc:0.3251 ===\n",
      "train loss:2.209539024260619\n",
      "train loss:2.193493892391997\n",
      "train loss:2.1842659650598293\n",
      "=== epoch:82, train acc:0.39, test acc:0.3243 ===\n",
      "train loss:2.184919072851673\n",
      "train loss:2.182217194709766\n",
      "train loss:2.163586742270089\n",
      "=== epoch:83, train acc:0.39, test acc:0.3287 ===\n",
      "train loss:2.223035483374577\n",
      "train loss:2.1707784586318986\n",
      "train loss:2.2140837252909593\n",
      "=== epoch:84, train acc:0.39666666666666667, test acc:0.3331 ===\n",
      "train loss:2.1990710546001067\n",
      "train loss:2.1849784740607556\n",
      "train loss:2.198677444263504\n",
      "=== epoch:85, train acc:0.4033333333333333, test acc:0.3413 ===\n",
      "train loss:2.1757103285876522\n",
      "train loss:2.158214968482066\n",
      "train loss:2.1957557759124966\n",
      "=== epoch:86, train acc:0.4, test acc:0.3404 ===\n",
      "train loss:2.166557358189466\n",
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      "train loss:1.0237574880480822\n",
      "=== epoch:276, train acc:0.6266666666666667, test acc:0.5317 ===\n",
      "train loss:1.155152726323903\n",
      "train loss:1.031565104568128\n",
      "train loss:1.1691996088585899\n",
      "=== epoch:277, train acc:0.6266666666666667, test acc:0.5299 ===\n",
      "train loss:1.2325562797362528\n",
      "train loss:1.2070689108618047\n",
      "train loss:1.1830774000725406\n",
      "=== epoch:278, train acc:0.6266666666666667, test acc:0.5318 ===\n",
      "train loss:1.201910463441897\n",
      "train loss:1.074443393459312\n",
      "train loss:1.1407386691190664\n",
      "=== epoch:279, train acc:0.6333333333333333, test acc:0.5332 ===\n",
      "train loss:1.15468432699182\n",
      "train loss:1.1422914495799024\n",
      "train loss:1.3186708770229751\n",
      "=== epoch:280, train acc:0.6366666666666667, test acc:0.5359 ===\n",
      "train loss:1.1294764615197168\n",
      "train loss:1.0604684812308922\n",
      "train loss:1.1199595840016243\n",
      "=== epoch:281, train acc:0.63, test acc:0.5397 ===\n",
      "train loss:1.249411387583402\n",
      "train loss:1.2452818664898557\n",
      "train loss:1.1806921296278234\n",
      "=== epoch:282, train acc:0.6366666666666667, test acc:0.5368 ===\n",
      "train loss:1.1946448015157312\n",
      "train loss:1.2132080423286258\n",
      "train loss:1.0639551810835914\n",
      "=== epoch:283, train acc:0.6433333333333333, test acc:0.5393 ===\n",
      "train loss:1.175445786422054\n",
      "train loss:1.0080100553263451\n",
      "train loss:1.082598164773164\n",
      "=== epoch:284, train acc:0.6433333333333333, test acc:0.5372 ===\n",
      "train loss:1.0701978813631066\n",
      "train loss:1.138288271421511\n",
      "train loss:1.1228006982784415\n",
      "=== epoch:285, train acc:0.6333333333333333, test acc:0.5365 ===\n",
      "train loss:1.1659185142164619\n",
      "train loss:1.2008899615039152\n",
      "train loss:1.100772367971567\n",
      "=== epoch:286, train acc:0.6366666666666667, test acc:0.5374 ===\n",
      "train loss:1.202979161743067\n",
      "train loss:1.107702012850778\n",
      "train loss:0.9956711966511429\n",
      "=== epoch:287, train acc:0.6466666666666666, test acc:0.5398 ===\n",
      "train loss:1.0793073780675235\n",
      "train loss:1.1415558657670473\n",
      "train loss:1.2113468830346852\n",
      "=== epoch:288, train acc:0.65, test acc:0.5437 ===\n",
      "train loss:0.9553901865378753\n",
      "train loss:1.0633233093230883\n",
      "train loss:1.1301852922158289\n",
      "=== epoch:289, train acc:0.6566666666666666, test acc:0.5423 ===\n",
      "train loss:1.1357455386014323\n",
      "train loss:1.0980677392175031\n",
      "train loss:1.1315768995628932\n",
      "=== epoch:290, train acc:0.65, test acc:0.5428 ===\n",
      "train loss:0.9631378837994969\n",
      "train loss:1.166843377297977\n",
      "train loss:0.9097884487826308\n",
      "=== epoch:291, train acc:0.6533333333333333, test acc:0.543 ===\n",
      "train loss:1.1130578128547794\n",
      "train loss:1.1061659594595974\n",
      "train loss:1.1548137676176182\n",
      "=== epoch:292, train acc:0.66, test acc:0.5423 ===\n",
      "train loss:1.0513984590646066\n",
      "train loss:1.075573503132981\n",
      "train loss:1.1033291799897071\n",
      "=== epoch:293, train acc:0.6533333333333333, test acc:0.5402 ===\n",
      "train loss:1.1740300567878448\n",
      "train loss:1.1481647505870383\n",
      "train loss:1.1675145939707519\n",
      "=== epoch:294, train acc:0.6633333333333333, test acc:0.5407 ===\n",
      "train loss:0.9491551560106373\n",
      "train loss:1.0930984512114716\n",
      "train loss:1.005902789243863\n",
      "=== epoch:295, train acc:0.6566666666666666, test acc:0.545 ===\n",
      "train loss:0.9250649888601856\n",
      "train loss:1.1460919282476265\n",
      "train loss:1.0692102543721649\n",
      "=== epoch:296, train acc:0.6666666666666666, test acc:0.5456 ===\n",
      "train loss:1.145653744419829\n",
      "train loss:1.0473556256969871\n",
      "train loss:1.0644068007526328\n",
      "=== epoch:297, train acc:0.6633333333333333, test acc:0.5463 ===\n",
      "train loss:1.0844160765251356\n",
      "train loss:1.0561417956903818\n",
      "train loss:1.0567661154231778\n",
      "=== epoch:298, train acc:0.6633333333333333, test acc:0.5464 ===\n",
      "train loss:1.0116232012901514\n",
      "train loss:0.9783626887929745\n",
      "train loss:1.1082893987655742\n",
      "=== epoch:299, train acc:0.6666666666666666, test acc:0.5462 ===\n",
      "train loss:1.167476195989586\n",
      "train loss:1.0926160661705602\n",
      "train loss:1.0865831773859447\n",
      "=== epoch:300, train acc:0.67, test acc:0.5458 ===\n",
      "train loss:0.9834808922828264\n",
      "train loss:0.9592428192391843\n",
      "train loss:1.119804214847421\n",
      "=== epoch:301, train acc:0.67, test acc:0.5476 ===\n",
      "train loss:1.0797918199757217\n",
      "train loss:1.0234256431896538\n",
      "=============== Final Test Accuracy ===============\n",
      "test acc:0.5478\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append(os.pardir)  # 親ディレクトリのファイルをインポートするための設定\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataset.mnist import load_mnist\n",
    "from common.multi_layer_net_extend import MultiLayerNetExtend\n",
    "from common.trainer import Trainer\n",
    "\n",
    "(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": "markdown",
   "metadata": {},
   "source": [
    "# ch06/hyperparameter_optimization.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val acc:0.09 | lr:1.901967382287021e-06, weight decay:1.3286113794384553e-06\n",
      "val acc:0.6 | lr:0.003978766790506623, weight decay:1.1494171840105422e-06\n",
      "val acc:0.09 | lr:1.2638554662080983e-06, weight decay:8.29552145479523e-08\n",
      "val acc:0.08 | lr:1.3645665298970673e-05, weight decay:1.6348890260392733e-06\n",
      "val acc:0.1 | lr:3.376169708502469e-05, weight decay:3.6223566248485454e-06\n",
      "val acc:0.2 | lr:1.054115233579305e-06, weight decay:3.6523741352877204e-06\n",
      "val acc:0.09 | lr:9.484682533482772e-06, weight decay:2.7166203905198513e-08\n",
      "val acc:0.19 | lr:0.0012822359991881067, weight decay:5.5083019157860015e-08\n",
      "val acc:0.09 | lr:2.197396063265889e-06, weight decay:2.6199327374116732e-05\n",
      "val acc:0.28 | lr:0.0028134935952994398, weight decay:9.173386087093646e-08\n",
      "val acc:0.05 | lr:1.0887322819354457e-05, weight decay:5.562455484096854e-07\n",
      "val acc:0.14 | lr:3.001538610842652e-05, weight decay:1.1534438081090565e-08\n",
      "val acc:0.13 | lr:4.919737600309615e-06, weight decay:3.5831659974137977e-06\n",
      "val acc:0.48 | lr:0.004837633240364256, weight decay:2.865624783315455e-07\n",
      "val acc:0.07 | lr:1.4136857488581338e-06, weight decay:3.204988714898054e-08\n",
      "val acc:0.42 | lr:0.002402091056369899, weight decay:8.121350224016941e-08\n",
      "val acc:0.04 | lr:3.1641912149766666e-05, weight decay:5.951681214798245e-08\n",
      "val acc:0.03 | lr:6.38942060957141e-06, weight decay:6.280055708160601e-06\n",
      "val acc:0.09 | lr:3.2446087901176983e-06, weight decay:2.114998582042998e-05\n",
      "val acc:0.09 | lr:3.418432865959453e-05, weight decay:9.990544246054949e-08\n",
      "val acc:0.1 | lr:1.2841484159863253e-06, weight decay:8.660037101607172e-05\n",
      "val acc:0.15 | lr:0.0004763573156268341, weight decay:3.206560463884061e-05\n",
      "val acc:0.11 | lr:4.202310138705138e-06, weight decay:2.1560294481579806e-07\n",
      "val acc:0.13 | lr:2.847991982219314e-05, weight decay:4.126560726346264e-07\n",
      "val acc:0.08 | lr:1.0769151818948026e-05, weight decay:8.525789541523559e-08\n",
      "val acc:0.25 | lr:0.0006387263594048124, weight decay:6.507917487879922e-08\n",
      "val acc:0.65 | lr:0.004950955950380995, weight decay:1.6962377368227627e-06\n",
      "val acc:0.11 | lr:1.106340743799198e-05, weight decay:8.523936507312819e-08\n",
      "val acc:0.1 | lr:1.5725905847918745e-06, weight decay:1.5692613829219046e-08\n",
      "val acc:0.04 | lr:3.003238209405467e-05, weight decay:2.0231815145314126e-08\n",
      "val acc:0.66 | lr:0.0053772401592052975, weight decay:1.2442392045589773e-07\n",
      "val acc:0.11 | lr:4.1277106868033884e-05, weight decay:1.5350205929045906e-06\n",
      "val acc:0.09 | lr:1.3390732142206328e-05, weight decay:1.8093855130344713e-06\n",
      "val acc:0.08 | lr:6.9449802719893935e-06, weight decay:1.0173354031675088e-07\n",
      "val acc:0.42 | lr:0.0028211277733491223, weight decay:7.473373898507497e-05\n",
      "val acc:0.09 | lr:2.744288669972092e-05, weight decay:7.249800202428613e-08\n",
      "val acc:0.07 | lr:2.201831513560607e-05, weight decay:3.9133196529158146e-06\n",
      "val acc:0.07 | lr:8.998888340335717e-05, weight decay:2.5531153136731583e-08\n",
      "val acc:0.12 | lr:1.5038931626327507e-05, weight decay:5.606989364605266e-07\n",
      "val acc:0.78 | lr:0.008139906126069275, weight decay:2.6800314516030073e-05\n",
      "val acc:0.15 | lr:9.698929048047387e-05, weight decay:8.160465335695844e-08\n",
      "val acc:0.84 | lr:0.009402354663034039, weight decay:2.7657425441943384e-06\n",
      "val acc:0.08 | lr:3.773392576539369e-05, weight decay:1.7586269548864255e-05\n",
      "val acc:0.31 | lr:0.003732011034447314, weight decay:2.155241304186123e-08\n",
      "val acc:0.18 | lr:0.0009572800452483858, weight decay:3.943291365497582e-06\n",
      "val acc:0.09 | lr:8.911381148939478e-05, weight decay:1.2485318166567018e-07\n",
      "val acc:0.77 | lr:0.005323475200869654, weight decay:3.0944362377617312e-06\n",
      "val acc:0.11 | lr:2.6268106287787297e-05, weight decay:4.386231145741845e-06\n",
      "val acc:0.08 | lr:4.1902739245488466e-05, weight decay:3.285997047507871e-08\n",
      "val acc:0.65 | lr:0.0051935445322904145, weight decay:7.741092465120487e-06\n",
      "val acc:0.09 | lr:9.179859599702348e-05, weight decay:2.8068890702971863e-06\n",
      "val acc:0.15 | lr:0.0002915753444031139, weight decay:3.1641408654039235e-05\n",
      "val acc:0.14 | lr:0.0004072362532858795, weight decay:1.0902325957069675e-05\n",
      "val acc:0.07 | lr:0.00024448803282801543, weight decay:3.3848000180817603e-06\n",
      "val acc:0.36 | lr:0.0031281482414736326, weight decay:3.0104605115650894e-06\n",
      "val acc:0.09 | lr:1.2784817190931389e-06, weight decay:4.7425157254763966e-07\n",
      "val acc:0.07 | lr:3.341666970907377e-05, weight decay:1.1119736271449125e-07\n",
      "val acc:0.24 | lr:0.0008028565424373312, weight decay:1.4221880181724088e-08\n",
      "val acc:0.27 | lr:0.0014063591388285026, weight decay:3.3571197152804646e-07\n",
      "val acc:0.1 | lr:0.0001204915548248102, weight decay:5.957277235461275e-07\n",
      "val acc:0.13 | lr:2.2975197346694937e-05, weight decay:3.9058019158505534e-06\n",
      "val acc:0.05 | lr:7.408572130573206e-05, weight decay:4.9129093455022614e-08\n",
      "val acc:0.12 | lr:3.532290597879644e-05, weight decay:3.1209506633832274e-07\n",
      "val acc:0.08 | lr:4.917669603883112e-05, weight decay:2.852554118631265e-07\n",
      "val acc:0.04 | lr:1.742827334097188e-05, weight decay:2.7733927097021054e-05\n",
      "val acc:0.05 | lr:2.5297129406224066e-06, weight decay:1.346747891051982e-08\n",
      "val acc:0.16 | lr:0.0018082803193271064, weight decay:2.678769099097837e-05\n",
      "val acc:0.38 | lr:0.0015867989928561375, weight decay:7.126947004109484e-06\n",
      "val acc:0.72 | lr:0.005933942380243135, weight decay:1.1700648591899939e-06\n",
      "val acc:0.1 | lr:1.1387844258820435e-06, weight decay:2.4575801143383534e-08\n",
      "val acc:0.09 | lr:0.00010049323208029319, weight decay:1.0538545325592876e-07\n",
      "val acc:0.16 | lr:0.00029615641163198983, weight decay:2.302191824101012e-05\n",
      "val acc:0.18 | lr:0.00015506791172618912, weight decay:4.5571634987719706e-07\n",
      "val acc:0.09 | lr:9.735771141282724e-05, weight decay:4.767367365455984e-05\n",
      "val acc:0.73 | lr:0.00971006195511063, weight decay:7.968405625869132e-07\n",
      "val acc:0.81 | lr:0.00966269782528176, weight decay:4.2016228896271936e-08\n",
      "val acc:0.2 | lr:0.0004776149464821907, weight decay:2.3462503575978135e-06\n",
      "val acc:0.1 | lr:0.0004050412515357628, weight decay:4.995702802761457e-07\n",
      "val acc:0.2 | lr:0.0021118285905870134, weight decay:4.0534239243075754e-06\n",
      "val acc:0.11 | lr:0.0005892951503131724, weight decay:3.6212698605925374e-08\n",
      "val acc:0.1 | lr:6.550111834020697e-05, weight decay:7.170659871482868e-07\n",
      "val acc:0.13 | lr:5.3691164604043244e-05, weight decay:4.3266323700103445e-06\n",
      "val acc:0.41 | lr:0.0032819652235552645, weight decay:5.454787693603591e-06\n",
      "val acc:0.09 | lr:4.598838448782383e-06, weight decay:2.7100896803981685e-06\n",
      "val acc:0.17 | lr:0.000609620160433721, weight decay:4.138463880743084e-08\n",
      "val acc:0.16 | lr:0.00039628092985887457, weight decay:1.6517481680606803e-05\n",
      "val acc:0.04 | lr:4.6707145991625234e-05, weight decay:3.7623925009451605e-06\n",
      "val acc:0.73 | lr:0.00495406975968389, weight decay:3.106906292614055e-07\n",
      "val acc:0.48 | lr:0.004374511525651062, weight decay:3.1057047768207077e-06\n",
      "val acc:0.07 | lr:1.5415469409240567e-05, weight decay:1.147728526767842e-08\n",
      "val acc:0.09 | lr:7.483295422416259e-06, weight decay:3.606049160959718e-07\n",
      "val acc:0.1 | lr:2.5898488257378162e-06, weight decay:1.2629223318183463e-06\n",
      "val acc:0.13 | lr:0.00016737540168973162, weight decay:1.2066029964500918e-05\n",
      "val acc:0.72 | lr:0.009976360661833212, weight decay:3.863360692619599e-05\n",
      "val acc:0.23 | lr:1.0523464242017625e-05, weight decay:4.6245369313029846e-05\n",
      "val acc:0.72 | lr:0.007689165478868014, weight decay:9.501747374925242e-08\n",
      "val acc:0.15 | lr:0.0006739240327174801, weight decay:1.6711282655262968e-06\n",
      "val acc:0.12 | lr:1.4232777053742872e-06, weight decay:7.98427751888876e-05\n",
      "val acc:0.1 | lr:3.705532330527043e-05, weight decay:2.321364545700686e-08\n",
      "val acc:0.12 | lr:0.0001845185976915193, weight decay:9.906254128490712e-05\n",
      "=========== Hyper-Parameter Optimization Result ===========\n",
      "Best-1(val acc:0.84) | lr:0.009402354663034039, weight decay:2.7657425441943384e-06\n",
      "Best-2(val acc:0.81) | lr:0.00966269782528176, weight decay:4.2016228896271936e-08\n",
      "Best-3(val acc:0.78) | lr:0.008139906126069275, weight decay:2.6800314516030073e-05\n",
      "Best-4(val acc:0.77) | lr:0.005323475200869654, weight decay:3.0944362377617312e-06\n",
      "Best-5(val acc:0.73) | lr:0.00971006195511063, weight decay:7.968405625869132e-07\n",
      "Best-6(val acc:0.73) | lr:0.00495406975968389, weight decay:3.106906292614055e-07\n",
      "Best-7(val acc:0.72) | lr:0.005933942380243135, weight decay:1.1700648591899939e-06\n",
      "Best-8(val acc:0.72) | lr:0.009976360661833212, weight decay:3.863360692619599e-05\n",
      "Best-9(val acc:0.72) | lr:0.007689165478868014, weight decay:9.501747374925242e-08\n",
      "Best-10(val acc:0.66) | lr:0.0053772401592052975, weight decay:1.2442392045589773e-07\n",
      "Best-11(val acc:0.65) | lr:0.004950955950380995, weight decay:1.6962377368227627e-06\n",
      "Best-12(val acc:0.65) | lr:0.0051935445322904145, weight decay:7.741092465120487e-06\n",
      "Best-13(val acc:0.6) | lr:0.003978766790506623, weight decay:1.1494171840105422e-06\n",
      "Best-14(val acc:0.48) | lr:0.004837633240364256, weight decay:2.865624783315455e-07\n",
      "Best-15(val acc:0.48) | lr:0.004374511525651062, weight decay:3.1057047768207077e-06\n",
      "Best-16(val acc:0.42) | lr:0.002402091056369899, weight decay:8.121350224016941e-08\n",
      "Best-17(val acc:0.42) | lr:0.0028211277733491223, weight decay:7.473373898507497e-05\n",
      "Best-18(val acc:0.41) | lr:0.0032819652235552645, weight decay:5.454787693603591e-06\n",
      "Best-19(val acc:0.38) | lr:0.0015867989928561375, weight decay:7.126947004109484e-06\n",
      "Best-20(val acc:0.36) | lr:0.0031281482414736326, weight decay:3.0104605115650894e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys, os\n",
    "sys.path.append(os.pardir)  # 親ディレクトリのファイルをインポートするための設定\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataset.mnist import load_mnist\n",
    "from common.multi_layer_net import MultiLayerNet\n",
    "from common.util import shuffle_dataset\n",
    "from common.trainer import Trainer\n",
    "\n",
    "(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 = int(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,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dezero:Python",
   "language": "python",
   "name": "conda-env-dezero-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}