Lab10/lab-10-4_Leehyogeun.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from time import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eager mode on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.enable_eager_execution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checkpoint function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load(model, checkpoint_dir):\n",
    "    print(\" [*] Reading checkpoints...\")\n",
    "\n",
    "    ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\n",
    "    if ckpt :\n",
    "        ckpt_name = os.path.basename(ckpt.model_checkpoint_path)\n",
    "        checkpoint = tf.train.Checkpoint(dnn=model)\n",
    "        checkpoint.restore(save_path=os.path.join(checkpoint_dir, ckpt_name))\n",
    "        counter = int(ckpt_name.split('-')[1])\n",
    "        print(\" [*] Success to read {}\".format(ckpt_name))\n",
    "        return True, counter\n",
    "    else:\n",
    "        print(\" [*] Failed to find a checkpoint\")\n",
    "        return False, 0\n",
    "\n",
    "def check_folder(dir):\n",
    "    if not os.path.exists(dir):\n",
    "        os.makedirs(dir)\n",
    "    return dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data load & pre-processing function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_mnist() :\n",
    "    (train_data, train_labels), (test_data, test_labels) = mnist.load_data()\n",
    "    train_data = np.expand_dims(train_data, axis=-1) # [N, 28, 28] -> [N, 28, 28, 1]\n",
    "    test_data = np.expand_dims(test_data, axis=-1) # [N, 28, 28] -> [N, 28, 28, 1]\n",
    "\n",
    "    train_data, test_data = normalize(train_data, test_data)\n",
    "\n",
    "    train_labels = to_categorical(train_labels, 10) # [N,] -> [N, 10]\n",
    "    test_labels = to_categorical(test_labels, 10) # [N,] -> [N, 10]\n",
    "\n",
    "    return train_data, train_labels, test_data, test_labels\n",
    "\n",
    "def normalize(train_data, test_data):\n",
    "    train_data = train_data.astype(np.float32) / 255.0\n",
    "    test_data = test_data.astype(np.float32) / 255.0\n",
    "\n",
    "    return train_data, test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_fn(model, images, labels):\n",
    "    logits = model(images, training=True)\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=labels))\n",
    "    return loss\n",
    "\n",
    "def accuracy_fn(model, images, labels):\n",
    "    logits = model(images, training=False)\n",
    "    prediction = tf.equal(tf.argmax(logits, -1), tf.argmax(labels, -1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))\n",
    "    return accuracy\n",
    "\n",
    "def grad(model, images, labels):\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss = loss_fn(model, images, labels)\n",
    "    return tape.gradient(loss, model.variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flatten() :\n",
    "    return tf.keras.layers.Flatten()\n",
    "\n",
    "def dense(label_dim, weight_init) :\n",
    "    return tf.keras.layers.Dense(units=label_dim, use_bias=True, kernel_initializer=weight_init)\n",
    "\n",
    "def relu() :\n",
    "    return tf.keras.layers.Activation(tf.keras.activations.relu)\n",
    "\n",
    "def batch_norm() :\n",
    "    return tf.keras.layers.BatchNormalization()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create model (class version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class create_model_class(tf.keras.Model):\n",
    "    def __init__(self, label_dim):\n",
    "        super(create_model_class, self).__init__()\n",
    "        weight_init = tf.keras.initializers.glorot_uniform()\n",
    "\n",
    "        self.model = tf.keras.Sequential()\n",
    "        self.model.add(flatten())\n",
    "\n",
    "        for i in range(4):\n",
    "            self.model.add(dense(512, weight_init))\n",
    "            self.model.add(batch_norm())\n",
    "            self.model.add(relu())\n",
    "\n",
    "        self.model.add(dense(label_dim, weight_init))\n",
    "\n",
    "    def call(self, x, training=None, mask=None):\n",
    "\n",
    "        x = self.model(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create model (function version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model_function(label_dim) :\n",
    "    weight_init = tf.keras.initializers.glorot_uniform()\n",
    "\n",
    "    model = tf.keras.Sequential()\n",
    "    model.add(flatten())\n",
    "\n",
    "    for i in range(4) :\n",
    "        model.add(dense(512, weight_init))\n",
    "        model.add(batch_norm())\n",
    "        model.add(relu())\n",
    "\n",
    "    model.add(dense(label_dim, weight_init))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define data & hyper-parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" dataset \"\"\"\n",
    "train_x, train_y, test_x, test_y = load_mnist()\n",
    "\n",
    "\"\"\" parameters \"\"\"\n",
    "learning_rate = 0.001\n",
    "batch_size = 128\n",
    "\n",
    "training_epochs = 1\n",
    "training_iterations = len(train_x) // batch_size\n",
    "\n",
    "label_dim = 10\n",
    "\n",
    "train_flag = True\n",
    "\n",
    "\"\"\" Graph Input using Dataset API \"\"\"\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y)).\\\n",
    "    shuffle(buffer_size=100000).\\\n",
    "    prefetch(buffer_size=batch_size).\\\n",
    "    batch(batch_size).\\\n",
    "    repeat()\n",
    "\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((test_x, test_y)).\\\n",
    "    shuffle(buffer_size=100000).\\\n",
    "    prefetch(buffer_size=len(test_x)).\\\n",
    "    batch(len(test_x)).\\\n",
    "    repeat()\n",
    "\n",
    "train_iterator = train_dataset.make_one_shot_iterator()\n",
    "test_iterator = test_dataset.make_one_shot_iterator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define model & optimizer & writer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Model \"\"\"\n",
    "network = create_model_function(label_dim)\n",
    "\n",
    "\"\"\" Training \"\"\"\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "\n",
    "\"\"\" Writer \"\"\"\n",
    "checkpoint_dir = 'checkpoints'\n",
    "logs_dir = 'logs'\n",
    "\n",
    "model_dir = 'nn_batchnorm'\n",
    "\n",
    "checkpoint_dir = os.path.join(checkpoint_dir, model_dir)\n",
    "check_folder(checkpoint_dir)\n",
    "checkpoint_prefix = os.path.join(checkpoint_dir, model_dir)\n",
    "logs_dir = os.path.join(logs_dir, model_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Restore checkpoint & start train or test phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [*] Reading checkpoints...\n",
      " [*] Failed to find a checkpoint\n",
      " [!] Load failed...\n",
      "Epoch: [ 0] [    0/  468] time: 0.4975, train_loss: 0.33036286, train_accuracy: 0.3359, test_Accuracy: 0.2291\n",
      "Epoch: [ 0] [    1/  468] time: 0.7914, train_loss: 0.29299241, train_accuracy: 0.3672, test_Accuracy: 0.3296\n",
      "Epoch: [ 0] [    2/  468] time: 1.0807, train_loss: 0.29535800, train_accuracy: 0.4297, test_Accuracy: 0.3945\n",
      "Epoch: [ 0] [    3/  468] time: 1.3561, train_loss: 0.30906296, train_accuracy: 0.5078, test_Accuracy: 0.4767\n",
      "Epoch: [ 0] [    4/  468] time: 1.6425, train_loss: 0.26806700, train_accuracy: 0.6250, test_Accuracy: 0.5505\n",
      "Epoch: [ 0] [    5/  468] time: 1.9350, train_loss: 0.32235762, train_accuracy: 0.5781, test_Accuracy: 0.5983\n",
      "Epoch: [ 0] [    6/  468] time: 2.2008, train_loss: 0.26554614, train_accuracy: 0.6641, test_Accuracy: 0.6494\n",
      "Epoch: [ 0] [    7/  468] time: 2.4638, train_loss: 0.20839277, train_accuracy: 0.7500, test_Accuracy: 0.6980\n",
      "Epoch: [ 0] [    8/  468] time: 2.7304, train_loss: 0.24621534, train_accuracy: 0.8203, test_Accuracy: 0.7361\n",
      "Epoch: [ 0] [    9/  468] time: 3.0195, train_loss: 0.30940843, train_accuracy: 0.7500, test_Accuracy: 0.7723\n",
      "Epoch: [ 0] [   10/  468] time: 3.3082, train_loss: 0.23286557, train_accuracy: 0.7422, test_Accuracy: 0.7954\n",
      "Epoch: [ 0] [   11/  468] time: 3.6021, train_loss: 0.37185064, train_accuracy: 0.7422, test_Accuracy: 0.8212\n",
      "Epoch: [ 0] [   12/  468] time: 3.8878, train_loss: 0.20489557, train_accuracy: 0.8594, test_Accuracy: 0.8423\n",
      "Epoch: [ 0] [   13/  468] time: 4.1745, train_loss: 0.14178658, train_accuracy: 0.8750, test_Accuracy: 0.8579\n",
      "Epoch: [ 0] [   14/  468] time: 4.4688, train_loss: 0.18831021, train_accuracy: 0.8906, test_Accuracy: 0.8664\n",
      "Epoch: [ 0] [   15/  468] time: 4.7581, train_loss: 0.22178584, train_accuracy: 0.8516, test_Accuracy: 0.8686\n",
      "Epoch: [ 0] [   16/  468] time: 5.0499, train_loss: 0.15011916, train_accuracy: 0.8906, test_Accuracy: 0.8705\n",
      "Epoch: [ 0] [   17/  468] time: 5.3366, train_loss: 0.11185556, train_accuracy: 0.8906, test_Accuracy: 0.8712\n",
      "Epoch: [ 0] [   18/  468] time: 5.6250, train_loss: 0.18777201, train_accuracy: 0.8750, test_Accuracy: 0.8737\n",
      "Epoch: [ 0] [   19/  468] time: 5.9215, train_loss: 0.24554151, train_accuracy: 0.8984, test_Accuracy: 0.8709\n",
      "Epoch: [ 0] [   20/  468] time: 6.2004, train_loss: 0.12950307, train_accuracy: 0.9141, test_Accuracy: 0.8627\n",
      "Epoch: [ 0] [   21/  468] time: 6.4901, train_loss: 0.30777186, train_accuracy: 0.8359, test_Accuracy: 0.8610\n",
      "Epoch: [ 0] [   22/  468] time: 6.7930, train_loss: 0.16224533, train_accuracy: 0.8516, test_Accuracy: 0.8616\n",
      "Epoch: [ 0] [   23/  468] time: 7.0895, train_loss: 0.17987403, train_accuracy: 0.8281, test_Accuracy: 0.8669\n",
      "Epoch: [ 0] [   24/  468] time: 7.3713, train_loss: 0.14167723, train_accuracy: 0.9062, test_Accuracy: 0.8668\n",
      "Epoch: [ 0] [   25/  468] time: 7.6630, train_loss: 0.23095348, train_accuracy: 0.8828, test_Accuracy: 0.8683\n",
      "Epoch: [ 0] [   26/  468] time: 7.9532, train_loss: 0.15378220, train_accuracy: 0.8750, test_Accuracy: 0.8665\n",
      "Epoch: [ 0] [   27/  468] time: 8.2432, train_loss: 0.18093681, train_accuracy: 0.8828, test_Accuracy: 0.8635\n",
      "Epoch: [ 0] [   28/  468] time: 8.5337, train_loss: 0.16689263, train_accuracy: 0.8984, test_Accuracy: 0.8606\n",
      "Epoch: [ 0] [   29/  468] time: 8.7974, train_loss: 0.16516356, train_accuracy: 0.8359, test_Accuracy: 0.8598\n",
      "Epoch: [ 0] [   30/  468] time: 9.0920, train_loss: 0.16654181, train_accuracy: 0.8672, test_Accuracy: 0.8638\n",
      "Epoch: [ 0] [   31/  468] time: 9.3805, train_loss: 0.23453392, train_accuracy: 0.8906, test_Accuracy: 0.8688\n",
      "Epoch: [ 0] [   32/  468] time: 9.6560, train_loss: 0.16296065, train_accuracy: 0.8828, test_Accuracy: 0.8730\n",
      "Epoch: [ 0] [   33/  468] time: 9.9461, train_loss: 0.19446757, train_accuracy: 0.8906, test_Accuracy: 0.8762\n",
      "Epoch: [ 0] [   34/  468] time: 10.2198, train_loss: 0.21550670, train_accuracy: 0.8984, test_Accuracy: 0.8809\n",
      "Epoch: [ 0] [   35/  468] time: 10.5128, train_loss: 0.18052366, train_accuracy: 0.9141, test_Accuracy: 0.8874\n",
      "Epoch: [ 0] [   36/  468] time: 10.8006, train_loss: 0.14563078, train_accuracy: 0.9141, test_Accuracy: 0.8918\n",
      "Epoch: [ 0] [   37/  468] time: 11.0921, train_loss: 0.19181316, train_accuracy: 0.9141, test_Accuracy: 0.8956\n",
      "Epoch: [ 0] [   38/  468] time: 11.3841, train_loss: 0.18485656, train_accuracy: 0.9297, test_Accuracy: 0.9015\n",
      "Epoch: [ 0] [   39/  468] time: 11.6669, train_loss: 0.21941191, train_accuracy: 0.9141, test_Accuracy: 0.9056\n",
      "Epoch: [ 0] [   40/  468] time: 11.9537, train_loss: 0.14315601, train_accuracy: 0.9453, test_Accuracy: 0.9075\n",
      "Epoch: [ 0] [   41/  468] time: 12.2379, train_loss: 0.11220451, train_accuracy: 0.9219, test_Accuracy: 0.9064\n",
      "Epoch: [ 0] [   42/  468] time: 12.5294, train_loss: 0.08643550, train_accuracy: 0.9688, test_Accuracy: 0.9033\n",
      "Epoch: [ 0] [   43/  468] time: 12.7989, train_loss: 0.16712800, train_accuracy: 0.8906, test_Accuracy: 0.9028\n",
      "Epoch: [ 0] [   44/  468] time: 13.0906, train_loss: 0.15595710, train_accuracy: 0.9141, test_Accuracy: 0.9045\n",
      "Epoch: [ 0] [   45/  468] time: 13.3590, train_loss: 0.20998164, train_accuracy: 0.9141, test_Accuracy: 0.9059\n",
      "Epoch: [ 0] [   46/  468] time: 13.6519, train_loss: 0.10192706, train_accuracy: 0.8984, test_Accuracy: 0.9097\n",
      "Epoch: [ 0] [   47/  468] time: 13.9382, train_loss: 0.10565534, train_accuracy: 0.9531, test_Accuracy: 0.9162\n",
      "Epoch: [ 0] [   48/  468] time: 14.2218, train_loss: 0.14988501, train_accuracy: 0.9453, test_Accuracy: 0.9177\n",
      "Epoch: [ 0] [   49/  468] time: 14.5139, train_loss: 0.14903027, train_accuracy: 0.9219, test_Accuracy: 0.9172\n",
      "Epoch: [ 0] [   50/  468] time: 14.7953, train_loss: 0.15871224, train_accuracy: 0.9297, test_Accuracy: 0.9171\n",
      "Epoch: [ 0] [   51/  468] time: 15.0769, train_loss: 0.24307902, train_accuracy: 0.8984, test_Accuracy: 0.9154\n",
      "Epoch: [ 0] [   52/  468] time: 15.3702, train_loss: 0.18263561, train_accuracy: 0.9219, test_Accuracy: 0.9141\n",
      "Epoch: [ 0] [   53/  468] time: 15.6631, train_loss: 0.14420322, train_accuracy: 0.9375, test_Accuracy: 0.9139\n",
      "Epoch: [ 0] [   54/  468] time: 15.9534, train_loss: 0.15244173, train_accuracy: 0.9062, test_Accuracy: 0.9150\n",
      "Epoch: [ 0] [   55/  468] time: 16.2415, train_loss: 0.10869100, train_accuracy: 0.9531, test_Accuracy: 0.9162\n",
      "Epoch: [ 0] [   56/  468] time: 16.5322, train_loss: 0.14378312, train_accuracy: 0.9375, test_Accuracy: 0.9161\n",
      "Epoch: [ 0] [   57/  468] time: 16.8042, train_loss: 0.07503418, train_accuracy: 0.9688, test_Accuracy: 0.9207\n",
      "Epoch: [ 0] [   58/  468] time: 17.0842, train_loss: 0.07075039, train_accuracy: 0.9531, test_Accuracy: 0.9236\n",
      "Epoch: [ 0] [   59/  468] time: 17.3789, train_loss: 0.15796611, train_accuracy: 0.9531, test_Accuracy: 0.9243\n",
      "Epoch: [ 0] [   60/  468] time: 17.6723, train_loss: 0.15890257, train_accuracy: 0.9531, test_Accuracy: 0.9263\n",
      "Epoch: [ 0] [   61/  468] time: 17.9480, train_loss: 0.07769872, train_accuracy: 0.9609, test_Accuracy: 0.9268\n",
      "Epoch: [ 0] [   62/  468] time: 18.2334, train_loss: 0.18216269, train_accuracy: 0.9062, test_Accuracy: 0.9271\n",
      "Epoch: [ 0] [   63/  468] time: 18.5226, train_loss: 0.15232329, train_accuracy: 0.8984, test_Accuracy: 0.9282\n",
      "Epoch: [ 0] [   64/  468] time: 18.8084, train_loss: 0.13572270, train_accuracy: 0.9688, test_Accuracy: 0.9302\n",
      "Epoch: [ 0] [   65/  468] time: 19.0933, train_loss: 0.18152100, train_accuracy: 0.9375, test_Accuracy: 0.9335\n",
      "Epoch: [ 0] [   66/  468] time: 19.3762, train_loss: 0.17971778, train_accuracy: 0.9297, test_Accuracy: 0.9348\n",
      "Epoch: [ 0] [   67/  468] time: 19.6686, train_loss: 0.08616480, train_accuracy: 0.9609, test_Accuracy: 0.9348\n",
      "Epoch: [ 0] [   68/  468] time: 19.9560, train_loss: 0.08750863, train_accuracy: 0.9375, test_Accuracy: 0.9345\n",
      "Epoch: [ 0] [   69/  468] time: 20.2475, train_loss: 0.19714886, train_accuracy: 0.9453, test_Accuracy: 0.9342\n",
      "Epoch: [ 0] [   70/  468] time: 20.5411, train_loss: 0.13898247, train_accuracy: 0.9688, test_Accuracy: 0.9354\n",
      "Epoch: [ 0] [   71/  468] time: 20.8265, train_loss: 0.19265622, train_accuracy: 0.9297, test_Accuracy: 0.9355\n",
      "Epoch: [ 0] [   72/  468] time: 21.1151, train_loss: 0.13917142, train_accuracy: 0.9609, test_Accuracy: 0.9375\n",
      "Epoch: [ 0] [   73/  468] time: 21.3939, train_loss: 0.15555823, train_accuracy: 0.8984, test_Accuracy: 0.9377\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [ 0] [   74/  468] time: 21.6645, train_loss: 0.17417696, train_accuracy: 0.9297, test_Accuracy: 0.9382\n",
      "Epoch: [ 0] [   75/  468] time: 21.9596, train_loss: 0.19491014, train_accuracy: 0.9531, test_Accuracy: 0.9366\n",
      "Epoch: [ 0] [   76/  468] time: 22.2544, train_loss: 0.26912385, train_accuracy: 0.9141, test_Accuracy: 0.9390\n",
      "Epoch: [ 0] [   77/  468] time: 22.5362, train_loss: 0.10218343, train_accuracy: 0.9609, test_Accuracy: 0.9426\n",
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      "Epoch: [ 0] [  366/  468] time: 103.1570, train_loss: 0.07647148, train_accuracy: 0.9766, test_Accuracy: 0.9666\n",
      "Epoch: [ 0] [  367/  468] time: 103.4331, train_loss: 0.06761818, train_accuracy: 0.9688, test_Accuracy: 0.9672\n",
      "Epoch: [ 0] [  368/  468] time: 103.7019, train_loss: 0.05172930, train_accuracy: 0.9922, test_Accuracy: 0.9669\n",
      "Epoch: [ 0] [  369/  468] time: 103.9765, train_loss: 0.07137258, train_accuracy: 0.9688, test_Accuracy: 0.9677\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [ 0] [  370/  468] time: 104.2466, train_loss: 0.07196055, train_accuracy: 0.9688, test_Accuracy: 0.9675\n",
      "Epoch: [ 0] [  371/  468] time: 104.5191, train_loss: 0.07451864, train_accuracy: 0.9688, test_Accuracy: 0.9679\n",
      "Epoch: [ 0] [  372/  468] time: 104.7896, train_loss: 0.09573793, train_accuracy: 0.9766, test_Accuracy: 0.9670\n",
      "Epoch: [ 0] [  373/  468] time: 105.0653, train_loss: 0.07326534, train_accuracy: 0.9609, test_Accuracy: 0.9675\n",
      "Epoch: [ 0] [  374/  468] time: 105.3373, train_loss: 0.12505338, train_accuracy: 0.9766, test_Accuracy: 0.9670\n",
      "Epoch: [ 0] [  375/  468] time: 105.6107, train_loss: 0.13861583, train_accuracy: 0.9609, test_Accuracy: 0.9648\n",
      "Epoch: [ 0] [  376/  468] time: 105.8843, train_loss: 0.05679125, train_accuracy: 0.9844, test_Accuracy: 0.9636\n",
      "Epoch: [ 0] [  377/  468] time: 106.1599, train_loss: 0.04744723, train_accuracy: 0.9766, test_Accuracy: 0.9639\n",
      "Epoch: [ 0] [  378/  468] time: 106.4317, train_loss: 0.05268546, train_accuracy: 0.9766, test_Accuracy: 0.9622\n",
      "Epoch: [ 0] [  379/  468] time: 106.7114, train_loss: 0.06168382, train_accuracy: 0.9766, test_Accuracy: 0.9616\n",
      "Epoch: [ 0] [  380/  468] time: 106.9873, train_loss: 0.13276374, train_accuracy: 0.9609, test_Accuracy: 0.9607\n",
      "Epoch: [ 0] [  381/  468] time: 107.2618, train_loss: 0.11481471, train_accuracy: 0.9453, test_Accuracy: 0.9606\n",
      "Epoch: [ 0] [  382/  468] time: 107.5348, train_loss: 0.06801298, train_accuracy: 0.9609, test_Accuracy: 0.9608\n",
      "Epoch: [ 0] [  383/  468] time: 107.8107, train_loss: 0.11362356, train_accuracy: 0.9453, test_Accuracy: 0.9617\n",
      "Epoch: [ 0] [  384/  468] time: 108.0891, train_loss: 0.09086045, train_accuracy: 0.9688, test_Accuracy: 0.9626\n",
      "Epoch: [ 0] [  385/  468] time: 108.3621, train_loss: 0.12973855, train_accuracy: 0.9688, test_Accuracy: 0.9636\n",
      "Epoch: [ 0] [  386/  468] time: 108.6362, train_loss: 0.05483389, train_accuracy: 0.9844, test_Accuracy: 0.9643\n",
      "Epoch: [ 0] [  387/  468] time: 108.9143, train_loss: 0.05093582, train_accuracy: 0.9844, test_Accuracy: 0.9645\n",
      "Epoch: [ 0] [  388/  468] time: 109.1860, train_loss: 0.12514561, train_accuracy: 0.9766, test_Accuracy: 0.9642\n",
      "Epoch: [ 0] [  389/  468] time: 109.4622, train_loss: 0.04488120, train_accuracy: 0.9844, test_Accuracy: 0.9642\n",
      "Epoch: [ 0] [  390/  468] time: 109.7359, train_loss: 0.07844785, train_accuracy: 0.9844, test_Accuracy: 0.9648\n",
      "Epoch: [ 0] [  391/  468] time: 110.0121, train_loss: 0.03005332, train_accuracy: 1.0000, test_Accuracy: 0.9643\n",
      "Epoch: [ 0] [  392/  468] time: 110.2873, train_loss: 0.11281857, train_accuracy: 0.9766, test_Accuracy: 0.9646\n",
      "Epoch: [ 0] [  393/  468] time: 110.5647, train_loss: 0.05506925, train_accuracy: 0.9922, test_Accuracy: 0.9658\n",
      "Epoch: [ 0] [  394/  468] time: 110.8382, train_loss: 0.05781502, train_accuracy: 0.9922, test_Accuracy: 0.9667\n",
      "Epoch: [ 0] [  395/  468] time: 111.1198, train_loss: 0.07646196, train_accuracy: 0.9688, test_Accuracy: 0.9677\n",
      "Epoch: [ 0] [  396/  468] time: 111.3957, train_loss: 0.08497742, train_accuracy: 0.9688, test_Accuracy: 0.9678\n",
      "Epoch: [ 0] [  397/  468] time: 111.6763, train_loss: 0.02395377, train_accuracy: 0.9922, test_Accuracy: 0.9684\n",
      "Epoch: [ 0] [  398/  468] time: 111.9520, train_loss: 0.05425565, train_accuracy: 0.9844, test_Accuracy: 0.9678\n",
      "Epoch: [ 0] [  399/  468] time: 112.2379, train_loss: 0.06788430, train_accuracy: 0.9922, test_Accuracy: 0.9685\n",
      "Epoch: [ 0] [  400/  468] time: 112.5216, train_loss: 0.08179754, train_accuracy: 0.9766, test_Accuracy: 0.9674\n",
      "Epoch: [ 0] [  401/  468] time: 112.8053, train_loss: 0.06286477, train_accuracy: 1.0000, test_Accuracy: 0.9676\n",
      "Epoch: [ 0] [  402/  468] time: 113.0863, train_loss: 0.04528212, train_accuracy: 0.9609, test_Accuracy: 0.9677\n",
      "Epoch: [ 0] [  403/  468] time: 113.3597, train_loss: 0.11551024, train_accuracy: 0.9531, test_Accuracy: 0.9677\n",
      "Epoch: [ 0] [  404/  468] time: 113.6399, train_loss: 0.06776837, train_accuracy: 0.9688, test_Accuracy: 0.9672\n",
      "Epoch: [ 0] [  405/  468] time: 113.9159, train_loss: 0.04432866, train_accuracy: 0.9922, test_Accuracy: 0.9672\n",
      "Epoch: [ 0] [  406/  468] time: 114.2022, train_loss: 0.04255662, train_accuracy: 1.0000, test_Accuracy: 0.9668\n",
      "Epoch: [ 0] [  407/  468] time: 114.4893, train_loss: 0.08487280, train_accuracy: 0.9688, test_Accuracy: 0.9667\n",
      "Epoch: [ 0] [  408/  468] time: 114.7734, train_loss: 0.03581078, train_accuracy: 0.9922, test_Accuracy: 0.9662\n",
      "Epoch: [ 0] [  409/  468] time: 115.0499, train_loss: 0.02377347, train_accuracy: 1.0000, test_Accuracy: 0.9656\n",
      "Epoch: [ 0] [  410/  468] time: 115.3388, train_loss: 0.08597452, train_accuracy: 0.9688, test_Accuracy: 0.9651\n",
      "Epoch: [ 0] [  411/  468] time: 115.6187, train_loss: 0.06086750, train_accuracy: 0.9688, test_Accuracy: 0.9649\n",
      "Epoch: [ 0] [  412/  468] time: 115.9059, train_loss: 0.05947427, train_accuracy: 0.9766, test_Accuracy: 0.9654\n",
      "Epoch: [ 0] [  413/  468] time: 116.1871, train_loss: 0.14364804, train_accuracy: 0.9531, test_Accuracy: 0.9650\n",
      "Epoch: [ 0] [  414/  468] time: 116.4699, train_loss: 0.14255790, train_accuracy: 0.9531, test_Accuracy: 0.9654\n",
      "Epoch: [ 0] [  415/  468] time: 116.7619, train_loss: 0.15623891, train_accuracy: 0.9453, test_Accuracy: 0.9665\n",
      "Epoch: [ 0] [  416/  468] time: 117.0394, train_loss: 0.06397348, train_accuracy: 0.9844, test_Accuracy: 0.9662\n",
      "Epoch: [ 0] [  417/  468] time: 117.3206, train_loss: 0.04354159, train_accuracy: 0.9766, test_Accuracy: 0.9661\n",
      "Epoch: [ 0] [  418/  468] time: 117.6046, train_loss: 0.16735369, train_accuracy: 0.9609, test_Accuracy: 0.9674\n",
      "Epoch: [ 0] [  419/  468] time: 117.8821, train_loss: 0.04083662, train_accuracy: 0.9844, test_Accuracy: 0.9684\n",
      "Epoch: [ 0] [  420/  468] time: 118.1619, train_loss: 0.09119068, train_accuracy: 0.9844, test_Accuracy: 0.9689\n",
      "Epoch: [ 0] [  421/  468] time: 118.4370, train_loss: 0.05534330, train_accuracy: 0.9766, test_Accuracy: 0.9685\n",
      "Epoch: [ 0] [  422/  468] time: 118.7203, train_loss: 0.07439876, train_accuracy: 0.9766, test_Accuracy: 0.9690\n",
      "Epoch: [ 0] [  423/  468] time: 118.9944, train_loss: 0.05504909, train_accuracy: 0.9922, test_Accuracy: 0.9692\n",
      "Epoch: [ 0] [  424/  468] time: 119.2791, train_loss: 0.06466702, train_accuracy: 0.9766, test_Accuracy: 0.9692\n",
      "Epoch: [ 0] [  425/  468] time: 119.5594, train_loss: 0.08699164, train_accuracy: 0.9688, test_Accuracy: 0.9688\n",
      "Epoch: [ 0] [  426/  468] time: 119.8396, train_loss: 0.07600092, train_accuracy: 0.9766, test_Accuracy: 0.9687\n",
      "Epoch: [ 0] [  427/  468] time: 120.1195, train_loss: 0.08780071, train_accuracy: 0.9766, test_Accuracy: 0.9686\n",
      "Epoch: [ 0] [  428/  468] time: 120.3960, train_loss: 0.11962836, train_accuracy: 0.9609, test_Accuracy: 0.9679\n",
      "Epoch: [ 0] [  429/  468] time: 120.6730, train_loss: 0.10566874, train_accuracy: 0.9609, test_Accuracy: 0.9682\n",
      "Epoch: [ 0] [  430/  468] time: 120.9582, train_loss: 0.09882703, train_accuracy: 0.9766, test_Accuracy: 0.9672\n",
      "Epoch: [ 0] [  431/  468] time: 121.2341, train_loss: 0.11905998, train_accuracy: 0.9609, test_Accuracy: 0.9670\n",
      "Epoch: [ 0] [  432/  468] time: 121.5075, train_loss: 0.07744007, train_accuracy: 0.9688, test_Accuracy: 0.9667\n",
      "Epoch: [ 0] [  433/  468] time: 121.7826, train_loss: 0.16874829, train_accuracy: 0.9609, test_Accuracy: 0.9675\n",
      "Epoch: [ 0] [  434/  468] time: 122.0607, train_loss: 0.15265706, train_accuracy: 0.9766, test_Accuracy: 0.9689\n",
      "Epoch: [ 0] [  435/  468] time: 122.3394, train_loss: 0.09362063, train_accuracy: 0.9766, test_Accuracy: 0.9691\n",
      "Epoch: [ 0] [  436/  468] time: 122.6190, train_loss: 0.05701040, train_accuracy: 0.9844, test_Accuracy: 0.9700\n",
      "Epoch: [ 0] [  437/  468] time: 122.9020, train_loss: 0.08446917, train_accuracy: 0.9609, test_Accuracy: 0.9701\n",
      "Epoch: [ 0] [  438/  468] time: 123.1771, train_loss: 0.04116307, train_accuracy: 0.9922, test_Accuracy: 0.9708\n",
      "Epoch: [ 0] [  439/  468] time: 123.4556, train_loss: 0.05500548, train_accuracy: 0.9844, test_Accuracy: 0.9708\n",
      "Epoch: [ 0] [  440/  468] time: 123.7356, train_loss: 0.08892813, train_accuracy: 0.9766, test_Accuracy: 0.9717\n",
      "Epoch: [ 0] [  441/  468] time: 124.0134, train_loss: 0.06729247, train_accuracy: 0.9688, test_Accuracy: 0.9719\n",
      "Epoch: [ 0] [  442/  468] time: 124.2954, train_loss: 0.06463272, train_accuracy: 0.9766, test_Accuracy: 0.9723\n",
      "Epoch: [ 0] [  443/  468] time: 124.5712, train_loss: 0.07042312, train_accuracy: 0.9609, test_Accuracy: 0.9723\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [ 0] [  444/  468] time: 124.8562, train_loss: 0.04957449, train_accuracy: 0.9688, test_Accuracy: 0.9721\n",
      "Epoch: [ 0] [  445/  468] time: 125.1316, train_loss: 0.14170647, train_accuracy: 0.9531, test_Accuracy: 0.9723\n",
      "Epoch: [ 0] [  446/  468] time: 125.4117, train_loss: 0.04400287, train_accuracy: 0.9766, test_Accuracy: 0.9717\n",
      "Epoch: [ 0] [  447/  468] time: 125.6919, train_loss: 0.05544932, train_accuracy: 0.9844, test_Accuracy: 0.9718\n",
      "Epoch: [ 0] [  448/  468] time: 125.9700, train_loss: 0.08386404, train_accuracy: 0.9609, test_Accuracy: 0.9715\n",
      "Epoch: [ 0] [  449/  468] time: 126.2484, train_loss: 0.09472851, train_accuracy: 0.9609, test_Accuracy: 0.9709\n",
      "Epoch: [ 0] [  450/  468] time: 126.5273, train_loss: 0.06384026, train_accuracy: 0.9922, test_Accuracy: 0.9706\n",
      "Epoch: [ 0] [  451/  468] time: 126.8147, train_loss: 0.03368580, train_accuracy: 0.9844, test_Accuracy: 0.9703\n",
      "Epoch: [ 0] [  452/  468] time: 127.0962, train_loss: 0.10920897, train_accuracy: 0.9766, test_Accuracy: 0.9694\n",
      "Epoch: [ 0] [  453/  468] time: 127.3796, train_loss: 0.10738675, train_accuracy: 0.9844, test_Accuracy: 0.9687\n",
      "Epoch: [ 0] [  454/  468] time: 127.6584, train_loss: 0.05605371, train_accuracy: 0.9844, test_Accuracy: 0.9683\n",
      "Epoch: [ 0] [  455/  468] time: 127.9428, train_loss: 0.08430134, train_accuracy: 0.9922, test_Accuracy: 0.9681\n",
      "Epoch: [ 0] [  456/  468] time: 128.2236, train_loss: 0.05011282, train_accuracy: 0.9688, test_Accuracy: 0.9684\n",
      "Epoch: [ 0] [  457/  468] time: 128.5106, train_loss: 0.06256937, train_accuracy: 0.9688, test_Accuracy: 0.9692\n",
      "Epoch: [ 0] [  458/  468] time: 128.7864, train_loss: 0.06745856, train_accuracy: 0.9922, test_Accuracy: 0.9703\n",
      "Epoch: [ 0] [  459/  468] time: 129.0722, train_loss: 0.13526490, train_accuracy: 0.9609, test_Accuracy: 0.9715\n",
      "Epoch: [ 0] [  460/  468] time: 129.3535, train_loss: 0.05344047, train_accuracy: 0.9766, test_Accuracy: 0.9729\n",
      "Epoch: [ 0] [  461/  468] time: 129.6380, train_loss: 0.05183939, train_accuracy: 0.9922, test_Accuracy: 0.9735\n",
      "Epoch: [ 0] [  462/  468] time: 129.9270, train_loss: 0.05679365, train_accuracy: 0.9922, test_Accuracy: 0.9741\n",
      "Epoch: [ 0] [  463/  468] time: 130.2190, train_loss: 0.04974241, train_accuracy: 0.9844, test_Accuracy: 0.9741\n",
      "Epoch: [ 0] [  464/  468] time: 130.4944, train_loss: 0.07346866, train_accuracy: 0.9844, test_Accuracy: 0.9743\n",
      "Epoch: [ 0] [  465/  468] time: 130.7829, train_loss: 0.04353916, train_accuracy: 0.9922, test_Accuracy: 0.9738\n",
      "Epoch: [ 0] [  466/  468] time: 131.0678, train_loss: 0.07985547, train_accuracy: 0.9688, test_Accuracy: 0.9739\n",
      "Epoch: [ 0] [  467/  468] time: 131.3470, train_loss: 0.04797248, train_accuracy: 0.9922, test_Accuracy: 0.9751\n"
     ]
    }
   ],
   "source": [
    "if train_flag :\n",
    "\n",
    "    checkpoint = tf.train.Checkpoint(dnn=network)\n",
    "\n",
    "    # create writer for tensorboard\n",
    "    summary_writer = tf.contrib.summary.create_file_writer(logdir=logs_dir)\n",
    "    start_time = time()\n",
    "\n",
    "    # restore check-point if it exits\n",
    "    could_load, checkpoint_counter = load(network, checkpoint_dir)\n",
    "    global_step = tf.train.create_global_step()\n",
    "\n",
    "    if could_load:\n",
    "        start_epoch = (int)(checkpoint_counter / training_iterations)\n",
    "        start_iteration = checkpoint_counter - start_epoch * training_iterations\n",
    "        counter = checkpoint_counter\n",
    "        global_step.assign(checkpoint_counter)\n",
    "        print(\" [*] Load SUCCESS\")\n",
    "    else:\n",
    "        start_epoch = 0\n",
    "        start_iteration = 0\n",
    "        counter = 0\n",
    "        print(\" [!] Load failed...\")\n",
    "    \n",
    "    # train phase\n",
    "    with summary_writer.as_default(), tf.contrib.summary.always_record_summaries():  # for tensorboard\n",
    "        for epoch in range(start_epoch, training_epochs):\n",
    "            for idx in range(start_iteration, training_iterations):\n",
    "                train_input, train_label = train_iterator.get_next()\n",
    "                grads = grad(network, train_input, train_label)\n",
    "                optimizer.apply_gradients(grads_and_vars=zip(grads, network.variables), global_step=global_step)\n",
    "\n",
    "                train_loss = loss_fn(network, train_input, train_label)\n",
    "                train_accuracy = accuracy_fn(network, train_input, train_label)\n",
    "\n",
    "                test_input, test_label = test_iterator.get_next()\n",
    "                test_accuracy = accuracy_fn(network, test_input, test_label)\n",
    "\n",
    "                tf.contrib.summary.scalar(name='train_loss', tensor=train_loss)\n",
    "                tf.contrib.summary.scalar(name='train_accuracy', tensor=train_accuracy)\n",
    "                tf.contrib.summary.scalar(name='test_accuracy', tensor=test_accuracy)\n",
    "\n",
    "                print(\n",
    "                    \"Epoch: [%2d] [%5d/%5d] time: %4.4f, train_loss: %.8f, train_accuracy: %.4f, test_Accuracy: %.4f\" \\\n",
    "                    % (epoch, idx, training_iterations, time() - start_time, train_loss, train_accuracy,\n",
    "                       test_accuracy))\n",
    "                counter += 1\n",
    "        checkpoint.save(file_prefix=checkpoint_prefix + '-{}'.format(counter))\n",
    "        \n",
    "# test phase      \n",
    "else :\n",
    "    _, _ = load(network, checkpoint_dir)\n",
    "    test_input, test_label = test_iterator.get_next()\n",
    "    test_accuracy = accuracy_fn(network, test_input, test_label)\n",
    "\n",
    "    print(\"test_Accuracy: %.4f\" % (test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test accuracy : 97.51 %"
   ]
  }
 ],
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