{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST-Neural Network-Two Hidden Layers with Variable Initializers"
   ]
  },
  {
   "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": [
    "## Initializers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Zero_Initializer:\n",
    "    def __init__(self, input_size, hidden_layer1_size, hidden_layer2_size, output_size):\n",
    "        self.input_size = input_size\n",
    "        self.hidden_layer1_size = hidden_layer1_size\n",
    "        self.hidden_layer2_size = hidden_layer2_size\n",
    "        self.output_size = output_size\n",
    "        self.params = {}        \n",
    "        self.params['W1'] = np.zeros((self.input_size, self.hidden_layer1_size))\n",
    "        self.params['b1'] = np.zeros(self.hidden_layer1_size)\n",
    "        self.params['W2'] = np.zeros((self.hidden_layer1_size, self.hidden_layer2_size))\n",
    "        self.params['b2'] = np.zeros(self.hidden_layer2_size)\n",
    "        self.params['W3'] = np.zeros((self.hidden_layer2_size, self.output_size))\n",
    "        self.params['b3'] = np.zeros(self.output_size)\n",
    "        \n",
    "    def get_params(self):\n",
    "        return self.params\n",
    "    \n",
    "class N1_Initializer:\n",
    "    def __init__(self, input_size, hidden_layer1_size, hidden_layer2_size, output_size):\n",
    "        self.input_size = input_size\n",
    "        self.hidden_layer1_size = hidden_layer1_size\n",
    "        self.hidden_layer2_size = hidden_layer2_size\n",
    "        self.output_size = output_size\n",
    "        self.params = {}\n",
    "        self.params['W1'] = np.random.randn(self.input_size, self.hidden_layer1_size)\n",
    "        self.params['b1'] = np.random.randn(self.hidden_layer1_size)\n",
    "        self.params['W2'] = np.random.randn(self.hidden_layer1_size, self.hidden_layer2_size)\n",
    "        self.params['b2'] = np.random.randn(self.hidden_layer2_size)\n",
    "        self.params['W3'] = np.random.randn(self.hidden_layer2_size, self.output_size)\n",
    "        self.params['b3'] = np.random.randn(self.output_size)\n",
    "        \n",
    "    def get_params(self):\n",
    "        return self.params\n",
    "    \n",
    "class N2_Initializer:\n",
    "    def __init__(self, input_size, hidden_layer1_size, hidden_layer2_size, output_size):\n",
    "        self.input_size = input_size\n",
    "        self.hidden_layer1_size = hidden_layer1_size\n",
    "        self.hidden_layer2_size = hidden_layer2_size\n",
    "        self.output_size = output_size\n",
    "        self.params = {}\n",
    "        self.params['W1'] = np.random.randn(self.input_size, self.hidden_layer1_size) * 0.01\n",
    "        self.params['b1'] = np.random.randn(self.hidden_layer1_size) * 0.01\n",
    "        self.params['W2'] = np.random.randn(self.hidden_layer1_size, self.hidden_layer2_size) * 0.01\n",
    "        self.params['b2'] = np.random.randn(self.hidden_layer2_size) * 0.01\n",
    "        self.params['W3'] = np.random.randn(self.hidden_layer2_size, self.output_size) * 0.01\n",
    "        self.params['b3'] = np.random.randn(self.output_size) * 0.01\n",
    "    \n",
    "    def get_params(self):\n",
    "        return self.params\n",
    "    \n",
    "class Xavier_Initializer:\n",
    "    def __init__(self, input_size, hidden_layer1_size, hidden_layer2_size, output_size):\n",
    "        self.input_size = input_size\n",
    "        self.hidden_layer1_size = hidden_layer1_size\n",
    "        self.hidden_layer2_size = hidden_layer2_size\n",
    "        self.output_size = output_size\n",
    "        self.params = {}\n",
    "        self.params['W1'] = np.random.randn(self.input_size, self.hidden_layer1_size) / np.sqrt(self.input_size)\n",
    "        self.params['b1'] = np.random.randn(self.hidden_layer1_size) / np.sqrt(self.input_size)\n",
    "        self.params['W2'] = np.random.randn(self.hidden_layer1_size, self.hidden_layer2_size) / np.sqrt(self.hidden_layer1_size)\n",
    "        self.params['b2'] = np.random.randn(self.hidden_layer2_size) / np.sqrt(self.hidden_layer1_size)\n",
    "        self.params['W3'] = np.random.randn(self.hidden_layer2_size, self.output_size) / np.sqrt(self.hidden_layer2_size)\n",
    "        self.params['b3'] = np.random.randn(self.output_size) / np.sqrt(self.hidden_layer2_size)\n",
    "    \n",
    "    def get_params(self):\n",
    "        return self.params\n",
    "\n",
    "class He_Initializer:\n",
    "    def __init__(self, input_size, hidden_layer1_size, hidden_layer2_size, output_size):\n",
    "        self.input_size = input_size\n",
    "        self.hidden_layer1_size = hidden_layer1_size\n",
    "        self.hidden_layer2_size = hidden_layer2_size\n",
    "        self.output_size = output_size\n",
    "        self.params = {}\n",
    "        self.params['W1'] = np.random.randn(self.input_size, self.hidden_layer1_size) * np.sqrt(2) / np.sqrt(self.input_size)\n",
    "        self.params['b1'] = np.random.randn(self.hidden_layer1_size) * np.sqrt(2) / np.sqrt(self.input_size)\n",
    "        self.params['W2'] = np.random.randn(self.hidden_layer1_size, self.hidden_layer2_size) * np.sqrt(2) / np.sqrt(self.hidden_layer1_size)\n",
    "        self.params['b2'] = np.random.randn(self.hidden_layer2_size) * np.sqrt(2) / np.sqrt(self.hidden_layer1_size)\n",
    "        self.params['W3'] = np.random.randn(self.hidden_layer2_size, self.output_size) * np.sqrt(2) / np.sqrt(self.hidden_layer2_size)\n",
    "        self.params['b3'] = np.random.randn(self.output_size) * np.sqrt(2) / np.sqrt(self.hidden_layer2_size)\n",
    "    \n",
    "    def get_params(self):\n",
    "        return self.params  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilayer Neural Network Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "markers = {\"Zero\": \"o\", \"N1\": \"x\", \"N2\": \"s\", \"Xavier\": \"o\", \"He\": \"x\"}\n",
    "\n",
    "class TwoLayerNet2:\n",
    "    def __init__(self, initializer):\n",
    "        self.params = initializer.get_params()\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.activations = OrderedDict()\n",
    "        self.activations[0] = None\n",
    "        \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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero  -Epoch:  0, Train Err.:2.30211, Validation Err.:2.30169, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../common/functions.py:56: RuntimeWarning: divide by zero encountered in log\n",
      "  return -np.sum(np.log(y[np.arange(batch_size), t])) / batch_size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N1    -Epoch:  0, Train Err.:    inf, Validation Err.:    inf, Test Accuracy:0.63990, Max Test Accuracy:0.63990\n",
      "N2    -Epoch:  0, Train Err.:2.30212, Validation Err.:2.30208, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  0, Train Err.:1.99279, Validation Err.:1.98679, Test Accuracy:0.45400, Max Test Accuracy:0.45400\n",
      "He    -Epoch:  0, Train Err.:1.80990, Validation Err.:1.80165, Test Accuracy:0.51980, Max Test Accuracy:0.51980\n",
      "\n",
      "Zero  -Epoch:  1, Train Err.:2.30212, Validation Err.:2.30169, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch:  1, Train Err.:    inf, Validation Err.:    inf, Test Accuracy:0.72610, Max Test Accuracy:0.72610\n",
      "N2    -Epoch:  1, Train Err.:2.30186, Validation Err.:2.30176, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  1, Train Err.:1.55536, Validation Err.:1.52620, Test Accuracy:0.62590, Max Test Accuracy:0.62590\n",
      "He    -Epoch:  1, Train Err.:1.27103, Validation Err.:1.24178, Test Accuracy:0.73610, Max Test Accuracy:0.73610\n",
      "\n",
      "Zero  -Epoch:  2, Train Err.:2.30213, Validation Err.:2.30169, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch:  2, Train Err.:    inf, Validation Err.:    inf, Test Accuracy:0.76480, Max Test Accuracy:0.76480\n",
      "N2    -Epoch:  2, Train Err.:2.30168, Validation Err.:2.30154, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  2, Train Err.:1.16768, Validation Err.:1.11996, Test Accuracy:0.73410, Max Test Accuracy:0.73410\n",
      "He    -Epoch:  2, Train Err.:0.91962, Validation Err.:0.86791, Test Accuracy:0.79910, Max Test Accuracy:0.79910\n",
      "\n",
      "Zero  -Epoch:  3, Train Err.:2.30214, Validation Err.:2.30169, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch:  3, Train Err.:    inf, Validation Err.:    inf, Test Accuracy:0.78750, Max Test Accuracy:0.78750\n",
      "N2    -Epoch:  3, Train Err.:2.30154, Validation Err.:2.30137, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  3, Train Err.:0.89864, Validation Err.:0.84130, Test Accuracy:0.78700, Max Test Accuracy:0.78700\n",
      "He    -Epoch:  3, Train Err.:0.73808, Validation Err.:0.67435, Test Accuracy:0.82790, Max Test Accuracy:0.82790\n",
      "\n",
      "Zero  -Epoch:  4, Train Err.:2.30215, Validation Err.:2.30170, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch:  4, Train Err.:    inf, Validation Err.:    inf, Test Accuracy:0.80310, Max Test Accuracy:0.80310\n",
      "N2    -Epoch:  4, Train Err.:2.30142, Validation Err.:2.30121, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  4, Train Err.:0.73909, Validation Err.:0.67867, Test Accuracy:0.81790, Max Test Accuracy:0.81790\n",
      "He    -Epoch:  4, Train Err.:0.63672, Validation Err.:0.56600, Test Accuracy:0.84640, Max Test Accuracy:0.84640\n",
      "\n",
      "Zero  -Epoch:  5, Train Err.:2.30216, Validation Err.:2.30171, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch:  5, Train Err.:    inf, Validation Err.:    inf, Test Accuracy:0.81480, Max Test Accuracy:0.81480\n",
      "N2    -Epoch:  5, Train Err.:2.30130, Validation Err.:2.30106, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  5, Train Err.:0.64144, Validation Err.:0.57992, Test Accuracy:0.83800, Max Test Accuracy:0.83800\n",
      "He    -Epoch:  5, Train Err.:0.57145, Validation Err.:0.49721, Test Accuracy:0.85920, Max Test Accuracy:0.85920\n",
      "\n",
      "Zero  -Epoch:  6, Train Err.:2.30217, Validation Err.:2.30171, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch:  6, Train Err.:    inf, Validation Err.:    inf, Test Accuracy:0.82320, Max Test Accuracy:0.82320\n",
      "N2    -Epoch:  6, Train Err.:2.30117, Validation Err.:2.30092, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  6, Train Err.:0.57679, Validation Err.:0.51468, Test Accuracy:0.85160, Max Test Accuracy:0.85160\n",
      "He    -Epoch:  6, Train Err.:0.52531, Validation Err.:0.44912, Test Accuracy:0.86880, Max Test Accuracy:0.86880\n",
      "\n",
      "Zero  -Epoch:  7, Train Err.:2.30218, Validation Err.:2.30172, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch:  7, Train Err.:25.82635, Validation Err.:    inf, Test Accuracy:0.83060, Max Test Accuracy:0.83060\n",
      "N2    -Epoch:  7, Train Err.:2.30104, Validation Err.:2.30076, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  7, Train Err.:0.53110, Validation Err.:0.46841, Test Accuracy:0.85990, Max Test Accuracy:0.85990\n",
      "He    -Epoch:  7, Train Err.:0.49032, Validation Err.:0.41337, Test Accuracy:0.87820, Max Test Accuracy:0.87820\n",
      "\n",
      "Zero  -Epoch:  8, Train Err.:2.30219, Validation Err.:2.30173, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch:  8, Train Err.:24.29886, Validation Err.:    inf, Test Accuracy:0.83710, Max Test Accuracy:0.83710\n",
      "N2    -Epoch:  8, Train Err.:2.30090, Validation Err.:2.30059, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  8, Train Err.:0.49731, Validation Err.:0.43389, Test Accuracy:0.86890, Max Test Accuracy:0.86890\n",
      "He    -Epoch:  8, Train Err.:0.46288, Validation Err.:0.38573, Test Accuracy:0.88560, Max Test Accuracy:0.88560\n",
      "\n",
      "Zero  -Epoch:  9, Train Err.:2.30220, Validation Err.:2.30173, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch:  9, Train Err.:22.92013, Validation Err.:    inf, Test Accuracy:0.84160, Max Test Accuracy:0.84160\n",
      "N2    -Epoch:  9, Train Err.:2.30073, Validation Err.:2.30041, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch:  9, Train Err.:0.47136, Validation Err.:0.40724, Test Accuracy:0.87580, Max Test Accuracy:0.87580\n",
      "He    -Epoch:  9, Train Err.:0.44062, Validation Err.:0.36367, Test Accuracy:0.88950, Max Test Accuracy:0.88950\n",
      "\n",
      "Zero  -Epoch: 10, Train Err.:2.30221, Validation Err.:2.30174, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 10, Train Err.:21.74523, Validation Err.:    inf, Test Accuracy:0.84640, Max Test Accuracy:0.84640\n",
      "N2    -Epoch: 10, Train Err.:2.30054, Validation Err.:2.30019, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch: 10, Train Err.:0.45090, Validation Err.:0.38611, Test Accuracy:0.88000, Max Test Accuracy:0.88000\n",
      "He    -Epoch: 10, Train Err.:0.42223, Validation Err.:0.34566, Test Accuracy:0.89290, Max Test Accuracy:0.89290\n",
      "\n",
      "Zero  -Epoch: 11, Train Err.:2.30222, Validation Err.:2.30174, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 11, Train Err.:20.72326, Validation Err.:    inf, Test Accuracy:0.84980, Max Test Accuracy:0.84980\n",
      "N2    -Epoch: 11, Train Err.:2.30031, Validation Err.:2.29995, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch: 11, Train Err.:0.43443, Validation Err.:0.36900, Test Accuracy:0.88320, Max Test Accuracy:0.88320\n",
      "He    -Epoch: 11, Train Err.:0.40670, Validation Err.:0.33065, Test Accuracy:0.89730, Max Test Accuracy:0.89730\n",
      "\n",
      "Zero  -Epoch: 12, Train Err.:2.30223, Validation Err.:2.30175, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 12, Train Err.:19.85780, Validation Err.:    inf, Test Accuracy:0.85310, Max Test Accuracy:0.85310\n",
      "N2    -Epoch: 12, Train Err.:2.30004, Validation Err.:2.29965, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch: 12, Train Err.:0.42087, Validation Err.:0.35487, Test Accuracy:0.88700, Max Test Accuracy:0.88700\n",
      "He    -Epoch: 12, Train Err.:0.39340, Validation Err.:0.31791, Test Accuracy:0.90090, Max Test Accuracy:0.90090\n",
      "\n",
      "Zero  -Epoch: 13, Train Err.:2.30224, Validation Err.:2.30175, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 13, Train Err.:19.08249, Validation Err.:    inf, Test Accuracy:0.85600, Max Test Accuracy:0.85600\n",
      "N2    -Epoch: 13, Train Err.:2.29971, Validation Err.:2.29930, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch: 13, Train Err.:0.40948, Validation Err.:0.34302, Test Accuracy:0.88890, Max Test Accuracy:0.88890\n",
      "He    -Epoch: 13, Train Err.:0.38190, Validation Err.:0.30701, Test Accuracy:0.90310, Max Test Accuracy:0.90310\n",
      "\n",
      "Zero  -Epoch: 14, Train Err.:2.30224, Validation Err.:2.30176, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 14, Train Err.:18.36657, Validation Err.:    inf, Test Accuracy:0.85860, Max Test Accuracy:0.85860\n",
      "N2    -Epoch: 14, Train Err.:2.29929, Validation Err.:2.29886, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch: 14, Train Err.:0.39973, Validation Err.:0.33291, Test Accuracy:0.89100, Max Test Accuracy:0.89100\n",
      "He    -Epoch: 14, Train Err.:0.37180, Validation Err.:0.29755, Test Accuracy:0.90530, Max Test Accuracy:0.90530\n",
      "\n",
      "Zero  -Epoch: 15, Train Err.:2.30225, Validation Err.:2.30176, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 15, Train Err.:17.69516, Validation Err.:    inf, Test Accuracy:0.86080, Max Test Accuracy:0.86080\n",
      "N2    -Epoch: 15, Train Err.:2.29876, Validation Err.:2.29831, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch: 15, Train Err.:0.39128, Validation Err.:0.32419, Test Accuracy:0.89400, Max Test Accuracy:0.89400\n",
      "He    -Epoch: 15, Train Err.:0.36285, Validation Err.:0.28926, Test Accuracy:0.90760, Max Test Accuracy:0.90760\n",
      "\n",
      "Zero  -Epoch: 16, Train Err.:2.30226, Validation Err.:2.30177, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 16, Train Err.:17.06487, Validation Err.:    inf, Test Accuracy:0.86250, Max Test Accuracy:0.86250\n",
      "N2    -Epoch: 16, Train Err.:2.29809, Validation Err.:2.29761, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch: 16, Train Err.:0.38383, Validation Err.:0.31657, Test Accuracy:0.89540, Max Test Accuracy:0.89540\n",
      "He    -Epoch: 16, Train Err.:0.35486, Validation Err.:0.28192, Test Accuracy:0.90990, Max Test Accuracy:0.90990\n",
      "\n",
      "Zero  -Epoch: 17, Train Err.:2.30227, Validation Err.:2.30177, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 17, Train Err.:16.50245, Validation Err.:    inf, Test Accuracy:0.86420, Max Test Accuracy:0.86420\n",
      "N2    -Epoch: 17, Train Err.:2.29720, Validation Err.:2.29670, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch: 17, Train Err.:0.37723, Validation Err.:0.30982, Test Accuracy:0.89730, Max Test Accuracy:0.89730\n",
      "He    -Epoch: 17, Train Err.:0.34763, Validation Err.:0.27539, Test Accuracy:0.91080, Max Test Accuracy:0.91080\n",
      "\n",
      "Zero  -Epoch: 18, Train Err.:2.30227, Validation Err.:2.30177, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 18, Train Err.:15.96660, Validation Err.:    inf, Test Accuracy:0.86560, Max Test Accuracy:0.86560\n",
      "N2    -Epoch: 18, Train Err.:2.29600, Validation Err.:2.29546, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "Xavier-Epoch: 18, Train Err.:0.37127, Validation Err.:0.30382, Test Accuracy:0.89820, Max Test Accuracy:0.89820\n",
      "He    -Epoch: 18, Train Err.:0.34103, Validation Err.:0.26950, Test Accuracy:0.91220, Max Test Accuracy:0.91220\n",
      "\n",
      "Zero  -Epoch: 19, Train Err.:2.30228, Validation Err.:2.30178, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 19, Train Err.:15.47522, Validation Err.:    inf, Test Accuracy:0.86730, Max Test Accuracy:0.86730\n",
      "N2    -Epoch: 19, Train Err.:2.29431, Validation Err.:2.29374, Test Accuracy:0.11400, Max Test Accuracy:0.11400\n",
      "Xavier-Epoch: 19, Train Err.:0.36583, Validation Err.:0.29841, Test Accuracy:0.89890, Max Test Accuracy:0.89890\n",
      "He    -Epoch: 19, Train Err.:0.33502, Validation Err.:0.26416, Test Accuracy:0.91280, Max Test Accuracy:0.91280\n",
      "\n",
      "Zero  -Epoch: 20, Train Err.:2.30228, Validation Err.:2.30178, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 20, Train Err.:15.02592, Validation Err.:    inf, Test Accuracy:0.86850, Max Test Accuracy:0.86850\n",
      "N2    -Epoch: 20, Train Err.:2.29184, Validation Err.:2.29123, Test Accuracy:0.13490, Max Test Accuracy:0.13490\n",
      "Xavier-Epoch: 20, Train Err.:0.36084, Validation Err.:0.29351, Test Accuracy:0.89980, Max Test Accuracy:0.89980\n",
      "He    -Epoch: 20, Train Err.:0.32943, Validation Err.:0.25932, Test Accuracy:0.91400, Max Test Accuracy:0.91400\n",
      "\n",
      "Zero  -Epoch: 21, Train Err.:2.30229, Validation Err.:2.30178, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 21, Train Err.:14.60892, Validation Err.:    inf, Test Accuracy:0.87060, Max Test Accuracy:0.87060\n",
      "N2    -Epoch: 21, Train Err.:2.28804, Validation Err.:2.28739, Test Accuracy:0.18360, Max Test Accuracy:0.18360\n",
      "Xavier-Epoch: 21, Train Err.:0.35622, Validation Err.:0.28905, Test Accuracy:0.90150, Max Test Accuracy:0.90150\n",
      "He    -Epoch: 21, Train Err.:0.32420, Validation Err.:0.25490, Test Accuracy:0.91470, Max Test Accuracy:0.91470\n",
      "\n",
      "Zero  -Epoch: 22, Train Err.:2.30229, Validation Err.:2.30179, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 22, Train Err.:14.21866, Validation Err.:    inf, Test Accuracy:0.87180, Max Test Accuracy:0.87180\n",
      "N2    -Epoch: 22, Train Err.:2.28187, Validation Err.:2.28116, Test Accuracy:0.22900, Max Test Accuracy:0.22900\n",
      "Xavier-Epoch: 22, Train Err.:0.35193, Validation Err.:0.28494, Test Accuracy:0.90300, Max Test Accuracy:0.90300\n",
      "He    -Epoch: 22, Train Err.:0.31933, Validation Err.:0.25083, Test Accuracy:0.91540, Max Test Accuracy:0.91540\n",
      "\n",
      "Zero  -Epoch: 23, Train Err.:2.30230, Validation Err.:2.30179, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 23, Train Err.:13.85946, Validation Err.:    inf, Test Accuracy:0.87340, Max Test Accuracy:0.87340\n",
      "N2    -Epoch: 23, Train Err.:2.27135, Validation Err.:2.27056, Test Accuracy:0.24610, Max Test Accuracy:0.24610\n",
      "Xavier-Epoch: 23, Train Err.:0.34791, Validation Err.:0.28115, Test Accuracy:0.90390, Max Test Accuracy:0.90390\n",
      "He    -Epoch: 23, Train Err.:0.31482, Validation Err.:0.24708, Test Accuracy:0.91590, Max Test Accuracy:0.91590\n",
      "\n",
      "Zero  -Epoch: 24, Train Err.:2.30230, Validation Err.:2.30179, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 24, Train Err.:13.51581, Validation Err.:    inf, Test Accuracy:0.87490, Max Test Accuracy:0.87490\n",
      "N2    -Epoch: 24, Train Err.:2.25307, Validation Err.:2.25220, Test Accuracy:0.24780, Max Test Accuracy:0.24780\n",
      "Xavier-Epoch: 24, Train Err.:0.34411, Validation Err.:0.27763, Test Accuracy:0.90440, Max Test Accuracy:0.90440\n",
      "He    -Epoch: 24, Train Err.:0.31060, Validation Err.:0.24359, Test Accuracy:0.91730, Max Test Accuracy:0.91730\n",
      "\n",
      "Zero  -Epoch: 25, Train Err.:2.30230, Validation Err.:2.30179, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 25, Train Err.:13.18274, Validation Err.:    inf, Test Accuracy:0.87580, Max Test Accuracy:0.87580\n",
      "N2    -Epoch: 25, Train Err.:2.22317, Validation Err.:2.22237, Test Accuracy:0.24820, Max Test Accuracy:0.24820\n",
      "Xavier-Epoch: 25, Train Err.:0.34053, Validation Err.:0.27436, Test Accuracy:0.90600, Max Test Accuracy:0.90600\n",
      "He    -Epoch: 25, Train Err.:0.30662, Validation Err.:0.24035, Test Accuracy:0.91800, Max Test Accuracy:0.91800\n",
      "\n",
      "Zero  -Epoch: 26, Train Err.:2.30231, Validation Err.:2.30180, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 26, Train Err.:12.87416, Validation Err.:    inf, Test Accuracy:0.87680, Max Test Accuracy:0.87680\n",
      "N2    -Epoch: 26, Train Err.:2.18291, Validation Err.:2.18254, Test Accuracy:0.25720, Max Test Accuracy:0.25720\n",
      "Xavier-Epoch: 26, Train Err.:0.33713, Validation Err.:0.27129, Test Accuracy:0.90670, Max Test Accuracy:0.90670\n",
      "He    -Epoch: 26, Train Err.:0.30286, Validation Err.:0.23731, Test Accuracy:0.91890, Max Test Accuracy:0.91890\n",
      "\n",
      "Zero  -Epoch: 27, Train Err.:2.30231, Validation Err.:2.30180, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 27, Train Err.:12.60325, Validation Err.:    inf, Test Accuracy:0.87760, Max Test Accuracy:0.87760\n",
      "N2    -Epoch: 27, Train Err.:2.14090, Validation Err.:2.14130, Test Accuracy:0.27190, Max Test Accuracy:0.27190\n",
      "Xavier-Epoch: 27, Train Err.:0.33391, Validation Err.:0.26840, Test Accuracy:0.90760, Max Test Accuracy:0.90760\n",
      "He    -Epoch: 27, Train Err.:0.29934, Validation Err.:0.23448, Test Accuracy:0.92000, Max Test Accuracy:0.92000\n",
      "\n",
      "Zero  -Epoch: 28, Train Err.:2.30231, Validation Err.:2.30180, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 28, Train Err.:12.34224, Validation Err.:    inf, Test Accuracy:0.87900, Max Test Accuracy:0.87900\n",
      "N2    -Epoch: 28, Train Err.:2.10159, Validation Err.:2.10259, Test Accuracy:0.28700, Max Test Accuracy:0.28700\n",
      "Xavier-Epoch: 28, Train Err.:0.33086, Validation Err.:0.26567, Test Accuracy:0.90840, Max Test Accuracy:0.90840\n",
      "He    -Epoch: 28, Train Err.:0.29601, Validation Err.:0.23183, Test Accuracy:0.92040, Max Test Accuracy:0.92040\n",
      "\n",
      "Zero  -Epoch: 29, Train Err.:2.30232, Validation Err.:2.30180, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 29, Train Err.:12.10488, Validation Err.:    inf, Test Accuracy:0.88010, Max Test Accuracy:0.88010\n",
      "N2    -Epoch: 29, Train Err.:2.06191, Validation Err.:2.06333, Test Accuracy:0.29930, Max Test Accuracy:0.29930\n",
      "Xavier-Epoch: 29, Train Err.:0.32795, Validation Err.:0.26309, Test Accuracy:0.90900, Max Test Accuracy:0.90900\n",
      "He    -Epoch: 29, Train Err.:0.29285, Validation Err.:0.22933, Test Accuracy:0.92100, Max Test Accuracy:0.92100\n",
      "\n",
      "Zero  -Epoch: 30, Train Err.:2.30232, Validation Err.:2.30180, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 30, Train Err.:11.88343, Validation Err.:    inf, Test Accuracy:0.88050, Max Test Accuracy:0.88050\n",
      "N2    -Epoch: 30, Train Err.:2.01704, Validation Err.:2.01853, Test Accuracy:0.30670, Max Test Accuracy:0.30670\n",
      "Xavier-Epoch: 30, Train Err.:0.32514, Validation Err.:0.26063, Test Accuracy:0.90980, Max Test Accuracy:0.90980\n",
      "He    -Epoch: 30, Train Err.:0.28985, Validation Err.:0.22697, Test Accuracy:0.92140, Max Test Accuracy:0.92140\n",
      "\n",
      "Zero  -Epoch: 31, Train Err.:2.30232, Validation Err.:2.30180, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 31, Train Err.:11.68085, Validation Err.:    inf, Test Accuracy:0.88180, Max Test Accuracy:0.88180\n",
      "N2    -Epoch: 31, Train Err.:1.96356, Validation Err.:1.96416, Test Accuracy:0.30510, Max Test Accuracy:0.30670\n",
      "Xavier-Epoch: 31, Train Err.:0.32242, Validation Err.:0.25830, Test Accuracy:0.91010, Max Test Accuracy:0.91010\n",
      "He    -Epoch: 31, Train Err.:0.28697, Validation Err.:0.22476, Test Accuracy:0.92190, Max Test Accuracy:0.92190\n",
      "\n",
      "Zero  -Epoch: 32, Train Err.:2.30232, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 32, Train Err.:11.50463, Validation Err.:    inf, Test Accuracy:0.88240, Max Test Accuracy:0.88240\n",
      "N2    -Epoch: 32, Train Err.:1.90156, Validation Err.:1.90006, Test Accuracy:0.30510, Max Test Accuracy:0.30670\n",
      "Xavier-Epoch: 32, Train Err.:0.31978, Validation Err.:0.25606, Test Accuracy:0.91060, Max Test Accuracy:0.91060\n",
      "He    -Epoch: 32, Train Err.:0.28422, Validation Err.:0.22265, Test Accuracy:0.92320, Max Test Accuracy:0.92320\n",
      "\n",
      "Zero  -Epoch: 33, Train Err.:2.30232, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 33, Train Err.:11.32855, Validation Err.:    inf, Test Accuracy:0.88290, Max Test Accuracy:0.88290\n",
      "N2    -Epoch: 33, Train Err.:1.83510, Validation Err.:1.83060, Test Accuracy:0.31820, Max Test Accuracy:0.31820\n",
      "Xavier-Epoch: 33, Train Err.:0.31722, Validation Err.:0.25393, Test Accuracy:0.91090, Max Test Accuracy:0.91090\n",
      "He    -Epoch: 33, Train Err.:0.28158, Validation Err.:0.22065, Test Accuracy:0.92390, Max Test Accuracy:0.92390\n",
      "\n",
      "Zero  -Epoch: 34, Train Err.:2.30232, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 34, Train Err.:11.15499, Validation Err.:    inf, Test Accuracy:0.88320, Max Test Accuracy:0.88320\n",
      "N2    -Epoch: 34, Train Err.:1.76730, Validation Err.:1.75975, Test Accuracy:0.34470, Max Test Accuracy:0.34470\n",
      "Xavier-Epoch: 34, Train Err.:0.31477, Validation Err.:0.25190, Test Accuracy:0.91150, Max Test Accuracy:0.91150\n",
      "He    -Epoch: 34, Train Err.:0.27906, Validation Err.:0.21874, Test Accuracy:0.92430, Max Test Accuracy:0.92430\n",
      "\n",
      "Zero  -Epoch: 35, Train Err.:2.30233, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 35, Train Err.:10.99644, Validation Err.:    inf, Test Accuracy:0.88330, Max Test Accuracy:0.88330\n",
      "N2    -Epoch: 35, Train Err.:1.69725, Validation Err.:1.68666, Test Accuracy:0.38870, Max Test Accuracy:0.38870\n",
      "Xavier-Epoch: 35, Train Err.:0.31241, Validation Err.:0.24995, Test Accuracy:0.91190, Max Test Accuracy:0.91190\n",
      "He    -Epoch: 35, Train Err.:0.27665, Validation Err.:0.21692, Test Accuracy:0.92510, Max Test Accuracy:0.92510\n",
      "\n",
      "Zero  -Epoch: 36, Train Err.:2.30233, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 36, Train Err.:10.83669, Validation Err.:    inf, Test Accuracy:0.88400, Max Test Accuracy:0.88400\n",
      "N2    -Epoch: 36, Train Err.:1.62150, Validation Err.:1.60803, Test Accuracy:0.42470, Max Test Accuracy:0.42470\n",
      "Xavier-Epoch: 36, Train Err.:0.31014, Validation Err.:0.24809, Test Accuracy:0.91240, Max Test Accuracy:0.91240\n",
      "He    -Epoch: 36, Train Err.:0.27434, Validation Err.:0.21520, Test Accuracy:0.92560, Max Test Accuracy:0.92560\n",
      "\n",
      "Zero  -Epoch: 37, Train Err.:2.30233, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 37, Train Err.:10.67719, Validation Err.:    inf, Test Accuracy:0.88430, Max Test Accuracy:0.88430\n",
      "N2    -Epoch: 37, Train Err.:1.53975, Validation Err.:1.52371, Test Accuracy:0.44970, Max Test Accuracy:0.44970\n",
      "Xavier-Epoch: 37, Train Err.:0.30793, Validation Err.:0.24630, Test Accuracy:0.91320, Max Test Accuracy:0.91320\n",
      "He    -Epoch: 37, Train Err.:0.27211, Validation Err.:0.21356, Test Accuracy:0.92590, Max Test Accuracy:0.92590\n",
      "\n",
      "Zero  -Epoch: 38, Train Err.:2.30233, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 38, Train Err.:10.52266, Validation Err.:    inf, Test Accuracy:0.88480, Max Test Accuracy:0.88480\n",
      "N2    -Epoch: 38, Train Err.:1.45775, Validation Err.:1.43971, Test Accuracy:0.47500, Max Test Accuracy:0.47500\n",
      "Xavier-Epoch: 38, Train Err.:0.30582, Validation Err.:0.24457, Test Accuracy:0.91390, Max Test Accuracy:0.91390\n",
      "He    -Epoch: 38, Train Err.:0.26993, Validation Err.:0.21198, Test Accuracy:0.92630, Max Test Accuracy:0.92630\n",
      "\n",
      "Zero  -Epoch: 39, Train Err.:2.30233, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 39, Train Err.:10.37247, Validation Err.:    inf, Test Accuracy:0.88510, Max Test Accuracy:0.88510\n",
      "N2    -Epoch: 39, Train Err.:1.38173, Validation Err.:1.36205, Test Accuracy:0.51080, Max Test Accuracy:0.51080\n",
      "Xavier-Epoch: 39, Train Err.:0.30377, Validation Err.:0.24290, Test Accuracy:0.91460, Max Test Accuracy:0.91460\n",
      "He    -Epoch: 39, Train Err.:0.26784, Validation Err.:0.21048, Test Accuracy:0.92700, Max Test Accuracy:0.92700\n",
      "\n",
      "Zero  -Epoch: 40, Train Err.:2.30233, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 40, Train Err.:10.22131, Validation Err.:    inf, Test Accuracy:0.88560, Max Test Accuracy:0.88560\n",
      "N2    -Epoch: 40, Train Err.:1.31435, Validation Err.:1.29278, Test Accuracy:0.54690, Max Test Accuracy:0.54690\n",
      "Xavier-Epoch: 40, Train Err.:0.30177, Validation Err.:0.24129, Test Accuracy:0.91490, Max Test Accuracy:0.91490\n",
      "He    -Epoch: 40, Train Err.:0.26581, Validation Err.:0.20903, Test Accuracy:0.92760, Max Test Accuracy:0.92760\n",
      "\n",
      "Zero  -Epoch: 41, Train Err.:2.30233, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 41, Train Err.:10.07976, Validation Err.:    inf, Test Accuracy:0.88650, Max Test Accuracy:0.88650\n",
      "N2    -Epoch: 41, Train Err.:1.25556, Validation Err.:1.23176, Test Accuracy:0.57420, Max Test Accuracy:0.57420\n",
      "Xavier-Epoch: 41, Train Err.:0.29981, Validation Err.:0.23973, Test Accuracy:0.91530, Max Test Accuracy:0.91530\n",
      "He    -Epoch: 41, Train Err.:0.26385, Validation Err.:0.20765, Test Accuracy:0.92820, Max Test Accuracy:0.92820\n",
      "\n",
      "Zero  -Epoch: 42, Train Err.:2.30233, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 42, Train Err.:9.94493, Validation Err.:    inf, Test Accuracy:0.88690, Max Test Accuracy:0.88690\n",
      "N2    -Epoch: 42, Train Err.:1.20456, Validation Err.:1.17817, Test Accuracy:0.59440, Max Test Accuracy:0.59440\n",
      "Xavier-Epoch: 42, Train Err.:0.29792, Validation Err.:0.23822, Test Accuracy:0.91550, Max Test Accuracy:0.91550\n",
      "He    -Epoch: 42, Train Err.:0.26195, Validation Err.:0.20631, Test Accuracy:0.92880, Max Test Accuracy:0.92880\n",
      "\n",
      "Zero  -Epoch: 43, Train Err.:2.30234, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 43, Train Err.:9.81044, Validation Err.:    inf, Test Accuracy:0.88780, Max Test Accuracy:0.88780\n",
      "N2    -Epoch: 43, Train Err.:1.16002, Validation Err.:1.13097, Test Accuracy:0.60890, Max Test Accuracy:0.60890\n",
      "Xavier-Epoch: 43, Train Err.:0.29603, Validation Err.:0.23676, Test Accuracy:0.91570, Max Test Accuracy:0.91570\n",
      "He    -Epoch: 43, Train Err.:0.26010, Validation Err.:0.20503, Test Accuracy:0.92910, Max Test Accuracy:0.92910\n",
      "\n",
      "Zero  -Epoch: 44, Train Err.:2.30234, Validation Err.:2.30181, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 44, Train Err.:9.67931, Validation Err.:    inf, Test Accuracy:0.88810, Max Test Accuracy:0.88810\n",
      "N2    -Epoch: 44, Train Err.:1.12095, Validation Err.:1.08926, Test Accuracy:0.62150, Max Test Accuracy:0.62150\n",
      "Xavier-Epoch: 44, Train Err.:0.29418, Validation Err.:0.23536, Test Accuracy:0.91660, Max Test Accuracy:0.91660\n",
      "He    -Epoch: 44, Train Err.:0.25831, Validation Err.:0.20379, Test Accuracy:0.92980, Max Test Accuracy:0.92980\n",
      "\n",
      "Zero  -Epoch: 45, Train Err.:2.30234, Validation Err.:2.30182, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 45, Train Err.:9.55656, Validation Err.:    inf, Test Accuracy:0.88890, Max Test Accuracy:0.88890\n",
      "N2    -Epoch: 45, Train Err.:1.08674, Validation Err.:1.05227, Test Accuracy:0.63450, Max Test Accuracy:0.63450\n",
      "Xavier-Epoch: 45, Train Err.:0.29238, Validation Err.:0.23400, Test Accuracy:0.91700, Max Test Accuracy:0.91700\n",
      "He    -Epoch: 45, Train Err.:0.25655, Validation Err.:0.20259, Test Accuracy:0.93040, Max Test Accuracy:0.93040\n",
      "\n",
      "Zero  -Epoch: 46, Train Err.:2.30234, Validation Err.:2.30182, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 46, Train Err.:9.43401, Validation Err.:    inf, Test Accuracy:0.88900, Max Test Accuracy:0.88900\n",
      "N2    -Epoch: 46, Train Err.:1.05650, Validation Err.:1.01938, Test Accuracy:0.64380, Max Test Accuracy:0.64380\n",
      "Xavier-Epoch: 46, Train Err.:0.29062, Validation Err.:0.23268, Test Accuracy:0.91760, Max Test Accuracy:0.91760\n",
      "He    -Epoch: 46, Train Err.:0.25483, Validation Err.:0.20144, Test Accuracy:0.93060, Max Test Accuracy:0.93060\n",
      "\n",
      "Zero  -Epoch: 47, Train Err.:2.30234, Validation Err.:2.30182, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 47, Train Err.:9.31201, Validation Err.:    inf, Test Accuracy:0.88940, Max Test Accuracy:0.88940\n",
      "N2    -Epoch: 47, Train Err.:1.02976, Validation Err.:0.98996, Test Accuracy:0.65430, Max Test Accuracy:0.65430\n",
      "Xavier-Epoch: 47, Train Err.:0.28890, Validation Err.:0.23140, Test Accuracy:0.91790, Max Test Accuracy:0.91790\n",
      "He    -Epoch: 47, Train Err.:0.25316, Validation Err.:0.20032, Test Accuracy:0.93080, Max Test Accuracy:0.93080\n",
      "\n",
      "Zero  -Epoch: 48, Train Err.:2.30234, Validation Err.:2.30182, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 48, Train Err.:9.19183, Validation Err.:    inf, Test Accuracy:0.89010, Max Test Accuracy:0.89010\n",
      "N2    -Epoch: 48, Train Err.:1.00585, Validation Err.:0.96336, Test Accuracy:0.66150, Max Test Accuracy:0.66150\n",
      "Xavier-Epoch: 48, Train Err.:0.28722, Validation Err.:0.23017, Test Accuracy:0.91810, Max Test Accuracy:0.91810\n",
      "He    -Epoch: 48, Train Err.:0.25154, Validation Err.:0.19924, Test Accuracy:0.93090, Max Test Accuracy:0.93090\n",
      "\n",
      "Zero  -Epoch: 49, Train Err.:2.30234, Validation Err.:2.30182, Test Accuracy:0.11350, Max Test Accuracy:0.11350\n",
      "N1    -Epoch: 49, Train Err.:9.07459, Validation Err.:    inf, Test Accuracy:0.89060, Max Test Accuracy:0.89060\n",
      "N2    -Epoch: 49, Train Err.:0.98422, Validation Err.:0.93909, Test Accuracy:0.66920, Max Test Accuracy:0.66920\n",
      "Xavier-Epoch: 49, Train Err.:0.28558, Validation Err.:0.22897, Test Accuracy:0.91830, Max Test Accuracy:0.91830\n",
      "He    -Epoch: 49, Train Err.:0.24996, Validation Err.:0.19820, Test Accuracy:0.93090, Max Test Accuracy:0.93090\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",
    "input_size=784\n",
    "hidden_layer1_size=128\n",
    "hidden_layer2_size=128\n",
    "output_size=10\n",
    "\n",
    "initializers = {}\n",
    "initializers['Zero'] = Zero_Initializer(input_size, hidden_layer1_size, hidden_layer2_size, output_size)\n",
    "initializers['N1'] = N1_Initializer(input_size, hidden_layer1_size, hidden_layer2_size, output_size)\n",
    "initializers['N2'] = N2_Initializer(input_size, hidden_layer1_size, hidden_layer2_size, output_size)\n",
    "initializers['Xavier'] = Xavier_Initializer(input_size, hidden_layer1_size, hidden_layer2_size, output_size)\n",
    "initializers['He'] = He_Initializer(input_size, hidden_layer1_size, hidden_layer2_size, output_size)\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",
    "optimizer = AdaGrad()\n",
    "\n",
    "for key in initializers.keys():\n",
    "    networks[key] = TwoLayerNet2(initializer=initializers[key])\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 initializers.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, optimizer)\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(\"{0:6s}-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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C/dZK+EREpEyKaiP3H9oHwP+2ZHDoiD/c/bWR+w/to02jGow6LbHYex/ZPn6V8hVfpXzF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\nNfOdJYUjEne8nPNQchViNPQf3CUtsn7ejTNpqu6ksqSJTat30LC/nd0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xfrTfZ7yOz8+G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\nFuBsz/lq3CtnOoIXUs9iMhsZc3YqGz7Mp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Ap2f1tEqM1M4b6qVnP4QqdOoWHrVnRTUxAj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179a2c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2, 2, figsize=(15,10))\n",
    "for key in initializers.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 initializers.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 initializers.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, 3.0)\n",
    "axarr[1, 0].set_title('Train Error (0.00 ~ 3.00)')\n",
    "axarr[1, 0].legend(loc='upper left')\n",
    "\n",
    "for key in initializers.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, 1.0)\n",
    "axarr[1, 1].set_title('Validation Error (0.00 ~ 1.00)')\n",
    "axarr[1, 1].legend(loc='upper right')\n",
    "\n",
    "f.subplots_adjust(hspace=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HvsBiVXg9GotV8Z+/bewu/Prg7Uc2heTffWzLgH52ACte2BmSX/3SrgHb+nAWA3hY93bR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\nMCaCkiRJktTB/H/6hteRo16NxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117733e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2, 1, figsize=(15,10))\n",
    "\n",
    "for key in initializers.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 initializers.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(0.7, 1.0)\n",
    "axarr[1].set_title('Test Accuracy (0.7 ~ 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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero                       - Epoch:  0, Max Test Accuracy: 0.11350\n",
      "N1                         - Epoch: 49, Max Test Accuracy: 0.89060\n",
      "N2                         - Epoch: 49, Max Test Accuracy: 0.66920\n",
      "Xavier                     - Epoch: 49, Max Test Accuracy: 0.91830\n",
      "He                         - Epoch: 48, Max Test Accuracy: 0.93090\n"
     ]
    }
   ],
   "source": [
    "for key in initializers.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": 2
}