TF_1_x/SelfNormalizingNetworks_CNN_CIFAR10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial on self-normalizing networks on the CIFAR-10 data set\n",
    "\n",
    "Adapted from CIFAR10 tutorial from [exelban](https://github.com/exelban/tensorflow-cifar-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.15.0\n"
     ]
    }
   ],
   "source": [
    "# imports\n",
    "\n",
    "import pickle\n",
    "import sys\n",
    "import tarfile\n",
    "import zipfile\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.framework import ops\n",
    "\n",
    "import numpy as np\n",
    "from math import sqrt\n",
    "\n",
    "from urllib.request import urlretrieve\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "print(tf.__version__)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Fetch Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_data_set(name=\"train\", cifar=10):\n",
    "    x = None\n",
    "    y = None\n",
    "    l = None\n",
    "    \n",
    "    maybe_download_and_extract()\n",
    "    \n",
    "    folder_name = \"cifar_10\" if cifar == 10 else \"cifar_100\"\n",
    "    \n",
    "    f = open('./data_set/' + folder_name + '/batches.meta', 'rb')\n",
    "    datadict = pickle.load(f, encoding='latin1')\n",
    "    f.close()\n",
    "    l = datadict['label_names']\n",
    "    \n",
    "    # mean and sdev of training set\n",
    "    mean_train = 0.4733630004850902\n",
    "    sdev_train = 0.2515689250632212\n",
    "    \n",
    "    if name is \"train\":\n",
    "        for i in range(5):\n",
    "            f = open('./data_set/' + folder_name + '/data_batch_' + str(i + 1), 'rb')\n",
    "            datadict = pickle.load(f, encoding='latin1')\n",
    "            f.close()\n",
    "            \n",
    "            _X = datadict[\"data\"]\n",
    "            _Y = datadict['labels']\n",
    "            \n",
    "            _X = np.array(_X, dtype=float) / 255.0\n",
    "            _X = _X.reshape([-1, 3, 32, 32])\n",
    "            _X = _X.transpose([0, 2, 3, 1])\n",
    "            _X = _X.reshape(-1, 32 * 32 * 3)\n",
    "            \n",
    "            if x is None:\n",
    "                x = _X\n",
    "                y = _Y\n",
    "            else:\n",
    "                x = np.concatenate((x, _X), axis=0)\n",
    "                y = np.concatenate((y, _Y), axis=0)\n",
    "        \n",
    "        # Normalize Data to mean = 0, stdev = 1\n",
    "        x = (x - mean_train) / sdev_train\n",
    "    \n",
    "    elif name is \"test\":\n",
    "        f = open('./data_set/' + folder_name + '/test_batch', 'rb')\n",
    "        datadict = pickle.load(f, encoding='latin1')\n",
    "        f.close()\n",
    "        \n",
    "        x = datadict[\"data\"]\n",
    "        y = np.array(datadict['labels'])\n",
    "        \n",
    "        x = np.array(x, dtype=float) / 255.0\n",
    "        x = x.reshape([-1, 3, 32, 32])\n",
    "        x = x.transpose([0, 2, 3, 1])\n",
    "        x = x.reshape(-1, 32 * 32 * 3)\n",
    "        \n",
    "        # Normalize Data according to mean and sdev of training set\n",
    "        x = (x - mean_train) / sdev_train\n",
    "    \n",
    "    def dense_to_one_hot(labels_dense, num_classes=10):\n",
    "        num_labels = labels_dense.shape[0]\n",
    "        index_offset = np.arange(num_labels) * num_classes\n",
    "        labels_one_hot = np.zeros((num_labels, num_classes))\n",
    "        labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n",
    "        \n",
    "        return labels_one_hot\n",
    "    \n",
    "    return x, dense_to_one_hot(y), l\n",
    "\n",
    "\n",
    "def _print_download_progress(count, block_size, total_size):\n",
    "    pct_complete = float(count * block_size) / total_size\n",
    "    msg = \"\\r- Download progress: {0:.1%}\".format(pct_complete)\n",
    "    sys.stdout.write(msg)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "\n",
    "def maybe_download_and_extract():\n",
    "    main_directory = \"./data_set/\"\n",
    "    cifar_10_directory = main_directory + \"cifar_10/\"\n",
    "    cifar_100_directory = main_directory + \"cifar_100/\"\n",
    "    if not os.path.exists(main_directory):\n",
    "        os.makedirs(main_directory)\n",
    "        \n",
    "        url = \"http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\"\n",
    "        filename = url.split('/')[-1]\n",
    "        file_path = os.path.join(main_directory, filename)\n",
    "        zip_cifar_10 = file_path\n",
    "        file_path, _ = urlretrieve(url=url, filename=file_path, reporthook=_print_download_progress)\n",
    "        \n",
    "        print()\n",
    "        print(\"Download finished. Extracting files.\")\n",
    "        if file_path.endswith(\".zip\"):\n",
    "            zipfile.ZipFile(file=file_path, mode=\"r\").extractall(main_directory)\n",
    "        elif file_path.endswith((\".tar.gz\", \".tgz\")):\n",
    "            tarfile.open(name=file_path, mode=\"r:gz\").extractall(main_directory)\n",
    "        print(\"Done.\")\n",
    "        \n",
    "        url = \"http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\"\n",
    "        filename = url.split('/')[-1]\n",
    "        file_path = os.path.join(main_directory, filename)\n",
    "        zip_cifar_100 = file_path\n",
    "        file_path, _ = urlretrieve(url=url, filename=file_path, reporthook=_print_download_progress)\n",
    "        \n",
    "        print()\n",
    "        print(\"Download finished. Extracting files.\")\n",
    "        if file_path.endswith(\".zip\"):\n",
    "            zipfile.ZipFile(file=file_path, mode=\"r\").extractall(main_directory)\n",
    "        elif file_path.endswith((\".tar.gz\", \".tgz\")):\n",
    "            tarfile.open(name=file_path, mode=\"r:gz\").extractall(main_directory)\n",
    "        print(\"Done.\")\n",
    "        \n",
    "        os.rename(main_directory + \"./cifar-10-batches-py\", cifar_10_directory)\n",
    "        os.rename(main_directory + \"./cifar-100-python\", cifar_100_directory)\n",
    "        os.remove(zip_cifar_10)\n",
    "        os.remove(zip_cifar_100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaled ELU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def selu(x, name=\"selu\"):\n",
    "    \"\"\" When using SELUs you have to keep the following in mind:\n",
    "    # (1) scale inputs to zero mean and unit variance\n",
    "    # (2) use SELUs\n",
    "    # (3) initialize weights with stddev sqrt(1/n)\n",
    "    # (4) use SELU dropout\n",
    "    \"\"\"\n",
    "    with ops.name_scope(name) as scope:\n",
    "        alpha = 1.6732632423543772848170429916717\n",
    "        scale = 1.0507009873554804934193349852946\n",
    "        return scale * tf.where(x >= 0.0, x, alpha * tf.nn.elu(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some helpers to build the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _variable_with_weight_decay(name, shape, activation, stddev, wd=None):    \n",
    "    # Determine number of input features from shape\n",
    "    f_in = np.prod(shape[:-1]) if len(shape) == 4 else shape[0]\n",
    "    \n",
    "    # Calculate sdev for initialization according to activation function\n",
    "    if activation == selu:\n",
    "        sdev = sqrt(1 / f_in)\n",
    "    elif activation == tf.nn.relu:\n",
    "        sdev = sqrt(2 / f_in)\n",
    "    elif activation == tf.nn.elu:\n",
    "        sdev = sqrt(1.5505188080679277 / f_in)\n",
    "    else:\n",
    "        sdev = stddev\n",
    "    \n",
    "    var = tf.get_variable(name=name, shape=shape,\n",
    "                          initializer=tf.truncated_normal_initializer(stddev=sdev, dtype=tf.float32))\n",
    "    if wd is not None:\n",
    "        weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')\n",
    "        tf.add_to_collection('losses', weight_decay)\n",
    "    return var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv2d(scope_name, input, activation, ksize, f_in, f_out, bias_init=0.0, stddev=5e-2):\n",
    "    with tf.variable_scope(scope_name) as scope:\n",
    "        kernel = _variable_with_weight_decay('weights', shape=[ksize, ksize, f_in, f_out], activation=activation,\n",
    "                                             stddev=stddev)\n",
    "        conv = tf.nn.conv2d(input, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "        biases = tf.get_variable('biases', [f_out], initializer=tf.constant_initializer(bias_init), dtype=tf.float32)\n",
    "        pre_activation = tf.nn.bias_add(conv, biases)\n",
    "        return activation(pre_activation, name=scope.name)\n",
    "\n",
    "\n",
    "def fc(scope_name, input, activation, n_in, n_out, stddev=0.04, bias_init=0.0, weight_decay=None):\n",
    "    with tf.variable_scope(scope_name) as scope:\n",
    "        weights = _variable_with_weight_decay('weights', shape=[n_in, n_out], activation=activation, stddev=stddev,\n",
    "                                              wd=weight_decay)\n",
    "        biases = tf.get_variable(name='biases', shape=[n_out], initializer=tf.constant_initializer(bias_init),\n",
    "                                 dtype=tf.float32)\n",
    "        return activation(tf.matmul(input, weights) + biases, name=scope.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model with a specified activation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model(activation):\n",
    "    _IMAGE_SIZE = 32\n",
    "    _IMAGE_CHANNELS = 3\n",
    "    _NUM_CLASSES = 10\n",
    "    _RESHAPE_SIZE = 4 * 4 * 128\n",
    "    \n",
    "    # set activation function\n",
    "    act = selu if activation == \"selu\" else tf.nn.elu if activation == \"elu\" else tf.nn.relu\n",
    "    \n",
    "    with tf.variable_scope(activation):\n",
    "        # input\n",
    "        with tf.name_scope('data'):\n",
    "            x = tf.placeholder(tf.float32, shape=[None, _IMAGE_SIZE * _IMAGE_SIZE * _IMAGE_CHANNELS], name='Input')\n",
    "            y = tf.placeholder(tf.float32, shape=[None, _NUM_CLASSES], name='Output')\n",
    "            x_image = tf.reshape(x, [-1, _IMAGE_SIZE, _IMAGE_SIZE, _IMAGE_CHANNELS], name='images')\n",
    "        \n",
    "        # Conv 1\n",
    "        conv1 = conv2d(\"conv1\", input=x_image, activation=act, ksize=5, f_in=3, f_out=64)\n",
    "        pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')\n",
    "        \n",
    "        # Conv 2\n",
    "        conv2 = conv2d(\"conv2\", input=pool1, activation=act, ksize=5, f_in=64, f_out=64, bias_init=0.1)\n",
    "        pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2')\n",
    "        \n",
    "        # Conv 3-5\n",
    "        conv3 = conv2d(\"conv3\", input=pool2, activation=act, ksize=3, f_in=64, f_out=128)\n",
    "        conv4 = conv2d(\"conv4\", input=conv3, activation=act, ksize=3, f_in=128, f_out=128)\n",
    "        conv5 = conv2d(\"conv5\", input=conv4, activation=act, ksize=3, f_in=128, f_out=128)\n",
    "        \n",
    "        # Pool\n",
    "        pool3 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool3')\n",
    "        \n",
    "        # Reshape\n",
    "        reshape = tf.reshape(pool3, [-1, _RESHAPE_SIZE])\n",
    "        dim = reshape.get_shape()[1].value\n",
    "        \n",
    "        # Fully Connected\n",
    "        fc1 = fc('fully_connected1', input=reshape, activation=act, n_in=dim, n_out=384, stddev=0.04, bias_init=0.1,\n",
    "                 weight_decay=0.004)\n",
    "        fc2 = fc('fully_connected2', input=fc1, activation=act, n_in=384, n_out=192, stddev=0.04, bias_init=0.1,\n",
    "                 weight_decay=0.004)\n",
    "        \n",
    "        # Softmax\n",
    "        with tf.variable_scope('output') as scope:\n",
    "            weights = _variable_with_weight_decay('weights', [192, _NUM_CLASSES], stddev=1 / 192.0,\n",
    "                                                  activation=activation,\n",
    "                                                  wd=0.0)\n",
    "            biases = tf.get_variable(name='biases', shape=[_NUM_CLASSES], initializer=tf.constant_initializer(0.0),\n",
    "                                     dtype=tf.float32)\n",
    "            softmax_linear = tf.add(tf.matmul(fc2, weights), biases, name=scope.name)\n",
    "            \n",
    "            # output\n",
    "            y_pred_cls = tf.argmax(softmax_linear, dimension=1)\n",
    "        \n",
    "        # Define Loss and Optimizer\n",
    "        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=softmax_linear, labels=y))\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)\n",
    "        \n",
    "        correct_prediction = tf.equal(y_pred_cls, tf.argmax(y, dimension=1))\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "        # tf.summary.scalar(\"Accuracy/train\", accuracy)\n",
    "    \n",
    "    return {\"x\": x, \"y\": y, \"output\": y_pred_cls, \"loss\": loss, \"accuracy\": accuracy, \"optimizer\": optimizer, \"name\": activation}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_test(test_x, test_y, models):\n",
    "    \"\"\"\n",
    "        Make prediction for all images in test_x\n",
    "    \"\"\"\n",
    "    i = 0\n",
    "    predicted_class = {\"selu\": np.zeros(shape=len(test_x), dtype=np.int32),\n",
    "                       \"elu\": np.zeros(shape=len(test_x), dtype=np.int32),\n",
    "                       \"relu\":np.zeros(shape=len(test_x), dtype=np.int32)}\n",
    "    while i < len(test_x):\n",
    "        j = min(i + _BATCH_SIZE, len(test_x))\n",
    "        batch_xs = test_x[i:j, :]\n",
    "        batch_ys = test_y[i:j, :]\n",
    "        for name, model in models.items():\n",
    "            predicted_class[name][i:j] = sess.run(model[\"output\"], feed_dict={model['x']: batch_xs, model['y']: batch_ys})\n",
    "        i = j\n",
    "    \n",
    "    accuracy = {\"selu\": 0, \"elu\": 0, \"relu\": 0}\n",
    "    for name, model in models.items():\n",
    "        correct = (np.argmax(test_y, axis=1) == predicted_class[name])        \n",
    "        accuracy[name] = correct.mean() * 100        \n",
    "    \n",
    "    print(\"Accuracy on Test-Set (SELU/ELU/RELU): {0:.2f}% | {1:.2f}% | {2:.2f}%\".format(\n",
    "        accuracy[\"selu\"], accuracy[\"elu\"], accuracy[\"relu\"]))\n",
    "    \n",
    "    return accuracy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_metric(title, ylabel, metric):\n",
    "    # Training Accuracy\n",
    "    plt.figure()    \n",
    "    plt.title(title, size=\"xx-large\")\n",
    "    plt.ylabel(ylabel, size=\"x-large\")    \n",
    "    plt.tick_params(axis=\"x\", bottom=\"off\", labelbottom=\"off\")\n",
    "    \n",
    "    # select manually for consistent colors\n",
    "    plt.plot(metric[\"selu\"], label=\"SELU\", linewidth=2)\n",
    "    plt.plot(metric[\"elu\"], label=\"ELU\", linewidth=2)\n",
    "    plt.plot(metric[\"relu\"], label=\"RELU\", linewidth=2)\n",
    "        \n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def plot(train_loss, train_accuracy, test_accuracy):    \n",
    "    # Training Loss\n",
    "    plot_metric(\"Training Loss\", \"Loss\", train_loss)\n",
    "    \n",
    "    # Training Accuracy\n",
    "    plot_metric(\"Training Accuracy\", \"Accuracy\", train_accuracy)\n",
    "    \n",
    "    # Test Accuracy\n",
    "    plot_metric(\"Test Accuracy\", \"Accuracy\", test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(session, num_iterations, train_x, train_y, test_x, test_y, models, global_step):\n",
    "    \"\"\"\n",
    "        Train CNN\n",
    "    \"\"\"    \n",
    "    train_loss = {\"selu\": [], \"elu\":[], \"relu\": []}\n",
    "    train_accuracy = {\"selu\": [], \"elu\":[], \"relu\": []}    \n",
    "    test_accuracy = {\"selu\": [], \"elu\":[], \"relu\": []}\n",
    "    \n",
    "    inc_step_op = tf.assign(global_step, global_step+1)\n",
    "    # start training\n",
    "    for i in range(num_iterations):\n",
    "        randidx = np.random.randint(len(train_x), size=_BATCH_SIZE)\n",
    "        batch_xs = train_x[randidx]\n",
    "        batch_ys = train_y[randidx]\n",
    "                \n",
    "        optimizers = []\n",
    "        feed_dict = {}\n",
    "        for name, model in models.items():\n",
    "            optimizers.append(model[\"optimizer\"])\n",
    "            feed_dict.update({model[\"x\"]: batch_xs, model[\"y\"]: batch_ys})\n",
    "        \n",
    "        # current step\n",
    "        i_global = session.run(global_step)\n",
    "        \n",
    "        # train\n",
    "        session.run( optimizers, feed_dict=feed_dict)\n",
    "        \n",
    "        # print training loss\n",
    "        if (i_global % 10 == 0) or (i == num_iterations - 1):\n",
    "            l_selu, l_elu, l_relu, acc_selu, acc_elu, acc_relu = session.run(\n",
    "                [models['selu']['loss'], models['elu']['loss'], models['relu']['loss'], \n",
    "                 models['selu']['accuracy'], models['elu']['accuracy'], models['relu']['accuracy']],\n",
    "                feed_dict=feed_dict)\n",
    "            \n",
    "            msg = \"Global Step: {0:>6}, \" \\\n",
    "                  \"accuracy (SELU/ELU/RELU): {1:>6.1%} | {2:>6.1%} | {3:>6.1%}, \" \\\n",
    "                  \"loss (SELU/ELU/RELU): {4:.2f} | {5:.2f} | {6:.2f}\"\n",
    "            print(msg.format(i_global, acc_selu, acc_elu, acc_relu, l_selu, l_elu, l_relu))            \n",
    "            \n",
    "            # collect metrics for plots                            \n",
    "            train_loss[\"selu\"].append(l_selu)\n",
    "            train_loss[\"elu\"].append(l_elu)\n",
    "            train_loss[\"relu\"].append(l_relu)\n",
    "            train_accuracy[\"selu\"].append(acc_selu)\n",
    "            train_accuracy[\"elu\"].append(acc_elu)\n",
    "            train_accuracy[\"relu\"].append(acc_relu)\n",
    "\n",
    "        # evaluate test set accuracy\n",
    "        if (i_global % 100 == 0) or (i == num_iterations - 1):\n",
    "            acc = predict_test(test_x, test_y, models)                \n",
    "            test_accuracy[\"selu\"].append(acc[\"selu\"])\n",
    "            test_accuracy[\"elu\"].append(acc[\"elu\"])\n",
    "            test_accuracy[\"relu\"].append(acc[\"relu\"])\n",
    "            saver.save(session, save_path=_SAVE_PATH + \"/checkpoint\", global_step=global_step)\n",
    "            print(\"Saved checkpoint.\")\n",
    "        \n",
    "        # increment global step\n",
    "        session.run(inc_step_op)\n",
    "    return train_loss, train_accuracy, test_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "_IMG_SIZE = 32\n",
    "_NUM_CHANNELS = 3\n",
    "_BATCH_SIZE = 128\n",
    "_CLASS_SIZE = 10\n",
    "_ITERATION = 10000\n",
    "_SAVE_PATH = \"./checkpoints/cifar-10\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Set GPU\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "if not os.path.exists(_SAVE_PATH):\n",
    "    os.makedirs(_SAVE_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-4-18ff1be5653d>:16: calling TruncatedNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From <ipython-input-6-08c2b124511b>:53: calling argmax (from tensorflow.python.ops.math_ops) with dimension is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use the `axis` argument instead\n",
      "WARNING:tensorflow:From <ipython-input-6-08c2b124511b>:56: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n",
      "WARNING:tensorflow:From <ipython-input-3-687a2dd06014>:11: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "# Build Graph\n",
    "relu = model(\"relu\")\n",
    "selu = model(\"selu\")\n",
    "elu = model(\"elu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Download progress: 100.0%\n",
      "Download finished. Extracting files.\n",
      "Done.\n",
      "- Download progress: 100.0%\n",
      "Download finished. Extracting files.\n",
      "Done.\n",
      "Trying to restore last checkpoint ...\n",
      "Failed to restore checkpoint. Initializing variables instead.\n",
      "Global Step:      0, accuracy (SELU/ELU/RELU):  14.8% |  14.8% |  10.9%, loss (SELU/ELU/RELU): 2.27 | 2.28 | 2.28\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 10.65% | 11.28% | 10.00%\n",
      "Saved checkpoint.\n",
      "Global Step:     10, accuracy (SELU/ELU/RELU):  21.9% |  22.7% |  18.0%, loss (SELU/ELU/RELU): 2.17 | 2.18 | 2.24\n",
      "Global Step:     20, accuracy (SELU/ELU/RELU):  32.0% |  32.8% |  25.8%, loss (SELU/ELU/RELU): 2.01 | 2.00 | 2.13\n",
      "Global Step:     30, accuracy (SELU/ELU/RELU):  32.8% |  35.9% |  29.7%, loss (SELU/ELU/RELU): 1.93 | 1.86 | 1.92\n",
      "Global Step:     40, accuracy (SELU/ELU/RELU):  34.4% |  34.4% |  32.0%, loss (SELU/ELU/RELU): 1.92 | 1.89 | 1.90\n",
      "Global Step:     50, accuracy (SELU/ELU/RELU):  35.2% |  33.6% |  27.3%, loss (SELU/ELU/RELU): 1.88 | 1.86 | 1.93\n",
      "Global Step:     60, accuracy (SELU/ELU/RELU):  29.7% |  34.4% |  28.9%, loss (SELU/ELU/RELU): 1.82 | 1.78 | 1.81\n",
      "Global Step:     70, accuracy (SELU/ELU/RELU):  38.3% |  42.2% |  36.7%, loss (SELU/ELU/RELU): 1.77 | 1.68 | 1.70\n",
      "Global Step:     80, accuracy (SELU/ELU/RELU):  39.1% |  42.2% |  32.8%, loss (SELU/ELU/RELU): 1.73 | 1.73 | 1.80\n",
      "Global Step:     90, accuracy (SELU/ELU/RELU):  35.2% |  25.8% |  34.4%, loss (SELU/ELU/RELU): 1.74 | 1.80 | 1.83\n",
      "Global Step:    100, accuracy (SELU/ELU/RELU):  35.9% |  37.5% |  36.7%, loss (SELU/ELU/RELU): 1.60 | 1.61 | 1.66\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 40.16% | 40.34% | 36.86%\n",
      "Saved checkpoint.\n",
      "Global Step:    110, accuracy (SELU/ELU/RELU):  43.0% |  45.3% |  35.9%, loss (SELU/ELU/RELU): 1.59 | 1.59 | 1.66\n",
      "Global Step:    120, accuracy (SELU/ELU/RELU):  46.9% |  43.8% |  47.7%, loss (SELU/ELU/RELU): 1.56 | 1.55 | 1.57\n",
      "Global Step:    130, accuracy (SELU/ELU/RELU):  43.8% |  41.4% |  37.5%, loss (SELU/ELU/RELU): 1.57 | 1.57 | 1.66\n",
      "Global Step:    140, accuracy (SELU/ELU/RELU):  46.1% |  43.8% |  39.1%, loss (SELU/ELU/RELU): 1.52 | 1.55 | 1.64\n",
      "Global Step:    150, accuracy (SELU/ELU/RELU):  44.5% |  38.3% |  42.2%, loss (SELU/ELU/RELU): 1.48 | 1.50 | 1.55\n",
      "Global Step:    160, accuracy (SELU/ELU/RELU):  41.4% |  38.3% |  42.2%, loss (SELU/ELU/RELU): 1.45 | 1.49 | 1.64\n",
      "Global Step:    170, accuracy (SELU/ELU/RELU):  52.3% |  49.2% |  41.4%, loss (SELU/ELU/RELU): 1.36 | 1.36 | 1.51\n",
      "Global Step:    180, accuracy (SELU/ELU/RELU):  44.5% |  43.0% |  42.2%, loss (SELU/ELU/RELU): 1.52 | 1.59 | 1.57\n",
      "Global Step:    190, accuracy (SELU/ELU/RELU):  56.2% |  49.2% |  50.8%, loss (SELU/ELU/RELU): 1.30 | 1.37 | 1.40\n",
      "Global Step:    200, accuracy (SELU/ELU/RELU):  46.9% |  41.4% |  38.3%, loss (SELU/ELU/RELU): 1.47 | 1.52 | 1.58\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 47.07% | 45.11% | 42.30%\n",
      "Saved checkpoint.\n",
      "Global Step:    210, accuracy (SELU/ELU/RELU):  51.6% |  46.1% |  46.9%, loss (SELU/ELU/RELU): 1.32 | 1.38 | 1.45\n",
      "Global Step:    220, accuracy (SELU/ELU/RELU):  55.5% |  57.0% |  50.8%, loss (SELU/ELU/RELU): 1.32 | 1.34 | 1.50\n",
      "Global Step:    230, accuracy (SELU/ELU/RELU):  47.7% |  48.4% |  43.8%, loss (SELU/ELU/RELU): 1.38 | 1.45 | 1.55\n",
      "Global Step:    240, accuracy (SELU/ELU/RELU):  47.7% |  43.8% |  46.1%, loss (SELU/ELU/RELU): 1.38 | 1.47 | 1.47\n",
      "Global Step:    250, accuracy (SELU/ELU/RELU):  50.0% |  53.9% |  44.5%, loss (SELU/ELU/RELU): 1.39 | 1.43 | 1.52\n",
      "Global Step:    260, accuracy (SELU/ELU/RELU):  51.6% |  49.2% |  40.6%, loss (SELU/ELU/RELU): 1.31 | 1.37 | 1.48\n",
      "Global Step:    270, accuracy (SELU/ELU/RELU):  54.7% |  50.0% |  45.3%, loss (SELU/ELU/RELU): 1.18 | 1.21 | 1.40\n",
      "Global Step:    280, accuracy (SELU/ELU/RELU):  47.7% |  56.2% |  47.7%, loss (SELU/ELU/RELU): 1.27 | 1.26 | 1.30\n",
      "Global Step:    290, accuracy (SELU/ELU/RELU):  56.2% |  56.2% |  50.8%, loss (SELU/ELU/RELU): 1.19 | 1.25 | 1.38\n",
      "Global Step:    300, accuracy (SELU/ELU/RELU):  56.2% |  53.9% |  44.5%, loss (SELU/ELU/RELU): 1.25 | 1.34 | 1.50\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 50.47% | 48.70% | 45.49%\n",
      "Saved checkpoint.\n",
      "Global Step:    310, accuracy (SELU/ELU/RELU):  54.7% |  56.2% |  52.3%, loss (SELU/ELU/RELU): 1.32 | 1.34 | 1.46\n",
      "Global Step:    320, accuracy (SELU/ELU/RELU):  55.5% |  54.7% |  53.9%, loss (SELU/ELU/RELU): 1.21 | 1.27 | 1.36\n",
      "Global Step:    330, accuracy (SELU/ELU/RELU):  60.9% |  57.0% |  57.8%, loss (SELU/ELU/RELU): 1.23 | 1.32 | 1.34\n",
      "Global Step:    340, accuracy (SELU/ELU/RELU):  52.3% |  46.9% |  46.1%, loss (SELU/ELU/RELU): 1.29 | 1.40 | 1.44\n",
      "Global Step:    350, accuracy (SELU/ELU/RELU):  53.1% |  48.4% |  46.1%, loss (SELU/ELU/RELU): 1.39 | 1.44 | 1.48\n",
      "Global Step:    360, accuracy (SELU/ELU/RELU):  61.7% |  57.8% |  54.7%, loss (SELU/ELU/RELU): 1.06 | 1.17 | 1.20\n",
      "Global Step:    370, accuracy (SELU/ELU/RELU):  43.8% |  43.8% |  43.0%, loss (SELU/ELU/RELU): 1.49 | 1.54 | 1.57\n",
      "Global Step:    380, accuracy (SELU/ELU/RELU):  57.0% |  60.9% |  59.4%, loss (SELU/ELU/RELU): 1.10 | 1.18 | 1.26\n",
      "Global Step:    390, accuracy (SELU/ELU/RELU):  64.8% |  59.4% |  55.5%, loss (SELU/ELU/RELU): 1.07 | 1.15 | 1.32\n",
      "Global Step:    400, accuracy (SELU/ELU/RELU):  60.2% |  51.6% |  54.7%, loss (SELU/ELU/RELU): 1.27 | 1.35 | 1.42\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 53.94% | 52.48% | 49.03%\n",
      "Saved checkpoint.\n",
      "Global Step:    410, accuracy (SELU/ELU/RELU):  44.5% |  46.1% |  44.5%, loss (SELU/ELU/RELU): 1.41 | 1.46 | 1.50\n",
      "Global Step:    420, accuracy (SELU/ELU/RELU):  51.6% |  53.9% |  49.2%, loss (SELU/ELU/RELU): 1.21 | 1.26 | 1.36\n",
      "Global Step:    430, accuracy (SELU/ELU/RELU):  66.4% |  64.8% |  57.8%, loss (SELU/ELU/RELU): 1.05 | 1.12 | 1.25\n",
      "Global Step:    440, accuracy (SELU/ELU/RELU):  64.1% |  60.9% |  57.0%, loss (SELU/ELU/RELU): 1.03 | 1.10 | 1.18\n",
      "Global Step:    450, accuracy (SELU/ELU/RELU):  64.1% |  62.5% |  54.7%, loss (SELU/ELU/RELU): 1.10 | 1.16 | 1.31\n",
      "Global Step:    460, accuracy (SELU/ELU/RELU):  52.3% |  46.1% |  40.6%, loss (SELU/ELU/RELU): 1.42 | 1.50 | 1.57\n",
      "Global Step:    470, accuracy (SELU/ELU/RELU):  60.2% |  55.5% |  48.4%, loss (SELU/ELU/RELU): 1.09 | 1.16 | 1.33\n",
      "Global Step:    480, accuracy (SELU/ELU/RELU):  54.7% |  53.1% |  52.3%, loss (SELU/ELU/RELU): 1.16 | 1.23 | 1.31\n",
      "Global Step:    490, accuracy (SELU/ELU/RELU):  57.8% |  55.5% |  57.0%, loss (SELU/ELU/RELU): 1.16 | 1.25 | 1.29\n",
      "Global Step:    500, accuracy (SELU/ELU/RELU):  53.1% |  51.6% |  46.1%, loss (SELU/ELU/RELU): 1.25 | 1.25 | 1.39\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 56.50% | 54.08% | 50.19%\n",
      "WARNING:tensorflow:From /home/kai/miniconda3/envs/snn_tf1_compat/lib/python3.7/site-packages/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to delete files with this prefix.\n",
      "Saved checkpoint.\n",
      "Global Step:    510, accuracy (SELU/ELU/RELU):  57.8% |  57.8% |  49.2%, loss (SELU/ELU/RELU): 1.22 | 1.23 | 1.33\n",
      "Global Step:    520, accuracy (SELU/ELU/RELU):  57.8% |  54.7% |  53.1%, loss (SELU/ELU/RELU): 1.23 | 1.30 | 1.34\n",
      "Global Step:    530, accuracy (SELU/ELU/RELU):  57.8% |  58.6% |  55.5%, loss (SELU/ELU/RELU): 1.13 | 1.17 | 1.32\n",
      "Global Step:    540, accuracy (SELU/ELU/RELU):  57.0% |  59.4% |  54.7%, loss (SELU/ELU/RELU): 1.12 | 1.15 | 1.33\n",
      "Global Step:    550, accuracy (SELU/ELU/RELU):  57.8% |  58.6% |  54.7%, loss (SELU/ELU/RELU): 1.19 | 1.23 | 1.29\n",
      "Global Step:    560, accuracy (SELU/ELU/RELU):  60.2% |  53.1% |  53.1%, loss (SELU/ELU/RELU): 1.07 | 1.20 | 1.28\n",
      "Global Step:    570, accuracy (SELU/ELU/RELU):  60.2% |  52.3% |  46.9%, loss (SELU/ELU/RELU): 1.03 | 1.18 | 1.28\n",
      "Global Step:    580, accuracy (SELU/ELU/RELU):  60.2% |  50.8% |  48.4%, loss (SELU/ELU/RELU): 1.23 | 1.42 | 1.49\n",
      "Global Step:    590, accuracy (SELU/ELU/RELU):  63.3% |  56.2% |  55.5%, loss (SELU/ELU/RELU): 1.02 | 1.12 | 1.21\n",
      "Global Step:    600, accuracy (SELU/ELU/RELU):  61.7% |  56.2% |  55.5%, loss (SELU/ELU/RELU): 1.12 | 1.22 | 1.27\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 58.26% | 55.55% | 52.55%\n",
      "Saved checkpoint.\n",
      "Global Step:    610, accuracy (SELU/ELU/RELU):  64.1% |  64.8% |  58.6%, loss (SELU/ELU/RELU): 1.05 | 1.07 | 1.19\n",
      "Global Step:    620, accuracy (SELU/ELU/RELU):  64.8% |  62.5% |  57.0%, loss (SELU/ELU/RELU): 0.99 | 1.10 | 1.22\n",
      "Global Step:    630, accuracy (SELU/ELU/RELU):  59.4% |  54.7% |  52.3%, loss (SELU/ELU/RELU): 1.06 | 1.13 | 1.25\n",
      "Global Step:    640, accuracy (SELU/ELU/RELU):  63.3% |  56.2% |  59.4%, loss (SELU/ELU/RELU): 1.05 | 1.19 | 1.29\n",
      "Global Step:    650, accuracy (SELU/ELU/RELU):  64.8% |  62.5% |  57.0%, loss (SELU/ELU/RELU): 1.05 | 1.17 | 1.21\n",
      "Global Step:    660, accuracy (SELU/ELU/RELU):  53.9% |  51.6% |  51.6%, loss (SELU/ELU/RELU): 1.21 | 1.25 | 1.37\n",
      "Global Step:    670, accuracy (SELU/ELU/RELU):  59.4% |  58.6% |  57.0%, loss (SELU/ELU/RELU): 1.01 | 1.05 | 1.14\n",
      "Global Step:    680, accuracy (SELU/ELU/RELU):  63.3% |  61.7% |  56.2%, loss (SELU/ELU/RELU): 1.00 | 1.04 | 1.20\n",
      "Global Step:    690, accuracy (SELU/ELU/RELU):  69.5% |  61.7% |  60.9%, loss (SELU/ELU/RELU): 0.93 | 1.08 | 1.15\n",
      "Global Step:    700, accuracy (SELU/ELU/RELU):  63.3% |  63.3% |  60.2%, loss (SELU/ELU/RELU): 0.98 | 1.03 | 1.13\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 61.13% | 58.17% | 53.68%\n",
      "Saved checkpoint.\n",
      "Global Step:    710, accuracy (SELU/ELU/RELU):  57.8% |  52.3% |  56.2%, loss (SELU/ELU/RELU): 1.15 | 1.17 | 1.32\n",
      "Global Step:    720, accuracy (SELU/ELU/RELU):  60.9% |  57.8% |  47.7%, loss (SELU/ELU/RELU): 1.12 | 1.24 | 1.29\n",
      "Global Step:    730, accuracy (SELU/ELU/RELU):  66.4% |  62.5% |  60.9%, loss (SELU/ELU/RELU): 0.97 | 1.06 | 1.21\n",
      "Global Step:    740, accuracy (SELU/ELU/RELU):  59.4% |  53.1% |  55.5%, loss (SELU/ELU/RELU): 1.06 | 1.21 | 1.25\n",
      "Global Step:    750, accuracy (SELU/ELU/RELU):  64.8% |  62.5% |  57.8%, loss (SELU/ELU/RELU): 1.04 | 1.09 | 1.22\n",
      "Global Step:    760, accuracy (SELU/ELU/RELU):  63.3% |  56.2% |  54.7%, loss (SELU/ELU/RELU): 1.03 | 1.19 | 1.28\n",
      "Global Step:    770, accuracy (SELU/ELU/RELU):  68.8% |  63.3% |  61.7%, loss (SELU/ELU/RELU): 0.88 | 1.03 | 1.06\n",
      "Global Step:    780, accuracy (SELU/ELU/RELU):  66.4% |  63.3% |  56.2%, loss (SELU/ELU/RELU): 0.96 | 1.06 | 1.26\n",
      "Global Step:    790, accuracy (SELU/ELU/RELU):  63.3% |  60.2% |  57.0%, loss (SELU/ELU/RELU): 1.10 | 1.22 | 1.23\n",
      "Global Step:    800, accuracy (SELU/ELU/RELU):  53.9% |  54.7% |  57.8%, loss (SELU/ELU/RELU): 1.06 | 1.15 | 1.20\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 62.34% | 59.26% | 56.14%\n",
      "Saved checkpoint.\n",
      "Global Step:    810, accuracy (SELU/ELU/RELU):  68.0% |  59.4% |  62.5%, loss (SELU/ELU/RELU): 0.93 | 1.05 | 1.13\n",
      "Global Step:    820, accuracy (SELU/ELU/RELU):  64.8% |  57.0% |  53.9%, loss (SELU/ELU/RELU): 0.97 | 1.09 | 1.24\n",
      "Global Step:    830, accuracy (SELU/ELU/RELU):  64.8% |  60.9% |  50.0%, loss (SELU/ELU/RELU): 0.99 | 1.02 | 1.20\n",
      "Global Step:    840, accuracy (SELU/ELU/RELU):  76.6% |  73.4% |  68.8%, loss (SELU/ELU/RELU): 0.83 | 0.92 | 0.98\n",
      "Global Step:    850, accuracy (SELU/ELU/RELU):  68.8% |  66.4% |  60.2%, loss (SELU/ELU/RELU): 0.97 | 1.00 | 1.21\n",
      "Global Step:    860, accuracy (SELU/ELU/RELU):  69.5% |  68.8% |  65.6%, loss (SELU/ELU/RELU): 0.87 | 0.96 | 1.04\n",
      "Global Step:    870, accuracy (SELU/ELU/RELU):  65.6% |  61.7% |  60.2%, loss (SELU/ELU/RELU): 0.98 | 1.05 | 1.09\n",
      "Global Step:    880, accuracy (SELU/ELU/RELU):  60.9% |  55.5% |  50.0%, loss (SELU/ELU/RELU): 1.07 | 1.14 | 1.30\n",
      "Global Step:    890, accuracy (SELU/ELU/RELU):  62.5% |  61.7% |  60.9%, loss (SELU/ELU/RELU): 0.97 | 1.00 | 1.06\n",
      "Global Step:    900, accuracy (SELU/ELU/RELU):  68.0% |  60.2% |  53.9%, loss (SELU/ELU/RELU): 0.89 | 1.10 | 1.21\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 63.93% | 61.38% | 58.38%\n",
      "Saved checkpoint.\n",
      "Global Step:    910, accuracy (SELU/ELU/RELU):  61.7% |  57.8% |  55.5%, loss (SELU/ELU/RELU): 1.08 | 1.12 | 1.23\n",
      "Global Step:    920, accuracy (SELU/ELU/RELU):  64.8% |  54.7% |  52.3%, loss (SELU/ELU/RELU): 1.09 | 1.27 | 1.34\n",
      "Global Step:    930, accuracy (SELU/ELU/RELU):  58.6% |  60.9% |  59.4%, loss (SELU/ELU/RELU): 1.13 | 1.15 | 1.25\n",
      "Global Step:    940, accuracy (SELU/ELU/RELU):  60.2% |  61.7% |  50.8%, loss (SELU/ELU/RELU): 1.04 | 1.12 | 1.27\n",
      "Global Step:    950, accuracy (SELU/ELU/RELU):  71.1% |  70.3% |  61.7%, loss (SELU/ELU/RELU): 0.87 | 0.92 | 1.01\n",
      "Global Step:    960, accuracy (SELU/ELU/RELU):  64.1% |  66.4% |  57.0%, loss (SELU/ELU/RELU): 1.06 | 1.11 | 1.22\n",
      "Global Step:    970, accuracy (SELU/ELU/RELU):  70.3% |  69.5% |  64.8%, loss (SELU/ELU/RELU): 0.84 | 0.96 | 1.06\n",
      "Global Step:    980, accuracy (SELU/ELU/RELU):  67.2% |  61.7% |  51.6%, loss (SELU/ELU/RELU): 0.95 | 1.06 | 1.28\n",
      "Global Step:    990, accuracy (SELU/ELU/RELU):  70.3% |  68.8% |  67.2%, loss (SELU/ELU/RELU): 0.77 | 0.93 | 0.95\n",
      "Global Step:   1000, accuracy (SELU/ELU/RELU):  64.8% |  65.6% |  60.9%, loss (SELU/ELU/RELU): 0.95 | 1.01 | 1.12\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 64.19% | 60.32% | 57.43%\n",
      "Saved checkpoint.\n",
      "Global Step:   1010, accuracy (SELU/ELU/RELU):  65.6% |  68.0% |  61.7%, loss (SELU/ELU/RELU): 0.99 | 1.01 | 1.22\n",
      "Global Step:   1020, accuracy (SELU/ELU/RELU):  65.6% |  63.3% |  56.2%, loss (SELU/ELU/RELU): 1.00 | 1.11 | 1.25\n",
      "Global Step:   1030, accuracy (SELU/ELU/RELU):  63.3% |  60.9% |  50.8%, loss (SELU/ELU/RELU): 1.00 | 1.10 | 1.33\n",
      "Global Step:   1040, accuracy (SELU/ELU/RELU):  60.2% |  59.4% |  53.1%, loss (SELU/ELU/RELU): 1.01 | 1.08 | 1.20\n",
      "Global Step:   1050, accuracy (SELU/ELU/RELU):  72.7% |  68.0% |  66.4%, loss (SELU/ELU/RELU): 0.82 | 0.90 | 0.98\n",
      "Global Step:   1060, accuracy (SELU/ELU/RELU):  66.4% |  66.4% |  60.2%, loss (SELU/ELU/RELU): 0.97 | 1.00 | 1.12\n",
      "Global Step:   1070, accuracy (SELU/ELU/RELU):  69.5% |  66.4% |  63.3%, loss (SELU/ELU/RELU): 0.86 | 0.93 | 1.00\n",
      "Global Step:   1080, accuracy (SELU/ELU/RELU):  69.5% |  66.4% |  65.6%, loss (SELU/ELU/RELU): 0.91 | 0.91 | 0.98\n",
      "Global Step:   1090, accuracy (SELU/ELU/RELU):  71.9% |  64.8% |  61.7%, loss (SELU/ELU/RELU): 0.84 | 0.95 | 1.13\n",
      "Global Step:   1100, accuracy (SELU/ELU/RELU):  67.2% |  66.4% |  57.8%, loss (SELU/ELU/RELU): 0.87 | 0.87 | 1.02\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 63.92% | 61.69% | 58.35%\n",
      "Saved checkpoint.\n",
      "Global Step:   1110, accuracy (SELU/ELU/RELU):  75.8% |  74.2% |  68.0%, loss (SELU/ELU/RELU): 0.77 | 0.83 | 0.93\n",
      "Global Step:   1120, accuracy (SELU/ELU/RELU):  72.7% |  71.1% |  68.0%, loss (SELU/ELU/RELU): 0.75 | 0.85 | 1.00\n",
      "Global Step:   1130, accuracy (SELU/ELU/RELU):  73.4% |  64.1% |  61.7%, loss (SELU/ELU/RELU): 0.83 | 0.95 | 1.06\n",
      "Global Step:   1140, accuracy (SELU/ELU/RELU):  72.7% |  69.5% |  56.2%, loss (SELU/ELU/RELU): 0.75 | 0.94 | 1.13\n",
      "Global Step:   1150, accuracy (SELU/ELU/RELU):  67.2% |  68.8% |  64.8%, loss (SELU/ELU/RELU): 0.83 | 0.82 | 0.98\n",
      "Global Step:   1160, accuracy (SELU/ELU/RELU):  65.6% |  59.4% |  59.4%, loss (SELU/ELU/RELU): 1.06 | 1.12 | 1.26\n",
      "Global Step:   1170, accuracy (SELU/ELU/RELU):  59.4% |  59.4% |  57.0%, loss (SELU/ELU/RELU): 1.00 | 1.04 | 1.17\n",
      "Global Step:   1180, accuracy (SELU/ELU/RELU):  68.0% |  73.4% |  64.1%, loss (SELU/ELU/RELU): 0.78 | 0.84 | 0.96\n",
      "Global Step:   1190, accuracy (SELU/ELU/RELU):  75.8% |  69.5% |  69.5%, loss (SELU/ELU/RELU): 0.75 | 0.82 | 0.89\n",
      "Global Step:   1200, accuracy (SELU/ELU/RELU):  74.2% |  68.0% |  65.6%, loss (SELU/ELU/RELU): 0.83 | 0.94 | 0.99\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 66.38% | 63.15% | 59.69%\n",
      "Saved checkpoint.\n",
      "Global Step:   1210, accuracy (SELU/ELU/RELU):  68.8% |  67.2% |  64.1%, loss (SELU/ELU/RELU): 0.87 | 0.94 | 1.07\n",
      "Global Step:   1220, accuracy (SELU/ELU/RELU):  68.0% |  67.2% |  59.4%, loss (SELU/ELU/RELU): 0.92 | 0.91 | 1.12\n",
      "Global Step:   1230, accuracy (SELU/ELU/RELU):  72.7% |  68.8% |  67.2%, loss (SELU/ELU/RELU): 0.78 | 0.87 | 0.93\n",
      "Global Step:   1240, accuracy (SELU/ELU/RELU):  67.2% |  61.7% |  60.2%, loss (SELU/ELU/RELU): 0.97 | 1.08 | 1.15\n",
      "Global Step:   1250, accuracy (SELU/ELU/RELU):  70.3% |  64.8% |  57.8%, loss (SELU/ELU/RELU): 0.84 | 0.95 | 1.06\n",
      "Global Step:   1260, accuracy (SELU/ELU/RELU):  68.8% |  64.1% |  64.1%, loss (SELU/ELU/RELU): 0.95 | 1.03 | 1.09\n",
      "Global Step:   1270, accuracy (SELU/ELU/RELU):  68.0% |  66.4% |  59.4%, loss (SELU/ELU/RELU): 0.90 | 0.93 | 1.07\n",
      "Global Step:   1280, accuracy (SELU/ELU/RELU):  69.5% |  68.0% |  64.8%, loss (SELU/ELU/RELU): 0.87 | 0.85 | 1.01\n",
      "Global Step:   1290, accuracy (SELU/ELU/RELU):  60.9% |  57.0% |  61.7%, loss (SELU/ELU/RELU): 0.94 | 0.98 | 1.03\n",
      "Global Step:   1300, accuracy (SELU/ELU/RELU):  67.2% |  68.8% |  58.6%, loss (SELU/ELU/RELU): 0.85 | 0.93 | 1.09\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 64.97% | 63.94% | 59.54%\n",
      "Saved checkpoint.\n",
      "Global Step:   1310, accuracy (SELU/ELU/RELU):  72.7% |  69.5% |  64.1%, loss (SELU/ELU/RELU): 0.83 | 0.91 | 1.05\n",
      "Global Step:   1320, accuracy (SELU/ELU/RELU):  75.0% |  69.5% |  67.2%, loss (SELU/ELU/RELU): 0.75 | 0.87 | 0.93\n",
      "Global Step:   1330, accuracy (SELU/ELU/RELU):  73.4% |  65.6% |  65.6%, loss (SELU/ELU/RELU): 0.73 | 0.86 | 0.98\n",
      "Global Step:   1340, accuracy (SELU/ELU/RELU):  66.4% |  60.9% |  57.8%, loss (SELU/ELU/RELU): 0.94 | 1.06 | 1.15\n",
      "Global Step:   1350, accuracy (SELU/ELU/RELU):  75.0% |  71.1% |  68.8%, loss (SELU/ELU/RELU): 0.77 | 0.86 | 0.95\n",
      "Global Step:   1360, accuracy (SELU/ELU/RELU):  70.3% |  66.4% |  71.9%, loss (SELU/ELU/RELU): 0.79 | 0.89 | 0.92\n",
      "Global Step:   1370, accuracy (SELU/ELU/RELU):  69.5% |  68.0% |  65.6%, loss (SELU/ELU/RELU): 0.78 | 0.89 | 1.06\n",
      "Global Step:   1380, accuracy (SELU/ELU/RELU):  75.8% |  70.3% |  64.1%, loss (SELU/ELU/RELU): 0.72 | 0.80 | 0.93\n",
      "Global Step:   1390, accuracy (SELU/ELU/RELU):  67.2% |  63.3% |  56.2%, loss (SELU/ELU/RELU): 0.98 | 1.01 | 1.15\n",
      "Global Step:   1400, accuracy (SELU/ELU/RELU):  68.8% |  64.1% |  62.5%, loss (SELU/ELU/RELU): 0.82 | 0.95 | 1.01\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 68.17% | 65.35% | 61.08%\n",
      "Saved checkpoint.\n",
      "Global Step:   1410, accuracy (SELU/ELU/RELU):  74.2% |  71.1% |  64.8%, loss (SELU/ELU/RELU): 0.82 | 0.85 | 0.94\n",
      "Global Step:   1420, accuracy (SELU/ELU/RELU):  72.7% |  71.9% |  63.3%, loss (SELU/ELU/RELU): 0.82 | 0.84 | 1.00\n",
      "Global Step:   1430, accuracy (SELU/ELU/RELU):  75.0% |  74.2% |  66.4%, loss (SELU/ELU/RELU): 0.78 | 0.81 | 0.96\n",
      "Global Step:   1440, accuracy (SELU/ELU/RELU):  68.8% |  65.6% |  62.5%, loss (SELU/ELU/RELU): 0.94 | 1.05 | 1.14\n",
      "Global Step:   1450, accuracy (SELU/ELU/RELU):  70.3% |  71.1% |  65.6%, loss (SELU/ELU/RELU): 0.78 | 0.86 | 0.99\n",
      "Global Step:   1460, accuracy (SELU/ELU/RELU):  75.0% |  71.1% |  70.3%, loss (SELU/ELU/RELU): 0.76 | 0.85 | 0.98\n",
      "Global Step:   1470, accuracy (SELU/ELU/RELU):  72.7% |  69.5% |  64.1%, loss (SELU/ELU/RELU): 0.85 | 0.91 | 1.02\n",
      "Global Step:   1480, accuracy (SELU/ELU/RELU):  74.2% |  70.3% |  59.4%, loss (SELU/ELU/RELU): 0.71 | 0.87 | 1.01\n",
      "Global Step:   1490, accuracy (SELU/ELU/RELU):  71.9% |  68.8% |  66.4%, loss (SELU/ELU/RELU): 0.84 | 0.90 | 1.02\n",
      "Global Step:   1500, accuracy (SELU/ELU/RELU):  70.3% |  67.2% |  60.2%, loss (SELU/ELU/RELU): 0.76 | 0.89 | 1.08\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 68.25% | 66.16% | 60.76%\n",
      "Saved checkpoint.\n",
      "Global Step:   1510, accuracy (SELU/ELU/RELU):  74.2% |  71.1% |  68.8%, loss (SELU/ELU/RELU): 0.85 | 0.99 | 1.07\n",
      "Global Step:   1520, accuracy (SELU/ELU/RELU):  69.5% |  64.8% |  57.8%, loss (SELU/ELU/RELU): 0.88 | 1.00 | 1.16\n",
      "Global Step:   1530, accuracy (SELU/ELU/RELU):  74.2% |  69.5% |  64.8%, loss (SELU/ELU/RELU): 0.74 | 0.86 | 0.92\n",
      "Global Step:   1540, accuracy (SELU/ELU/RELU):  74.2% |  72.7% |  70.3%, loss (SELU/ELU/RELU): 0.75 | 0.75 | 0.84\n",
      "Global Step:   1550, accuracy (SELU/ELU/RELU):  68.8% |  62.5% |  60.2%, loss (SELU/ELU/RELU): 0.82 | 0.90 | 1.00\n",
      "Global Step:   1560, accuracy (SELU/ELU/RELU):  73.4% |  70.3% |  67.2%, loss (SELU/ELU/RELU): 0.79 | 0.80 | 0.89\n",
      "Global Step:   1570, accuracy (SELU/ELU/RELU):  74.2% |  72.7% |  67.2%, loss (SELU/ELU/RELU): 0.73 | 0.83 | 0.94\n",
      "Global Step:   1580, accuracy (SELU/ELU/RELU):  72.7% |  66.4% |  66.4%, loss (SELU/ELU/RELU): 0.76 | 0.83 | 0.96\n",
      "Global Step:   1590, accuracy (SELU/ELU/RELU):  71.9% |  70.3% |  67.2%, loss (SELU/ELU/RELU): 0.77 | 0.86 | 0.96\n",
      "Global Step:   1600, accuracy (SELU/ELU/RELU):  70.3% |  68.0% |  67.2%, loss (SELU/ELU/RELU): 0.82 | 0.87 | 0.90\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 68.31% | 65.80% | 63.05%\n",
      "Saved checkpoint.\n",
      "Global Step:   1610, accuracy (SELU/ELU/RELU):  78.1% |  74.2% |  73.4%, loss (SELU/ELU/RELU): 0.64 | 0.70 | 0.75\n",
      "Global Step:   1620, accuracy (SELU/ELU/RELU):  71.9% |  71.9% |  69.5%, loss (SELU/ELU/RELU): 0.84 | 0.99 | 0.97\n",
      "Global Step:   1630, accuracy (SELU/ELU/RELU):  78.9% |  68.8% |  64.8%, loss (SELU/ELU/RELU): 0.68 | 0.84 | 1.00\n",
      "Global Step:   1640, accuracy (SELU/ELU/RELU):  72.7% |  68.0% |  65.6%, loss (SELU/ELU/RELU): 0.77 | 0.85 | 0.97\n",
      "Global Step:   1650, accuracy (SELU/ELU/RELU):  74.2% |  73.4% |  67.2%, loss (SELU/ELU/RELU): 0.74 | 0.82 | 0.92\n",
      "Global Step:   1660, accuracy (SELU/ELU/RELU):  71.9% |  68.8% |  65.6%, loss (SELU/ELU/RELU): 0.73 | 0.80 | 0.97\n",
      "Global Step:   1670, accuracy (SELU/ELU/RELU):  74.2% |  72.7% |  69.5%, loss (SELU/ELU/RELU): 0.81 | 0.90 | 1.04\n",
      "Global Step:   1680, accuracy (SELU/ELU/RELU):  79.7% |  78.1% |  74.2%, loss (SELU/ELU/RELU): 0.58 | 0.65 | 0.75\n",
      "Global Step:   1690, accuracy (SELU/ELU/RELU):  74.2% |  73.4% |  71.1%, loss (SELU/ELU/RELU): 0.69 | 0.85 | 0.89\n",
      "Global Step:   1700, accuracy (SELU/ELU/RELU):  75.0% |  71.9% |  65.6%, loss (SELU/ELU/RELU): 0.78 | 0.88 | 1.06\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 69.28% | 66.81% | 62.98%\n",
      "Saved checkpoint.\n",
      "Global Step:   1710, accuracy (SELU/ELU/RELU):  78.9% |  71.1% |  64.1%, loss (SELU/ELU/RELU): 0.69 | 0.80 | 0.97\n",
      "Global Step:   1720, accuracy (SELU/ELU/RELU):  76.6% |  75.8% |  71.1%, loss (SELU/ELU/RELU): 0.70 | 0.84 | 0.93\n",
      "Global Step:   1730, accuracy (SELU/ELU/RELU):  73.4% |  69.5% |  70.3%, loss (SELU/ELU/RELU): 0.73 | 0.86 | 0.84\n",
      "Global Step:   1740, accuracy (SELU/ELU/RELU):  74.2% |  71.1% |  68.8%, loss (SELU/ELU/RELU): 0.73 | 0.82 | 0.86\n",
      "Global Step:   1750, accuracy (SELU/ELU/RELU):  77.3% |  72.7% |  67.2%, loss (SELU/ELU/RELU): 0.80 | 0.85 | 0.93\n",
      "Global Step:   1760, accuracy (SELU/ELU/RELU):  74.2% |  74.2% |  69.5%, loss (SELU/ELU/RELU): 0.76 | 0.79 | 0.89\n",
      "Global Step:   1770, accuracy (SELU/ELU/RELU):  71.1% |  73.4% |  70.3%, loss (SELU/ELU/RELU): 0.76 | 0.72 | 0.87\n",
      "Global Step:   1780, accuracy (SELU/ELU/RELU):  71.1% |  67.2% |  67.2%, loss (SELU/ELU/RELU): 0.77 | 0.83 | 0.94\n",
      "Global Step:   1790, accuracy (SELU/ELU/RELU):  77.3% |  77.3% |  70.3%, loss (SELU/ELU/RELU): 0.65 | 0.67 | 0.88\n",
      "Global Step:   1800, accuracy (SELU/ELU/RELU):  75.0% |  71.9% |  71.1%, loss (SELU/ELU/RELU): 0.70 | 0.78 | 0.89\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 70.42% | 67.97% | 63.92%\n",
      "Saved checkpoint.\n",
      "Global Step:   1810, accuracy (SELU/ELU/RELU):  76.6% |  70.3% |  68.8%, loss (SELU/ELU/RELU): 0.68 | 0.77 | 0.88\n",
      "Global Step:   1820, accuracy (SELU/ELU/RELU):  75.0% |  69.5% |  64.8%, loss (SELU/ELU/RELU): 0.73 | 0.81 | 0.88\n",
      "Global Step:   1830, accuracy (SELU/ELU/RELU):  75.0% |  72.7% |  68.0%, loss (SELU/ELU/RELU): 0.77 | 0.84 | 0.88\n",
      "Global Step:   1840, accuracy (SELU/ELU/RELU):  73.4% |  68.8% |  66.4%, loss (SELU/ELU/RELU): 0.78 | 0.91 | 1.02\n",
      "Global Step:   1850, accuracy (SELU/ELU/RELU):  68.0% |  69.5% |  62.5%, loss (SELU/ELU/RELU): 0.79 | 0.82 | 0.95\n",
      "Global Step:   1860, accuracy (SELU/ELU/RELU):  75.8% |  75.8% |  71.9%, loss (SELU/ELU/RELU): 0.72 | 0.75 | 0.92\n",
      "Global Step:   1870, accuracy (SELU/ELU/RELU):  77.3% |  75.8% |  68.8%, loss (SELU/ELU/RELU): 0.62 | 0.77 | 0.82\n",
      "Global Step:   1880, accuracy (SELU/ELU/RELU):  71.9% |  71.1% |  54.7%, loss (SELU/ELU/RELU): 0.87 | 0.92 | 1.23\n",
      "Global Step:   1890, accuracy (SELU/ELU/RELU):  75.0% |  71.1% |  67.2%, loss (SELU/ELU/RELU): 0.67 | 0.80 | 0.93\n",
      "Global Step:   1900, accuracy (SELU/ELU/RELU):  76.6% |  73.4% |  71.9%, loss (SELU/ELU/RELU): 0.73 | 0.83 | 0.88\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 69.45% | 68.63% | 63.74%\n",
      "Saved checkpoint.\n",
      "Global Step:   1910, accuracy (SELU/ELU/RELU):  70.3% |  71.9% |  60.9%, loss (SELU/ELU/RELU): 0.83 | 0.88 | 1.00\n",
      "Global Step:   1920, accuracy (SELU/ELU/RELU):  74.2% |  70.3% |  67.2%, loss (SELU/ELU/RELU): 0.75 | 0.81 | 0.85\n",
      "Global Step:   1930, accuracy (SELU/ELU/RELU):  82.0% |  79.7% |  75.0%, loss (SELU/ELU/RELU): 0.56 | 0.63 | 0.77\n",
      "Global Step:   1940, accuracy (SELU/ELU/RELU):  74.2% |  72.7% |  70.3%, loss (SELU/ELU/RELU): 0.68 | 0.78 | 0.87\n",
      "Global Step:   1950, accuracy (SELU/ELU/RELU):  80.5% |  80.5% |  69.5%, loss (SELU/ELU/RELU): 0.62 | 0.65 | 0.82\n",
      "Global Step:   1960, accuracy (SELU/ELU/RELU):  68.8% |  68.0% |  67.2%, loss (SELU/ELU/RELU): 0.74 | 0.80 | 0.96\n",
      "Global Step:   1970, accuracy (SELU/ELU/RELU):  78.9% |  78.1% |  72.7%, loss (SELU/ELU/RELU): 0.66 | 0.69 | 0.82\n",
      "Global Step:   1980, accuracy (SELU/ELU/RELU):  81.2% |  75.0% |  73.4%, loss (SELU/ELU/RELU): 0.67 | 0.72 | 0.77\n",
      "Global Step:   1990, accuracy (SELU/ELU/RELU):  81.2% |  75.0% |  69.5%, loss (SELU/ELU/RELU): 0.60 | 0.72 | 0.86\n",
      "Global Step:   2000, accuracy (SELU/ELU/RELU):  68.8% |  60.2% |  64.1%, loss (SELU/ELU/RELU): 0.82 | 1.01 | 1.01\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 71.58% | 68.86% | 65.29%\n",
      "Saved checkpoint.\n",
      "Global Step:   2010, accuracy (SELU/ELU/RELU):  75.8% |  68.8% |  65.6%, loss (SELU/ELU/RELU): 0.67 | 0.86 | 0.99\n",
      "Global Step:   2020, accuracy (SELU/ELU/RELU):  82.0% |  82.8% |  77.3%, loss (SELU/ELU/RELU): 0.54 | 0.60 | 0.75\n",
      "Global Step:   2030, accuracy (SELU/ELU/RELU):  71.1% |  62.5% |  68.0%, loss (SELU/ELU/RELU): 0.83 | 0.92 | 0.95\n",
      "Global Step:   2040, accuracy (SELU/ELU/RELU):  78.9% |  75.8% |  68.0%, loss (SELU/ELU/RELU): 0.60 | 0.72 | 0.90\n",
      "Global Step:   2050, accuracy (SELU/ELU/RELU):  85.2% |  79.7% |  78.9%, loss (SELU/ELU/RELU): 0.53 | 0.64 | 0.72\n",
      "Global Step:   2060, accuracy (SELU/ELU/RELU):  73.4% |  75.0% |  64.1%, loss (SELU/ELU/RELU): 0.69 | 0.76 | 1.00\n",
      "Global Step:   2070, accuracy (SELU/ELU/RELU):  85.9% |  79.7% |  78.1%, loss (SELU/ELU/RELU): 0.51 | 0.63 | 0.69\n",
      "Global Step:   2080, accuracy (SELU/ELU/RELU):  70.3% |  71.1% |  68.8%, loss (SELU/ELU/RELU): 0.78 | 0.89 | 0.95\n",
      "Global Step:   2090, accuracy (SELU/ELU/RELU):  78.1% |  75.0% |  70.3%, loss (SELU/ELU/RELU): 0.64 | 0.69 | 0.90\n",
      "Global Step:   2100, accuracy (SELU/ELU/RELU):  75.0% |  75.8% |  69.5%, loss (SELU/ELU/RELU): 0.68 | 0.71 | 0.83\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 71.05% | 69.08% | 64.81%\n",
      "Saved checkpoint.\n",
      "Global Step:   2110, accuracy (SELU/ELU/RELU):  77.3% |  76.6% |  70.3%, loss (SELU/ELU/RELU): 0.63 | 0.67 | 0.82\n",
      "Global Step:   2120, accuracy (SELU/ELU/RELU):  87.5% |  80.5% |  72.7%, loss (SELU/ELU/RELU): 0.48 | 0.63 | 0.72\n",
      "Global Step:   2130, accuracy (SELU/ELU/RELU):  76.6% |  75.0% |  66.4%, loss (SELU/ELU/RELU): 0.65 | 0.80 | 0.89\n",
      "Global Step:   2140, accuracy (SELU/ELU/RELU):  80.5% |  75.0% |  73.4%, loss (SELU/ELU/RELU): 0.55 | 0.62 | 0.75\n",
      "Global Step:   2150, accuracy (SELU/ELU/RELU):  75.0% |  69.5% |  67.2%, loss (SELU/ELU/RELU): 0.66 | 0.84 | 0.93\n",
      "Global Step:   2160, accuracy (SELU/ELU/RELU):  76.6% |  71.9% |  68.0%, loss (SELU/ELU/RELU): 0.70 | 0.85 | 0.96\n",
      "Global Step:   2170, accuracy (SELU/ELU/RELU):  78.1% |  75.0% |  69.5%, loss (SELU/ELU/RELU): 0.63 | 0.80 | 0.93\n",
      "Global Step:   2180, accuracy (SELU/ELU/RELU):  77.3% |  70.3% |  68.8%, loss (SELU/ELU/RELU): 0.74 | 0.78 | 0.83\n",
      "Global Step:   2190, accuracy (SELU/ELU/RELU):  78.1% |  74.2% |  71.1%, loss (SELU/ELU/RELU): 0.71 | 0.76 | 0.82\n",
      "Global Step:   2200, accuracy (SELU/ELU/RELU):  68.0% |  68.0% |  67.2%, loss (SELU/ELU/RELU): 0.84 | 0.93 | 1.03\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 72.23% | 70.38% | 65.81%\n",
      "Saved checkpoint.\n",
      "Global Step:   2210, accuracy (SELU/ELU/RELU):  70.3% |  72.7% |  65.6%, loss (SELU/ELU/RELU): 0.74 | 0.74 | 0.87\n",
      "Global Step:   2220, accuracy (SELU/ELU/RELU):  71.9% |  72.7% |  69.5%, loss (SELU/ELU/RELU): 0.74 | 0.83 | 0.88\n",
      "Global Step:   2230, accuracy (SELU/ELU/RELU):  78.1% |  73.4% |  71.1%, loss (SELU/ELU/RELU): 0.64 | 0.73 | 0.86\n",
      "Global Step:   2240, accuracy (SELU/ELU/RELU):  82.8% |  76.6% |  69.5%, loss (SELU/ELU/RELU): 0.67 | 0.76 | 0.89\n",
      "Global Step:   2250, accuracy (SELU/ELU/RELU):  75.8% |  74.2% |  64.1%, loss (SELU/ELU/RELU): 0.60 | 0.74 | 0.88\n",
      "Global Step:   2260, accuracy (SELU/ELU/RELU):  76.6% |  75.0% |  69.5%, loss (SELU/ELU/RELU): 0.63 | 0.78 | 0.85\n",
      "Global Step:   2270, accuracy (SELU/ELU/RELU):  75.8% |  76.6% |  71.1%, loss (SELU/ELU/RELU): 0.69 | 0.74 | 0.84\n",
      "Global Step:   2280, accuracy (SELU/ELU/RELU):  83.6% |  82.8% |  74.2%, loss (SELU/ELU/RELU): 0.54 | 0.60 | 0.64\n",
      "Global Step:   2290, accuracy (SELU/ELU/RELU):  78.9% |  77.3% |  71.9%, loss (SELU/ELU/RELU): 0.59 | 0.62 | 0.79\n",
      "Global Step:   2300, accuracy (SELU/ELU/RELU):  79.7% |  76.6% |  77.3%, loss (SELU/ELU/RELU): 0.59 | 0.60 | 0.71\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 72.15% | 70.88% | 66.97%\n",
      "Saved checkpoint.\n",
      "Global Step:   2310, accuracy (SELU/ELU/RELU):  76.6% |  69.5% |  66.4%, loss (SELU/ELU/RELU): 0.73 | 0.82 | 0.97\n",
      "Global Step:   2320, accuracy (SELU/ELU/RELU):  81.2% |  78.1% |  70.3%, loss (SELU/ELU/RELU): 0.62 | 0.72 | 0.86\n",
      "Global Step:   2330, accuracy (SELU/ELU/RELU):  74.2% |  73.4% |  68.0%, loss (SELU/ELU/RELU): 0.79 | 0.74 | 0.89\n",
      "Global Step:   2340, accuracy (SELU/ELU/RELU):  79.7% |  76.6% |  68.0%, loss (SELU/ELU/RELU): 0.73 | 0.78 | 0.91\n",
      "Global Step:   2350, accuracy (SELU/ELU/RELU):  80.5% |  77.3% |  71.1%, loss (SELU/ELU/RELU): 0.58 | 0.64 | 0.83\n",
      "Global Step:   2360, accuracy (SELU/ELU/RELU):  77.3% |  75.8% |  77.3%, loss (SELU/ELU/RELU): 0.63 | 0.69 | 0.79\n",
      "Global Step:   2370, accuracy (SELU/ELU/RELU):  75.8% |  70.3% |  64.8%, loss (SELU/ELU/RELU): 0.68 | 0.73 | 0.86\n",
      "Global Step:   2380, accuracy (SELU/ELU/RELU):  82.0% |  77.3% |  71.1%, loss (SELU/ELU/RELU): 0.60 | 0.71 | 0.88\n",
      "Global Step:   2390, accuracy (SELU/ELU/RELU):  82.8% |  79.7% |  73.4%, loss (SELU/ELU/RELU): 0.49 | 0.58 | 0.76\n",
      "Global Step:   2400, accuracy (SELU/ELU/RELU):  82.8% |  80.5% |  71.1%, loss (SELU/ELU/RELU): 0.51 | 0.64 | 0.77\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 72.66% | 70.21% | 67.02%\n",
      "Saved checkpoint.\n",
      "Global Step:   2410, accuracy (SELU/ELU/RELU):  74.2% |  74.2% |  71.9%, loss (SELU/ELU/RELU): 0.70 | 0.70 | 0.75\n",
      "Global Step:   2420, accuracy (SELU/ELU/RELU):  72.7% |  72.7% |  68.8%, loss (SELU/ELU/RELU): 0.74 | 0.84 | 0.92\n",
      "Global Step:   2430, accuracy (SELU/ELU/RELU):  83.6% |  81.2% |  78.1%, loss (SELU/ELU/RELU): 0.58 | 0.61 | 0.74\n",
      "Global Step:   2440, accuracy (SELU/ELU/RELU):  76.6% |  72.7% |  71.1%, loss (SELU/ELU/RELU): 0.67 | 0.75 | 0.86\n",
      "Global Step:   2450, accuracy (SELU/ELU/RELU):  74.2% |  72.7% |  69.5%, loss (SELU/ELU/RELU): 0.62 | 0.75 | 0.80\n",
      "Global Step:   2460, accuracy (SELU/ELU/RELU):  74.2% |  67.2% |  68.8%, loss (SELU/ELU/RELU): 0.81 | 0.88 | 0.96\n",
      "Global Step:   2470, accuracy (SELU/ELU/RELU):  82.0% |  80.5% |  79.7%, loss (SELU/ELU/RELU): 0.50 | 0.62 | 0.72\n",
      "Global Step:   2480, accuracy (SELU/ELU/RELU):  78.9% |  78.1% |  71.9%, loss (SELU/ELU/RELU): 0.56 | 0.63 | 0.80\n",
      "Global Step:   2490, accuracy (SELU/ELU/RELU):  78.9% |  73.4% |  72.7%, loss (SELU/ELU/RELU): 0.57 | 0.69 | 0.80\n",
      "Global Step:   2500, accuracy (SELU/ELU/RELU):  84.4% |  78.9% |  71.1%, loss (SELU/ELU/RELU): 0.55 | 0.62 | 0.87\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 73.31% | 71.90% | 66.80%\n",
      "Saved checkpoint.\n",
      "Global Step:   2510, accuracy (SELU/ELU/RELU):  71.9% |  68.0% |  63.3%, loss (SELU/ELU/RELU): 0.81 | 0.89 | 0.94\n",
      "Global Step:   2520, accuracy (SELU/ELU/RELU):  78.9% |  78.1% |  75.0%, loss (SELU/ELU/RELU): 0.59 | 0.63 | 0.77\n",
      "Global Step:   2530, accuracy (SELU/ELU/RELU):  85.2% |  76.6% |  75.0%, loss (SELU/ELU/RELU): 0.46 | 0.62 | 0.71\n",
      "Global Step:   2540, accuracy (SELU/ELU/RELU):  83.6% |  75.8% |  73.4%, loss (SELU/ELU/RELU): 0.65 | 0.73 | 0.77\n",
      "Global Step:   2550, accuracy (SELU/ELU/RELU):  78.9% |  81.2% |  76.6%, loss (SELU/ELU/RELU): 0.48 | 0.57 | 0.70\n",
      "Global Step:   2560, accuracy (SELU/ELU/RELU):  78.1% |  73.4% |  68.8%, loss (SELU/ELU/RELU): 0.63 | 0.70 | 0.81\n",
      "Global Step:   2570, accuracy (SELU/ELU/RELU):  83.6% |  79.7% |  75.0%, loss (SELU/ELU/RELU): 0.57 | 0.57 | 0.73\n",
      "Global Step:   2580, accuracy (SELU/ELU/RELU):  85.2% |  82.0% |  74.2%, loss (SELU/ELU/RELU): 0.51 | 0.57 | 0.75\n",
      "Global Step:   2590, accuracy (SELU/ELU/RELU):  79.7% |  79.7% |  78.9%, loss (SELU/ELU/RELU): 0.59 | 0.62 | 0.80\n",
      "Global Step:   2600, accuracy (SELU/ELU/RELU):  84.4% |  84.4% |  73.4%, loss (SELU/ELU/RELU): 0.48 | 0.53 | 0.67\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 72.85% | 70.75% | 65.86%\n",
      "Saved checkpoint.\n",
      "Global Step:   2610, accuracy (SELU/ELU/RELU):  84.4% |  78.9% |  75.0%, loss (SELU/ELU/RELU): 0.50 | 0.57 | 0.78\n",
      "Global Step:   2620, accuracy (SELU/ELU/RELU):  81.2% |  80.5% |  74.2%, loss (SELU/ELU/RELU): 0.49 | 0.61 | 0.72\n",
      "Global Step:   2630, accuracy (SELU/ELU/RELU):  82.8% |  74.2% |  72.7%, loss (SELU/ELU/RELU): 0.52 | 0.65 | 0.73\n",
      "Global Step:   2640, accuracy (SELU/ELU/RELU):  82.8% |  78.9% |  77.3%, loss (SELU/ELU/RELU): 0.53 | 0.59 | 0.73\n",
      "Global Step:   2650, accuracy (SELU/ELU/RELU):  82.0% |  78.1% |  69.5%, loss (SELU/ELU/RELU): 0.55 | 0.62 | 0.76\n",
      "Global Step:   2660, accuracy (SELU/ELU/RELU):  79.7% |  77.3% |  68.8%, loss (SELU/ELU/RELU): 0.65 | 0.76 | 0.91\n",
      "Global Step:   2670, accuracy (SELU/ELU/RELU):  81.2% |  76.6% |  77.3%, loss (SELU/ELU/RELU): 0.60 | 0.69 | 0.76\n",
      "Global Step:   2680, accuracy (SELU/ELU/RELU):  82.0% |  80.5% |  73.4%, loss (SELU/ELU/RELU): 0.49 | 0.57 | 0.75\n",
      "Global Step:   2690, accuracy (SELU/ELU/RELU):  83.6% |  76.6% |  77.3%, loss (SELU/ELU/RELU): 0.53 | 0.64 | 0.66\n",
      "Global Step:   2700, accuracy (SELU/ELU/RELU):  85.2% |  78.1% |  75.0%, loss (SELU/ELU/RELU): 0.50 | 0.62 | 0.67\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 72.96% | 70.49% | 67.70%\n",
      "Saved checkpoint.\n",
      "Global Step:   2710, accuracy (SELU/ELU/RELU):  78.9% |  80.5% |  77.3%, loss (SELU/ELU/RELU): 0.55 | 0.54 | 0.68\n",
      "Global Step:   2720, accuracy (SELU/ELU/RELU):  88.3% |  86.7% |  79.7%, loss (SELU/ELU/RELU): 0.39 | 0.39 | 0.59\n",
      "Global Step:   2730, accuracy (SELU/ELU/RELU):  83.6% |  79.7% |  76.6%, loss (SELU/ELU/RELU): 0.52 | 0.62 | 0.78\n",
      "Global Step:   2740, accuracy (SELU/ELU/RELU):  78.9% |  73.4% |  71.9%, loss (SELU/ELU/RELU): 0.52 | 0.60 | 0.74\n",
      "Global Step:   2750, accuracy (SELU/ELU/RELU):  79.7% |  80.5% |  73.4%, loss (SELU/ELU/RELU): 0.59 | 0.71 | 0.79\n",
      "Global Step:   2760, accuracy (SELU/ELU/RELU):  82.8% |  75.8% |  72.7%, loss (SELU/ELU/RELU): 0.52 | 0.61 | 0.74\n",
      "Global Step:   2770, accuracy (SELU/ELU/RELU):  85.2% |  80.5% |  68.8%, loss (SELU/ELU/RELU): 0.57 | 0.68 | 0.93\n",
      "Global Step:   2780, accuracy (SELU/ELU/RELU):  79.7% |  79.7% |  78.9%, loss (SELU/ELU/RELU): 0.64 | 0.60 | 0.70\n",
      "Global Step:   2790, accuracy (SELU/ELU/RELU):  79.7% |  76.6% |  68.8%, loss (SELU/ELU/RELU): 0.68 | 0.77 | 0.90\n",
      "Global Step:   2800, accuracy (SELU/ELU/RELU):  85.2% |  77.3% |  72.7%, loss (SELU/ELU/RELU): 0.56 | 0.68 | 0.83\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 73.27% | 71.97% | 68.39%\n",
      "Saved checkpoint.\n",
      "Global Step:   2810, accuracy (SELU/ELU/RELU):  75.0% |  76.6% |  73.4%, loss (SELU/ELU/RELU): 0.65 | 0.67 | 0.71\n",
      "Global Step:   2820, accuracy (SELU/ELU/RELU):  82.0% |  75.0% |  73.4%, loss (SELU/ELU/RELU): 0.53 | 0.66 | 0.81\n",
      "Global Step:   2830, accuracy (SELU/ELU/RELU):  85.2% |  82.0% |  79.7%, loss (SELU/ELU/RELU): 0.44 | 0.56 | 0.64\n",
      "Global Step:   2840, accuracy (SELU/ELU/RELU):  80.5% |  77.3% |  71.9%, loss (SELU/ELU/RELU): 0.62 | 0.70 | 0.84\n",
      "Global Step:   2850, accuracy (SELU/ELU/RELU):  84.4% |  81.2% |  73.4%, loss (SELU/ELU/RELU): 0.52 | 0.58 | 0.72\n",
      "Global Step:   2860, accuracy (SELU/ELU/RELU):  82.8% |  75.0% |  71.9%, loss (SELU/ELU/RELU): 0.53 | 0.61 | 0.72\n",
      "Global Step:   2870, accuracy (SELU/ELU/RELU):  79.7% |  75.0% |  76.6%, loss (SELU/ELU/RELU): 0.57 | 0.66 | 0.72\n",
      "Global Step:   2880, accuracy (SELU/ELU/RELU):  80.5% |  74.2% |  69.5%, loss (SELU/ELU/RELU): 0.57 | 0.67 | 0.88\n",
      "Global Step:   2890, accuracy (SELU/ELU/RELU):  80.5% |  75.0% |  74.2%, loss (SELU/ELU/RELU): 0.65 | 0.75 | 0.81\n",
      "Global Step:   2900, accuracy (SELU/ELU/RELU):  78.9% |  78.1% |  78.9%, loss (SELU/ELU/RELU): 0.52 | 0.61 | 0.67\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 73.97% | 72.19% | 68.61%\n",
      "Saved checkpoint.\n",
      "Global Step:   2910, accuracy (SELU/ELU/RELU):  82.0% |  76.6% |  74.2%, loss (SELU/ELU/RELU): 0.51 | 0.66 | 0.78\n",
      "Global Step:   2920, accuracy (SELU/ELU/RELU):  78.1% |  72.7% |  67.2%, loss (SELU/ELU/RELU): 0.70 | 0.77 | 0.91\n",
      "Global Step:   2930, accuracy (SELU/ELU/RELU):  82.0% |  80.5% |  75.8%, loss (SELU/ELU/RELU): 0.48 | 0.55 | 0.70\n",
      "Global Step:   2940, accuracy (SELU/ELU/RELU):  84.4% |  82.8% |  75.0%, loss (SELU/ELU/RELU): 0.50 | 0.55 | 0.71\n",
      "Global Step:   2950, accuracy (SELU/ELU/RELU):  79.7% |  74.2% |  71.9%, loss (SELU/ELU/RELU): 0.63 | 0.76 | 0.84\n",
      "Global Step:   2960, accuracy (SELU/ELU/RELU):  84.4% |  82.8% |  78.9%, loss (SELU/ELU/RELU): 0.53 | 0.61 | 0.67\n",
      "Global Step:   2970, accuracy (SELU/ELU/RELU):  83.6% |  82.0% |  79.7%, loss (SELU/ELU/RELU): 0.51 | 0.52 | 0.77\n",
      "Global Step:   2980, accuracy (SELU/ELU/RELU):  78.1% |  75.8% |  68.0%, loss (SELU/ELU/RELU): 0.58 | 0.68 | 0.87\n",
      "Global Step:   2990, accuracy (SELU/ELU/RELU):  85.2% |  82.8% |  82.0%, loss (SELU/ELU/RELU): 0.41 | 0.46 | 0.64\n",
      "Global Step:   3000, accuracy (SELU/ELU/RELU):  82.0% |  79.7% |  68.0%, loss (SELU/ELU/RELU): 0.52 | 0.61 | 0.79\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.16% | 72.48% | 68.69%\n",
      "Saved checkpoint.\n",
      "Global Step:   3010, accuracy (SELU/ELU/RELU):  85.2% |  83.6% |  83.6%, loss (SELU/ELU/RELU): 0.37 | 0.48 | 0.57\n",
      "Global Step:   3020, accuracy (SELU/ELU/RELU):  83.6% |  77.3% |  75.8%, loss (SELU/ELU/RELU): 0.44 | 0.60 | 0.74\n",
      "Global Step:   3030, accuracy (SELU/ELU/RELU):  87.5% |  83.6% |  77.3%, loss (SELU/ELU/RELU): 0.45 | 0.49 | 0.64\n",
      "Global Step:   3040, accuracy (SELU/ELU/RELU):  85.9% |  85.9% |  84.4%, loss (SELU/ELU/RELU): 0.38 | 0.40 | 0.55\n",
      "Global Step:   3050, accuracy (SELU/ELU/RELU):  82.0% |  78.9% |  77.3%, loss (SELU/ELU/RELU): 0.47 | 0.55 | 0.65\n",
      "Global Step:   3060, accuracy (SELU/ELU/RELU):  85.2% |  78.9% |  74.2%, loss (SELU/ELU/RELU): 0.55 | 0.65 | 0.74\n",
      "Global Step:   3070, accuracy (SELU/ELU/RELU):  86.7% |  78.9% |  74.2%, loss (SELU/ELU/RELU): 0.46 | 0.52 | 0.69\n",
      "Global Step:   3080, accuracy (SELU/ELU/RELU):  84.4% |  81.2% |  78.1%, loss (SELU/ELU/RELU): 0.51 | 0.58 | 0.70\n",
      "Global Step:   3090, accuracy (SELU/ELU/RELU):  85.2% |  81.2% |  74.2%, loss (SELU/ELU/RELU): 0.45 | 0.54 | 0.75\n",
      "Global Step:   3100, accuracy (SELU/ELU/RELU):  87.5% |  81.2% |  76.6%, loss (SELU/ELU/RELU): 0.44 | 0.50 | 0.60\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.21% | 73.14% | 68.74%\n",
      "Saved checkpoint.\n",
      "Global Step:   3110, accuracy (SELU/ELU/RELU):  79.7% |  80.5% |  68.0%, loss (SELU/ELU/RELU): 0.58 | 0.60 | 0.89\n",
      "Global Step:   3120, accuracy (SELU/ELU/RELU):  78.9% |  76.6% |  69.5%, loss (SELU/ELU/RELU): 0.62 | 0.65 | 0.86\n",
      "Global Step:   3130, accuracy (SELU/ELU/RELU):  82.0% |  74.2% |  67.2%, loss (SELU/ELU/RELU): 0.58 | 0.65 | 0.86\n",
      "Global Step:   3140, accuracy (SELU/ELU/RELU):  88.3% |  83.6% |  77.3%, loss (SELU/ELU/RELU): 0.47 | 0.50 | 0.66\n",
      "Global Step:   3150, accuracy (SELU/ELU/RELU):  84.4% |  82.0% |  77.3%, loss (SELU/ELU/RELU): 0.42 | 0.48 | 0.70\n",
      "Global Step:   3160, accuracy (SELU/ELU/RELU):  84.4% |  78.9% |  74.2%, loss (SELU/ELU/RELU): 0.43 | 0.56 | 0.62\n",
      "Global Step:   3170, accuracy (SELU/ELU/RELU):  85.2% |  82.8% |  77.3%, loss (SELU/ELU/RELU): 0.43 | 0.58 | 0.69\n",
      "Global Step:   3180, accuracy (SELU/ELU/RELU):  78.9% |  81.2% |  82.0%, loss (SELU/ELU/RELU): 0.51 | 0.54 | 0.64\n",
      "Global Step:   3190, accuracy (SELU/ELU/RELU):  84.4% |  76.6% |  79.7%, loss (SELU/ELU/RELU): 0.55 | 0.61 | 0.66\n",
      "Global Step:   3200, accuracy (SELU/ELU/RELU):  88.3% |  84.4% |  78.1%, loss (SELU/ELU/RELU): 0.44 | 0.49 | 0.72\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 73.77% | 73.40% | 68.82%\n",
      "Saved checkpoint.\n",
      "Global Step:   3210, accuracy (SELU/ELU/RELU):  87.5% |  84.4% |  78.1%, loss (SELU/ELU/RELU): 0.46 | 0.46 | 0.67\n",
      "Global Step:   3220, accuracy (SELU/ELU/RELU):  82.0% |  76.6% |  75.8%, loss (SELU/ELU/RELU): 0.45 | 0.57 | 0.68\n",
      "Global Step:   3230, accuracy (SELU/ELU/RELU):  86.7% |  80.5% |  78.1%, loss (SELU/ELU/RELU): 0.39 | 0.47 | 0.64\n",
      "Global Step:   3240, accuracy (SELU/ELU/RELU):  81.2% |  75.8% |  71.9%, loss (SELU/ELU/RELU): 0.55 | 0.61 | 0.76\n",
      "Global Step:   3250, accuracy (SELU/ELU/RELU):  90.6% |  83.6% |  82.8%, loss (SELU/ELU/RELU): 0.36 | 0.45 | 0.57\n",
      "Global Step:   3260, accuracy (SELU/ELU/RELU):  82.8% |  79.7% |  78.9%, loss (SELU/ELU/RELU): 0.49 | 0.60 | 0.67\n",
      "Global Step:   3270, accuracy (SELU/ELU/RELU):  82.8% |  81.2% |  73.4%, loss (SELU/ELU/RELU): 0.44 | 0.52 | 0.68\n",
      "Global Step:   3280, accuracy (SELU/ELU/RELU):  80.5% |  74.2% |  71.9%, loss (SELU/ELU/RELU): 0.62 | 0.76 | 0.85\n",
      "Global Step:   3290, accuracy (SELU/ELU/RELU):  83.6% |  80.5% |  78.1%, loss (SELU/ELU/RELU): 0.50 | 0.56 | 0.71\n",
      "Global Step:   3300, accuracy (SELU/ELU/RELU):  83.6% |  84.4% |  75.8%, loss (SELU/ELU/RELU): 0.37 | 0.48 | 0.69\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.53% | 73.26% | 70.86%\n",
      "Saved checkpoint.\n",
      "Global Step:   3310, accuracy (SELU/ELU/RELU):  83.6% |  84.4% |  82.0%, loss (SELU/ELU/RELU): 0.39 | 0.49 | 0.52\n",
      "Global Step:   3320, accuracy (SELU/ELU/RELU):  83.6% |  80.5% |  75.0%, loss (SELU/ELU/RELU): 0.53 | 0.58 | 0.72\n",
      "Global Step:   3330, accuracy (SELU/ELU/RELU):  80.5% |  77.3% |  79.7%, loss (SELU/ELU/RELU): 0.55 | 0.63 | 0.67\n",
      "Global Step:   3340, accuracy (SELU/ELU/RELU):  82.8% |  82.0% |  80.5%, loss (SELU/ELU/RELU): 0.43 | 0.50 | 0.56\n",
      "Global Step:   3350, accuracy (SELU/ELU/RELU):  84.4% |  84.4% |  71.9%, loss (SELU/ELU/RELU): 0.48 | 0.55 | 0.76\n",
      "Global Step:   3360, accuracy (SELU/ELU/RELU):  87.5% |  82.0% |  78.9%, loss (SELU/ELU/RELU): 0.35 | 0.51 | 0.65\n",
      "Global Step:   3370, accuracy (SELU/ELU/RELU):  85.9% |  86.7% |  80.5%, loss (SELU/ELU/RELU): 0.35 | 0.44 | 0.64\n",
      "Global Step:   3380, accuracy (SELU/ELU/RELU):  84.4% |  85.2% |  76.6%, loss (SELU/ELU/RELU): 0.39 | 0.45 | 0.59\n",
      "Global Step:   3390, accuracy (SELU/ELU/RELU):  83.6% |  85.9% |  75.8%, loss (SELU/ELU/RELU): 0.42 | 0.46 | 0.62\n",
      "Global Step:   3400, accuracy (SELU/ELU/RELU):  84.4% |  78.9% |  71.1%, loss (SELU/ELU/RELU): 0.45 | 0.55 | 0.71\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 73.34% | 73.05% | 69.40%\n",
      "Saved checkpoint.\n",
      "Global Step:   3410, accuracy (SELU/ELU/RELU):  85.2% |  82.8% |  73.4%, loss (SELU/ELU/RELU): 0.47 | 0.56 | 0.77\n",
      "Global Step:   3420, accuracy (SELU/ELU/RELU):  82.8% |  77.3% |  78.1%, loss (SELU/ELU/RELU): 0.46 | 0.58 | 0.68\n",
      "Global Step:   3430, accuracy (SELU/ELU/RELU):  84.4% |  85.2% |  81.2%, loss (SELU/ELU/RELU): 0.45 | 0.43 | 0.55\n",
      "Global Step:   3440, accuracy (SELU/ELU/RELU):  82.0% |  80.5% |  78.1%, loss (SELU/ELU/RELU): 0.57 | 0.61 | 0.63\n",
      "Global Step:   3450, accuracy (SELU/ELU/RELU):  85.2% |  81.2% |  78.1%, loss (SELU/ELU/RELU): 0.50 | 0.53 | 0.64\n",
      "Global Step:   3460, accuracy (SELU/ELU/RELU):  82.8% |  82.8% |  78.9%, loss (SELU/ELU/RELU): 0.43 | 0.50 | 0.64\n",
      "Global Step:   3470, accuracy (SELU/ELU/RELU):  93.0% |  86.7% |  84.4%, loss (SELU/ELU/RELU): 0.28 | 0.33 | 0.43\n",
      "Global Step:   3480, accuracy (SELU/ELU/RELU):  85.9% |  81.2% |  79.7%, loss (SELU/ELU/RELU): 0.39 | 0.42 | 0.53\n",
      "Global Step:   3490, accuracy (SELU/ELU/RELU):  88.3% |  82.0% |  78.1%, loss (SELU/ELU/RELU): 0.40 | 0.50 | 0.64\n",
      "Global Step:   3500, accuracy (SELU/ELU/RELU):  89.1% |  88.3% |  82.0%, loss (SELU/ELU/RELU): 0.35 | 0.43 | 0.54\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.33% | 73.60% | 67.94%\n",
      "Saved checkpoint.\n",
      "Global Step:   3510, accuracy (SELU/ELU/RELU):  82.8% |  78.9% |  77.3%, loss (SELU/ELU/RELU): 0.46 | 0.56 | 0.70\n",
      "Global Step:   3520, accuracy (SELU/ELU/RELU):  82.8% |  82.8% |  76.6%, loss (SELU/ELU/RELU): 0.43 | 0.49 | 0.71\n",
      "Global Step:   3530, accuracy (SELU/ELU/RELU):  83.6% |  78.9% |  76.6%, loss (SELU/ELU/RELU): 0.49 | 0.57 | 0.61\n",
      "Global Step:   3540, accuracy (SELU/ELU/RELU):  85.2% |  78.1% |  80.5%, loss (SELU/ELU/RELU): 0.48 | 0.51 | 0.56\n",
      "Global Step:   3550, accuracy (SELU/ELU/RELU):  82.8% |  82.0% |  79.7%, loss (SELU/ELU/RELU): 0.47 | 0.55 | 0.70\n",
      "Global Step:   3560, accuracy (SELU/ELU/RELU):  83.6% |  85.2% |  78.9%, loss (SELU/ELU/RELU): 0.49 | 0.45 | 0.59\n",
      "Global Step:   3570, accuracy (SELU/ELU/RELU):  83.6% |  75.8% |  75.0%, loss (SELU/ELU/RELU): 0.52 | 0.67 | 0.72\n",
      "Global Step:   3580, accuracy (SELU/ELU/RELU):  89.1% |  82.0% |  78.9%, loss (SELU/ELU/RELU): 0.39 | 0.47 | 0.61\n",
      "Global Step:   3590, accuracy (SELU/ELU/RELU):  85.9% |  82.8% |  76.6%, loss (SELU/ELU/RELU): 0.42 | 0.52 | 0.62\n",
      "Global Step:   3600, accuracy (SELU/ELU/RELU):  84.4% |  81.2% |  75.8%, loss (SELU/ELU/RELU): 0.49 | 0.60 | 0.70\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.68% | 74.19% | 70.12%\n",
      "Saved checkpoint.\n",
      "Global Step:   3610, accuracy (SELU/ELU/RELU):  85.2% |  81.2% |  73.4%, loss (SELU/ELU/RELU): 0.46 | 0.51 | 0.75\n",
      "Global Step:   3620, accuracy (SELU/ELU/RELU):  83.6% |  79.7% |  73.4%, loss (SELU/ELU/RELU): 0.42 | 0.59 | 0.73\n",
      "Global Step:   3630, accuracy (SELU/ELU/RELU):  86.7% |  85.9% |  78.9%, loss (SELU/ELU/RELU): 0.40 | 0.49 | 0.64\n",
      "Global Step:   3640, accuracy (SELU/ELU/RELU):  85.2% |  84.4% |  78.1%, loss (SELU/ELU/RELU): 0.54 | 0.56 | 0.73\n",
      "Global Step:   3650, accuracy (SELU/ELU/RELU):  81.2% |  83.6% |  76.6%, loss (SELU/ELU/RELU): 0.43 | 0.45 | 0.61\n",
      "Global Step:   3660, accuracy (SELU/ELU/RELU):  87.5% |  83.6% |  82.0%, loss (SELU/ELU/RELU): 0.34 | 0.44 | 0.57\n",
      "Global Step:   3670, accuracy (SELU/ELU/RELU):  88.3% |  83.6% |  78.9%, loss (SELU/ELU/RELU): 0.38 | 0.43 | 0.62\n",
      "Global Step:   3680, accuracy (SELU/ELU/RELU):  88.3% |  83.6% |  77.3%, loss (SELU/ELU/RELU): 0.37 | 0.57 | 0.65\n",
      "Global Step:   3690, accuracy (SELU/ELU/RELU):  87.5% |  85.2% |  79.7%, loss (SELU/ELU/RELU): 0.40 | 0.44 | 0.55\n",
      "Global Step:   3700, accuracy (SELU/ELU/RELU):  85.2% |  81.2% |  78.1%, loss (SELU/ELU/RELU): 0.43 | 0.55 | 0.68\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.30% | 72.49% | 69.95%\n",
      "Saved checkpoint.\n",
      "Global Step:   3710, accuracy (SELU/ELU/RELU):  88.3% |  85.9% |  75.8%, loss (SELU/ELU/RELU): 0.43 | 0.48 | 0.61\n",
      "Global Step:   3720, accuracy (SELU/ELU/RELU):  91.4% |  83.6% |  79.7%, loss (SELU/ELU/RELU): 0.29 | 0.37 | 0.53\n",
      "Global Step:   3730, accuracy (SELU/ELU/RELU):  89.1% |  84.4% |  79.7%, loss (SELU/ELU/RELU): 0.42 | 0.43 | 0.71\n",
      "Global Step:   3740, accuracy (SELU/ELU/RELU):  88.3% |  85.2% |  78.1%, loss (SELU/ELU/RELU): 0.36 | 0.46 | 0.59\n",
      "Global Step:   3750, accuracy (SELU/ELU/RELU):  87.5% |  81.2% |  81.2%, loss (SELU/ELU/RELU): 0.40 | 0.43 | 0.55\n",
      "Global Step:   3760, accuracy (SELU/ELU/RELU):  84.4% |  85.2% |  76.6%, loss (SELU/ELU/RELU): 0.49 | 0.50 | 0.68\n",
      "Global Step:   3770, accuracy (SELU/ELU/RELU):  85.9% |  82.0% |  77.3%, loss (SELU/ELU/RELU): 0.43 | 0.51 | 0.63\n",
      "Global Step:   3780, accuracy (SELU/ELU/RELU):  85.2% |  82.8% |  78.9%, loss (SELU/ELU/RELU): 0.42 | 0.53 | 0.52\n",
      "Global Step:   3790, accuracy (SELU/ELU/RELU):  82.0% |  78.1% |  71.1%, loss (SELU/ELU/RELU): 0.60 | 0.75 | 0.91\n",
      "Global Step:   3800, accuracy (SELU/ELU/RELU):  85.9% |  82.8% |  79.7%, loss (SELU/ELU/RELU): 0.43 | 0.56 | 0.67\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.37% | 73.89% | 71.08%\n",
      "Saved checkpoint.\n",
      "Global Step:   3810, accuracy (SELU/ELU/RELU):  84.4% |  82.8% |  75.0%, loss (SELU/ELU/RELU): 0.48 | 0.53 | 0.76\n",
      "Global Step:   3820, accuracy (SELU/ELU/RELU):  93.0% |  86.7% |  77.3%, loss (SELU/ELU/RELU): 0.30 | 0.43 | 0.57\n",
      "Global Step:   3830, accuracy (SELU/ELU/RELU):  83.6% |  78.9% |  75.0%, loss (SELU/ELU/RELU): 0.56 | 0.56 | 0.76\n",
      "Global Step:   3840, accuracy (SELU/ELU/RELU):  79.7% |  82.0% |  74.2%, loss (SELU/ELU/RELU): 0.53 | 0.54 | 0.70\n",
      "Global Step:   3850, accuracy (SELU/ELU/RELU):  89.1% |  86.7% |  79.7%, loss (SELU/ELU/RELU): 0.37 | 0.46 | 0.57\n",
      "Global Step:   3860, accuracy (SELU/ELU/RELU):  84.4% |  82.8% |  76.6%, loss (SELU/ELU/RELU): 0.44 | 0.44 | 0.61\n",
      "Global Step:   3870, accuracy (SELU/ELU/RELU):  78.9% |  84.4% |  78.1%, loss (SELU/ELU/RELU): 0.52 | 0.52 | 0.73\n",
      "Global Step:   3880, accuracy (SELU/ELU/RELU):  85.9% |  82.0% |  79.7%, loss (SELU/ELU/RELU): 0.44 | 0.53 | 0.70\n",
      "Global Step:   3890, accuracy (SELU/ELU/RELU):  85.2% |  84.4% |  79.7%, loss (SELU/ELU/RELU): 0.46 | 0.53 | 0.63\n",
      "Global Step:   3900, accuracy (SELU/ELU/RELU):  82.0% |  83.6% |  75.8%, loss (SELU/ELU/RELU): 0.48 | 0.54 | 0.71\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.28% | 74.23% | 71.60%\n",
      "Saved checkpoint.\n",
      "Global Step:   3910, accuracy (SELU/ELU/RELU):  87.5% |  88.3% |  82.8%, loss (SELU/ELU/RELU): 0.39 | 0.42 | 0.60\n",
      "Global Step:   3920, accuracy (SELU/ELU/RELU):  82.8% |  84.4% |  78.1%, loss (SELU/ELU/RELU): 0.47 | 0.51 | 0.69\n",
      "Global Step:   3930, accuracy (SELU/ELU/RELU):  84.4% |  76.6% |  75.0%, loss (SELU/ELU/RELU): 0.51 | 0.61 | 0.68\n",
      "Global Step:   3940, accuracy (SELU/ELU/RELU):  88.3% |  88.3% |  83.6%, loss (SELU/ELU/RELU): 0.32 | 0.35 | 0.48\n",
      "Global Step:   3950, accuracy (SELU/ELU/RELU):  87.5% |  85.2% |  85.2%, loss (SELU/ELU/RELU): 0.34 | 0.44 | 0.47\n",
      "Global Step:   3960, accuracy (SELU/ELU/RELU):  90.6% |  92.2% |  82.0%, loss (SELU/ELU/RELU): 0.33 | 0.41 | 0.53\n",
      "Global Step:   3970, accuracy (SELU/ELU/RELU):  85.9% |  82.8% |  75.0%, loss (SELU/ELU/RELU): 0.52 | 0.51 | 0.73\n",
      "Global Step:   3980, accuracy (SELU/ELU/RELU):  82.0% |  78.9% |  73.4%, loss (SELU/ELU/RELU): 0.51 | 0.55 | 0.75\n",
      "Global Step:   3990, accuracy (SELU/ELU/RELU):  84.4% |  78.9% |  78.1%, loss (SELU/ELU/RELU): 0.47 | 0.55 | 0.72\n",
      "Global Step:   4000, accuracy (SELU/ELU/RELU):  84.4% |  85.2% |  76.6%, loss (SELU/ELU/RELU): 0.41 | 0.41 | 0.67\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.96% | 72.89% | 69.46%\n",
      "Saved checkpoint.\n",
      "Global Step:   4010, accuracy (SELU/ELU/RELU):  85.9% |  82.8% |  77.3%, loss (SELU/ELU/RELU): 0.41 | 0.58 | 0.72\n",
      "Global Step:   4020, accuracy (SELU/ELU/RELU):  85.9% |  83.6% |  81.2%, loss (SELU/ELU/RELU): 0.40 | 0.44 | 0.55\n",
      "Global Step:   4030, accuracy (SELU/ELU/RELU):  92.2% |  85.2% |  83.6%, loss (SELU/ELU/RELU): 0.28 | 0.39 | 0.46\n",
      "Global Step:   4040, accuracy (SELU/ELU/RELU):  87.5% |  85.2% |  81.2%, loss (SELU/ELU/RELU): 0.32 | 0.34 | 0.52\n",
      "Global Step:   4050, accuracy (SELU/ELU/RELU):  90.6% |  87.5% |  82.0%, loss (SELU/ELU/RELU): 0.38 | 0.41 | 0.57\n",
      "Global Step:   4060, accuracy (SELU/ELU/RELU):  88.3% |  86.7% |  77.3%, loss (SELU/ELU/RELU): 0.29 | 0.39 | 0.62\n",
      "Global Step:   4070, accuracy (SELU/ELU/RELU):  89.1% |  88.3% |  78.1%, loss (SELU/ELU/RELU): 0.35 | 0.38 | 0.55\n",
      "Global Step:   4080, accuracy (SELU/ELU/RELU):  87.5% |  82.0% |  81.2%, loss (SELU/ELU/RELU): 0.36 | 0.42 | 0.50\n",
      "Global Step:   4090, accuracy (SELU/ELU/RELU):  87.5% |  85.2% |  81.2%, loss (SELU/ELU/RELU): 0.34 | 0.45 | 0.58\n",
      "Global Step:   4100, accuracy (SELU/ELU/RELU):  83.6% |  79.7% |  74.2%, loss (SELU/ELU/RELU): 0.41 | 0.53 | 0.65\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.66% | 74.26% | 71.85%\n",
      "Saved checkpoint.\n",
      "Global Step:   4110, accuracy (SELU/ELU/RELU):  89.1% |  85.2% |  83.6%, loss (SELU/ELU/RELU): 0.36 | 0.41 | 0.54\n",
      "Global Step:   4120, accuracy (SELU/ELU/RELU):  83.6% |  86.7% |  82.8%, loss (SELU/ELU/RELU): 0.41 | 0.39 | 0.53\n",
      "Global Step:   4130, accuracy (SELU/ELU/RELU):  90.6% |  89.1% |  85.9%, loss (SELU/ELU/RELU): 0.29 | 0.34 | 0.48\n",
      "Global Step:   4140, accuracy (SELU/ELU/RELU):  82.8% |  85.9% |  82.8%, loss (SELU/ELU/RELU): 0.50 | 0.47 | 0.59\n",
      "Global Step:   4150, accuracy (SELU/ELU/RELU):  91.4% |  85.9% |  81.2%, loss (SELU/ELU/RELU): 0.30 | 0.36 | 0.56\n",
      "Global Step:   4160, accuracy (SELU/ELU/RELU):  89.8% |  82.0% |  75.8%, loss (SELU/ELU/RELU): 0.38 | 0.47 | 0.57\n",
      "Global Step:   4170, accuracy (SELU/ELU/RELU):  85.9% |  78.1% |  74.2%, loss (SELU/ELU/RELU): 0.39 | 0.54 | 0.70\n",
      "Global Step:   4180, accuracy (SELU/ELU/RELU):  87.5% |  89.8% |  80.5%, loss (SELU/ELU/RELU): 0.33 | 0.36 | 0.51\n",
      "Global Step:   4190, accuracy (SELU/ELU/RELU):  89.1% |  84.4% |  79.7%, loss (SELU/ELU/RELU): 0.37 | 0.48 | 0.62\n",
      "Global Step:   4200, accuracy (SELU/ELU/RELU):  90.6% |  83.6% |  78.1%, loss (SELU/ELU/RELU): 0.37 | 0.42 | 0.52\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.08% | 74.23% | 71.46%\n",
      "Saved checkpoint.\n",
      "Global Step:   4210, accuracy (SELU/ELU/RELU):  90.6% |  84.4% |  73.4%, loss (SELU/ELU/RELU): 0.36 | 0.44 | 0.66\n",
      "Global Step:   4220, accuracy (SELU/ELU/RELU):  84.4% |  85.2% |  81.2%, loss (SELU/ELU/RELU): 0.38 | 0.41 | 0.53\n",
      "Global Step:   4230, accuracy (SELU/ELU/RELU):  85.2% |  82.0% |  75.0%, loss (SELU/ELU/RELU): 0.44 | 0.49 | 0.70\n",
      "Global Step:   4240, accuracy (SELU/ELU/RELU):  88.3% |  79.7% |  81.2%, loss (SELU/ELU/RELU): 0.39 | 0.53 | 0.62\n",
      "Global Step:   4250, accuracy (SELU/ELU/RELU):  96.1% |  91.4% |  84.4%, loss (SELU/ELU/RELU): 0.23 | 0.30 | 0.46\n",
      "Global Step:   4260, accuracy (SELU/ELU/RELU):  86.7% |  87.5% |  82.0%, loss (SELU/ELU/RELU): 0.37 | 0.36 | 0.55\n",
      "Global Step:   4270, accuracy (SELU/ELU/RELU):  87.5% |  87.5% |  81.2%, loss (SELU/ELU/RELU): 0.41 | 0.39 | 0.58\n",
      "Global Step:   4280, accuracy (SELU/ELU/RELU):  91.4% |  89.8% |  85.2%, loss (SELU/ELU/RELU): 0.32 | 0.43 | 0.54\n",
      "Global Step:   4290, accuracy (SELU/ELU/RELU):  85.2% |  87.5% |  78.9%, loss (SELU/ELU/RELU): 0.38 | 0.40 | 0.56\n",
      "Global Step:   4300, accuracy (SELU/ELU/RELU):  90.6% |  84.4% |  81.2%, loss (SELU/ELU/RELU): 0.33 | 0.46 | 0.58\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.35% | 73.43% | 69.26%\n",
      "Saved checkpoint.\n",
      "Global Step:   4310, accuracy (SELU/ELU/RELU):  90.6% |  89.1% |  85.9%, loss (SELU/ELU/RELU): 0.26 | 0.31 | 0.46\n",
      "Global Step:   4320, accuracy (SELU/ELU/RELU):  93.0% |  89.1% |  84.4%, loss (SELU/ELU/RELU): 0.25 | 0.38 | 0.49\n",
      "Global Step:   4330, accuracy (SELU/ELU/RELU):  88.3% |  84.4% |  80.5%, loss (SELU/ELU/RELU): 0.38 | 0.40 | 0.53\n",
      "Global Step:   4340, accuracy (SELU/ELU/RELU):  88.3% |  85.9% |  77.3%, loss (SELU/ELU/RELU): 0.38 | 0.48 | 0.61\n",
      "Global Step:   4350, accuracy (SELU/ELU/RELU):  85.9% |  81.2% |  79.7%, loss (SELU/ELU/RELU): 0.35 | 0.44 | 0.54\n",
      "Global Step:   4360, accuracy (SELU/ELU/RELU):  91.4% |  85.9% |  85.9%, loss (SELU/ELU/RELU): 0.36 | 0.40 | 0.49\n",
      "Global Step:   4370, accuracy (SELU/ELU/RELU):  88.3% |  88.3% |  81.2%, loss (SELU/ELU/RELU): 0.29 | 0.42 | 0.49\n",
      "Global Step:   4380, accuracy (SELU/ELU/RELU):  85.9% |  87.5% |  80.5%, loss (SELU/ELU/RELU): 0.31 | 0.35 | 0.51\n",
      "Global Step:   4390, accuracy (SELU/ELU/RELU):  85.2% |  84.4% |  78.9%, loss (SELU/ELU/RELU): 0.43 | 0.49 | 0.65\n",
      "Global Step:   4400, accuracy (SELU/ELU/RELU):  89.8% |  87.5% |  75.0%, loss (SELU/ELU/RELU): 0.29 | 0.36 | 0.64\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.91% | 75.08% | 70.72%\n",
      "Saved checkpoint.\n",
      "Global Step:   4410, accuracy (SELU/ELU/RELU):  85.9% |  82.0% |  78.1%, loss (SELU/ELU/RELU): 0.39 | 0.43 | 0.60\n",
      "Global Step:   4420, accuracy (SELU/ELU/RELU):  89.8% |  90.6% |  81.2%, loss (SELU/ELU/RELU): 0.29 | 0.35 | 0.57\n",
      "Global Step:   4430, accuracy (SELU/ELU/RELU):  87.5% |  84.4% |  83.6%, loss (SELU/ELU/RELU): 0.35 | 0.45 | 0.49\n",
      "Global Step:   4440, accuracy (SELU/ELU/RELU):  89.8% |  87.5% |  85.2%, loss (SELU/ELU/RELU): 0.33 | 0.38 | 0.50\n",
      "Global Step:   4450, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  77.3%, loss (SELU/ELU/RELU): 0.28 | 0.29 | 0.60\n",
      "Global Step:   4460, accuracy (SELU/ELU/RELU):  86.7% |  80.5% |  80.5%, loss (SELU/ELU/RELU): 0.47 | 0.55 | 0.67\n",
      "Global Step:   4470, accuracy (SELU/ELU/RELU):  90.6% |  84.4% |  78.9%, loss (SELU/ELU/RELU): 0.40 | 0.45 | 0.69\n",
      "Global Step:   4480, accuracy (SELU/ELU/RELU):  93.0% |  86.7% |  83.6%, loss (SELU/ELU/RELU): 0.24 | 0.33 | 0.54\n",
      "Global Step:   4490, accuracy (SELU/ELU/RELU):  89.8% |  91.4% |  82.0%, loss (SELU/ELU/RELU): 0.28 | 0.35 | 0.51\n",
      "Global Step:   4500, accuracy (SELU/ELU/RELU):  91.4% |  88.3% |  83.6%, loss (SELU/ELU/RELU): 0.25 | 0.28 | 0.46\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.85% | 74.79% | 71.97%\n",
      "Saved checkpoint.\n",
      "Global Step:   4510, accuracy (SELU/ELU/RELU):  85.9% |  80.5% |  74.2%, loss (SELU/ELU/RELU): 0.39 | 0.46 | 0.68\n",
      "Global Step:   4520, accuracy (SELU/ELU/RELU):  94.5% |  91.4% |  85.9%, loss (SELU/ELU/RELU): 0.30 | 0.37 | 0.51\n",
      "Global Step:   4530, accuracy (SELU/ELU/RELU):  88.3% |  85.9% |  81.2%, loss (SELU/ELU/RELU): 0.33 | 0.37 | 0.52\n",
      "Global Step:   4540, accuracy (SELU/ELU/RELU):  92.2% |  89.1% |  82.8%, loss (SELU/ELU/RELU): 0.32 | 0.33 | 0.52\n",
      "Global Step:   4550, accuracy (SELU/ELU/RELU):  94.5% |  93.8% |  91.4%, loss (SELU/ELU/RELU): 0.23 | 0.25 | 0.30\n",
      "Global Step:   4560, accuracy (SELU/ELU/RELU):  85.9% |  82.0% |  78.9%, loss (SELU/ELU/RELU): 0.43 | 0.48 | 0.57\n",
      "Global Step:   4570, accuracy (SELU/ELU/RELU):  90.6% |  83.6% |  81.2%, loss (SELU/ELU/RELU): 0.37 | 0.49 | 0.59\n",
      "Global Step:   4580, accuracy (SELU/ELU/RELU):  85.2% |  88.3% |  85.9%, loss (SELU/ELU/RELU): 0.38 | 0.39 | 0.56\n",
      "Global Step:   4590, accuracy (SELU/ELU/RELU):  93.8% |  91.4% |  85.9%, loss (SELU/ELU/RELU): 0.22 | 0.29 | 0.41\n",
      "Global Step:   4600, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  83.6%, loss (SELU/ELU/RELU): 0.29 | 0.33 | 0.47\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.81% | 75.45% | 72.83%\n",
      "Saved checkpoint.\n",
      "Global Step:   4610, accuracy (SELU/ELU/RELU):  86.7% |  82.8% |  79.7%, loss (SELU/ELU/RELU): 0.38 | 0.47 | 0.54\n",
      "Global Step:   4620, accuracy (SELU/ELU/RELU):  84.4% |  79.7% |  75.8%, loss (SELU/ELU/RELU): 0.35 | 0.44 | 0.70\n",
      "Global Step:   4630, accuracy (SELU/ELU/RELU):  91.4% |  89.1% |  80.5%, loss (SELU/ELU/RELU): 0.30 | 0.36 | 0.51\n",
      "Global Step:   4640, accuracy (SELU/ELU/RELU):  94.5% |  83.6% |  82.8%, loss (SELU/ELU/RELU): 0.25 | 0.37 | 0.51\n",
      "Global Step:   4650, accuracy (SELU/ELU/RELU):  89.1% |  85.9% |  76.6%, loss (SELU/ELU/RELU): 0.37 | 0.45 | 0.63\n",
      "Global Step:   4660, accuracy (SELU/ELU/RELU):  89.8% |  85.2% |  80.5%, loss (SELU/ELU/RELU): 0.37 | 0.43 | 0.57\n",
      "Global Step:   4670, accuracy (SELU/ELU/RELU):  88.3% |  86.7% |  78.1%, loss (SELU/ELU/RELU): 0.36 | 0.43 | 0.61\n",
      "Global Step:   4680, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  87.5%, loss (SELU/ELU/RELU): 0.22 | 0.33 | 0.44\n",
      "Global Step:   4690, accuracy (SELU/ELU/RELU):  90.6% |  84.4% |  78.9%, loss (SELU/ELU/RELU): 0.29 | 0.39 | 0.53\n",
      "Global Step:   4700, accuracy (SELU/ELU/RELU):  88.3% |  87.5% |  78.9%, loss (SELU/ELU/RELU): 0.33 | 0.40 | 0.62\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.98% | 74.47% | 71.19%\n",
      "Saved checkpoint.\n",
      "Global Step:   4710, accuracy (SELU/ELU/RELU):  91.4% |  88.3% |  76.6%, loss (SELU/ELU/RELU): 0.27 | 0.36 | 0.60\n",
      "Global Step:   4720, accuracy (SELU/ELU/RELU):  89.8% |  88.3% |  75.8%, loss (SELU/ELU/RELU): 0.31 | 0.35 | 0.56\n",
      "Global Step:   4730, accuracy (SELU/ELU/RELU):  96.1% |  88.3% |  84.4%, loss (SELU/ELU/RELU): 0.20 | 0.34 | 0.47\n",
      "Global Step:   4740, accuracy (SELU/ELU/RELU):  94.5% |  88.3% |  81.2%, loss (SELU/ELU/RELU): 0.23 | 0.37 | 0.55\n",
      "Global Step:   4750, accuracy (SELU/ELU/RELU):  92.2% |  91.4% |  85.2%, loss (SELU/ELU/RELU): 0.26 | 0.29 | 0.40\n",
      "Global Step:   4760, accuracy (SELU/ELU/RELU):  95.3% |  85.9% |  82.8%, loss (SELU/ELU/RELU): 0.19 | 0.36 | 0.50\n",
      "Global Step:   4770, accuracy (SELU/ELU/RELU):  88.3% |  87.5% |  78.9%, loss (SELU/ELU/RELU): 0.32 | 0.43 | 0.55\n",
      "Global Step:   4780, accuracy (SELU/ELU/RELU):  85.2% |  83.6% |  79.7%, loss (SELU/ELU/RELU): 0.40 | 0.39 | 0.59\n",
      "Global Step:   4790, accuracy (SELU/ELU/RELU):  89.1% |  83.6% |  78.1%, loss (SELU/ELU/RELU): 0.36 | 0.42 | 0.59\n",
      "Global Step:   4800, accuracy (SELU/ELU/RELU):  91.4% |  90.6% |  89.8%, loss (SELU/ELU/RELU): 0.26 | 0.26 | 0.34\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.93% | 75.18% | 72.30%\n",
      "Saved checkpoint.\n",
      "Global Step:   4810, accuracy (SELU/ELU/RELU):  95.3% |  92.2% |  82.8%, loss (SELU/ELU/RELU): 0.21 | 0.32 | 0.55\n",
      "Global Step:   4820, accuracy (SELU/ELU/RELU):  89.1% |  90.6% |  85.9%, loss (SELU/ELU/RELU): 0.32 | 0.33 | 0.45\n",
      "Global Step:   4830, accuracy (SELU/ELU/RELU):  89.1% |  85.2% |  78.9%, loss (SELU/ELU/RELU): 0.30 | 0.39 | 0.57\n",
      "Global Step:   4840, accuracy (SELU/ELU/RELU):  95.3% |  87.5% |  83.6%, loss (SELU/ELU/RELU): 0.17 | 0.30 | 0.44\n",
      "Global Step:   4850, accuracy (SELU/ELU/RELU):  83.6% |  86.7% |  82.8%, loss (SELU/ELU/RELU): 0.43 | 0.40 | 0.58\n",
      "Global Step:   4860, accuracy (SELU/ELU/RELU):  86.7% |  89.1% |  86.7%, loss (SELU/ELU/RELU): 0.31 | 0.31 | 0.36\n",
      "Global Step:   4870, accuracy (SELU/ELU/RELU):  86.7% |  82.0% |  77.3%, loss (SELU/ELU/RELU): 0.36 | 0.47 | 0.60\n",
      "Global Step:   4880, accuracy (SELU/ELU/RELU):  92.2% |  87.5% |  75.0%, loss (SELU/ELU/RELU): 0.32 | 0.35 | 0.63\n",
      "Global Step:   4890, accuracy (SELU/ELU/RELU):  91.4% |  93.8% |  90.6%, loss (SELU/ELU/RELU): 0.20 | 0.24 | 0.40\n",
      "Global Step:   4900, accuracy (SELU/ELU/RELU):  90.6% |  87.5% |  82.8%, loss (SELU/ELU/RELU): 0.30 | 0.38 | 0.49\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.96% | 75.72% | 72.51%\n",
      "Saved checkpoint.\n",
      "Global Step:   4910, accuracy (SELU/ELU/RELU):  92.2% |  85.9% |  79.7%, loss (SELU/ELU/RELU): 0.28 | 0.32 | 0.51\n",
      "Global Step:   4920, accuracy (SELU/ELU/RELU):  82.0% |  89.1% |  79.7%, loss (SELU/ELU/RELU): 0.38 | 0.37 | 0.61\n",
      "Global Step:   4930, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  85.2%, loss (SELU/ELU/RELU): 0.21 | 0.30 | 0.48\n",
      "Global Step:   4940, accuracy (SELU/ELU/RELU):  90.6% |  90.6% |  78.1%, loss (SELU/ELU/RELU): 0.27 | 0.33 | 0.61\n",
      "Global Step:   4950, accuracy (SELU/ELU/RELU):  93.0% |  91.4% |  85.2%, loss (SELU/ELU/RELU): 0.27 | 0.33 | 0.46\n",
      "Global Step:   4960, accuracy (SELU/ELU/RELU):  92.2% |  91.4% |  82.8%, loss (SELU/ELU/RELU): 0.27 | 0.30 | 0.52\n",
      "Global Step:   4970, accuracy (SELU/ELU/RELU):  89.1% |  88.3% |  78.9%, loss (SELU/ELU/RELU): 0.30 | 0.34 | 0.55\n",
      "Global Step:   4980, accuracy (SELU/ELU/RELU):  88.3% |  88.3% |  77.3%, loss (SELU/ELU/RELU): 0.32 | 0.33 | 0.57\n",
      "Global Step:   4990, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  85.2%, loss (SELU/ELU/RELU): 0.19 | 0.32 | 0.49\n",
      "Global Step:   5000, accuracy (SELU/ELU/RELU):  91.4% |  89.8% |  78.1%, loss (SELU/ELU/RELU): 0.33 | 0.32 | 0.50\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.16% | 74.18% | 73.06%\n",
      "Saved checkpoint.\n",
      "Global Step:   5010, accuracy (SELU/ELU/RELU):  93.0% |  88.3% |  83.6%, loss (SELU/ELU/RELU): 0.26 | 0.41 | 0.54\n",
      "Global Step:   5020, accuracy (SELU/ELU/RELU):  89.1% |  86.7% |  75.8%, loss (SELU/ELU/RELU): 0.28 | 0.41 | 0.62\n",
      "Global Step:   5030, accuracy (SELU/ELU/RELU):  89.1% |  88.3% |  79.7%, loss (SELU/ELU/RELU): 0.33 | 0.41 | 0.52\n",
      "Global Step:   5040, accuracy (SELU/ELU/RELU):  92.2% |  93.0% |  85.9%, loss (SELU/ELU/RELU): 0.18 | 0.19 | 0.39\n",
      "Global Step:   5050, accuracy (SELU/ELU/RELU):  91.4% |  92.2% |  82.8%, loss (SELU/ELU/RELU): 0.27 | 0.28 | 0.49\n",
      "Global Step:   5060, accuracy (SELU/ELU/RELU):  93.0% |  88.3% |  85.2%, loss (SELU/ELU/RELU): 0.32 | 0.40 | 0.54\n",
      "Global Step:   5070, accuracy (SELU/ELU/RELU):  96.1% |  90.6% |  85.9%, loss (SELU/ELU/RELU): 0.18 | 0.25 | 0.41\n",
      "Global Step:   5080, accuracy (SELU/ELU/RELU):  90.6% |  90.6% |  80.5%, loss (SELU/ELU/RELU): 0.29 | 0.32 | 0.47\n",
      "Global Step:   5090, accuracy (SELU/ELU/RELU):  94.5% |  93.0% |  82.0%, loss (SELU/ELU/RELU): 0.22 | 0.27 | 0.52\n",
      "Global Step:   5100, accuracy (SELU/ELU/RELU):  89.1% |  86.7% |  78.9%, loss (SELU/ELU/RELU): 0.31 | 0.36 | 0.55\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.20% | 75.83% | 72.61%\n",
      "Saved checkpoint.\n",
      "Global Step:   5110, accuracy (SELU/ELU/RELU):  87.5% |  85.2% |  82.0%, loss (SELU/ELU/RELU): 0.27 | 0.39 | 0.52\n",
      "Global Step:   5120, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  83.6%, loss (SELU/ELU/RELU): 0.26 | 0.28 | 0.53\n",
      "Global Step:   5130, accuracy (SELU/ELU/RELU):  94.5% |  95.3% |  87.5%, loss (SELU/ELU/RELU): 0.21 | 0.20 | 0.38\n",
      "Global Step:   5140, accuracy (SELU/ELU/RELU):  92.2% |  86.7% |  83.6%, loss (SELU/ELU/RELU): 0.21 | 0.35 | 0.42\n",
      "Global Step:   5150, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  84.4%, loss (SELU/ELU/RELU): 0.19 | 0.20 | 0.37\n",
      "Global Step:   5160, accuracy (SELU/ELU/RELU):  86.7% |  86.7% |  83.6%, loss (SELU/ELU/RELU): 0.37 | 0.36 | 0.51\n",
      "Global Step:   5170, accuracy (SELU/ELU/RELU):  92.2% |  87.5% |  78.1%, loss (SELU/ELU/RELU): 0.25 | 0.38 | 0.66\n",
      "Global Step:   5180, accuracy (SELU/ELU/RELU):  91.4% |  86.7% |  80.5%, loss (SELU/ELU/RELU): 0.36 | 0.43 | 0.64\n",
      "Global Step:   5190, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  86.7%, loss (SELU/ELU/RELU): 0.26 | 0.26 | 0.39\n",
      "Global Step:   5200, accuracy (SELU/ELU/RELU):  91.4% |  91.4% |  83.6%, loss (SELU/ELU/RELU): 0.24 | 0.30 | 0.51\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.37% | 75.38% | 72.61%\n",
      "Saved checkpoint.\n",
      "Global Step:   5210, accuracy (SELU/ELU/RELU):  90.6% |  87.5% |  82.0%, loss (SELU/ELU/RELU): 0.29 | 0.34 | 0.48\n",
      "Global Step:   5220, accuracy (SELU/ELU/RELU):  89.8% |  91.4% |  82.0%, loss (SELU/ELU/RELU): 0.25 | 0.26 | 0.52\n",
      "Global Step:   5230, accuracy (SELU/ELU/RELU):  89.8% |  88.3% |  82.0%, loss (SELU/ELU/RELU): 0.29 | 0.37 | 0.60\n",
      "Global Step:   5240, accuracy (SELU/ELU/RELU):  93.8% |  89.8% |  85.2%, loss (SELU/ELU/RELU): 0.23 | 0.27 | 0.44\n",
      "Global Step:   5250, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  85.9%, loss (SELU/ELU/RELU): 0.20 | 0.30 | 0.42\n",
      "Global Step:   5260, accuracy (SELU/ELU/RELU):  93.8% |  92.2% |  85.9%, loss (SELU/ELU/RELU): 0.21 | 0.27 | 0.40\n",
      "Global Step:   5270, accuracy (SELU/ELU/RELU):  93.0% |  90.6% |  85.9%, loss (SELU/ELU/RELU): 0.19 | 0.28 | 0.41\n",
      "Global Step:   5280, accuracy (SELU/ELU/RELU):  94.5% |  91.4% |  82.8%, loss (SELU/ELU/RELU): 0.20 | 0.27 | 0.51\n",
      "Global Step:   5290, accuracy (SELU/ELU/RELU):  92.2% |  96.1% |  83.6%, loss (SELU/ELU/RELU): 0.19 | 0.21 | 0.44\n",
      "Global Step:   5300, accuracy (SELU/ELU/RELU):  92.2% |  91.4% |  80.5%, loss (SELU/ELU/RELU): 0.20 | 0.22 | 0.44\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.01% | 75.22% | 72.40%\n",
      "Saved checkpoint.\n",
      "Global Step:   5310, accuracy (SELU/ELU/RELU):  96.1% |  92.2% |  86.7%, loss (SELU/ELU/RELU): 0.16 | 0.23 | 0.40\n",
      "Global Step:   5320, accuracy (SELU/ELU/RELU):  90.6% |  87.5% |  88.3%, loss (SELU/ELU/RELU): 0.24 | 0.30 | 0.38\n",
      "Global Step:   5330, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  85.9%, loss (SELU/ELU/RELU): 0.22 | 0.27 | 0.45\n",
      "Global Step:   5340, accuracy (SELU/ELU/RELU):  96.1% |  90.6% |  85.9%, loss (SELU/ELU/RELU): 0.15 | 0.28 | 0.36\n",
      "Global Step:   5350, accuracy (SELU/ELU/RELU):  89.8% |  88.3% |  84.4%, loss (SELU/ELU/RELU): 0.28 | 0.43 | 0.55\n",
      "Global Step:   5360, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  86.7%, loss (SELU/ELU/RELU): 0.19 | 0.29 | 0.44\n",
      "Global Step:   5370, accuracy (SELU/ELU/RELU):  90.6% |  85.9% |  85.9%, loss (SELU/ELU/RELU): 0.23 | 0.34 | 0.40\n",
      "Global Step:   5380, accuracy (SELU/ELU/RELU):  93.0% |  90.6% |  82.8%, loss (SELU/ELU/RELU): 0.25 | 0.31 | 0.49\n",
      "Global Step:   5390, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  89.1%, loss (SELU/ELU/RELU): 0.22 | 0.31 | 0.37\n",
      "Global Step:   5400, accuracy (SELU/ELU/RELU):  91.4% |  89.1% |  81.2%, loss (SELU/ELU/RELU): 0.29 | 0.28 | 0.45\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.97% | 75.28% | 73.17%\n",
      "Saved checkpoint.\n",
      "Global Step:   5410, accuracy (SELU/ELU/RELU):  93.8% |  90.6% |  89.8%, loss (SELU/ELU/RELU): 0.20 | 0.26 | 0.35\n",
      "Global Step:   5420, accuracy (SELU/ELU/RELU):  89.8% |  86.7% |  82.8%, loss (SELU/ELU/RELU): 0.30 | 0.37 | 0.56\n",
      "Global Step:   5430, accuracy (SELU/ELU/RELU):  93.0% |  89.8% |  83.6%, loss (SELU/ELU/RELU): 0.24 | 0.31 | 0.46\n",
      "Global Step:   5440, accuracy (SELU/ELU/RELU):  93.0% |  85.2% |  79.7%, loss (SELU/ELU/RELU): 0.35 | 0.41 | 0.51\n",
      "Global Step:   5450, accuracy (SELU/ELU/RELU):  94.5% |  92.2% |  82.0%, loss (SELU/ELU/RELU): 0.20 | 0.27 | 0.49\n",
      "Global Step:   5460, accuracy (SELU/ELU/RELU):  92.2% |  92.2% |  84.4%, loss (SELU/ELU/RELU): 0.25 | 0.27 | 0.56\n",
      "Global Step:   5470, accuracy (SELU/ELU/RELU):  90.6% |  86.7% |  78.9%, loss (SELU/ELU/RELU): 0.33 | 0.34 | 0.61\n",
      "Global Step:   5480, accuracy (SELU/ELU/RELU):  89.8% |  88.3% |  82.8%, loss (SELU/ELU/RELU): 0.26 | 0.34 | 0.47\n",
      "Global Step:   5490, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  85.9%, loss (SELU/ELU/RELU): 0.14 | 0.19 | 0.39\n",
      "Global Step:   5500, accuracy (SELU/ELU/RELU):  92.2% |  91.4% |  81.2%, loss (SELU/ELU/RELU): 0.28 | 0.31 | 0.49\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.34% | 74.66% | 73.57%\n",
      "Saved checkpoint.\n",
      "Global Step:   5510, accuracy (SELU/ELU/RELU):  89.1% |  90.6% |  85.9%, loss (SELU/ELU/RELU): 0.35 | 0.31 | 0.45\n",
      "Global Step:   5520, accuracy (SELU/ELU/RELU):  92.2% |  92.2% |  82.0%, loss (SELU/ELU/RELU): 0.26 | 0.31 | 0.51\n",
      "Global Step:   5530, accuracy (SELU/ELU/RELU):  95.3% |  92.2% |  85.9%, loss (SELU/ELU/RELU): 0.22 | 0.25 | 0.46\n",
      "Global Step:   5540, accuracy (SELU/ELU/RELU):  91.4% |  91.4% |  82.8%, loss (SELU/ELU/RELU): 0.28 | 0.35 | 0.49\n",
      "Global Step:   5550, accuracy (SELU/ELU/RELU):  87.5% |  89.1% |  78.9%, loss (SELU/ELU/RELU): 0.31 | 0.34 | 0.53\n",
      "Global Step:   5560, accuracy (SELU/ELU/RELU):  95.3% |  92.2% |  85.2%, loss (SELU/ELU/RELU): 0.17 | 0.19 | 0.41\n",
      "Global Step:   5570, accuracy (SELU/ELU/RELU):  89.8% |  86.7% |  81.2%, loss (SELU/ELU/RELU): 0.27 | 0.35 | 0.54\n",
      "Global Step:   5580, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  90.6%, loss (SELU/ELU/RELU): 0.16 | 0.20 | 0.37\n",
      "Global Step:   5590, accuracy (SELU/ELU/RELU):  91.4% |  91.4% |  80.5%, loss (SELU/ELU/RELU): 0.23 | 0.32 | 0.56\n",
      "Global Step:   5600, accuracy (SELU/ELU/RELU):  89.8% |  87.5% |  81.2%, loss (SELU/ELU/RELU): 0.27 | 0.27 | 0.50\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.38% | 75.05% | 73.96%\n",
      "Saved checkpoint.\n",
      "Global Step:   5610, accuracy (SELU/ELU/RELU):  86.7% |  89.1% |  74.2%, loss (SELU/ELU/RELU): 0.39 | 0.40 | 0.60\n",
      "Global Step:   5620, accuracy (SELU/ELU/RELU):  96.1% |  93.0% |  86.7%, loss (SELU/ELU/RELU): 0.18 | 0.26 | 0.35\n",
      "Global Step:   5630, accuracy (SELU/ELU/RELU):  89.8% |  92.2% |  87.5%, loss (SELU/ELU/RELU): 0.31 | 0.33 | 0.45\n",
      "Global Step:   5640, accuracy (SELU/ELU/RELU):  92.2% |  95.3% |  89.1%, loss (SELU/ELU/RELU): 0.19 | 0.18 | 0.38\n",
      "Global Step:   5650, accuracy (SELU/ELU/RELU):  93.0% |  94.5% |  88.3%, loss (SELU/ELU/RELU): 0.17 | 0.20 | 0.45\n",
      "Global Step:   5660, accuracy (SELU/ELU/RELU):  93.0% |  91.4% |  85.9%, loss (SELU/ELU/RELU): 0.21 | 0.25 | 0.40\n",
      "Global Step:   5670, accuracy (SELU/ELU/RELU):  94.5% |  94.5% |  85.2%, loss (SELU/ELU/RELU): 0.17 | 0.20 | 0.46\n",
      "Global Step:   5680, accuracy (SELU/ELU/RELU):  93.8% |  90.6% |  84.4%, loss (SELU/ELU/RELU): 0.21 | 0.26 | 0.41\n",
      "Global Step:   5690, accuracy (SELU/ELU/RELU):  92.2% |  90.6% |  85.2%, loss (SELU/ELU/RELU): 0.27 | 0.29 | 0.39\n",
      "Global Step:   5700, accuracy (SELU/ELU/RELU):  92.2% |  88.3% |  81.2%, loss (SELU/ELU/RELU): 0.28 | 0.33 | 0.48\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.53% | 75.24% | 73.38%\n",
      "Saved checkpoint.\n",
      "Global Step:   5710, accuracy (SELU/ELU/RELU):  93.0% |  92.2% |  85.9%, loss (SELU/ELU/RELU): 0.22 | 0.27 | 0.46\n",
      "Global Step:   5720, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  85.9%, loss (SELU/ELU/RELU): 0.13 | 0.19 | 0.43\n",
      "Global Step:   5730, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  88.3%, loss (SELU/ELU/RELU): 0.17 | 0.24 | 0.37\n",
      "Global Step:   5740, accuracy (SELU/ELU/RELU):  95.3% |  91.4% |  81.2%, loss (SELU/ELU/RELU): 0.22 | 0.26 | 0.56\n",
      "Global Step:   5750, accuracy (SELU/ELU/RELU):  93.0% |  93.0% |  84.4%, loss (SELU/ELU/RELU): 0.22 | 0.26 | 0.51\n",
      "Global Step:   5760, accuracy (SELU/ELU/RELU):  93.0% |  93.8% |  85.9%, loss (SELU/ELU/RELU): 0.20 | 0.17 | 0.37\n",
      "Global Step:   5770, accuracy (SELU/ELU/RELU):  93.0% |  90.6% |  82.8%, loss (SELU/ELU/RELU): 0.29 | 0.31 | 0.53\n",
      "Global Step:   5780, accuracy (SELU/ELU/RELU):  93.8% |  94.5% |  91.4%, loss (SELU/ELU/RELU): 0.18 | 0.21 | 0.33\n",
      "Global Step:   5790, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  86.7%, loss (SELU/ELU/RELU): 0.16 | 0.18 | 0.37\n",
      "Global Step:   5800, accuracy (SELU/ELU/RELU):  98.4% |  92.2% |  91.4%, loss (SELU/ELU/RELU): 0.09 | 0.22 | 0.32\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.32% | 75.15% | 74.25%\n",
      "Saved checkpoint.\n",
      "Global Step:   5810, accuracy (SELU/ELU/RELU):  93.0% |  93.0% |  85.2%, loss (SELU/ELU/RELU): 0.19 | 0.31 | 0.49\n",
      "Global Step:   5820, accuracy (SELU/ELU/RELU):  92.2% |  89.1% |  85.9%, loss (SELU/ELU/RELU): 0.23 | 0.29 | 0.48\n",
      "Global Step:   5830, accuracy (SELU/ELU/RELU):  94.5% |  93.0% |  82.8%, loss (SELU/ELU/RELU): 0.22 | 0.24 | 0.45\n",
      "Global Step:   5840, accuracy (SELU/ELU/RELU):  94.5% |  90.6% |  86.7%, loss (SELU/ELU/RELU): 0.20 | 0.28 | 0.46\n",
      "Global Step:   5850, accuracy (SELU/ELU/RELU):  89.1% |  89.1% |  76.6%, loss (SELU/ELU/RELU): 0.31 | 0.35 | 0.59\n",
      "Global Step:   5860, accuracy (SELU/ELU/RELU):  93.0% |  93.0% |  86.7%, loss (SELU/ELU/RELU): 0.22 | 0.24 | 0.40\n",
      "Global Step:   5870, accuracy (SELU/ELU/RELU):  94.5% |  94.5% |  89.8%, loss (SELU/ELU/RELU): 0.19 | 0.22 | 0.35\n",
      "Global Step:   5880, accuracy (SELU/ELU/RELU):  93.8% |  92.2% |  89.1%, loss (SELU/ELU/RELU): 0.18 | 0.24 | 0.37\n",
      "Global Step:   5890, accuracy (SELU/ELU/RELU):  95.3% |  92.2% |  91.4%, loss (SELU/ELU/RELU): 0.18 | 0.24 | 0.33\n",
      "Global Step:   5900, accuracy (SELU/ELU/RELU):  95.3% |  96.1% |  85.2%, loss (SELU/ELU/RELU): 0.18 | 0.20 | 0.44\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.04% | 74.85% | 73.36%\n",
      "Saved checkpoint.\n",
      "Global Step:   5910, accuracy (SELU/ELU/RELU):  95.3% |  94.5% |  91.4%, loss (SELU/ELU/RELU): 0.15 | 0.20 | 0.29\n",
      "Global Step:   5920, accuracy (SELU/ELU/RELU):  97.7% |  95.3% |  89.1%, loss (SELU/ELU/RELU): 0.13 | 0.15 | 0.32\n",
      "Global Step:   5930, accuracy (SELU/ELU/RELU):  94.5% |  92.2% |  85.9%, loss (SELU/ELU/RELU): 0.16 | 0.25 | 0.44\n",
      "Global Step:   5940, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  86.7%, loss (SELU/ELU/RELU): 0.15 | 0.17 | 0.32\n",
      "Global Step:   5950, accuracy (SELU/ELU/RELU):  94.5% |  90.6% |  84.4%, loss (SELU/ELU/RELU): 0.15 | 0.21 | 0.37\n",
      "Global Step:   5960, accuracy (SELU/ELU/RELU):  96.1% |  93.0% |  82.8%, loss (SELU/ELU/RELU): 0.18 | 0.23 | 0.52\n",
      "Global Step:   5970, accuracy (SELU/ELU/RELU):  97.7% |  97.7% |  93.8%, loss (SELU/ELU/RELU): 0.13 | 0.17 | 0.32\n",
      "Global Step:   5980, accuracy (SELU/ELU/RELU):  96.9% |  92.2% |  87.5%, loss (SELU/ELU/RELU): 0.17 | 0.25 | 0.42\n",
      "Global Step:   5990, accuracy (SELU/ELU/RELU):  93.8% |  93.0% |  85.2%, loss (SELU/ELU/RELU): 0.30 | 0.30 | 0.54\n",
      "Global Step:   6000, accuracy (SELU/ELU/RELU):  94.5% |  95.3% |  84.4%, loss (SELU/ELU/RELU): 0.16 | 0.23 | 0.44\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.56% | 75.90% | 73.04%\n",
      "Saved checkpoint.\n",
      "Global Step:   6010, accuracy (SELU/ELU/RELU):  96.9% |  92.2% |  85.9%, loss (SELU/ELU/RELU): 0.15 | 0.18 | 0.40\n",
      "Global Step:   6020, accuracy (SELU/ELU/RELU):  90.6% |  89.8% |  90.6%, loss (SELU/ELU/RELU): 0.23 | 0.23 | 0.39\n",
      "Global Step:   6030, accuracy (SELU/ELU/RELU):  93.8% |  91.4% |  89.1%, loss (SELU/ELU/RELU): 0.18 | 0.21 | 0.33\n",
      "Global Step:   6040, accuracy (SELU/ELU/RELU):  96.9% |  93.0% |  87.5%, loss (SELU/ELU/RELU): 0.16 | 0.19 | 0.44\n",
      "Global Step:   6050, accuracy (SELU/ELU/RELU):  91.4% |  88.3% |  83.6%, loss (SELU/ELU/RELU): 0.25 | 0.31 | 0.45\n",
      "Global Step:   6060, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  83.6%, loss (SELU/ELU/RELU): 0.17 | 0.27 | 0.42\n",
      "Global Step:   6070, accuracy (SELU/ELU/RELU):  95.3% |  95.3% |  89.8%, loss (SELU/ELU/RELU): 0.15 | 0.17 | 0.34\n",
      "Global Step:   6080, accuracy (SELU/ELU/RELU):  95.3% |  89.8% |  85.9%, loss (SELU/ELU/RELU): 0.18 | 0.22 | 0.39\n",
      "Global Step:   6090, accuracy (SELU/ELU/RELU):  95.3% |  94.5% |  87.5%, loss (SELU/ELU/RELU): 0.17 | 0.21 | 0.42\n",
      "Global Step:   6100, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  89.8%, loss (SELU/ELU/RELU): 0.13 | 0.19 | 0.36\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.44% | 75.50% | 73.99%\n",
      "Saved checkpoint.\n",
      "Global Step:   6110, accuracy (SELU/ELU/RELU):  93.8% |  89.8% |  92.2%, loss (SELU/ELU/RELU): 0.23 | 0.26 | 0.33\n",
      "Global Step:   6120, accuracy (SELU/ELU/RELU):  89.1% |  92.2% |  84.4%, loss (SELU/ELU/RELU): 0.24 | 0.24 | 0.40\n",
      "Global Step:   6130, accuracy (SELU/ELU/RELU):  94.5% |  93.8% |  87.5%, loss (SELU/ELU/RELU): 0.20 | 0.23 | 0.41\n",
      "Global Step:   6140, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  84.4%, loss (SELU/ELU/RELU): 0.15 | 0.19 | 0.41\n",
      "Global Step:   6150, accuracy (SELU/ELU/RELU):  99.2% |  96.1% |  90.6%, loss (SELU/ELU/RELU): 0.08 | 0.15 | 0.28\n",
      "Global Step:   6160, accuracy (SELU/ELU/RELU):  93.0% |  93.0% |  84.4%, loss (SELU/ELU/RELU): 0.24 | 0.21 | 0.39\n",
      "Global Step:   6170, accuracy (SELU/ELU/RELU):  97.7% |  91.4% |  85.9%, loss (SELU/ELU/RELU): 0.14 | 0.22 | 0.46\n",
      "Global Step:   6180, accuracy (SELU/ELU/RELU):  93.0% |  90.6% |  85.2%, loss (SELU/ELU/RELU): 0.16 | 0.25 | 0.39\n",
      "Global Step:   6190, accuracy (SELU/ELU/RELU):  93.8% |  93.0% |  88.3%, loss (SELU/ELU/RELU): 0.18 | 0.26 | 0.36\n",
      "Global Step:   6200, accuracy (SELU/ELU/RELU):  96.1% |  89.8% |  89.8%, loss (SELU/ELU/RELU): 0.15 | 0.31 | 0.36\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 74.86% | 75.31% | 74.20%\n",
      "Saved checkpoint.\n",
      "Global Step:   6210, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  93.8%, loss (SELU/ELU/RELU): 0.12 | 0.13 | 0.25\n",
      "Global Step:   6220, accuracy (SELU/ELU/RELU):  95.3% |  92.2% |  87.5%, loss (SELU/ELU/RELU): 0.16 | 0.28 | 0.36\n",
      "Global Step:   6230, accuracy (SELU/ELU/RELU):  93.0% |  92.2% |  86.7%, loss (SELU/ELU/RELU): 0.21 | 0.25 | 0.39\n",
      "Global Step:   6240, accuracy (SELU/ELU/RELU):  95.3% |  94.5% |  90.6%, loss (SELU/ELU/RELU): 0.18 | 0.19 | 0.36\n",
      "Global Step:   6250, accuracy (SELU/ELU/RELU):  97.7% |  91.4% |  85.9%, loss (SELU/ELU/RELU): 0.15 | 0.21 | 0.34\n",
      "Global Step:   6260, accuracy (SELU/ELU/RELU):  96.1% |  93.8% |  89.1%, loss (SELU/ELU/RELU): 0.13 | 0.22 | 0.31\n",
      "Global Step:   6270, accuracy (SELU/ELU/RELU):  95.3% |  95.3% |  88.3%, loss (SELU/ELU/RELU): 0.16 | 0.18 | 0.39\n",
      "Global Step:   6280, accuracy (SELU/ELU/RELU):  98.4% |  95.3% |  85.9%, loss (SELU/ELU/RELU): 0.09 | 0.15 | 0.41\n",
      "Global Step:   6290, accuracy (SELU/ELU/RELU):  97.7% |  94.5% |  86.7%, loss (SELU/ELU/RELU): 0.16 | 0.23 | 0.38\n",
      "Global Step:   6300, accuracy (SELU/ELU/RELU):  96.1% |  90.6% |  85.2%, loss (SELU/ELU/RELU): 0.12 | 0.21 | 0.41\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.75% | 74.91% | 72.87%\n",
      "Saved checkpoint.\n",
      "Global Step:   6310, accuracy (SELU/ELU/RELU):  97.7% |  94.5% |  89.8%, loss (SELU/ELU/RELU): 0.17 | 0.18 | 0.29\n",
      "Global Step:   6320, accuracy (SELU/ELU/RELU):  96.1% |  95.3% |  92.2%, loss (SELU/ELU/RELU): 0.12 | 0.16 | 0.25\n",
      "Global Step:   6330, accuracy (SELU/ELU/RELU):  95.3% |  91.4% |  85.9%, loss (SELU/ELU/RELU): 0.14 | 0.26 | 0.48\n",
      "Global Step:   6340, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  86.7%, loss (SELU/ELU/RELU): 0.15 | 0.24 | 0.37\n",
      "Global Step:   6350, accuracy (SELU/ELU/RELU):  97.7% |  93.0% |  85.2%, loss (SELU/ELU/RELU): 0.12 | 0.20 | 0.45\n",
      "Global Step:   6360, accuracy (SELU/ELU/RELU):  94.5% |  93.0% |  86.7%, loss (SELU/ELU/RELU): 0.17 | 0.20 | 0.40\n",
      "Global Step:   6370, accuracy (SELU/ELU/RELU):  98.4% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.07 | 0.12 | 0.28\n",
      "Global Step:   6380, accuracy (SELU/ELU/RELU):  92.2% |  89.8% |  84.4%, loss (SELU/ELU/RELU): 0.22 | 0.27 | 0.50\n",
      "Global Step:   6390, accuracy (SELU/ELU/RELU):  95.3% |  93.8% |  92.2%, loss (SELU/ELU/RELU): 0.16 | 0.19 | 0.37\n",
      "Global Step:   6400, accuracy (SELU/ELU/RELU):  95.3% |  92.2% |  86.7%, loss (SELU/ELU/RELU): 0.20 | 0.32 | 0.41\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.36% | 75.40% | 73.89%\n",
      "Saved checkpoint.\n",
      "Global Step:   6410, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  88.3%, loss (SELU/ELU/RELU): 0.10 | 0.13 | 0.35\n",
      "Global Step:   6420, accuracy (SELU/ELU/RELU):  95.3% |  96.9% |  88.3%, loss (SELU/ELU/RELU): 0.13 | 0.17 | 0.36\n",
      "Global Step:   6430, accuracy (SELU/ELU/RELU):  97.7% |  93.0% |  89.8%, loss (SELU/ELU/RELU): 0.11 | 0.21 | 0.29\n",
      "Global Step:   6440, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  87.5%, loss (SELU/ELU/RELU): 0.13 | 0.22 | 0.36\n",
      "Global Step:   6450, accuracy (SELU/ELU/RELU):  97.7% |  95.3% |  86.7%, loss (SELU/ELU/RELU): 0.14 | 0.21 | 0.37\n",
      "Global Step:   6460, accuracy (SELU/ELU/RELU):  96.9% |  97.7% |  87.5%, loss (SELU/ELU/RELU): 0.12 | 0.13 | 0.34\n",
      "Global Step:   6470, accuracy (SELU/ELU/RELU):  97.7% |  93.0% |  89.1%, loss (SELU/ELU/RELU): 0.14 | 0.18 | 0.32\n",
      "Global Step:   6480, accuracy (SELU/ELU/RELU):  96.1% |  92.2% |  87.5%, loss (SELU/ELU/RELU): 0.15 | 0.22 | 0.40\n",
      "Global Step:   6490, accuracy (SELU/ELU/RELU):  93.8% |  93.0% |  85.2%, loss (SELU/ELU/RELU): 0.21 | 0.26 | 0.49\n",
      "Global Step:   6500, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  87.5%, loss (SELU/ELU/RELU): 0.10 | 0.18 | 0.34\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.52% | 75.34% | 73.56%\n",
      "Saved checkpoint.\n",
      "Global Step:   6510, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  89.1%, loss (SELU/ELU/RELU): 0.11 | 0.11 | 0.32\n",
      "Global Step:   6520, accuracy (SELU/ELU/RELU):  98.4% |  94.5% |  83.6%, loss (SELU/ELU/RELU): 0.11 | 0.16 | 0.38\n",
      "Global Step:   6530, accuracy (SELU/ELU/RELU):  97.7% |  95.3% |  89.8%, loss (SELU/ELU/RELU): 0.10 | 0.15 | 0.33\n",
      "Global Step:   6540, accuracy (SELU/ELU/RELU):  94.5% |  95.3% |  86.7%, loss (SELU/ELU/RELU): 0.19 | 0.24 | 0.42\n",
      "Global Step:   6550, accuracy (SELU/ELU/RELU):  98.4% |  96.1% |  94.5%, loss (SELU/ELU/RELU): 0.09 | 0.13 | 0.27\n",
      "Global Step:   6560, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.09 | 0.14 | 0.25\n",
      "Global Step:   6570, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  90.6%, loss (SELU/ELU/RELU): 0.12 | 0.15 | 0.36\n",
      "Global Step:   6580, accuracy (SELU/ELU/RELU):  98.4% |  93.0% |  88.3%, loss (SELU/ELU/RELU): 0.11 | 0.16 | 0.29\n",
      "Global Step:   6590, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  88.3%, loss (SELU/ELU/RELU): 0.10 | 0.13 | 0.33\n",
      "Global Step:   6600, accuracy (SELU/ELU/RELU):  95.3% |  94.5% |  86.7%, loss (SELU/ELU/RELU): 0.20 | 0.28 | 0.50\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.82% | 75.28% | 73.58%\n",
      "Saved checkpoint.\n",
      "Global Step:   6610, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  89.1%, loss (SELU/ELU/RELU): 0.10 | 0.14 | 0.34\n",
      "Global Step:   6620, accuracy (SELU/ELU/RELU):  96.1% |  96.9% |  85.9%, loss (SELU/ELU/RELU): 0.15 | 0.16 | 0.34\n",
      "Global Step:   6630, accuracy (SELU/ELU/RELU):  95.3% |  93.8% |  86.7%, loss (SELU/ELU/RELU): 0.17 | 0.20 | 0.39\n",
      "Global Step:   6640, accuracy (SELU/ELU/RELU):  95.3% |  95.3% |  85.9%, loss (SELU/ELU/RELU): 0.16 | 0.15 | 0.34\n",
      "Global Step:   6650, accuracy (SELU/ELU/RELU):  96.9% |  90.6% |  85.9%, loss (SELU/ELU/RELU): 0.15 | 0.25 | 0.36\n",
      "Global Step:   6660, accuracy (SELU/ELU/RELU):  94.5% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.18 | 0.19 | 0.32\n",
      "Global Step:   6670, accuracy (SELU/ELU/RELU):  96.1% |  96.9% |  93.8%, loss (SELU/ELU/RELU): 0.11 | 0.12 | 0.21\n",
      "Global Step:   6680, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  89.1%, loss (SELU/ELU/RELU): 0.12 | 0.17 | 0.30\n",
      "Global Step:   6690, accuracy (SELU/ELU/RELU):  95.3% |  94.5% |  88.3%, loss (SELU/ELU/RELU): 0.22 | 0.18 | 0.36\n",
      "Global Step:   6700, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  87.5%, loss (SELU/ELU/RELU): 0.12 | 0.12 | 0.33\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.37% | 75.79% | 73.73%\n",
      "Saved checkpoint.\n",
      "Global Step:   6710, accuracy (SELU/ELU/RELU):  96.9% |  97.7% |  88.3%, loss (SELU/ELU/RELU): 0.11 | 0.16 | 0.31\n",
      "Global Step:   6720, accuracy (SELU/ELU/RELU):  96.1% |  93.8% |  83.6%, loss (SELU/ELU/RELU): 0.21 | 0.25 | 0.48\n",
      "Global Step:   6730, accuracy (SELU/ELU/RELU):  94.5% |  96.9% |  94.5%, loss (SELU/ELU/RELU): 0.12 | 0.14 | 0.22\n",
      "Global Step:   6740, accuracy (SELU/ELU/RELU):  97.7% |  93.8% |  89.8%, loss (SELU/ELU/RELU): 0.13 | 0.17 | 0.32\n",
      "Global Step:   6750, accuracy (SELU/ELU/RELU):  93.8% |  90.6% |  85.2%, loss (SELU/ELU/RELU): 0.13 | 0.23 | 0.35\n",
      "Global Step:   6760, accuracy (SELU/ELU/RELU):  95.3% |  94.5% |  89.1%, loss (SELU/ELU/RELU): 0.13 | 0.15 | 0.34\n",
      "Global Step:   6770, accuracy (SELU/ELU/RELU):  93.8% |  93.8% |  89.1%, loss (SELU/ELU/RELU): 0.16 | 0.17 | 0.37\n",
      "Global Step:   6780, accuracy (SELU/ELU/RELU):  96.9% |  96.9% |  91.4%, loss (SELU/ELU/RELU): 0.12 | 0.13 | 0.30\n",
      "Global Step:   6790, accuracy (SELU/ELU/RELU):  97.7% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.07 | 0.07 | 0.19\n",
      "Global Step:   6800, accuracy (SELU/ELU/RELU):  98.4% |  93.0% |  85.9%, loss (SELU/ELU/RELU): 0.08 | 0.18 | 0.38\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.65% | 75.64% | 73.68%\n",
      "Saved checkpoint.\n",
      "Global Step:   6810, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  89.1%, loss (SELU/ELU/RELU): 0.09 | 0.12 | 0.32\n",
      "Global Step:   6820, accuracy (SELU/ELU/RELU):  93.8% |  92.2% |  82.0%, loss (SELU/ELU/RELU): 0.18 | 0.23 | 0.43\n",
      "Global Step:   6830, accuracy (SELU/ELU/RELU):  95.3% |  95.3% |  86.7%, loss (SELU/ELU/RELU): 0.14 | 0.14 | 0.34\n",
      "Global Step:   6840, accuracy (SELU/ELU/RELU):  96.1% |  96.9% |  85.9%, loss (SELU/ELU/RELU): 0.13 | 0.19 | 0.35\n",
      "Global Step:   6850, accuracy (SELU/ELU/RELU):  95.3% |  93.8% |  87.5%, loss (SELU/ELU/RELU): 0.16 | 0.20 | 0.41\n",
      "Global Step:   6860, accuracy (SELU/ELU/RELU):  97.7% |  95.3% |  95.3%, loss (SELU/ELU/RELU): 0.09 | 0.15 | 0.20\n",
      "Global Step:   6870, accuracy (SELU/ELU/RELU):  96.1% |  98.4% |  86.7%, loss (SELU/ELU/RELU): 0.12 | 0.14 | 0.35\n",
      "Global Step:   6880, accuracy (SELU/ELU/RELU):  94.5% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.14 | 0.17 | 0.29\n",
      "Global Step:   6890, accuracy (SELU/ELU/RELU):  97.7% |  94.5% |  86.7%, loss (SELU/ELU/RELU): 0.11 | 0.20 | 0.43\n",
      "Global Step:   6900, accuracy (SELU/ELU/RELU):  93.8% |  93.0% |  87.5%, loss (SELU/ELU/RELU): 0.24 | 0.24 | 0.44\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.07% | 75.97% | 74.01%\n",
      "Saved checkpoint.\n",
      "Global Step:   6910, accuracy (SELU/ELU/RELU):  96.1% |  97.7% |  89.8%, loss (SELU/ELU/RELU): 0.11 | 0.12 | 0.32\n",
      "Global Step:   6920, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  91.4%, loss (SELU/ELU/RELU): 0.09 | 0.13 | 0.30\n",
      "Global Step:   6930, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.08 | 0.12 | 0.26\n",
      "Global Step:   6940, accuracy (SELU/ELU/RELU):  95.3% |  93.0% |  88.3%, loss (SELU/ELU/RELU): 0.17 | 0.18 | 0.40\n",
      "Global Step:   6950, accuracy (SELU/ELU/RELU):  97.7% |  93.0% |  85.9%, loss (SELU/ELU/RELU): 0.10 | 0.13 | 0.34\n",
      "Global Step:   6960, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  85.9%, loss (SELU/ELU/RELU): 0.10 | 0.18 | 0.38\n",
      "Global Step:   6970, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  93.0%, loss (SELU/ELU/RELU): 0.06 | 0.12 | 0.25\n",
      "Global Step:   6980, accuracy (SELU/ELU/RELU):  96.1% |  93.0% |  89.1%, loss (SELU/ELU/RELU): 0.21 | 0.21 | 0.38\n",
      "Global Step:   6990, accuracy (SELU/ELU/RELU):  95.3% |  95.3% |  83.6%, loss (SELU/ELU/RELU): 0.14 | 0.16 | 0.42\n",
      "Global Step:   7000, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.08 | 0.13 | 0.28\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.26% | 75.20% | 73.87%\n",
      "Saved checkpoint.\n",
      "Global Step:   7010, accuracy (SELU/ELU/RELU):  96.9% |  96.9% |  94.5%, loss (SELU/ELU/RELU): 0.13 | 0.14 | 0.24\n",
      "Global Step:   7020, accuracy (SELU/ELU/RELU):  97.7% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.08 | 0.11 | 0.24\n",
      "Global Step:   7030, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  93.8%, loss (SELU/ELU/RELU): 0.09 | 0.12 | 0.25\n",
      "Global Step:   7040, accuracy (SELU/ELU/RELU):  94.5% |  95.3% |  86.7%, loss (SELU/ELU/RELU): 0.16 | 0.18 | 0.35\n",
      "Global Step:   7050, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.08 | 0.13 | 0.30\n",
      "Global Step:   7060, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  91.4%, loss (SELU/ELU/RELU): 0.12 | 0.12 | 0.26\n",
      "Global Step:   7070, accuracy (SELU/ELU/RELU):  98.4% |  94.5% |  91.4%, loss (SELU/ELU/RELU): 0.09 | 0.15 | 0.27\n",
      "Global Step:   7080, accuracy (SELU/ELU/RELU):  96.9% |  98.4% |  89.1%, loss (SELU/ELU/RELU): 0.10 | 0.13 | 0.36\n",
      "Global Step:   7090, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  89.8%, loss (SELU/ELU/RELU): 0.08 | 0.10 | 0.31\n",
      "Global Step:   7100, accuracy (SELU/ELU/RELU):  96.9% |  96.9% |  92.2%, loss (SELU/ELU/RELU): 0.12 | 0.11 | 0.25\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.06% | 75.16% | 73.22%\n",
      "Saved checkpoint.\n",
      "Global Step:   7110, accuracy (SELU/ELU/RELU):  97.7% |  94.5% |  90.6%, loss (SELU/ELU/RELU): 0.09 | 0.14 | 0.27\n",
      "Global Step:   7120, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  90.6%, loss (SELU/ELU/RELU): 0.09 | 0.13 | 0.29\n",
      "Global Step:   7130, accuracy (SELU/ELU/RELU):  96.1% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.14 | 0.13 | 0.31\n",
      "Global Step:   7140, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.09 | 0.09 | 0.17\n",
      "Global Step:   7150, accuracy (SELU/ELU/RELU):  96.1% |  97.7% |  89.8%, loss (SELU/ELU/RELU): 0.12 | 0.09 | 0.31\n",
      "Global Step:   7160, accuracy (SELU/ELU/RELU):  97.7% |  94.5% |  90.6%, loss (SELU/ELU/RELU): 0.12 | 0.14 | 0.28\n",
      "Global Step:   7170, accuracy (SELU/ELU/RELU):  96.9% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.11 | 0.10 | 0.36\n",
      "Global Step:   7180, accuracy (SELU/ELU/RELU):  97.7% |  97.7% |  92.2%, loss (SELU/ELU/RELU): 0.09 | 0.10 | 0.31\n",
      "Global Step:   7190, accuracy (SELU/ELU/RELU):  98.4% |  96.1% |  93.0%, loss (SELU/ELU/RELU): 0.08 | 0.12 | 0.22\n",
      "Global Step:   7200, accuracy (SELU/ELU/RELU):  96.9% |  96.9% |  91.4%, loss (SELU/ELU/RELU): 0.12 | 0.12 | 0.33\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 77.07% | 76.24% | 74.48%\n",
      "Saved checkpoint.\n",
      "Global Step:   7210, accuracy (SELU/ELU/RELU):  98.4% |  96.1% |  89.1%, loss (SELU/ELU/RELU): 0.09 | 0.16 | 0.29\n",
      "Global Step:   7220, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.08 | 0.12 | 0.29\n",
      "Global Step:   7230, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  95.3%, loss (SELU/ELU/RELU): 0.06 | 0.08 | 0.21\n",
      "Global Step:   7240, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  85.2%, loss (SELU/ELU/RELU): 0.15 | 0.19 | 0.39\n",
      "Global Step:   7250, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.09 | 0.09 | 0.25\n",
      "Global Step:   7260, accuracy (SELU/ELU/RELU):  95.3% |  95.3% |  89.1%, loss (SELU/ELU/RELU): 0.14 | 0.16 | 0.37\n",
      "Global Step:   7270, accuracy (SELU/ELU/RELU):  98.4% |  95.3% |  90.6%, loss (SELU/ELU/RELU): 0.09 | 0.14 | 0.27\n",
      "Global Step:   7280, accuracy (SELU/ELU/RELU):  99.2% |  96.1% |  87.5%, loss (SELU/ELU/RELU): 0.07 | 0.13 | 0.38\n",
      "Global Step:   7290, accuracy (SELU/ELU/RELU):  96.9% | 100.0% |  89.1%, loss (SELU/ELU/RELU): 0.11 | 0.06 | 0.24\n",
      "Global Step:   7300, accuracy (SELU/ELU/RELU):  96.9% |  97.7% |  89.1%, loss (SELU/ELU/RELU): 0.10 | 0.12 | 0.26\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.82% | 75.79% | 73.56%\n",
      "Saved checkpoint.\n",
      "Global Step:   7310, accuracy (SELU/ELU/RELU):  96.9% |  93.8% |  87.5%, loss (SELU/ELU/RELU): 0.11 | 0.16 | 0.33\n",
      "Global Step:   7320, accuracy (SELU/ELU/RELU):  96.1% |  93.8% |  86.7%, loss (SELU/ELU/RELU): 0.14 | 0.19 | 0.40\n",
      "Global Step:   7330, accuracy (SELU/ELU/RELU):  98.4% |  93.8% |  89.1%, loss (SELU/ELU/RELU): 0.13 | 0.16 | 0.30\n",
      "Global Step:   7340, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.06 | 0.08 | 0.20\n",
      "Global Step:   7350, accuracy (SELU/ELU/RELU):  98.4% |  93.0% |  90.6%, loss (SELU/ELU/RELU): 0.09 | 0.20 | 0.32\n",
      "Global Step:   7360, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.09 | 0.21\n",
      "Global Step:   7370, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  91.4%, loss (SELU/ELU/RELU): 0.04 | 0.10 | 0.21\n",
      "Global Step:   7380, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.18\n",
      "Global Step:   7390, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  89.8%, loss (SELU/ELU/RELU): 0.05 | 0.10 | 0.26\n",
      "Global Step:   7400, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  93.8%, loss (SELU/ELU/RELU): 0.06 | 0.08 | 0.17\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.06% | 75.61% | 73.96%\n",
      "Saved checkpoint.\n",
      "Global Step:   7410, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  88.3%, loss (SELU/ELU/RELU): 0.11 | 0.11 | 0.28\n",
      "Global Step:   7420, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  93.8%, loss (SELU/ELU/RELU): 0.07 | 0.10 | 0.22\n",
      "Global Step:   7430, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  87.5%, loss (SELU/ELU/RELU): 0.10 | 0.13 | 0.37\n",
      "Global Step:   7440, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  90.6%, loss (SELU/ELU/RELU): 0.07 | 0.11 | 0.26\n",
      "Global Step:   7450, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.10 | 0.12 | 0.25\n",
      "Global Step:   7460, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.17\n",
      "Global Step:   7470, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  91.4%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.24\n",
      "Global Step:   7480, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  89.1%, loss (SELU/ELU/RELU): 0.04 | 0.09 | 0.34\n",
      "Global Step:   7490, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.06 | 0.09 | 0.21\n",
      "Global Step:   7500, accuracy (SELU/ELU/RELU):  97.7% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.07 | 0.10 | 0.27\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.56% | 76.13% | 75.21%\n",
      "Saved checkpoint.\n",
      "Global Step:   7510, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  93.0%, loss (SELU/ELU/RELU): 0.09 | 0.13 | 0.21\n",
      "Global Step:   7520, accuracy (SELU/ELU/RELU):  96.9% |  96.9% |  93.8%, loss (SELU/ELU/RELU): 0.10 | 0.09 | 0.21\n",
      "Global Step:   7530, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  95.3%, loss (SELU/ELU/RELU): 0.10 | 0.09 | 0.22\n",
      "Global Step:   7540, accuracy (SELU/ELU/RELU):  99.2% |  96.1% |  89.8%, loss (SELU/ELU/RELU): 0.11 | 0.14 | 0.35\n",
      "Global Step:   7550, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  91.4%, loss (SELU/ELU/RELU): 0.06 | 0.06 | 0.29\n",
      "Global Step:   7560, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.07 | 0.26\n",
      "Global Step:   7570, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.08 | 0.22\n",
      "Global Step:   7580, accuracy (SELU/ELU/RELU):  95.3% |  98.4% |  91.4%, loss (SELU/ELU/RELU): 0.11 | 0.08 | 0.23\n",
      "Global Step:   7590, accuracy (SELU/ELU/RELU):  96.9% |  97.7% |  90.6%, loss (SELU/ELU/RELU): 0.11 | 0.08 | 0.26\n",
      "Global Step:   7600, accuracy (SELU/ELU/RELU):  96.1% |  94.5% |  84.4%, loss (SELU/ELU/RELU): 0.12 | 0.14 | 0.48\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.75% | 74.99% | 73.39%\n",
      "Saved checkpoint.\n",
      "Global Step:   7610, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  92.2%, loss (SELU/ELU/RELU): 0.09 | 0.13 | 0.28\n",
      "Global Step:   7620, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  88.3%, loss (SELU/ELU/RELU): 0.06 | 0.11 | 0.27\n",
      "Global Step:   7630, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.11 | 0.12 | 0.24\n",
      "Global Step:   7640, accuracy (SELU/ELU/RELU):  99.2% |  94.5% |  92.2%, loss (SELU/ELU/RELU): 0.06 | 0.15 | 0.23\n",
      "Global Step:   7650, accuracy (SELU/ELU/RELU):  98.4% |  93.8% |  86.7%, loss (SELU/ELU/RELU): 0.10 | 0.19 | 0.31\n",
      "Global Step:   7660, accuracy (SELU/ELU/RELU):  96.9% |  94.5% |  89.8%, loss (SELU/ELU/RELU): 0.11 | 0.16 | 0.34\n",
      "Global Step:   7670, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.07 | 0.09 | 0.36\n",
      "Global Step:   7680, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.06 | 0.07 | 0.19\n",
      "Global Step:   7690, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  85.9%, loss (SELU/ELU/RELU): 0.10 | 0.12 | 0.36\n",
      "Global Step:   7700, accuracy (SELU/ELU/RELU): 100.0% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.06 | 0.12 | 0.25\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.55% | 74.90% | 73.43%\n",
      "Saved checkpoint.\n",
      "Global Step:   7710, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.07 | 0.10 | 0.24\n",
      "Global Step:   7720, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  95.3%, loss (SELU/ELU/RELU): 0.07 | 0.09 | 0.19\n",
      "Global Step:   7730, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  87.5%, loss (SELU/ELU/RELU): 0.07 | 0.10 | 0.36\n",
      "Global Step:   7740, accuracy (SELU/ELU/RELU):  96.1% |  96.9% |  89.8%, loss (SELU/ELU/RELU): 0.12 | 0.15 | 0.33\n",
      "Global Step:   7750, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  89.1%, loss (SELU/ELU/RELU): 0.14 | 0.12 | 0.34\n",
      "Global Step:   7760, accuracy (SELU/ELU/RELU):  96.9% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.14 | 0.17 | 0.33\n",
      "Global Step:   7770, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.07 | 0.09 | 0.19\n",
      "Global Step:   7780, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  91.4%, loss (SELU/ELU/RELU): 0.07 | 0.11 | 0.26\n",
      "Global Step:   7790, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  89.8%, loss (SELU/ELU/RELU): 0.06 | 0.07 | 0.28\n",
      "Global Step:   7800, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.08 | 0.12 | 0.30\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.94% | 75.80% | 74.10%\n",
      "Saved checkpoint.\n",
      "Global Step:   7810, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.07 | 0.06 | 0.20\n",
      "Global Step:   7820, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  91.4%, loss (SELU/ELU/RELU): 0.11 | 0.16 | 0.30\n",
      "Global Step:   7830, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  92.2%, loss (SELU/ELU/RELU): 0.06 | 0.11 | 0.27\n",
      "Global Step:   7840, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  91.4%, loss (SELU/ELU/RELU): 0.04 | 0.08 | 0.24\n",
      "Global Step:   7850, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  91.4%, loss (SELU/ELU/RELU): 0.07 | 0.10 | 0.30\n",
      "Global Step:   7860, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.07 | 0.06 | 0.18\n",
      "Global Step:   7870, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  90.6%, loss (SELU/ELU/RELU): 0.06 | 0.10 | 0.27\n",
      "Global Step:   7880, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.06 | 0.07 | 0.14\n",
      "Global Step:   7890, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  89.1%, loss (SELU/ELU/RELU): 0.07 | 0.09 | 0.34\n",
      "Global Step:   7900, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  93.0%, loss (SELU/ELU/RELU): 0.05 | 0.10 | 0.22\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.49% | 75.54% | 74.42%\n",
      "Saved checkpoint.\n",
      "Global Step:   7910, accuracy (SELU/ELU/RELU):  96.9% |  98.4% |  89.8%, loss (SELU/ELU/RELU): 0.08 | 0.08 | 0.26\n",
      "Global Step:   7920, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.19\n",
      "Global Step:   7930, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.06 | 0.10 | 0.30\n",
      "Global Step:   7940, accuracy (SELU/ELU/RELU):  96.9% |  96.1% |  89.1%, loss (SELU/ELU/RELU): 0.09 | 0.11 | 0.31\n",
      "Global Step:   7950, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.09 | 0.18\n",
      "Global Step:   7960, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.11\n",
      "Global Step:   7970, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  90.6%, loss (SELU/ELU/RELU): 0.05 | 0.09 | 0.24\n",
      "Global Step:   7980, accuracy (SELU/ELU/RELU):  99.2% |  96.1% |  95.3%, loss (SELU/ELU/RELU): 0.07 | 0.10 | 0.22\n",
      "Global Step:   7990, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.06 | 0.07 | 0.22\n",
      "Global Step:   8000, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.19\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.47% | 75.49% | 73.66%\n",
      "Saved checkpoint.\n",
      "Global Step:   8010, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  89.8%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.23\n",
      "Global Step:   8020, accuracy (SELU/ELU/RELU):  99.2% |  96.1% |  96.1%, loss (SELU/ELU/RELU): 0.08 | 0.12 | 0.22\n",
      "Global Step:   8030, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  91.4%, loss (SELU/ELU/RELU): 0.07 | 0.07 | 0.24\n",
      "Global Step:   8040, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  98.4%, loss (SELU/ELU/RELU): 0.06 | 0.07 | 0.14\n",
      "Global Step:   8050, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.06 | 0.06 | 0.23\n",
      "Global Step:   8060, accuracy (SELU/ELU/RELU):  97.7% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.13 | 0.09 | 0.24\n",
      "Global Step:   8070, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  91.4%, loss (SELU/ELU/RELU): 0.11 | 0.12 | 0.23\n",
      "Global Step:   8080, accuracy (SELU/ELU/RELU):  97.7% |  99.2% |  93.0%, loss (SELU/ELU/RELU): 0.10 | 0.09 | 0.21\n",
      "Global Step:   8090, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  93.8%, loss (SELU/ELU/RELU): 0.08 | 0.11 | 0.18\n",
      "Global Step:   8100, accuracy (SELU/ELU/RELU):  96.9% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.09 | 0.10 | 0.30\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.88% | 75.04% | 74.73%\n",
      "Saved checkpoint.\n",
      "Global Step:   8110, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.24\n",
      "Global Step:   8120, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  93.8%, loss (SELU/ELU/RELU): 0.09 | 0.13 | 0.20\n",
      "Global Step:   8130, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.05 | 0.08 | 0.19\n",
      "Global Step:   8140, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.04 | 0.09 | 0.19\n",
      "Global Step:   8150, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.15\n",
      "Global Step:   8160, accuracy (SELU/ELU/RELU):  98.4% |  96.9% |  91.4%, loss (SELU/ELU/RELU): 0.09 | 0.14 | 0.34\n",
      "Global Step:   8170, accuracy (SELU/ELU/RELU):  98.4% |  94.5% |  95.3%, loss (SELU/ELU/RELU): 0.08 | 0.15 | 0.20\n",
      "Global Step:   8180, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.05 | 0.09 | 0.26\n",
      "Global Step:   8190, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.13\n",
      "Global Step:   8200, accuracy (SELU/ELU/RELU):  97.7% |  96.1% |  91.4%, loss (SELU/ELU/RELU): 0.08 | 0.11 | 0.33\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.54% | 75.30% | 74.24%\n",
      "Saved checkpoint.\n",
      "Global Step:   8210, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  88.3%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.27\n",
      "Global Step:   8220, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  93.8%, loss (SELU/ELU/RELU): 0.08 | 0.10 | 0.22\n",
      "Global Step:   8230, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.16\n",
      "Global Step:   8240, accuracy (SELU/ELU/RELU): 100.0% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.03 | 0.09 | 0.24\n",
      "Global Step:   8250, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.22\n",
      "Global Step:   8260, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  92.2%, loss (SELU/ELU/RELU): 0.03 | 0.09 | 0.19\n",
      "Global Step:   8270, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.18\n",
      "Global Step:   8280, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.23\n",
      "Global Step:   8290, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.21\n",
      "Global Step:   8300, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.15\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.48% | 76.00% | 73.29%\n",
      "Saved checkpoint.\n",
      "Global Step:   8310, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  90.6%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.23\n",
      "Global Step:   8320, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  91.4%, loss (SELU/ELU/RELU): 0.12 | 0.16 | 0.28\n",
      "Global Step:   8330, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.07 | 0.22\n",
      "Global Step:   8340, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.19\n",
      "Global Step:   8350, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.20\n",
      "Global Step:   8360, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.18\n",
      "Global Step:   8370, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  89.8%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.24\n",
      "Global Step:   8380, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.22\n",
      "Global Step:   8390, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  93.0%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.20\n",
      "Global Step:   8400, accuracy (SELU/ELU/RELU):  97.7% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.06 | 0.08 | 0.20\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.98% | 75.39% | 74.06%\n",
      "Saved checkpoint.\n",
      "Global Step:   8410, accuracy (SELU/ELU/RELU):  99.2% |  96.1% |  85.9%, loss (SELU/ELU/RELU): 0.05 | 0.10 | 0.32\n",
      "Global Step:   8420, accuracy (SELU/ELU/RELU):  97.7% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.06 | 0.08 | 0.17\n",
      "Global Step:   8430, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  89.8%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.24\n",
      "Global Step:   8440, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.18\n",
      "Global Step:   8450, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.07 | 0.12\n",
      "Global Step:   8460, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  93.0%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.20\n",
      "Global Step:   8470, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.17\n",
      "Global Step:   8480, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  89.8%, loss (SELU/ELU/RELU): 0.06 | 0.10 | 0.30\n",
      "Global Step:   8490, accuracy (SELU/ELU/RELU):  96.9% |  95.3% |  96.1%, loss (SELU/ELU/RELU): 0.13 | 0.26 | 0.26\n",
      "Global Step:   8500, accuracy (SELU/ELU/RELU):  99.2% |  96.1% |  93.8%, loss (SELU/ELU/RELU): 0.05 | 0.11 | 0.19\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.14% | 75.65% | 74.68%\n",
      "Saved checkpoint.\n",
      "Global Step:   8510, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  93.0%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.18\n",
      "Global Step:   8520, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  91.4%, loss (SELU/ELU/RELU): 0.06 | 0.08 | 0.22\n",
      "Global Step:   8530, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.05 | 0.18\n",
      "Global Step:   8540, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.16\n",
      "Global Step:   8550, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.20\n",
      "Global Step:   8560, accuracy (SELU/ELU/RELU):  99.2% |  97.7% |  96.9%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.13\n",
      "Global Step:   8570, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  99.2%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.09\n",
      "Global Step:   8580, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.14\n",
      "Global Step:   8590, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.08 | 0.18\n",
      "Global Step:   8600, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.16\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.22% | 75.69% | 73.21%\n",
      "Saved checkpoint.\n",
      "Global Step:   8610, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  93.0%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.22\n",
      "Global Step:   8620, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.14\n",
      "Global Step:   8630, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.14\n",
      "Global Step:   8640, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  92.2%, loss (SELU/ELU/RELU): 0.09 | 0.09 | 0.31\n",
      "Global Step:   8650, accuracy (SELU/ELU/RELU):  98.4% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.15\n",
      "Global Step:   8660, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.23\n",
      "Global Step:   8670, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.15\n",
      "Global Step:   8680, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.16\n",
      "Global Step:   8690, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  89.8%, loss (SELU/ELU/RELU): 0.05 | 0.06 | 0.31\n",
      "Global Step:   8700, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.15\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.52% | 76.22% | 74.46%\n",
      "Saved checkpoint.\n",
      "Global Step:   8710, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.15\n",
      "Global Step:   8720, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.05 | 0.03 | 0.14\n",
      "Global Step:   8730, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.08 | 0.18\n",
      "Global Step:   8740, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.24\n",
      "Global Step:   8750, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.22\n",
      "Global Step:   8760, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  92.2%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.22\n",
      "Global Step:   8770, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.06 | 0.05 | 0.23\n",
      "Global Step:   8780, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.18\n",
      "Global Step:   8790, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.07 | 0.16\n",
      "Global Step:   8800, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.20\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.74% | 76.03% | 74.87%\n",
      "Saved checkpoint.\n",
      "Global Step:   8810, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.22\n",
      "Global Step:   8820, accuracy (SELU/ELU/RELU):  97.7% |  96.9% |  93.0%, loss (SELU/ELU/RELU): 0.06 | 0.06 | 0.23\n",
      "Global Step:   8830, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.16\n",
      "Global Step:   8840, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.22\n",
      "Global Step:   8850, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.18\n",
      "Global Step:   8860, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.14\n",
      "Global Step:   8870, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.07 | 0.21\n",
      "Global Step:   8880, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.18\n",
      "Global Step:   8890, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  91.4%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.19\n",
      "Global Step:   8900, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.16\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.19% | 75.40% | 75.17%\n",
      "Saved checkpoint.\n",
      "Global Step:   8910, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.06 | 0.17\n",
      "Global Step:   8920, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  93.0%, loss (SELU/ELU/RELU): 0.02 | 0.06 | 0.28\n",
      "Global Step:   8930, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.14\n",
      "Global Step:   8940, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.17\n",
      "Global Step:   8950, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  93.0%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.18\n",
      "Global Step:   8960, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.12\n",
      "Global Step:   8970, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.09\n",
      "Global Step:   8980, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.18\n",
      "Global Step:   8990, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.15\n",
      "Global Step:   9000, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.21\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.23% | 75.01% | 73.18%\n",
      "Saved checkpoint.\n",
      "Global Step:   9010, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.24\n",
      "Global Step:   9020, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  91.4%, loss (SELU/ELU/RELU): 0.07 | 0.07 | 0.19\n",
      "Global Step:   9030, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  93.0%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.21\n",
      "Global Step:   9040, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.13\n",
      "Global Step:   9050, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.06 | 0.04 | 0.14\n",
      "Global Step:   9060, accuracy (SELU/ELU/RELU): 100.0% |  97.7% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.06 | 0.11\n",
      "Global Step:   9070, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.17\n",
      "Global Step:   9080, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.08 | 0.06 | 0.18\n",
      "Global Step:   9090, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.17\n",
      "Global Step:   9100, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.11\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.21% | 74.70% | 74.99%\n",
      "Saved checkpoint.\n",
      "Global Step:   9110, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.20\n",
      "Global Step:   9120, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.19\n",
      "Global Step:   9130, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.17\n",
      "Global Step:   9140, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.05 | 0.18\n",
      "Global Step:   9150, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.25\n",
      "Global Step:   9160, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.12\n",
      "Global Step:   9170, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.20\n",
      "Global Step:   9180, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  93.0%, loss (SELU/ELU/RELU): 0.02 | 0.05 | 0.20\n",
      "Global Step:   9190, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  93.0%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.27\n",
      "Global Step:   9200, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.15\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.79% | 75.87% | 75.01%\n",
      "Saved checkpoint.\n",
      "Global Step:   9210, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.08\n",
      "Global Step:   9220, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.18\n",
      "Global Step:   9230, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.19\n",
      "Global Step:   9240, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.16\n",
      "Global Step:   9250, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.18\n",
      "Global Step:   9260, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.14\n",
      "Global Step:   9270, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.07\n",
      "Global Step:   9280, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.04 | 0.06 | 0.14\n",
      "Global Step:   9290, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.03 | 0.07 | 0.19\n",
      "Global Step:   9300, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.12\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.48% | 75.85% | 74.85%\n",
      "Saved checkpoint.\n",
      "Global Step:   9310, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.11\n",
      "Global Step:   9320, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.14\n",
      "Global Step:   9330, accuracy (SELU/ELU/RELU):  97.7% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.06 | 0.04 | 0.13\n",
      "Global Step:   9340, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.06 | 0.03 | 0.15\n",
      "Global Step:   9350, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.14\n",
      "Global Step:   9360, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.07 | 0.11 | 0.17\n",
      "Global Step:   9370, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.16\n",
      "Global Step:   9380, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  93.8%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.19\n",
      "Global Step:   9390, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.04 | 0.08 | 0.17\n",
      "Global Step:   9400, accuracy (SELU/ELU/RELU):  99.2% |  96.9% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.07 | 0.15\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.90% | 76.01% | 73.77%\n",
      "Saved checkpoint.\n",
      "Global Step:   9410, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.11\n",
      "Global Step:   9420, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  96.1%, loss (SELU/ELU/RELU): 0.06 | 0.05 | 0.15\n",
      "Global Step:   9430, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.19\n",
      "Global Step:   9440, accuracy (SELU/ELU/RELU): 100.0% | 100.0% | 100.0%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.08\n",
      "Global Step:   9450, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.18\n",
      "Global Step:   9460, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  90.6%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.23\n",
      "Global Step:   9470, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.05 | 0.15\n",
      "Global Step:   9480, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.09\n",
      "Global Step:   9490, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.17\n",
      "Global Step:   9500, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.14\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.87% | 75.83% | 74.68%\n",
      "Saved checkpoint.\n",
      "Global Step:   9510, accuracy (SELU/ELU/RELU):  98.4% |  97.7% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.07 | 0.18\n",
      "Global Step:   9520, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  93.8%, loss (SELU/ELU/RELU): 0.04 | 0.05 | 0.18\n",
      "Global Step:   9530, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.19\n",
      "Global Step:   9540, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.12\n",
      "Global Step:   9550, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.11\n",
      "Global Step:   9560, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.05 | 0.04 | 0.16\n",
      "Global Step:   9570, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  93.8%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.16\n",
      "Global Step:   9580, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.04 | 0.02 | 0.09\n",
      "Global Step:   9590, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.14\n",
      "Global Step:   9600, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.04 | 0.04 | 0.13\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.56% | 75.30% | 74.91%\n",
      "Saved checkpoint.\n",
      "Global Step:   9610, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.11\n",
      "Global Step:   9620, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.13\n",
      "Global Step:   9630, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.10\n",
      "Global Step:   9640, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.15\n",
      "Global Step:   9650, accuracy (SELU/ELU/RELU):  98.4% | 100.0% |  93.0%, loss (SELU/ELU/RELU): 0.04 | 0.03 | 0.19\n",
      "Global Step:   9660, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.05 | 0.10\n",
      "Global Step:   9670, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.17\n",
      "Global Step:   9680, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.10\n",
      "Global Step:   9690, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.10\n",
      "Global Step:   9700, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.09\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.17% | 75.91% | 74.90%\n",
      "Saved checkpoint.\n",
      "Global Step:   9710, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.14\n",
      "Global Step:   9720, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.10\n",
      "Global Step:   9730, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.10\n",
      "Global Step:   9740, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.07\n",
      "Global Step:   9750, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.08 | 0.14\n",
      "Global Step:   9760, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.09\n",
      "Global Step:   9770, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.13\n",
      "Global Step:   9780, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.05 | 0.12\n",
      "Global Step:   9790, accuracy (SELU/ELU/RELU):  99.2% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.13\n",
      "Global Step:   9800, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.02 | 0.11\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.40% | 75.82% | 74.63%\n",
      "Saved checkpoint.\n",
      "Global Step:   9810, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  94.5%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.12\n",
      "Global Step:   9820, accuracy (SELU/ELU/RELU):  98.4% |  99.2% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.10\n",
      "Global Step:   9830, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.10\n",
      "Global Step:   9840, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  92.2%, loss (SELU/ELU/RELU): 0.03 | 0.04 | 0.19\n",
      "Global Step:   9850, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.11\n",
      "Global Step:   9860, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  98.4%, loss (SELU/ELU/RELU): 0.01 | 0.03 | 0.09\n",
      "Global Step:   9870, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.16\n",
      "Global Step:   9880, accuracy (SELU/ELU/RELU):  99.2% |  98.4% |  97.7%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.13\n",
      "Global Step:   9890, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  95.3%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.13\n",
      "Global Step:   9900, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  97.7%, loss (SELU/ELU/RELU): 0.01 | 0.02 | 0.09\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 75.54% | 74.78% | 74.73%\n",
      "Saved checkpoint.\n",
      "Global Step:   9910, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  94.5%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.21\n",
      "Global Step:   9920, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.13\n",
      "Global Step:   9930, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  92.2%, loss (SELU/ELU/RELU): 0.03 | 0.03 | 0.19\n",
      "Global Step:   9940, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  99.2%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.09\n",
      "Global Step:   9950, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  98.4%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.08\n",
      "Global Step:   9960, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.03 | 0.02 | 0.10\n",
      "Global Step:   9970, accuracy (SELU/ELU/RELU): 100.0% | 100.0% |  96.1%, loss (SELU/ELU/RELU): 0.02 | 0.03 | 0.14\n",
      "Global Step:   9980, accuracy (SELU/ELU/RELU):  99.2% | 100.0% |  96.9%, loss (SELU/ELU/RELU): 0.02 | 0.02 | 0.09\n",
      "Global Step:   9990, accuracy (SELU/ELU/RELU): 100.0% |  98.4% |  96.1%, loss (SELU/ELU/RELU): 0.04 | 0.07 | 0.13\n",
      "Global Step:   9999, accuracy (SELU/ELU/RELU): 100.0% |  99.2% |  95.3%, loss (SELU/ELU/RELU): 0.02 | 0.04 | 0.16\n",
      "Accuracy on Test-Set (SELU/ELU/RELU): 76.72% | 75.33% | 74.48%\n",
      "Saved checkpoint.\n"
     ]
    }
   ],
   "source": [
    "# Some Tensorflow configuration\n",
    "tf_config = tf.ConfigProto()\n",
    "tf_config.gpu_options.allow_growth = True\n",
    "\n",
    "# Initialize Dataset\n",
    "train_x, train_y, train_l = get_data_set(\"train\", cifar=10)\n",
    "test_x, test_y, test_l = get_data_set(\"test\", cifar=10)\n",
    "\n",
    "# step counter\n",
    "global_step = tf.Variable(initial_value=0, name='global_step', trainable=False)\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "with tf.Session(config=tf_config) as sess:\n",
    "    try:\n",
    "        print(\"Trying to restore last checkpoint ...\")\n",
    "        last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)\n",
    "        saver.restore(sess, save_path=last_chk_path)\n",
    "        print(\"Restored checkpoint from:\", last_chk_path)\n",
    "    except:\n",
    "        print(\"Failed to restore checkpoint. Initializing variables instead.\")\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    if _ITERATION != 0:\n",
    "        train_loss, train_accuracy, test_accuracy = train(\n",
    "            sess, _ITERATION, train_x, train_y, test_x, test_y, \n",
    "            models={\"relu\": relu, \"selu\": selu, \"elu\": elu}, \n",
    "            global_step=global_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Training Loss, Training Accuracy and Test Accuracy for the three activation functions\n",
    "plot(train_loss, train_accuracy, test_accuracy)        "
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}