{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST-Neural Network-Two Hidden Layers with Variable Optimziers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# coding: utf-8\n",
    "import sys, os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "sys.path.append(os.pardir)\n",
    "from deeplink.mnist import *\n",
    "from deeplink.functions import *\n",
    "from deeplink.layers import *\n",
    "from deeplink.util import *\n",
    "from deeplink.optimizers import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilayer Neural Network Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys, os\n",
    "from collections import OrderedDict\n",
    "from scipy import stats\n",
    "from pandas import DataFrame\n",
    "\n",
    "class TwoLayerNet:\n",
    "    def __init__(self, input_size, hidden_layer1_size, hidden_layer2_size, output_size, weight_init_std = 0.01):\n",
    "        self.params = {}\n",
    "        self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_layer1_size)\n",
    "        self.params['b1'] = np.zeros(hidden_layer1_size)\n",
    "        self.params['W2'] = weight_init_std * np.random.randn(hidden_layer1_size, hidden_layer2_size)\n",
    "        self.params['b2'] = np.zeros(hidden_layer2_size)\n",
    "        self.params['W3'] = weight_init_std * np.random.randn(hidden_layer2_size, output_size)\n",
    "        self.params['b3'] = np.zeros(output_size)\n",
    "\n",
    "        self.layers = OrderedDict()\n",
    "        self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])\n",
    "        self.layers['Relu1'] = ReLU()\n",
    "        self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])\n",
    "        self.layers['Relu2'] = ReLU()\n",
    "        self.layers['Affine3'] = Affine(self.params['W3'], self.params['b3'])\n",
    "\n",
    "        self.lastLayer = SoftmaxWithCrossEntropyLoss()\n",
    "            \n",
    "    def predict(self, x):\n",
    "        for layer in self.layers.values():\n",
    "            x = layer.forward(x)\n",
    "        \n",
    "        return x\n",
    "        \n",
    "    def loss(self, x, t):\n",
    "        y = self.predict(x)\n",
    "        return self.lastLayer.forward(y, t)\n",
    "    \n",
    "    def accuracy(self, x, t):\n",
    "        y = self.predict(x)\n",
    "        y = np.argmax(y, axis=1)\n",
    "        if t.ndim != 1 : t = np.argmax(t, axis=1)\n",
    "        \n",
    "        accuracy = np.sum(y == t) / float(x.shape[0])\n",
    "        return accuracy\n",
    "        \n",
    "    def backpropagation_gradient(self, x, t):\n",
    "        # forward\n",
    "        self.loss(x, t)\n",
    "\n",
    "        # backward\n",
    "        din = 1\n",
    "        din = self.lastLayer.backward(din)\n",
    "        \n",
    "        layers = list(self.layers.values())\n",
    "        layers.reverse()\n",
    "        for layer in layers:\n",
    "            din = layer.backward(din)\n",
    "\n",
    "        grads = {}\n",
    "        grads['W1'], grads['b1'] = self.layers['Affine1'].dW, self.layers['Affine1'].db\n",
    "        grads['W2'], grads['b2'] = self.layers['Affine2'].dW, self.layers['Affine2'].db\n",
    "        grads['W3'], grads['b3'] = self.layers['Affine3'].dW, self.layers['Affine3'].db\n",
    "\n",
    "        return grads\n",
    "    \n",
    "    def learning(self, learning_rate, x_batch, t_batch, optimizer):\n",
    "        grads = self.backpropagation_gradient(x_batch, t_batch)\n",
    "        optimizer.update(self.params, grads)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning and Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGD     -Epoch:  0, Train Err.:2.30247, Validation Err.:2.30244, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  0, Train Err.:2.30181, Validation Err.:2.30151, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Nesterov-Epoch:  0, Train Err.:2.30159, Validation Err.:2.30133, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "AdaGrad -Epoch:  0, Train Err.:0.42933, Validation Err.:0.37621, Test Accuracy:0.87570, Max Test Accuracy:0.87570\n",
      "RMSprop -Epoch:  0, Train Err.:0.31946, Validation Err.:0.29008, Test Accuracy:0.89900, Max Test Accuracy:0.89900\n",
      "Adam    -Epoch:  0, Train Err.:0.59923, Validation Err.:0.53937, Test Accuracy:0.81020, Max Test Accuracy:0.81020\n",
      "\n",
      "SGD     -Epoch:  1, Train Err.:2.30234, Validation Err.:2.30228, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  1, Train Err.:2.30104, Validation Err.:2.30057, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Nesterov-Epoch:  1, Train Err.:2.30056, Validation Err.:2.30016, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "AdaGrad -Epoch:  1, Train Err.:0.32972, Validation Err.:0.27351, Test Accuracy:0.90430, Max Test Accuracy:0.90430\n",
      "RMSprop -Epoch:  1, Train Err.:0.24382, Validation Err.:0.22124, Test Accuracy:0.92090, Max Test Accuracy:0.92090\n",
      "Adam    -Epoch:  1, Train Err.:0.38069, Validation Err.:0.31509, Test Accuracy:0.88850, Max Test Accuracy:0.88850\n",
      "\n",
      "SGD     -Epoch:  2, Train Err.:2.30223, Validation Err.:2.30213, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  2, Train Err.:2.29943, Validation Err.:2.29885, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Nesterov-Epoch:  2, Train Err.:2.29815, Validation Err.:2.29765, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "AdaGrad -Epoch:  2, Train Err.:0.28053, Validation Err.:0.23047, Test Accuracy:0.91800, Max Test Accuracy:0.91800\n",
      "RMSprop -Epoch:  2, Train Err.:0.16622, Validation Err.:0.16929, Test Accuracy:0.94030, Max Test Accuracy:0.94030\n",
      "Adam    -Epoch:  2, Train Err.:0.32200, Validation Err.:0.26421, Test Accuracy:0.91080, Max Test Accuracy:0.91080\n",
      "\n",
      "SGD     -Epoch:  3, Train Err.:2.30212, Validation Err.:2.30200, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  3, Train Err.:2.29418, Validation Err.:2.29345, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Nesterov-Epoch:  3, Train Err.:2.28885, Validation Err.:2.28833, Test Accuracy:0.14390, Max Test Accuracy:0.14390\n",
      "AdaGrad -Epoch:  3, Train Err.:0.24584, Validation Err.:0.20466, Test Accuracy:0.92790, Max Test Accuracy:0.92790\n",
      "RMSprop -Epoch:  3, Train Err.:0.23688, Validation Err.:0.22960, Test Accuracy:0.91110, Max Test Accuracy:0.94030\n",
      "Adam    -Epoch:  3, Train Err.:0.28410, Validation Err.:0.23280, Test Accuracy:0.91940, Max Test Accuracy:0.91940\n",
      "\n",
      "SGD     -Epoch:  4, Train Err.:2.30201, Validation Err.:2.30187, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  4, Train Err.:2.26443, Validation Err.:2.26338, Test Accuracy:0.21050, Max Test Accuracy:0.21050\n",
      "Nesterov-Epoch:  4, Train Err.:2.21612, Validation Err.:2.21657, Test Accuracy:0.22940, Max Test Accuracy:0.22940\n",
      "AdaGrad -Epoch:  4, Train Err.:0.21909, Validation Err.:0.18696, Test Accuracy:0.93260, Max Test Accuracy:0.93260\n",
      "RMSprop -Epoch:  4, Train Err.:0.09962, Validation Err.:0.15052, Test Accuracy:0.94970, Max Test Accuracy:0.94970\n",
      "Adam    -Epoch:  4, Train Err.:0.25371, Validation Err.:0.20968, Test Accuracy:0.92550, Max Test Accuracy:0.92550\n",
      "\n",
      "SGD     -Epoch:  5, Train Err.:2.30192, Validation Err.:2.30175, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  5, Train Err.:2.05104, Validation Err.:2.04930, Test Accuracy:0.28610, Max Test Accuracy:0.28610\n",
      "Nesterov-Epoch:  5, Train Err.:1.88057, Validation Err.:1.88027, Test Accuracy:0.37970, Max Test Accuracy:0.37970\n",
      "AdaGrad -Epoch:  5, Train Err.:0.19800, Validation Err.:0.17408, Test Accuracy:0.93650, Max Test Accuracy:0.93650\n",
      "RMSprop -Epoch:  5, Train Err.:0.10011, Validation Err.:0.14826, Test Accuracy:0.94520, Max Test Accuracy:0.94970\n",
      "Adam    -Epoch:  5, Train Err.:0.22606, Validation Err.:0.19102, Test Accuracy:0.93270, Max Test Accuracy:0.93270\n",
      "\n",
      "SGD     -Epoch:  6, Train Err.:2.30183, Validation Err.:2.30163, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  6, Train Err.:1.39538, Validation Err.:1.36707, Test Accuracy:0.57280, Max Test Accuracy:0.57280\n",
      "Nesterov-Epoch:  6, Train Err.:1.04205, Validation Err.:1.00803, Test Accuracy:0.68280, Max Test Accuracy:0.68280\n",
      "AdaGrad -Epoch:  6, Train Err.:0.18097, Validation Err.:0.16429, Test Accuracy:0.93940, Max Test Accuracy:0.93940\n",
      "RMSprop -Epoch:  6, Train Err.:0.08246, Validation Err.:0.13258, Test Accuracy:0.95160, Max Test Accuracy:0.95160\n",
      "Adam    -Epoch:  6, Train Err.:0.19867, Validation Err.:0.17376, Test Accuracy:0.93830, Max Test Accuracy:0.93830\n",
      "\n",
      "SGD     -Epoch:  7, Train Err.:2.30174, Validation Err.:2.30153, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  7, Train Err.:0.86912, Validation Err.:0.81786, Test Accuracy:0.71560, Max Test Accuracy:0.71560\n",
      "Nesterov-Epoch:  7, Train Err.:0.71625, Validation Err.:0.65079, Test Accuracy:0.77400, Max Test Accuracy:0.77400\n",
      "AdaGrad -Epoch:  7, Train Err.:0.16689, Validation Err.:0.15640, Test Accuracy:0.94150, Max Test Accuracy:0.94150\n",
      "RMSprop -Epoch:  7, Train Err.:0.07401, Validation Err.:0.13384, Test Accuracy:0.95320, Max Test Accuracy:0.95320\n",
      "Adam    -Epoch:  7, Train Err.:0.17460, Validation Err.:0.15896, Test Accuracy:0.94170, Max Test Accuracy:0.94170\n",
      "\n",
      "SGD     -Epoch:  8, Train Err.:2.30166, Validation Err.:2.30142, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  8, Train Err.:0.68047, Validation Err.:0.61326, Test Accuracy:0.78600, Max Test Accuracy:0.78600\n",
      "Nesterov-Epoch:  8, Train Err.:0.62916, Validation Err.:0.55671, Test Accuracy:0.80720, Max Test Accuracy:0.80720\n",
      "AdaGrad -Epoch:  8, Train Err.:0.15531, Validation Err.:0.14993, Test Accuracy:0.94400, Max Test Accuracy:0.94400\n",
      "RMSprop -Epoch:  8, Train Err.:0.06335, Validation Err.:0.12631, Test Accuracy:0.95580, Max Test Accuracy:0.95580\n",
      "Adam    -Epoch:  8, Train Err.:0.15562, Validation Err.:0.14709, Test Accuracy:0.94590, Max Test Accuracy:0.94590\n",
      "\n",
      "SGD     -Epoch:  9, Train Err.:2.30158, Validation Err.:2.30132, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch:  9, Train Err.:0.60429, Validation Err.:0.53441, Test Accuracy:0.81610, Max Test Accuracy:0.81610\n",
      "Nesterov-Epoch:  9, Train Err.:0.58002, Validation Err.:0.50528, Test Accuracy:0.82710, Max Test Accuracy:0.82710\n",
      "AdaGrad -Epoch:  9, Train Err.:0.14571, Validation Err.:0.14456, Test Accuracy:0.94580, Max Test Accuracy:0.94580\n",
      "RMSprop -Epoch:  9, Train Err.:0.08532, Validation Err.:0.13603, Test Accuracy:0.95320, Max Test Accuracy:0.95580\n",
      "Adam    -Epoch:  9, Train Err.:0.13979, Validation Err.:0.13698, Test Accuracy:0.94910, Max Test Accuracy:0.94910\n",
      "\n",
      "SGD     -Epoch: 10, Train Err.:2.30149, Validation Err.:2.30122, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 10, Train Err.:0.55084, Validation Err.:0.47956, Test Accuracy:0.83440, Max Test Accuracy:0.83440\n",
      "Nesterov-Epoch: 10, Train Err.:0.53941, Validation Err.:0.46344, Test Accuracy:0.83970, Max Test Accuracy:0.83970\n",
      "AdaGrad -Epoch: 10, Train Err.:0.13767, Validation Err.:0.13995, Test Accuracy:0.94750, Max Test Accuracy:0.94750\n",
      "RMSprop -Epoch: 10, Train Err.:0.06467, Validation Err.:0.12934, Test Accuracy:0.95700, Max Test Accuracy:0.95700\n",
      "Adam    -Epoch: 10, Train Err.:0.12606, Validation Err.:0.12829, Test Accuracy:0.95450, Max Test Accuracy:0.95450\n",
      "\n",
      "SGD     -Epoch: 11, Train Err.:2.30141, Validation Err.:2.30112, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 11, Train Err.:0.50678, Validation Err.:0.43303, Test Accuracy:0.85030, Max Test Accuracy:0.85030\n",
      "Nesterov-Epoch: 11, Train Err.:0.50059, Validation Err.:0.42296, Test Accuracy:0.85270, Max Test Accuracy:0.85270\n",
      "AdaGrad -Epoch: 11, Train Err.:0.13069, Validation Err.:0.13588, Test Accuracy:0.94910, Max Test Accuracy:0.94910\n",
      "RMSprop -Epoch: 11, Train Err.:0.07355, Validation Err.:0.14455, Test Accuracy:0.95300, Max Test Accuracy:0.95700\n",
      "Adam    -Epoch: 11, Train Err.:0.11478, Validation Err.:0.12094, Test Accuracy:0.95760, Max Test Accuracy:0.95760\n",
      "\n",
      "SGD     -Epoch: 12, Train Err.:2.30132, Validation Err.:2.30101, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 12, Train Err.:0.47396, Validation Err.:0.39680, Test Accuracy:0.86210, Max Test Accuracy:0.86210\n",
      "Nesterov-Epoch: 12, Train Err.:0.46651, Validation Err.:0.38704, Test Accuracy:0.86430, Max Test Accuracy:0.86430\n",
      "AdaGrad -Epoch: 12, Train Err.:0.12445, Validation Err.:0.13215, Test Accuracy:0.95060, Max Test Accuracy:0.95060\n",
      "RMSprop -Epoch: 12, Train Err.:0.05375, Validation Err.:0.13627, Test Accuracy:0.95540, Max Test Accuracy:0.95700\n",
      "Adam    -Epoch: 12, Train Err.:0.10467, Validation Err.:0.11469, Test Accuracy:0.96040, Max Test Accuracy:0.96040\n",
      "\n",
      "SGD     -Epoch: 13, Train Err.:2.30123, Validation Err.:2.30090, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 13, Train Err.:0.44863, Validation Err.:0.36989, Test Accuracy:0.87000, Max Test Accuracy:0.87000\n",
      "Nesterov-Epoch: 13, Train Err.:0.43954, Validation Err.:0.35940, Test Accuracy:0.87380, Max Test Accuracy:0.87380\n",
      "AdaGrad -Epoch: 13, Train Err.:0.11853, Validation Err.:0.12905, Test Accuracy:0.95210, Max Test Accuracy:0.95210\n",
      "RMSprop -Epoch: 13, Train Err.:0.06131, Validation Err.:0.13693, Test Accuracy:0.95860, Max Test Accuracy:0.95860\n",
      "Adam    -Epoch: 13, Train Err.:0.09671, Validation Err.:0.10963, Test Accuracy:0.96190, Max Test Accuracy:0.96190\n",
      "\n",
      "SGD     -Epoch: 14, Train Err.:2.30114, Validation Err.:2.30079, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 14, Train Err.:0.42726, Validation Err.:0.34864, Test Accuracy:0.87830, Max Test Accuracy:0.87830\n",
      "Nesterov-Epoch: 14, Train Err.:0.41657, Validation Err.:0.33721, Test Accuracy:0.88060, Max Test Accuracy:0.88060\n",
      "AdaGrad -Epoch: 14, Train Err.:0.11360, Validation Err.:0.12599, Test Accuracy:0.95370, Max Test Accuracy:0.95370\n",
      "RMSprop -Epoch: 14, Train Err.:0.05116, Validation Err.:0.13572, Test Accuracy:0.95810, Max Test Accuracy:0.95860\n",
      "Adam    -Epoch: 14, Train Err.:0.08908, Validation Err.:0.10528, Test Accuracy:0.96390, Max Test Accuracy:0.96390\n",
      "\n",
      "SGD     -Epoch: 15, Train Err.:2.30104, Validation Err.:2.30068, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 15, Train Err.:0.40835, Validation Err.:0.33086, Test Accuracy:0.88300, Max Test Accuracy:0.88300\n",
      "Nesterov-Epoch: 15, Train Err.:0.39500, Validation Err.:0.31848, Test Accuracy:0.88630, Max Test Accuracy:0.88630\n",
      "AdaGrad -Epoch: 15, Train Err.:0.10891, Validation Err.:0.12331, Test Accuracy:0.95540, Max Test Accuracy:0.95540\n",
      "RMSprop -Epoch: 15, Train Err.:0.04457, Validation Err.:0.12839, Test Accuracy:0.96020, Max Test Accuracy:0.96020\n",
      "Adam    -Epoch: 15, Train Err.:0.08205, Validation Err.:0.10177, Test Accuracy:0.96540, Max Test Accuracy:0.96540\n",
      "\n",
      "SGD     -Epoch: 16, Train Err.:2.30093, Validation Err.:2.30055, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 16, Train Err.:0.39084, Validation Err.:0.31534, Test Accuracy:0.88820, Max Test Accuracy:0.88820\n",
      "Nesterov-Epoch: 16, Train Err.:0.37549, Validation Err.:0.30217, Test Accuracy:0.89150, Max Test Accuracy:0.89150\n",
      "AdaGrad -Epoch: 16, Train Err.:0.10476, Validation Err.:0.12082, Test Accuracy:0.95660, Max Test Accuracy:0.95660\n",
      "RMSprop -Epoch: 16, Train Err.:0.05032, Validation Err.:0.13215, Test Accuracy:0.95810, Max Test Accuracy:0.96020\n",
      "Adam    -Epoch: 16, Train Err.:0.07562, Validation Err.:0.09847, Test Accuracy:0.96620, Max Test Accuracy:0.96620\n",
      "\n",
      "SGD     -Epoch: 17, Train Err.:2.30081, Validation Err.:2.30042, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 17, Train Err.:0.37443, Validation Err.:0.30125, Test Accuracy:0.89250, Max Test Accuracy:0.89250\n",
      "Nesterov-Epoch: 17, Train Err.:0.35779, Validation Err.:0.28763, Test Accuracy:0.89800, Max Test Accuracy:0.89800\n",
      "AdaGrad -Epoch: 17, Train Err.:0.10106, Validation Err.:0.11865, Test Accuracy:0.95800, Max Test Accuracy:0.95800\n",
      "RMSprop -Epoch: 17, Train Err.:0.04925, Validation Err.:0.15284, Test Accuracy:0.95780, Max Test Accuracy:0.96020\n",
      "Adam    -Epoch: 17, Train Err.:0.06987, Validation Err.:0.09597, Test Accuracy:0.96810, Max Test Accuracy:0.96810\n",
      "\n",
      "SGD     -Epoch: 18, Train Err.:2.30068, Validation Err.:2.30028, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 18, Train Err.:0.35884, Validation Err.:0.28849, Test Accuracy:0.89620, Max Test Accuracy:0.89620\n",
      "Nesterov-Epoch: 18, Train Err.:0.34170, Validation Err.:0.27473, Test Accuracy:0.90220, Max Test Accuracy:0.90220\n",
      "AdaGrad -Epoch: 18, Train Err.:0.09753, Validation Err.:0.11673, Test Accuracy:0.95840, Max Test Accuracy:0.95840\n",
      "RMSprop -Epoch: 18, Train Err.:0.03862, Validation Err.:0.14301, Test Accuracy:0.95870, Max Test Accuracy:0.96020\n",
      "Adam    -Epoch: 18, Train Err.:0.06533, Validation Err.:0.09376, Test Accuracy:0.96930, Max Test Accuracy:0.96930\n",
      "\n",
      "SGD     -Epoch: 19, Train Err.:2.30055, Validation Err.:2.30013, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 19, Train Err.:0.34389, Validation Err.:0.27676, Test Accuracy:0.90160, Max Test Accuracy:0.90160\n",
      "Nesterov-Epoch: 19, Train Err.:0.32665, Validation Err.:0.26310, Test Accuracy:0.90600, Max Test Accuracy:0.90600\n",
      "AdaGrad -Epoch: 19, Train Err.:0.09428, Validation Err.:0.11492, Test Accuracy:0.95960, Max Test Accuracy:0.95960\n",
      "RMSprop -Epoch: 19, Train Err.:0.22136, Validation Err.:0.26585, Test Accuracy:0.92940, Max Test Accuracy:0.96020\n",
      "Adam    -Epoch: 19, Train Err.:0.06103, Validation Err.:0.09210, Test Accuracy:0.97000, Max Test Accuracy:0.97000\n",
      "\n",
      "SGD     -Epoch: 20, Train Err.:2.30040, Validation Err.:2.29996, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 20, Train Err.:0.32962, Validation Err.:0.26594, Test Accuracy:0.90620, Max Test Accuracy:0.90620\n",
      "Nesterov-Epoch: 20, Train Err.:0.31265, Validation Err.:0.25262, Test Accuracy:0.91080, Max Test Accuracy:0.91080\n",
      "AdaGrad -Epoch: 20, Train Err.:0.09126, Validation Err.:0.11333, Test Accuracy:0.96050, Max Test Accuracy:0.96050\n",
      "RMSprop -Epoch: 20, Train Err.:0.03511, Validation Err.:0.14248, Test Accuracy:0.95960, Max Test Accuracy:0.96020\n",
      "Adam    -Epoch: 20, Train Err.:0.05714, Validation Err.:0.09074, Test Accuracy:0.97060, Max Test Accuracy:0.97060\n",
      "\n",
      "SGD     -Epoch: 21, Train Err.:2.30023, Validation Err.:2.29978, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 21, Train Err.:0.31613, Validation Err.:0.25574, Test Accuracy:0.90910, Max Test Accuracy:0.90910\n",
      "Nesterov-Epoch: 21, Train Err.:0.29968, Validation Err.:0.24292, Test Accuracy:0.91420, Max Test Accuracy:0.91420\n",
      "AdaGrad -Epoch: 21, Train Err.:0.08849, Validation Err.:0.11183, Test Accuracy:0.96070, Max Test Accuracy:0.96070\n",
      "RMSprop -Epoch: 21, Train Err.:0.03603, Validation Err.:0.15121, Test Accuracy:0.95860, Max Test Accuracy:0.96020\n",
      "Adam    -Epoch: 21, Train Err.:0.05360, Validation Err.:0.08980, Test Accuracy:0.97070, Max Test Accuracy:0.97070\n",
      "\n",
      "SGD     -Epoch: 22, Train Err.:2.30005, Validation Err.:2.29959, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 22, Train Err.:0.30322, Validation Err.:0.24610, Test Accuracy:0.91110, Max Test Accuracy:0.91110\n",
      "Nesterov-Epoch: 22, Train Err.:0.28736, Validation Err.:0.23391, Test Accuracy:0.91720, Max Test Accuracy:0.91720\n",
      "AdaGrad -Epoch: 22, Train Err.:0.08588, Validation Err.:0.11042, Test Accuracy:0.96120, Max Test Accuracy:0.96120\n",
      "RMSprop -Epoch: 22, Train Err.:0.05252, Validation Err.:0.14305, Test Accuracy:0.95840, Max Test Accuracy:0.96020\n",
      "Adam    -Epoch: 22, Train Err.:0.05050, Validation Err.:0.08917, Test Accuracy:0.97150, Max Test Accuracy:0.97150\n",
      "\n",
      "SGD     -Epoch: 23, Train Err.:2.29984, Validation Err.:2.29937, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 23, Train Err.:0.29050, Validation Err.:0.23703, Test Accuracy:0.91330, Max Test Accuracy:0.91330\n",
      "Nesterov-Epoch: 23, Train Err.:0.27556, Validation Err.:0.22551, Test Accuracy:0.92060, Max Test Accuracy:0.92060\n",
      "AdaGrad -Epoch: 23, Train Err.:0.08344, Validation Err.:0.10912, Test Accuracy:0.96180, Max Test Accuracy:0.96180\n",
      "RMSprop -Epoch: 23, Train Err.:0.03476, Validation Err.:0.12857, Test Accuracy:0.96210, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 23, Train Err.:0.04751, Validation Err.:0.08868, Test Accuracy:0.97250, Max Test Accuracy:0.97250\n",
      "\n",
      "SGD     -Epoch: 24, Train Err.:2.29961, Validation Err.:2.29913, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 24, Train Err.:0.27857, Validation Err.:0.22851, Test Accuracy:0.91650, Max Test Accuracy:0.91650\n",
      "Nesterov-Epoch: 24, Train Err.:0.26437, Validation Err.:0.21754, Test Accuracy:0.92370, Max Test Accuracy:0.92370\n",
      "AdaGrad -Epoch: 24, Train Err.:0.08117, Validation Err.:0.10794, Test Accuracy:0.96260, Max Test Accuracy:0.96260\n",
      "RMSprop -Epoch: 24, Train Err.:0.02699, Validation Err.:0.13965, Test Accuracy:0.96090, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 24, Train Err.:0.04498, Validation Err.:0.08851, Test Accuracy:0.97280, Max Test Accuracy:0.97280\n",
      "\n",
      "SGD     -Epoch: 25, Train Err.:2.29936, Validation Err.:2.29886, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 25, Train Err.:0.26706, Validation Err.:0.22039, Test Accuracy:0.91950, Max Test Accuracy:0.91950\n",
      "Nesterov-Epoch: 25, Train Err.:0.25377, Validation Err.:0.20999, Test Accuracy:0.92590, Max Test Accuracy:0.92590\n",
      "AdaGrad -Epoch: 25, Train Err.:0.07900, Validation Err.:0.10687, Test Accuracy:0.96270, Max Test Accuracy:0.96270\n",
      "RMSprop -Epoch: 25, Train Err.:0.03537, Validation Err.:0.15894, Test Accuracy:0.95850, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 25, Train Err.:0.04244, Validation Err.:0.08818, Test Accuracy:0.97300, Max Test Accuracy:0.97300\n",
      "\n",
      "SGD     -Epoch: 26, Train Err.:2.29908, Validation Err.:2.29857, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 26, Train Err.:0.25591, Validation Err.:0.21261, Test Accuracy:0.92300, Max Test Accuracy:0.92300\n",
      "Nesterov-Epoch: 26, Train Err.:0.24368, Validation Err.:0.20279, Test Accuracy:0.92920, Max Test Accuracy:0.92920\n",
      "AdaGrad -Epoch: 26, Train Err.:0.07697, Validation Err.:0.10584, Test Accuracy:0.96300, Max Test Accuracy:0.96300\n",
      "RMSprop -Epoch: 26, Train Err.:0.03275, Validation Err.:0.14621, Test Accuracy:0.96050, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 26, Train Err.:0.03977, Validation Err.:0.08783, Test Accuracy:0.97350, Max Test Accuracy:0.97350\n",
      "\n",
      "SGD     -Epoch: 27, Train Err.:2.29876, Validation Err.:2.29824, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 27, Train Err.:0.24504, Validation Err.:0.20522, Test Accuracy:0.92530, Max Test Accuracy:0.92530\n",
      "Nesterov-Epoch: 27, Train Err.:0.23380, Validation Err.:0.19589, Test Accuracy:0.93160, Max Test Accuracy:0.93160\n",
      "AdaGrad -Epoch: 27, Train Err.:0.07503, Validation Err.:0.10485, Test Accuracy:0.96320, Max Test Accuracy:0.96320\n",
      "RMSprop -Epoch: 27, Train Err.:0.03175, Validation Err.:0.16259, Test Accuracy:0.95770, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 27, Train Err.:0.03691, Validation Err.:0.08701, Test Accuracy:0.97380, Max Test Accuracy:0.97380\n",
      "\n",
      "SGD     -Epoch: 28, Train Err.:2.29840, Validation Err.:2.29786, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 28, Train Err.:0.23472, Validation Err.:0.19819, Test Accuracy:0.92750, Max Test Accuracy:0.92750\n",
      "Nesterov-Epoch: 28, Train Err.:0.22421, Validation Err.:0.18927, Test Accuracy:0.93390, Max Test Accuracy:0.93390\n",
      "AdaGrad -Epoch: 28, Train Err.:0.07324, Validation Err.:0.10394, Test Accuracy:0.96330, Max Test Accuracy:0.96330\n",
      "RMSprop -Epoch: 28, Train Err.:0.17835, Validation Err.:0.24013, Test Accuracy:0.93630, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 28, Train Err.:0.03338, Validation Err.:0.08594, Test Accuracy:0.97440, Max Test Accuracy:0.97440\n",
      "\n",
      "SGD     -Epoch: 29, Train Err.:2.29800, Validation Err.:2.29744, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 29, Train Err.:0.22499, Validation Err.:0.19161, Test Accuracy:0.93020, Max Test Accuracy:0.93020\n",
      "Nesterov-Epoch: 29, Train Err.:0.21478, Validation Err.:0.18299, Test Accuracy:0.93610, Max Test Accuracy:0.93610\n",
      "AdaGrad -Epoch: 29, Train Err.:0.07145, Validation Err.:0.10316, Test Accuracy:0.96350, Max Test Accuracy:0.96350\n",
      "RMSprop -Epoch: 29, Train Err.:0.02845, Validation Err.:0.16085, Test Accuracy:0.95980, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 29, Train Err.:0.03040, Validation Err.:0.08523, Test Accuracy:0.97440, Max Test Accuracy:0.97440\n",
      "\n",
      "SGD     -Epoch: 30, Train Err.:2.29754, Validation Err.:2.29697, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 30, Train Err.:0.21594, Validation Err.:0.18552, Test Accuracy:0.93310, Max Test Accuracy:0.93310\n",
      "Nesterov-Epoch: 30, Train Err.:0.20573, Validation Err.:0.17704, Test Accuracy:0.93720, Max Test Accuracy:0.93720\n",
      "AdaGrad -Epoch: 30, Train Err.:0.06974, Validation Err.:0.10239, Test Accuracy:0.96380, Max Test Accuracy:0.96380\n",
      "RMSprop -Epoch: 30, Train Err.:0.02792, Validation Err.:0.15828, Test Accuracy:0.96130, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 30, Train Err.:0.02746, Validation Err.:0.08450, Test Accuracy:0.97490, Max Test Accuracy:0.97490\n",
      "\n",
      "SGD     -Epoch: 31, Train Err.:2.29701, Validation Err.:2.29642, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 31, Train Err.:0.20734, Validation Err.:0.17987, Test Accuracy:0.93520, Max Test Accuracy:0.93520\n",
      "Nesterov-Epoch: 31, Train Err.:0.19709, Validation Err.:0.17147, Test Accuracy:0.93880, Max Test Accuracy:0.93880\n",
      "AdaGrad -Epoch: 31, Train Err.:0.06815, Validation Err.:0.10168, Test Accuracy:0.96440, Max Test Accuracy:0.96440\n",
      "RMSprop -Epoch: 31, Train Err.:0.16350, Validation Err.:0.25763, Test Accuracy:0.93890, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 31, Train Err.:0.02490, Validation Err.:0.08450, Test Accuracy:0.97530, Max Test Accuracy:0.97530\n",
      "\n",
      "SGD     -Epoch: 32, Train Err.:2.29640, Validation Err.:2.29580, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 32, Train Err.:0.19935, Validation Err.:0.17455, Test Accuracy:0.93810, Max Test Accuracy:0.93810\n",
      "Nesterov-Epoch: 32, Train Err.:0.18893, Validation Err.:0.16627, Test Accuracy:0.94110, Max Test Accuracy:0.94110\n",
      "AdaGrad -Epoch: 32, Train Err.:0.06662, Validation Err.:0.10104, Test Accuracy:0.96500, Max Test Accuracy:0.96500\n",
      "RMSprop -Epoch: 32, Train Err.:0.02141, Validation Err.:0.15125, Test Accuracy:0.96140, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 32, Train Err.:0.02262, Validation Err.:0.08507, Test Accuracy:0.97580, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 33, Train Err.:2.29570, Validation Err.:2.29508, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 33, Train Err.:0.19193, Validation Err.:0.16955, Test Accuracy:0.93960, Max Test Accuracy:0.93960\n",
      "Nesterov-Epoch: 33, Train Err.:0.18109, Validation Err.:0.16139, Test Accuracy:0.94270, Max Test Accuracy:0.94270\n",
      "AdaGrad -Epoch: 33, Train Err.:0.06514, Validation Err.:0.10040, Test Accuracy:0.96530, Max Test Accuracy:0.96530\n",
      "RMSprop -Epoch: 33, Train Err.:0.02137, Validation Err.:0.15856, Test Accuracy:0.96120, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 33, Train Err.:0.02097, Validation Err.:0.08539, Test Accuracy:0.97520, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 34, Train Err.:2.29489, Validation Err.:2.29424, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 34, Train Err.:0.18498, Validation Err.:0.16488, Test Accuracy:0.94080, Max Test Accuracy:0.94080\n",
      "Nesterov-Epoch: 34, Train Err.:0.17368, Validation Err.:0.15684, Test Accuracy:0.94490, Max Test Accuracy:0.94490\n",
      "AdaGrad -Epoch: 34, Train Err.:0.06373, Validation Err.:0.09985, Test Accuracy:0.96570, Max Test Accuracy:0.96570\n",
      "RMSprop -Epoch: 34, Train Err.:0.15996, Validation Err.:0.31347, Test Accuracy:0.93310, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 34, Train Err.:0.01955, Validation Err.:0.08661, Test Accuracy:0.97520, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 35, Train Err.:2.29395, Validation Err.:2.29327, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 35, Train Err.:0.17851, Validation Err.:0.16048, Test Accuracy:0.94180, Max Test Accuracy:0.94180\n",
      "Nesterov-Epoch: 35, Train Err.:0.16684, Validation Err.:0.15265, Test Accuracy:0.94660, Max Test Accuracy:0.94660\n",
      "AdaGrad -Epoch: 35, Train Err.:0.06240, Validation Err.:0.09927, Test Accuracy:0.96600, Max Test Accuracy:0.96600\n",
      "RMSprop -Epoch: 35, Train Err.:0.04172, Validation Err.:0.16044, Test Accuracy:0.95570, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 35, Train Err.:0.01830, Validation Err.:0.08720, Test Accuracy:0.97500, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 36, Train Err.:2.29283, Validation Err.:2.29212, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 36, Train Err.:0.17243, Validation Err.:0.15633, Test Accuracy:0.94400, Max Test Accuracy:0.94400\n",
      "Nesterov-Epoch: 36, Train Err.:0.16029, Validation Err.:0.14876, Test Accuracy:0.94790, Max Test Accuracy:0.94790\n",
      "AdaGrad -Epoch: 36, Train Err.:0.06108, Validation Err.:0.09881, Test Accuracy:0.96650, Max Test Accuracy:0.96650\n",
      "RMSprop -Epoch: 36, Train Err.:0.02288, Validation Err.:0.15130, Test Accuracy:0.96100, Max Test Accuracy:0.96210\n",
      "Adam    -Epoch: 36, Train Err.:0.01720, Validation Err.:0.08735, Test Accuracy:0.97500, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 37, Train Err.:2.29150, Validation Err.:2.29077, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 37, Train Err.:0.16678, Validation Err.:0.15252, Test Accuracy:0.94540, Max Test Accuracy:0.94540\n",
      "Nesterov-Epoch: 37, Train Err.:0.15404, Validation Err.:0.14512, Test Accuracy:0.94920, Max Test Accuracy:0.94920\n",
      "AdaGrad -Epoch: 37, Train Err.:0.05982, Validation Err.:0.09829, Test Accuracy:0.96660, Max Test Accuracy:0.96660\n",
      "RMSprop -Epoch: 37, Train Err.:0.01641, Validation Err.:0.15179, Test Accuracy:0.96230, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 37, Train Err.:0.01607, Validation Err.:0.08739, Test Accuracy:0.97530, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 38, Train Err.:2.28992, Validation Err.:2.28914, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Momentum-Epoch: 38, Train Err.:0.16153, Validation Err.:0.14891, Test Accuracy:0.94610, Max Test Accuracy:0.94610\n",
      "Nesterov-Epoch: 38, Train Err.:0.14825, Validation Err.:0.14177, Test Accuracy:0.95140, Max Test Accuracy:0.95140\n",
      "AdaGrad -Epoch: 38, Train Err.:0.05856, Validation Err.:0.09782, Test Accuracy:0.96690, Max Test Accuracy:0.96690\n",
      "RMSprop -Epoch: 38, Train Err.:0.01397, Validation Err.:0.16313, Test Accuracy:0.96130, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 38, Train Err.:0.01479, Validation Err.:0.08712, Test Accuracy:0.97560, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 39, Train Err.:2.28801, Validation Err.:2.28719, Test Accuracy:0.11360, Max Test Accuracy:0.11360\n",
      "Momentum-Epoch: 39, Train Err.:0.15648, Validation Err.:0.14556, Test Accuracy:0.94710, Max Test Accuracy:0.94710\n",
      "Nesterov-Epoch: 39, Train Err.:0.14277, Validation Err.:0.13866, Test Accuracy:0.95230, Max Test Accuracy:0.95230\n",
      "AdaGrad -Epoch: 39, Train Err.:0.05739, Validation Err.:0.09737, Test Accuracy:0.96690, Max Test Accuracy:0.96690\n",
      "RMSprop -Epoch: 39, Train Err.:0.02124, Validation Err.:0.15782, Test Accuracy:0.96080, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 39, Train Err.:0.01373, Validation Err.:0.08674, Test Accuracy:0.97550, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 40, Train Err.:2.28569, Validation Err.:2.28480, Test Accuracy:0.11690, Max Test Accuracy:0.11690\n",
      "Momentum-Epoch: 40, Train Err.:0.15166, Validation Err.:0.14237, Test Accuracy:0.94890, Max Test Accuracy:0.94890\n",
      "Nesterov-Epoch: 40, Train Err.:0.13763, Validation Err.:0.13579, Test Accuracy:0.95290, Max Test Accuracy:0.95290\n",
      "AdaGrad -Epoch: 40, Train Err.:0.05628, Validation Err.:0.09692, Test Accuracy:0.96720, Max Test Accuracy:0.96720\n",
      "RMSprop -Epoch: 40, Train Err.:0.01887, Validation Err.:0.17953, Test Accuracy:0.96080, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 40, Train Err.:0.01423, Validation Err.:0.08902, Test Accuracy:0.97550, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 41, Train Err.:2.28282, Validation Err.:2.28187, Test Accuracy:0.13060, Max Test Accuracy:0.13060\n",
      "Momentum-Epoch: 41, Train Err.:0.14708, Validation Err.:0.13941, Test Accuracy:0.95020, Max Test Accuracy:0.95020\n",
      "Nesterov-Epoch: 41, Train Err.:0.13284, Validation Err.:0.13308, Test Accuracy:0.95420, Max Test Accuracy:0.95420\n",
      "AdaGrad -Epoch: 41, Train Err.:0.05518, Validation Err.:0.09652, Test Accuracy:0.96750, Max Test Accuracy:0.96750\n",
      "RMSprop -Epoch: 41, Train Err.:0.01823, Validation Err.:0.16061, Test Accuracy:0.95930, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 41, Train Err.:0.01692, Validation Err.:0.09520, Test Accuracy:0.97390, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 42, Train Err.:2.27925, Validation Err.:2.27820, Test Accuracy:0.15500, Max Test Accuracy:0.15500\n",
      "Momentum-Epoch: 42, Train Err.:0.14273, Validation Err.:0.13656, Test Accuracy:0.95170, Max Test Accuracy:0.95170\n",
      "Nesterov-Epoch: 42, Train Err.:0.12842, Validation Err.:0.13059, Test Accuracy:0.95520, Max Test Accuracy:0.95520\n",
      "AdaGrad -Epoch: 42, Train Err.:0.05416, Validation Err.:0.09617, Test Accuracy:0.96780, Max Test Accuracy:0.96780\n",
      "RMSprop -Epoch: 42, Train Err.:0.01753, Validation Err.:0.16497, Test Accuracy:0.96070, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 42, Train Err.:0.01751, Validation Err.:0.10192, Test Accuracy:0.97230, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 43, Train Err.:2.27473, Validation Err.:2.27357, Test Accuracy:0.19250, Max Test Accuracy:0.19250\n",
      "Momentum-Epoch: 43, Train Err.:0.13867, Validation Err.:0.13391, Test Accuracy:0.95190, Max Test Accuracy:0.95190\n",
      "Nesterov-Epoch: 43, Train Err.:0.12420, Validation Err.:0.12831, Test Accuracy:0.95550, Max Test Accuracy:0.95550\n",
      "AdaGrad -Epoch: 43, Train Err.:0.05315, Validation Err.:0.09582, Test Accuracy:0.96800, Max Test Accuracy:0.96800\n",
      "RMSprop -Epoch: 43, Train Err.:0.01176, Validation Err.:0.17764, Test Accuracy:0.96170, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 43, Train Err.:0.01311, Validation Err.:0.09795, Test Accuracy:0.97300, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 44, Train Err.:2.26893, Validation Err.:2.26763, Test Accuracy:0.23160, Max Test Accuracy:0.23160\n",
      "Momentum-Epoch: 44, Train Err.:0.13481, Validation Err.:0.13142, Test Accuracy:0.95260, Max Test Accuracy:0.95260\n",
      "Nesterov-Epoch: 44, Train Err.:0.12018, Validation Err.:0.12616, Test Accuracy:0.95590, Max Test Accuracy:0.95590\n",
      "AdaGrad -Epoch: 44, Train Err.:0.05221, Validation Err.:0.09550, Test Accuracy:0.96820, Max Test Accuracy:0.96820\n",
      "RMSprop -Epoch: 44, Train Err.:0.02055, Validation Err.:0.21524, Test Accuracy:0.96050, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 44, Train Err.:0.01011, Validation Err.:0.09299, Test Accuracy:0.97580, Max Test Accuracy:0.97580\n",
      "\n",
      "SGD     -Epoch: 45, Train Err.:2.26138, Validation Err.:2.25988, Test Accuracy:0.26570, Max Test Accuracy:0.26570\n",
      "Momentum-Epoch: 45, Train Err.:0.13131, Validation Err.:0.12913, Test Accuracy:0.95330, Max Test Accuracy:0.95330\n",
      "Nesterov-Epoch: 45, Train Err.:0.11642, Validation Err.:0.12419, Test Accuracy:0.95610, Max Test Accuracy:0.95610\n",
      "AdaGrad -Epoch: 45, Train Err.:0.05132, Validation Err.:0.09516, Test Accuracy:0.96850, Max Test Accuracy:0.96850\n",
      "RMSprop -Epoch: 45, Train Err.:0.15238, Validation Err.:0.32637, Test Accuracy:0.93070, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 45, Train Err.:0.00816, Validation Err.:0.09157, Test Accuracy:0.97590, Max Test Accuracy:0.97590\n",
      "\n",
      "SGD     -Epoch: 46, Train Err.:2.25138, Validation Err.:2.24962, Test Accuracy:0.29880, Max Test Accuracy:0.29880\n",
      "Momentum-Epoch: 46, Train Err.:0.12793, Validation Err.:0.12690, Test Accuracy:0.95470, Max Test Accuracy:0.95470\n",
      "Nesterov-Epoch: 46, Train Err.:0.11282, Validation Err.:0.12229, Test Accuracy:0.95720, Max Test Accuracy:0.95720\n",
      "AdaGrad -Epoch: 46, Train Err.:0.05040, Validation Err.:0.09490, Test Accuracy:0.96880, Max Test Accuracy:0.96880\n",
      "RMSprop -Epoch: 46, Train Err.:0.02356, Validation Err.:0.17880, Test Accuracy:0.96000, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 46, Train Err.:0.00721, Validation Err.:0.09468, Test Accuracy:0.97480, Max Test Accuracy:0.97590\n",
      "\n",
      "SGD     -Epoch: 47, Train Err.:2.23791, Validation Err.:2.23581, Test Accuracy:0.32260, Max Test Accuracy:0.32260\n",
      "Momentum-Epoch: 47, Train Err.:0.12461, Validation Err.:0.12479, Test Accuracy:0.95550, Max Test Accuracy:0.95550\n",
      "Nesterov-Epoch: 47, Train Err.:0.10946, Validation Err.:0.12048, Test Accuracy:0.95730, Max Test Accuracy:0.95730\n",
      "AdaGrad -Epoch: 47, Train Err.:0.04944, Validation Err.:0.09460, Test Accuracy:0.96890, Max Test Accuracy:0.96890\n",
      "RMSprop -Epoch: 47, Train Err.:0.01843, Validation Err.:0.17483, Test Accuracy:0.95740, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 47, Train Err.:0.00699, Validation Err.:0.09929, Test Accuracy:0.97400, Max Test Accuracy:0.97590\n",
      "\n",
      "SGD     -Epoch: 48, Train Err.:2.21949, Validation Err.:2.21693, Test Accuracy:0.33610, Max Test Accuracy:0.33610\n",
      "Momentum-Epoch: 48, Train Err.:0.12145, Validation Err.:0.12283, Test Accuracy:0.95590, Max Test Accuracy:0.95590\n",
      "Nesterov-Epoch: 48, Train Err.:0.10632, Validation Err.:0.11882, Test Accuracy:0.95770, Max Test Accuracy:0.95770\n",
      "AdaGrad -Epoch: 48, Train Err.:0.04856, Validation Err.:0.09435, Test Accuracy:0.96910, Max Test Accuracy:0.96910\n",
      "RMSprop -Epoch: 48, Train Err.:0.01105, Validation Err.:0.18191, Test Accuracy:0.96160, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 48, Train Err.:0.00694, Validation Err.:0.10309, Test Accuracy:0.97330, Max Test Accuracy:0.97590\n",
      "\n",
      "SGD     -Epoch: 49, Train Err.:2.19402, Validation Err.:2.19086, Test Accuracy:0.33860, Max Test Accuracy:0.33860\n",
      "Momentum-Epoch: 49, Train Err.:0.11848, Validation Err.:0.12097, Test Accuracy:0.95670, Max Test Accuracy:0.95670\n",
      "Nesterov-Epoch: 49, Train Err.:0.10323, Validation Err.:0.11721, Test Accuracy:0.95820, Max Test Accuracy:0.95820\n",
      "AdaGrad -Epoch: 49, Train Err.:0.04767, Validation Err.:0.09413, Test Accuracy:0.96920, Max Test Accuracy:0.96920\n",
      "RMSprop -Epoch: 49, Train Err.:0.02792, Validation Err.:0.19250, Test Accuracy:0.95830, Max Test Accuracy:0.96230\n",
      "Adam    -Epoch: 49, Train Err.:0.00680, Validation Err.:0.10461, Test Accuracy:0.97380, Max Test Accuracy:0.97590\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = mnist_data(\"/Users/yhhan/git/aiclass/0.Professor/data/MNIST_data/.\")\n",
    "(img_train, label_train), (img_validation, label_validation), (img_test, label_test) = data.load_mnist(flatten=True, normalize=True, one_hot_label=True)\n",
    "\n",
    "optimizers = {}\n",
    "optimizers['SGD'] = SGD()\n",
    "optimizers['Momentum'] = Momentum()\n",
    "optimizers['Nesterov'] = Nesterov()\n",
    "optimizers['AdaGrad'] = AdaGrad()\n",
    "optimizers['RMSprop'] = RMSprop()\n",
    "optimizers['Adam'] = Adam()\n",
    "\n",
    "num_epochs = 50\n",
    "train_size = img_train.shape[0]\n",
    "batch_size = 1000\n",
    "learning_rate = 0.1\n",
    "\n",
    "networks = {}\n",
    "train_errors = {}\n",
    "validation_errors = {}\n",
    "test_accuracy_values = {}\n",
    "max_test_accuracy_epoch = {}\n",
    "max_test_accuracy_value = {}\n",
    "\n",
    "for key in optimizers.keys():\n",
    "    networks[key] = TwoLayerNet(input_size=784, hidden_layer1_size=128, hidden_layer2_size=128, output_size=10)\n",
    "    train_errors[key] = [] \n",
    "    validation_errors[key] = []\n",
    "    test_accuracy_values[key] = []\n",
    "    max_test_accuracy_epoch[key] = 0\n",
    "    max_test_accuracy_value[key] = 0.0\n",
    "\n",
    "num_batch = math.ceil(train_size / batch_size)\n",
    "epoch_list = []\n",
    "\n",
    "for i in range(num_epochs):\n",
    "    epoch_list.append(i)\n",
    "    for key in optimizers.keys():\n",
    "        for k in range(num_batch):\n",
    "            x_batch = img_train[k * batch_size : k * batch_size + batch_size]\n",
    "            t_batch = label_train[k * batch_size : k * batch_size + batch_size]\n",
    "            networks[key].learning(learning_rate, x_batch, t_batch, optimizers[key])\n",
    "\n",
    "        train_loss = networks[key].loss(x_batch, t_batch)\n",
    "        train_errors[key].append(train_loss)\n",
    "\n",
    "        validation_loss = networks[key].loss(img_validation, label_validation)\n",
    "        validation_errors[key].append(validation_loss)    \n",
    "\n",
    "        test_accuracy = networks[key].accuracy(img_test, label_test)\n",
    "        test_accuracy_values[key].append(test_accuracy)\n",
    "        if test_accuracy > max_test_accuracy_value[key]:\n",
    "            max_test_accuracy_epoch[key] = i            \n",
    "            max_test_accuracy_value[key] = test_accuracy\n",
    "        print(\"{0:8s}-Epoch:{1:3d}, Train Err.:{2:7.5f}, Validation Err.:{3:7.5f}, Test Accuracy:{4:7.5f}, Max Test Accuracy:{5:7.5f}\".format(\n",
    "            key,\n",
    "            i,\n",
    "            train_loss,\n",
    "            validation_loss,\n",
    "            test_accuracy,\n",
    "            max_test_accuracy_value[key]\n",
    "        ))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O6tWrKSoqYubMmcybN48BAwYwb948Zs2axauvvuquz4UXXkhqaiqpqan07NmT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fmY8/Iw5eX6/5ubhzk/j9aKtez/1a+6zCY6W4LZ1GPBebQAMrik19HilkCwD8n8KIQQor/1\n2b/AlVJG4H7gXKAGWKWUeklrvTnstPOBsYE/c4AHA/8NOlNrfbCvnlH0jmG5dsbMyKF6YwMX3FRO\naXlWzPPzRqaz8IYpvPLABl59aCMX3jIVoym+f9z3FpfDS1JqYtlIi81Ic6Dn3YjJ5YyYXI6juZn7\nv35Fl9e6nV5aGp3t9lH2t9AewQTaR4A/e5c8LPoyTp9bY7YaMcYRsNmCewTj6Fvnb1If/19ZSclm\nGmqb4z4/KFqri67EyiYCGFOSMY4bh2vvXjLmzw+d07p2bcQA0pCWSvpFF+Petw/Pvn04Nm+OWMym\nI+1wsO+Xv/LvYczNxZSTg6PJP87B8Y61HNVa5P8r2NnqJnW4LeFxEF2T+VEIIcRA0JepmNnADq31\nLgCl1DPAJUD4RHcJ8KTWWgMfK6WGKaXytdZ1ffhcog+cc81EPG5v3PvNSiZnctbV43nriS289cQW\nzv3aRJSh5/uR4m1i73Z6Sc9OLNsRqhoaxpYSu03Hi7//BWUnn8b2VUnU7Wrjqp+fTErGwNhX6Gzz\nYDAqTOb4gvBgwNhVU3mvqxtZxjiXhsb7+wVgTTHjaD5+RU+6m02MFkDm3XFHp2t9Lhee+no8+/bh\n3refvd/9bsR7+hobqf3WraHPD2ROgSnf4OAdP0AP07RWVLT7ehBYjnr3n7A/9DwAjpbIP5OBXBl1\nEJH5UQghRL/ry0CwENgT9nkN7d/NjHZOIVAHaOBNpZQX+KvW+uE+fFbRQ0azAWOcAUXQ+JPzaWl0\n8vGLu2g76uKcayfGzDSF83l9bP6gjqaDbaQOt5Ey3EbqcCvD8uyhpvVFZRnUbDscsYm9y+FJqGoo\ngNlmCvUfjNf+XTvYufoTwIjBPIo3/raPC265GEuSnQdvuCrqnsTjsbfQFciwxVsQJDwjGIs/EIyz\nAE2CS0Pj2eMWlJRixuOK3pqiL3Qnm5hIAGmwWLAUFWEpKvJfc/fdkZej5uZS/OADuOvr8eyv5+h2\nF9RCUk4G3oNV6La2iM/iqatj341fg/Hfoub392DIdmLKzTmWWdy2nYa//hXt9K//TbSwjQSRITI/\nCiGE6HcDuVjMqVrrWqVUDvCGUmqr1vrdjicppW4AbgDIzc1lxYoVXd64ubk5rvOGquM5PtqqKZil\nqK04zD/u/IDCOYrUwtiBSdshzd5VGsdhQAEd6nMoAyz+81psGeBugeJ5ih1169kR9j66o8XH/gN1\nrFixP+5n3X/Ah6MVmptb4x6fsZddw/YltbiatuF1fsqu1f/kvv95mrQRpVH3ELY2Hjku419T5cML\ncX8tZ6N/oNet3sBnB6L/jFxtXrzG+H+HlBF2bPuMZnt1zPMOHfBhSY3/eQ/V+p93+ZvvYrYPnOqX\nEf//Sk2FO+8IfVoLEMf3aVuwgNSnnsIQVmzGZ7HQsGgRtfX1/gO5ORw8oqFWU3vp+Rgtiqwf/z+M\nhw51up8vKQnnuFIAGo+0cqjyXQxNTaiwthkdaYeDmp/cwfYVK/ClpeFLT8ebnu7/OC0NzIFlqZ+s\nbPesnr17qfl/P2HL5i045syOPT6iI5kf+4mMT2wyPrHJ+MQm4xNbX4xPXwaCtUBx2OdFgWNxnaO1\nDv63Xin1Av6lNJ0musA7oQ8DzJw5U88P23sTzYoVK4jnvKHquI/PmXD4ghZef3QTu99rZsoZBcz7\nwphOWRyXw8MnL+3is+U1JKVZWHjDOEZNy6b1qIvmQ06aDzs4eshB8yEnVZUHaaxvI2dEKou+NLNd\n1kv7NJv+vZxRY0qZM39U3I/5ydFdNGyrIjk5ud34bPnX36Jm9vKTxrPFAYtuXkBuaQpP/vgFUjNq\ncDRtjfm1jsf4L9m4HqtyMX/+rLjObzniZMerHzB65Dgmn14Y9bwdr75N3ohs5s/v3KcvkqpX3ycn\nM5P58yfEPu+1DygaMbzL84J2pR+gbtVGpk2ZSXZxalzXHA+9+v/X/Pk0TpzQZZbtk6Zd1K+r4qxz\n5qMMisYf/TDictSin99F2YJFrP3WO2RddxOTF/4O7fXiaWjAs7+eqi9+MeJjGJxOUpa+GrFqqjE9\nHVNONq7duztVRzW4XGQuW8bYH3w/dGyI/P0s8+MgJeMTm4xPbDI+scn4xNYX49OXgeAqYKxSaiT+\nyesK4MsdznkJuCWwP2IO0Ki1rlNKJQMGrfXRwMfnAT/vw2cV/SwjL5nLvj+TjxbvZP2be9hRUc/c\nz41m4ikFAOxad4Dl/9yKo9nN5DMKmfu50aGlisnpVpLTreSO9Depr9l2mG0r95E7Mo39nzXx7jPb\nOePKstDXcru8oEl4aajF6v96vg4rI6Mt43Q7vTx150fkjkxj9PRslFKUnz2LjSsKuPLOW3js1suj\nfq2qdWsonDi5T/sUutrcCRVf6Ys9guCvZBlf+wh33IVtwF81FDiu+wT7QzzLUR0tbqx2c2gfblfL\nUY1mQ6jSqDIaMefkYM7JwVRQEL3P4uvL8DQcwnPwAJ4Dnf84P90R8dk8dUNyy5vMj0IIIfpdnwWC\nWmuPUuoWYBn+8tiPaa03KaW+EXj9IWAp/tLYO/CXx/5a4PJc4IVAFscE/Etr/VpfPasYGIxmA6de\nNpYRE4az7NFNLP/HVuqrmmg76mbXugMoA5x2+TjKzyyKeo/wPYGFY4fxwt0VVL5Tiz3NwqwL/P3f\ngvv8LAmWxg8Gjh0DwWjWvbmblkYXC26YEspITl9Qwub397J6aVXMa5//zU8xmS0UTphEaflJlE6d\nTmZxSeg+vbG/0Nkau/pnRyazAYNRdVnhM+FAMMnU5R5Br8eHx+VLaI9gqA3CCR4IxsPZ0jnojxVA\n2uwmHBF+JjH7LJpMmHNzMOfmRLznp2edHTmIzM9P5Fs5Icj8KIQQYiDo0z2CWuul+Cez8GMPhX2s\ngZsjXLcLmNqXzyYGrhGTMvnKz+byygMb2PTeXpQBTBYD5980hRETMmNeW1/VFCoUA3DRt6bx7K9W\nsnppFSWTM8kpSQtV/jQn2L8w2FPNF0dc0dLopOL13Yyenk3+6PTQ8eR0K+VnFlHx+u6Y13/+R3dR\ntb6CqvUVvPPPx3jnn4+RMjyT0qnTKZ06Peb+wni52jxxN2cHUEphSTLFzN553T60N/5iMeAPGpuP\nOGOeE2omH+d9K5ZVk56dBPizYRC5euxQ4Wz1tOsh2BVrsjmUEQzXkz6LXbXXGGpkfhRCCNHfBnKx\nGDGE2dMsXPaDGSx7ZBM7K+qZds6ILoNAoNM/8s0WI5/7znSe/90aXrl/A5f9cCYuh/8fuIk2yw4F\ngnFkBFe+/Bk+j4+5nxvd6bWTzi1h4zu1uC0peF2d+9zZ04cxctoMRk6bAUDTwQNUra+gen0Fn678\nkMrlbyT03NE42zxYEgjYwJ+9i7U0NJhFssXRTD7IYjfh3NsS85xgxjDeTGNOaRrLHqkEoK3ZHbV6\n7FDhaHGHlsrGw5ZsDgXQHXW3z2JPgkghhBBC9D4JBMWAVbv9CLXbDzNzUSmV79ZSWJbRrj9gvJLT\nrVxwSzn//b81vHzfeuZe4i8QY0l0j2CcS0Mb9jaz5f29TDmzKGIDeVuKmWlnF7PqlRv4yq9mkT0i\ndiGTtKxsys9eQPnZC/B5vezbuZ2n7/he1PMdLc3YkmP3NwwutUwkIwj+QCzW0tBEM3fBc7vad5jo\nfYvKMlhw/WQW/2ktVRsOsnFFTbtM8VDjaPWQHuF3MRpbspkj9a29/hzdDSKFEEII0fsSa/wmxHES\nnsGZc/EoFlw/mWWPVFKz7XC37pdZkMLCG6dwZF8rK57aBnRnaWigWEwXS0M/+u9OzDYTsxaNjHrO\n1HNGYLWb+GTJroSewWA0UjAudtXMB677Mv/+2Q9Zufg/NNTsQUeo5BgMvBLZywf+BvCxgjZnS2KZ\nOwjsEWzzoH2dnzN039bEn7eoLIP0nCQO7D7KhHn5QzYIBP/PJZH9ldZkU+hnKYQQQogTk2QExYDU\nca9fMMNTX9XU7X/QF08YzhlfKWP5P/ytGxJeGholI1ixrJqc0jSKyjLYs+UQ1ZUNTDqtgM0f7I26\nH82aZOKk80bw8Yu72LerkbxR6RHP647Zl1zGropVvPevx3nvX4+TkV/ImFlzGTNrLov/8Kt2+whf\nf9D/J94iM9YkEy2N0TNFoYAtkf1odhNof3uQaBm/RJeGgv/NhLaj/us2rqhhxKTMIRkMap/G2ZbY\nHkGb3Yyji6JAQgghhBjcJCMoBqTpC0o6/aO9qCyjx4U+Jp5SwIzzSzCaDNjTLAldG1wa6u3w7+Pg\nfrQ9Ww7xwfM7SEozs3PtAXJK02Lez+fVWJKMfPLSsaxgzbbDVCyL3VgdwJwUeTmpOSmVU6/4Kl/9\nv3u5/v6/c/bXv0ladg5rXnmRp+/4Xo+LzFiSTLhiVPjsTsAWPLc3l5wGM8rnf2MKReMzMJgMLHu4\n+xnlwczZ5gF9rIpqPKzJJrxuHx6Xtw+fTAghhBD9STKCYsiZe8loZpxfitnSzWIxYXGQz6fJyLMz\n9+JRLH1wAx6XD7PNyKKbyrvMPuWPGUbFsmpqth6mdtthNMRd0OTzP3ogdG5RWUbEYihpWdlMO28R\n085bhKOlmc/WrmbpvX9I6HvuyGI34XREDw6CWSRbgnsEoatAMBBgxlmEJjyjrBS8ePdapswv6lFG\nebBKdOwgrPVGi5uUBP8/EUIIIcTgIIGgGJISDQLhWCB4eKfmud+soqXRRWuTq9PetqlnFcUVbBSV\nZbDwhsm8fP8G3nx8Mx63L+6CJkVlGZx59Xhevncd6Tl2jjY4OOfaCVGvtSWnMOHU+TEDwYpXlzDh\ntPkkpUQvXmNNMuFxevF6fRiNnRcUBPeVJdL4PViwJlYvQUerB5PVGPFrRhKeOS4cl0H+mHQ+W3+A\nq35xctzPdaJwtHSvgE/w2pShFTcLIYQQQ4YsDRUiTgajgdIpmSiDP2NSPHE40xeM4PQrxjH7wpFY\nkkxMXziCynf3xr0EsWRyFiMmZdJ82ElyuoX8OPcKNh928sniXXi9mkN7W3A7vbz6cCXP/XY1K5fs\nYt9njfhiFF+JZPnjf+WvN17Ny3/+HVXrK/B5O2f+LIGgzd0WOSvobPVgMIPBoOL+usFMVaz+hM7W\nxHoedjRzUSnNh51s/aiu2/cYrIIBdiLFYoKtJqRgjBBCCHHikoygEAm44OaprFixgvnzp4WOtduP\nVpZB8YTMdss2Ywk2OS8YO4y9nx7h2d+s4tLvTI/Z8+3I/lZe+ss6Wo+6sNiMlM8vYsOKWkZNy+Lw\nvlZWLa1i1StV2FLM5JamkZJhJSXDSvIwW8xnufp391C5/A22vLecbR+9R1JqGqNnzmXc3FMYMbmc\nh7/5tdBewvu/fnfouvBCM85WD8bEtl6GgsuYS0Nb3AlXOA1XPGE4OSWpVCyrZsK8fAxxZhZPBMHG\n8AkViwkE544YWVohhBBCDG4SCArRQ92tcBq+r6+oLINPFu9i9atVPPOLT7jk2yeRkZfc+WtVN/Hy\nfevxenwYTYZQ8Fk4fnjoXlmFKeze0kB1ZQOH9rZQX90Uqp6JsoPuXPXTnj6MnNJRnPW1Gzn9K19j\n19pVfPrJh2z/+D0ql7+O1Z6MszVy0/fwQjPOVnfCgaAtrj2Cnh4FgkopZl4wkqUPbODTVfspm5vf\n7XsNNsHG8AkViwn+TFqkcqgQQghxopJAUIgeilTJtKgso8tsYMcAcs4lo0hKs/DRCzt4/v/WsPDG\nKe3uUbP1EEsf3Igt2UzZ3HxKJmdGDT7Hzcpj3Ky80LUet5eWIy5ajpxE82Enmz+oo3bbYWYuKmXO\nxaPaPZfJYmHcnFMYN+cUPC4X1RvX8eknH7LpnTe7HAtHiwdj/PEG4N97qVTsPYLOVg+pmbEzml0p\nnZJJZlEKa16rZuzsvISWrw5m3ankGl4spreEt1kJCmbEe1oNWAghhBCJGzrro4QYYCK1yCg/s4gr\n75yDPd3Kkr+sY/MHewHYWVHPkvvWk5pp4wvfn8EpXxiTUHsNk9lIenYSBWMzsKdbaahtZuaiUirf\nrY25n9FksTB6xmwWfvO2mN/L2tdeo+VIoz8jaI3nuz9GGRQWuwlXrIxgW2IN0SN+HaWYeX4ph/e1\nsrOivkf3GkxChXZM8f91b7IYMJhUrwaCwTYrVRsOAscy4l21WRFCCCFE35CMoBADTFpWEl/4/gyW\nPbyR5f/YymfrD1K18SD5o9JZ9M3yhJb4ddRxOWphWUbc+xljefvv9/H23+/HYComJX8Uh/aWkZFf\ngFKKB2+4KmKfwvC9hdYkU8wG5v6lod3/voNGnZRNRp6dNa9WMWZ6DmoIZAWdLYkH0UopbHZzrxaL\nKSrL4OxrJvDKAxsoHJdBQ21zj3/vhBBCCNF9khEUYgCyJpm44JapTDq9kKoNBymZnMlFt07rURAI\nsfcz9sTsz/+I4innYk1y0LRnBX//9o08+q3rePNvD8TVxN5qN+OKUjXU5/XhdngT6oMXjcGgmHF+\nKQ21LVRtPNjj+w0GjhZPQoVigmwp5pjBeXfUVx8FDbXbDjP59EIJAoUQQoh+JBlBIQYoo9HAGVeO\nY9JpBWQWJPdKpcvu7mcEsKWk42hujHj8tMtPAU4BYNlLL5Jrs1C1fg2b3307ruey2k1R9wgG20r0\npFhMuLEzc1i5ZBerl1ZRWp6FUid2VtDZ6g5VAU2E1W7q1Yxga5OLNcuqMZgU088rofLdWgrj/N0T\nQgghRO+TQFCIAUwpRXZx9Abvx9PJX/p11GIf4axpw5g2fz7TzluE1+Pmz1+5NOo9P3nhWYomTsFs\nhZYj7bNP91x7Je62o6HP33jI/8eclMq3Hn+629+HwWhgxsJSlv9zK3s2H2LEpMxu3yvcQC2G4mjx\nkJFnT/g6W7KZw/ta8Xl9vfImxPJ/bsXn9nH2NeMZf3JBry1LFkIIIUT3yNJQIURcIhW3iVWgBsBo\nir0k8f1nnuSZO7/H1nd/zoHPnmT5E49QufwN9u/a0S4IDBfteCLK5uaRkmHlvWc/pWFvc4/vB8eK\noQSL7wyUYijO1gHLnmsAABATSURBVO4V2skdmcaR/a0888tVVFc2oLXu9jMc2d9K9caDlJZnMf7k\nAqD3liULIYQQonskIyiE6Dc3PfIUtVs2sXLJ++zftY21r76C1j3fl9ZVgRqjycDZ107k9b9V8txv\nVjPv82OYMr+wR8tEi8oyOPOqMl6+bz25JakcrG1hwfWTusx29WUmUWuNs5t7BKcvKCEjL5kPn9/B\ny/etp3hCBsMLUigtz0r4WT9evBOjxcj8r5S1Ox7vsmQhhBBC9D4JBIUQfcqePixqUGZPS2fsnHmk\n502m4rVqvF4vrtZDOFrq2Ls5+vLPx267gYyCIoYXFDG8sIiM3ALS8/JIGTYcZTDEVaCmqCyDy38y\nm7ef3Mp7/95OdWUDZ311PMnpCfa/CDiw+yjvPfspXrePvTv8eylffWgjBWOG+ffCjc8gK8Iy34+e\n/XHUvZfTFzzVrWcJqtrYgNfjw55mSfhapRSjpmVTMjmTyndqWfXKZ+zZcpjKd2o5538mMGZ6brsq\ntNHs+6yRnRUHmHlBabfHVgghhBC9TwJBIUSfCraIiCWnJI2FN05pd+yPl0cPBLNHjOTQ3hqq11fg\n9RzLIJrMFtJz8+J+tuR0KxfeUk7lO7V88PwOHvrG1eBr7XReV/sSt35cx4qntmG2GrEkmZh4aj6b\n3ttL0fgMjuxv46MXdgL+AiyGJB/NleuxJpuw2c0Rg0AAR3MjWutuZyl3rKnnjUc3kVOSyviT87t1\nDwCjycDUs4spm5vH6qVVbFi+h2UPb6JyXC0Ne1ti7vHTWvPRf3eSlGrmpHNHdPsZhBBCCNH7JBAU\nQgw6F33nRwD4fF6a6us5sr+OI/v3cWR/HY3762io2R312ie/dwup2TmkZeWQlp1DenYO2UU5XPy/\nZTx9R+cgEKLvS/R6fXzwnx1sXF5DVlEyzYednP+NKRSVZVAyOSuULcvIs1O7/TC1Ww9TvaOO1iYX\nh/e14GiJvQz2nmvvxGB0oZQLi83CKV/6PBNPn91lcLjt4zreemILeaPSueCWqViTev5XvS3ZzKlf\nHMvkMwp55f4N1G4/QlqWjayilKjXVFc2sPfTI5x+xTgsNpluhBBCiIFEZmYhxIBkTkqNGICZk44t\nrzQYjAzLy2dYXvuM1x8vvzDqfVOzsmk6UE/N5kpcbZEDv0jcLidmy7Glja1NLl57eCN1OxqZek4x\nFUvuxO04yr/vbH/df3/jzyaOm5XHuFl5rFixn/nzZ+H1uKnZson//DL619TeraBsKGMSzYdqee2B\nX/DBs6Wcec3VjJk1m4duvDryMlhlZ/ScH3HBN8sxW41xf4/xaD7sxNHipmRKJtUbG3j6ro+5+NaT\nyCxsHxD6fJqPXthJWnYSE08t6NVnEEIIIUTPSSAohBiQetIiIpZLf/DT0MeOlmaaDtT7/xysZ/nj\nD0e97p6rv4DFPoz0nDxSM3Op2+nD501m+sJJTDjFzifPRa9y6nY5cbW24mxt4eDWjbxU8SHVG9bi\namuL+ay3/fO50MdHG47yyr3/pnbbW7z0x18wLK8k6l5IdCsX3lyOydK7QWD4nsCisgw2LK/hvWe3\n8+xvV3Pe/0xk9Ek5oXO3fVzHob0tnHfdJIwmKVAthBBCDDQSCAohTjixCtSEsyWnYEtOIad0FEDM\nQDA9/0yaG/ZzcM8RDlTvAd0CwEfPvcZHz0W9DPAHkeFShmcyft4ZjJw+i8W//0U83xKpmalc8bPr\nqN54Mcsefp7G+g9int/bQSBAfVVTuz2B5WcWYU8z88F/dvDaXyuZuaiU2ReOxOvxsXLJZ+SUpDJm\nRk4XdxVCCCFEf5BAUAhxwomnQE2irvvz7TjbPNRuO8yR+lbKZmXjdjbSdPAARxsO8NoDf4p67alX\nXoM1yY7Fbqdq/wHO/8IXQ/v84g1ag0qm5PC1P97AqiXn8NGz3+6dby5OkVpEjJmRS2l5Fu88vZ3V\nS6s4uOcoWcWpNB92cs61E3vUkkMIIYQQfUcCQSGECOhqX6I1ycSoadlhr9hD+xNjBYJzPvfF0Mf1\nK1a0C466E7SaLUbmfWEsHz0b/ZzFf/gl4+aeyqjps7Ha7e1e66rPYqJMZiNnXT2e7OJU3n/uU6o2\nNlAyOZNC6REohBBCDFgSCAohREBf7UvsD/t2bGfHqo8xmkzkjh5H/pix5I0pI39MWVx9FiPpKoAs\nP7OIzIJkVi2t4pTLxiR0rRBCCCGOLwkEhRCiFyS6xLOvv+YNDzzO3k+3sWPVR+zdtoX1r7/KmlcW\n9+jrxRNAFpZlRMwEdjf4FEIIIUTfkEBQCCF6QX9ktS66/d52VTzDq3oqg4HCsgkUlk0AwOvxcHB3\nFft2bufNvz0Q9Z4P3nAVKRmZpAwfTlJqGkaTGYPJhNEUe7qoWLoYlPIve1UKg8GIwRj4Y5CqoUII\nIcRAI4GgEEIMUh2reBaVZbDg+snUVzWFjgUZTSZyR40hd9SYmIHg6JlzaDl8iKOHGjiwuwqf14vX\n7cbr8cR8luVPPNLzb0gIIYQQx40EgkIIMUhFquJZVJbRKQhMxHk3/G/U1/54+YVRX7v50WfQ2ofW\nGrTG5/Ph83rRPi8+r5fHbrux288khBBCiN7Xp+t1lFILlVLblFI7lFI/jPC6UkrdE3h9g1JqerzX\nCiGE6J5o+xZ7sp/RlpJCUmoa9rR07OnDSMkYTlpWNuk5eWTkF3b7vicqmR+FEEL0tz7LCCqljMD9\nwLlADbBKKfWS1npz2GnnA2MDf+YADwJz4rxWCCFEN3R3P2NPCuL0RzGdgUrmRyGEEANBXy4NnQ3s\n0FrvAlBKPQNcAoRPVpcAT2qtNfCxUmqYUiofKI3jWiGEEMdRTwriSIuIdmR+FEII0e/6cmloIbAn\n7POawLF4zonnWiGEEGIwkvlRCCFEvxv0xWKUUjcANwQ+bVZKbYvjsizgYN891aAn4xObjE9sMj6x\nyfjEFu/4dK6UI9qR+bFPyPjEJuMTm4xPbDI+sSUyPnHNkX0ZCNYCxWGfFwWOxXOOOY5rAdBaPww8\nnMiDKaVWa61nJnLNUCLjE5uMT2wyPrHJ+MQ2RMZH5sdBSsYnNhmf2GR8YpPxia0vxqcvl4auAsYq\npUYqpSzAFcBLHc55CfhqoDraXKBRa10X57VCCCHEYCTzoxBCiH7XZxlBrbVHKXULsAwwAo9prTcp\npb4ReP0hYCmwCNgBtAJfi3VtXz2rEEIIcbzI/CiEEGIg6NM9glrrpfgns/BjD4V9rIGb4722FyW0\nVGYIkvGJTcYnNhmf2GR8YhsS4yPz46Al4xObjE9sMj6xyfjE1uvjo/xzjRBCCCGEEEKIoaIv9wgK\nIYQQQgghhBiAhlQgqJRaqJTappTaoZT6YX8/T39TSj2mlKpXSlWGHRuulHpDKfVp4L8Z/fmM/Ukp\nVayUWq6U2qyU2qSUujVwXMYIUErZlFIrlVLrA+NzV+C4jE8YpZRRKbVWKfVy4HMZnwClVJVSaqNS\nap1SanXgmIxPP5E5sj2ZI2OTOTI2mSO7JvNjbMdjjhwygaBSygjcD5wPTASuVEpN7N+n6nePAws7\nHPsh8JbWeizwVuDzocoD3K61ngjMBW4O/M7IGPk5gbO01lOBacDCQHVDGZ/2bgW2hH0u49PemVrr\naWElsWV8+oHMkRE9jsyRscgcGZvMkV2T+bFrfTpHDplAEJgN7NBa79Jau4BngEv6+Zn6ldb6XeBQ\nh8OXAE8EPn4C+NxxfagBRGtdp7WuCHx8FP9fVoXIGAH+YhZa6+bAp+bAH42MT4hSqgi4APhb2GEZ\nn9hkfPqHzJEdyBwZm8yRsckcGZvMj93Wq2M0lALBQmBP2Oc1gWOivdxAryqAfUBufz7MQKGUKgVO\nAj5BxigksKxjHVAPvKG1lvFp78/A9wFf2DEZn2M08KZSao1S6obAMRmf/iFzZHzk9zMCmSMjkzky\nJpkfu9bnc2Sfto8Qg5vWWiulhnxZWaVUCvA8cJvWukkpFXptqI+R1toLTFNKDQNeUEpN7vD6kB0f\npdSFQL3Weo1San6kc4by+AScqrWuVUrlAG8opbaGvyjjIwYy+f30kzkyOpkjI5P5MW59PkcOpYxg\nLVAc9nlR4Jhob79SKh8g8N/6fn6efqWUMuOf4J7SWv83cFjGqAOt9RFgOf79NDI+fqcAFyulqvAv\nsztLKfVPZHxCtNa1gf/WAy/gX54o49M/ZI6Mj/x+hpE5Mj4yR3Yi82McjsccOZQCwVXAWKXUSKWU\nBbgCeKmfn2kgegm4JvDxNcDifnyWfqX8b2s+CmzRWt8d9pKMEaCUyg68y4lSKgk4F9iKjA8AWusf\naa2LtNal+P++eVtrfRUyPgAopZKVUqnBj4HzgEpkfPqLzJHxkd/PAJkjY5M5MjqZH7t2vObIIdVQ\nXim1CP+aZCPwmNb6V/38SP1KKfU0MB/IAvYDPwVeBJ4FRgDVwJe01h03yw8JSqlTgfeAjRxbw/5j\n/HsghvwYKaXK8W9UNuJ/U+lZrfXPlVKZyPi0E1j68l2t9YUyPn5KqVH43+EE/zaFf2mtfyXj039k\njmxP5sjYZI6MTebI+Mj8GNnxmiOHVCAohBBCCCGEEGJoLQ0VQgghhBBCCIEEgkIIIYQQQggx5Egg\nKIQQQgghhBBDjASCQgghhBBCCDHESCAohBBCCCGEEEOMBIJC9COllFcptS7szw978d6lSqnK3rqf\nEEIIcTzJHClE3zL19wMIMcS1aa2n9fdDCCGEEAOQzJFC9CHJCAoxACmlqpRS/6eU2qiUWqmUGhM4\nXqqUelsptUEp9ZZSakTgeK5S6gWl1PrAn3mBWxmVUo8opTYppV5XSiUFzv+WUmpz4D7P9NO3KYQQ\nQiRM5kgheocEgkL0r6QOy14uD3utUWs9BbgP+HPg2L3AE1rrcuAp4J7A8XuAd7TWU4HpwKbA8bHA\n/VrrScAR4AuB4z8ETgrc5xt99c0JIYQQPSBzpBB9SGmt+/sZhBiylFLNWuuUCMergLO01ruUUmZg\nn9Y6Uyl1EMjXWrsDx+u01llKqQNAkdbaGXaPUuANrfXYwOc/AMxa618qpV4DmoEXgRe11s19/K0K\nIYQQCZE5Uoi+JRlBIQYuHeXjRDjDPvZybF/wBcD9+N8ZXaWUkv3CQgghBhOZI4XoIQkEhRi4Lg/7\n70eBjz8Ergh8/BXgvcDHbwE3ASiljEqp9Gg3VUoZgGKt9XLgB0A60OkdVyGEEGIAkzlSiB6SdziE\n6F9JSql1YZ+/prUOlsfOUEptwP+O5ZWBY/8L/F0p9T3gAPC1wPFbgYeVUl/H/67mTUBdlK9pBP4Z\nmAgVcI/W+kivfUdCCCFE75A5Uog+JHsEhRiAAvsfZmqtD/b3swghhBADicyRQvQOWRoqhBBCCCGE\nEEOMZASFEEIIIYQQYoiRjKAQQgghhBBCDDESCAohhBBCCCHEECOBoBBCCCGEEEIMMRIICiGEEEII\nIcQQI4GgEEIIIYQQQgwxEggKIYQQQgghxBDz/wHPM4BdE2gIAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11886e908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "markers = {\"SGD\": \"o\", \"Momentum\": \"x\", \"Nesterov\": \"s\", \"AdaGrad\": \"o\", \"RMSprop\": \"x\", \"Adam\": \"s\"}\n",
    "\n",
    "f, axarr = plt.subplots(2, 2, figsize=(15,10))\n",
    "for key in optimizers.keys():\n",
    "    axarr[0, 0].plot(epoch_list[1:], train_errors[key][1:], marker=markers[key], markevery=2, label=key)\n",
    "axarr[0, 0].set_ylabel('Train - Total Error')\n",
    "axarr[0, 0].set_xlabel('Epochs')\n",
    "axarr[0, 0].grid(True)\n",
    "axarr[0, 0].set_title('Train Error')\n",
    "axarr[0, 0].legend(loc='upper left')\n",
    "\n",
    "for key in optimizers.keys():\n",
    "    axarr[0, 1].plot(epoch_list[1:], validation_errors[key][1:], marker=markers[key], markevery=2, label=key)\n",
    "axarr[0, 1].set_ylabel('Validation - Total Error')\n",
    "axarr[0, 1].set_xlabel('Epochs')\n",
    "axarr[0, 1].grid(True)\n",
    "axarr[0, 1].set_title('Validation Error')\n",
    "axarr[0, 1].legend(loc='upper left')\n",
    "\n",
    "for key in optimizers.keys():\n",
    "    axarr[1, 0].plot(epoch_list[1:], train_errors[key][1:], marker=markers[key], markevery=2, label=key)\n",
    "axarr[1, 0].set_ylabel('Train - Total Error')\n",
    "axarr[1, 0].set_xlabel('Epochs')\n",
    "axarr[1, 0].grid(True)\n",
    "axarr[1, 0].set_ylim(0, 0.3)\n",
    "axarr[1, 0].set_title('Train Error (0.00 ~ 0.30)')\n",
    "axarr[1, 0].legend(loc='upper left')\n",
    "\n",
    "for key in optimizers.keys():\n",
    "    axarr[1, 1].plot(epoch_list[1:], validation_errors[key][1:], marker=markers[key], markevery=2, label=key)\n",
    "axarr[1, 1].set_ylabel('Validation - Total Error')\n",
    "axarr[1, 1].set_xlabel('Epochs')\n",
    "axarr[1, 1].grid(True)\n",
    "axarr[1, 1].set_ylim(0, 0.3)\n",
    "axarr[1, 1].set_title('Validation Error (0.00 ~ 0.30)')\n",
    "axarr[1, 1].legend(loc='upper left')\n",
    "\n",
    "f.subplots_adjust(hspace=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+m209Jrr2l5F37vFt2k8oujJN+WNatZFSYsZt4hGL6rdmsPLx/7C2z9fJatxG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\nSZIG5ncMQVFPAUWh9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188b0860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2, 1, figsize=(15,10))\n",
    "for key in optimizers.keys():\n",
    "    axarr[0].plot(epoch_list[1:], test_accuracy_values[key][1:], marker=markers[key], markevery=1, label=key)\n",
    "axarr[0].set_ylabel('Test Accuracy')\n",
    "axarr[0].set_xlabel('Epochs')\n",
    "axarr[0].grid(True)\n",
    "axarr[0].set_title('Test Accuracy')\n",
    "axarr[0].legend(loc='upper left')\n",
    "\n",
    "for key in optimizers.keys():\n",
    "    axarr[1].plot(epoch_list[1:], test_accuracy_values[key][1:], marker=markers[key], markevery=1, label=key)\n",
    "axarr[1].set_ylabel('Test Accuracy')\n",
    "axarr[1].set_xlabel('Epochs')\n",
    "axarr[1].grid(True)\n",
    "axarr[1].set_ylim(0.9, 1.0)\n",
    "axarr[1].set_title('Test Accuracy (0.9 ~ 1.0)')\n",
    "axarr[1].legend(loc='upper left')\n",
    "\n",
    "f.subplots_adjust(hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGD                        - Epoch: 49, Max Test Accuracy: 0.33860\n",
      "Momentum                   - Epoch: 49, Max Test Accuracy: 0.95670\n",
      "Nesterov                   - Epoch: 49, Max Test Accuracy: 0.95820\n",
      "AdaGrad                    - Epoch: 49, Max Test Accuracy: 0.96920\n",
      "RMSprop                    - Epoch: 37, Max Test Accuracy: 0.96230\n",
      "Adam                       - Epoch: 45, Max Test Accuracy: 0.97590\n"
     ]
    }
   ],
   "source": [
    "for key in optimizers.keys():\n",
    "    print(\"{0:26s} - Epoch:{1:3d}, Max Test Accuracy: {2:7.5f}\".format(key, max_test_accuracy_epoch[key], max_test_accuracy_value[key]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}