zirconCNN_MWE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f588f52e-556b-436b-a3c3-3b8909e03b00",
   "metadata": {},
   "source": [
    "# Classification of zircon CL images using convolutional neural networks\n",
    "\n",
    "This Jupyter notebook is a supplmentary file to the manuscript entitled: Mineral texture classification using deep convolutional neural networks: an application to zircons from porphyry copper deposits by Chetan Nathwani et al.\n",
    "\n",
    "This notebook contains code to:\n",
    "* Segment images of individual crystals from mosaics of grain mounts\n",
    "* Train a convolutional neural network to classify images from the Quellaveco porphyry copper deposits (including regularisation approaches)\n",
    "* Apply transfer learning to the same classification task using the VGG-16 model trained on the ImageNet database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17dced4-24b6-485e-a4c5-ab793aa5158e",
   "metadata": {},
   "source": [
    "## Load packages and instantiate workbook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bacad33-df1f-4058-9fe9-b910207d1db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This version has code that saves segmented images, which are manually inspected, and then re-imported\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import glob\n",
    "import random as python_random\n",
    "import sklearn.model_selection\n",
    "from PIL import Image, ImageOps\n",
    "import cv2\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "# For reproducibility\n",
    "np.random.seed(123)\n",
    "python_random.seed(123)\n",
    "tf.random.set_seed(123)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f4d13c7-ad99-4b48-ab40-f64ec702a549",
   "metadata": {},
   "source": [
    "## Segment images of individual crystals from input mosaics of grain mounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d83153f8-a709-41cf-b048-5fb4f67e983b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set directories\n",
    "root_dir = 'C:/Users/chen/Desktop/Python_Projects/Zircon_Images/TIMA_Images'\n",
    "save_dir = 'C:/Users/chen/Desktop/Python_Projects/Zircon_Images/Segmented_Images'\n",
    "samples = []\n",
    "x_train = []\n",
    "\n",
    "# Import files\n",
    "for filename in glob.iglob(root_dir + '**/*.png', recursive=True):\n",
    "    img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    canny = cv2.Canny(img, 20, 255, 1) # Edge detection\n",
    "    kernel = np.ones((4,4),np.uint8) # 4x4 kernel\n",
    "    dilate = cv2.dilate(canny, kernel, iterations=1) # Thicken contours\n",
    "\n",
    "    # Code from https://stackoverflow.com/questions/56604151/python-extract-multiple-objects-from-image-opencv\n",
    "    # Find contours\n",
    "    cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    cnts = cnts[0] if len(cnts) == 2 else cnts[1]\n",
    "\n",
    "    image_number = 0\n",
    "    os.chdir(save_dir) # Change directory to save images\n",
    "    for c in cnts:\n",
    "        x,y,w,h = cv2.boundingRect(c)\n",
    "        # Only save images within a certain size\n",
    "        # This filters failed segmentations (e.g. multiple zircons together or fragments)\n",
    "        if w > 40 and h > 40 and w < 90 and h < 90:\n",
    "            ROI = img[y:y+h, x:x+w]\n",
    "            x_train.append(ROI)\n",
    "            samples.append(filename[-10:-4])\n",
    "            cv2.imwrite(\"{}_{}.png\".format(filename[-10:-4], image_number), ROI)\n",
    "        image_number += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d8ff466-9886-499f-8338-9048e5b17ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import segmented images \n",
    "root_dir = 'C:/Users/chen/Desktop/Python_Projects/Zircon_Images/Segmented_Images_Filtered'\n",
    "images = []\n",
    "samples = []\n",
    "for filename in glob.iglob(root_dir + '**/*.png', recursive=True):\n",
    "    img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)\n",
    "    images.append(img)\n",
    "    samples.append(filename[78:84])\n",
    "    \n",
    "# Assign class labels\n",
    "samples_mapper = {'QVC009':1,\n",
    " 'QVC024':1,\n",
    " 'QVC028':1,\n",
    " 'QVC029':1,\n",
    " 'QVC034':1,\n",
    "'QVC040':1,\n",
    " 'QVC041':1,\n",
    " 'QVC055':1,\n",
    " 'QVC058':1,\n",
    " 'QVC060':1,\n",
    " 'QVC061':1,\n",
    " 'QVC074':0,\n",
    " 'QVC075':0,\n",
    " 'QVC082':0,\n",
    " 'QVC083':0,\n",
    "'QVC091':1}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc718bc-d6c7-442d-9b20-8670f0c4072c",
   "metadata": {},
   "source": [
    "## Preprocess images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01ae9f32-b75e-4910-b9ff-d2b7239c23e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert images to tensors\n",
    "def convert_to_tensor(n):\n",
    "    out = tf.convert_to_tensor(n, dtype=None, dtype_hint=None, name=None)\n",
    "    return out\n",
    "\n",
    "out = []\n",
    "for i in images:\n",
    "    i = tf.convert_to_tensor(i, dtype=None, dtype_hint=None, name=None)\n",
    "    i = tf.reshape(i, (i.shape[0],i.shape[1],1), name=None) # reshape to be 3D\n",
    "    i = tf.image.resize_with_crop_or_pad(i, 90,90) # Crop or pad if larger/smaller than 70 pixel long or wide\n",
    "    i = tf.image.resize(i, [32,32]) # all images to 32x32\n",
    "    # i = tf.image.resize(i, [224,224]) # if larger image size required\n",
    "    # i = tf.image.grayscale_to_rgb(i, name=None) # if RGB image required - default is grayscale\n",
    "    out.append(i)\n",
    "\n",
    "zircons = out\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(out,samples, test_size = 0.2)\n",
    "y_train_samples = y_train\n",
    "# Assign class labels\n",
    "y_train = [*map(samples_mapper.get, y_train)]\n",
    "y_test = [*map(samples_mapper.get, y_test)]\n",
    "\n",
    "x_train_LN = tf.convert_to_tensor(x_train)\n",
    "y_train = tf.convert_to_tensor(y_train, dtype = tf.int32)\n",
    "x_test_LN = tf.convert_to_tensor(x_test)\n",
    "y_test = tf.convert_to_tensor(y_test, dtype = tf.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1925e83a-2b74-4990-99d2-3178db50ec7b",
   "metadata": {},
   "source": [
    "## Train and validate a CNN model using tensorflow-keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ecce8b0d-71c4-4ae1-92a9-c25481e2aff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
      "WARNING:tensorflow:Using a while_loop for converting AdjustContrastv2\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
      "WARNING:tensorflow:Using a while_loop for converting AdjustContrastv2\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " sequential (Sequential)     (None, 32, 32, 1)         0         \n",
      "                                                                 \n",
      " rescaling (Rescaling)       (None, 32, 32, 1)         0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 32, 32, 6)         60        \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 16, 6)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 16, 16, 16)        880       \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 8, 8, 16)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 8, 8, 32)          4640      \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 4, 4, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 512)               0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 512)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                16416     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 22,029\n",
      "Trainable params: 22,029\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build a LeNet model\n",
    "\n",
    "# Data Augmentation\n",
    "data_augmentation = tf.keras.Sequential([\n",
    "  tf.keras.layers.experimental.preprocessing.RandomContrast(0.2),\n",
    "    tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),\n",
    "    tf.keras.layers.experimental.preprocessing.RandomZoom(height_factor = (-0.4,0.4)),\n",
    "])\n",
    "\n",
    "model = tf.keras.models.Sequential()\n",
    "model.add(data_augmentation)\n",
    "model.add(tf.keras.layers.experimental.preprocessing.Rescaling(1./127.5, offset=-1))\n",
    "model.add(layers.Conv2D(6, 3, activation='relu',strides = (1,1), padding = 'same', input_shape=(32, 32, 1)))\n",
    "model.add(layers.MaxPooling2D())\n",
    "model.add(layers.Conv2D(16, 3,strides = (1,1), padding = 'same', activation='relu'))\n",
    "model.add(layers.MaxPooling2D())\n",
    "model.add(layers.Conv2D(32,3,strides = (1,1), padding = 'same',activation='relu'))\n",
    "model.add(layers.MaxPooling2D())\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dropout(0.5)) # Drop out significantly reduces overfitting\n",
    "model.add(layers.Dense(32, activation='relu'))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "model.build(input_shape=(None, 32, 32, 1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "852a42e3-9263-4a54-9516-31c939bd6aab",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
      "WARNING:tensorflow:Using a while_loop for converting AdjustContrastv2\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
      "WARNING:tensorflow:Using a while_loop for converting AdjustContrastv2\n",
      "17/17 [==============================] - 4s 46ms/step - loss: 0.6862 - accuracy: 0.5609 - auc: 0.5466 - val_loss: 0.6832 - val_accuracy: 0.5956 - val_auc: 0.4147\n",
      "Epoch 2/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.6803 - accuracy: 0.5996 - auc: 0.4895 - val_loss: 0.6780 - val_accuracy: 0.5956 - val_auc: 0.4814\n",
      "Epoch 3/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.6764 - accuracy: 0.5996 - auc: 0.4917 - val_loss: 0.6786 - val_accuracy: 0.5956 - val_auc: 0.4859\n",
      "Epoch 4/200\n",
      "17/17 [==============================] - 0s 26ms/step - loss: 0.6770 - accuracy: 0.5793 - auc: 0.5038 - val_loss: 0.6783 - val_accuracy: 0.5956 - val_auc: 0.5282\n",
      "Epoch 5/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.6792 - accuracy: 0.6125 - auc: 0.5102 - val_loss: 0.6779 - val_accuracy: 0.5956 - val_auc: 0.5541\n",
      "Epoch 6/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.6730 - accuracy: 0.6033 - auc: 0.5297 - val_loss: 0.6742 - val_accuracy: 0.5956 - val_auc: 0.5705\n",
      "Epoch 7/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.6648 - accuracy: 0.6089 - auc: 0.5567 - val_loss: 0.6740 - val_accuracy: 0.5956 - val_auc: 0.5842\n",
      "Epoch 8/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.6586 - accuracy: 0.6107 - auc: 0.6026 - val_loss: 0.6664 - val_accuracy: 0.5956 - val_auc: 0.6434\n",
      "Epoch 9/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.6654 - accuracy: 0.6181 - auc: 0.5767 - val_loss: 0.6654 - val_accuracy: 0.5956 - val_auc: 0.6485\n",
      "Epoch 10/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.6630 - accuracy: 0.6033 - auc: 0.5849 - val_loss: 0.6593 - val_accuracy: 0.5956 - val_auc: 0.6684\n",
      "Epoch 11/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.6509 - accuracy: 0.6347 - auc: 0.6312 - val_loss: 0.6489 - val_accuracy: 0.5956 - val_auc: 0.7016\n",
      "Epoch 12/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.6495 - accuracy: 0.6089 - auc: 0.6420 - val_loss: 0.6412 - val_accuracy: 0.6324 - val_auc: 0.7467\n",
      "Epoch 13/200\n",
      "17/17 [==============================] - 0s 19ms/step - loss: 0.6540 - accuracy: 0.6421 - auc: 0.6259 - val_loss: 0.6346 - val_accuracy: 0.6176 - val_auc: 0.7725\n",
      "Epoch 14/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.6294 - accuracy: 0.6384 - auc: 0.6670 - val_loss: 0.6266 - val_accuracy: 0.6838 - val_auc: 0.7604\n",
      "Epoch 15/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.6198 - accuracy: 0.6587 - auc: 0.6996 - val_loss: 0.5816 - val_accuracy: 0.7500 - val_auc: 0.8209\n",
      "Epoch 16/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.6267 - accuracy: 0.6827 - auc: 0.6844 - val_loss: 0.5831 - val_accuracy: 0.7132 - val_auc: 0.8274\n",
      "Epoch 17/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.6040 - accuracy: 0.7066 - auc: 0.7246 - val_loss: 0.5515 - val_accuracy: 0.7132 - val_auc: 0.8328\n",
      "Epoch 18/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5848 - accuracy: 0.6993 - auc: 0.7477 - val_loss: 0.5430 - val_accuracy: 0.7647 - val_auc: 0.8373\n",
      "Epoch 19/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.6048 - accuracy: 0.6956 - auc: 0.7302 - val_loss: 0.5682 - val_accuracy: 0.7426 - val_auc: 0.8193\n",
      "Epoch 20/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.6018 - accuracy: 0.6956 - auc: 0.7152 - val_loss: 0.5334 - val_accuracy: 0.7941 - val_auc: 0.8467\n",
      "Epoch 21/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.5590 - accuracy: 0.7380 - auc: 0.7889 - val_loss: 0.5259 - val_accuracy: 0.7279 - val_auc: 0.8350\n",
      "Epoch 22/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5668 - accuracy: 0.7159 - auc: 0.7611 - val_loss: 0.5013 - val_accuracy: 0.7647 - val_auc: 0.8558\n",
      "Epoch 23/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5466 - accuracy: 0.7380 - auc: 0.7889 - val_loss: 0.5111 - val_accuracy: 0.7647 - val_auc: 0.8324\n",
      "Epoch 24/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5618 - accuracy: 0.7066 - auc: 0.7708 - val_loss: 0.5177 - val_accuracy: 0.7647 - val_auc: 0.8318\n",
      "Epoch 25/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5437 - accuracy: 0.7454 - auc: 0.8037 - val_loss: 0.4941 - val_accuracy: 0.7721 - val_auc: 0.8416\n",
      "Epoch 26/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5590 - accuracy: 0.7103 - auc: 0.7696 - val_loss: 0.5023 - val_accuracy: 0.7794 - val_auc: 0.8407\n",
      "Epoch 27/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5569 - accuracy: 0.7214 - auc: 0.7709 - val_loss: 0.5318 - val_accuracy: 0.7206 - val_auc: 0.8110\n",
      "Epoch 28/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.5634 - accuracy: 0.7196 - auc: 0.7685 - val_loss: 0.5195 - val_accuracy: 0.7500 - val_auc: 0.8455\n",
      "Epoch 29/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5287 - accuracy: 0.7435 - auc: 0.8005 - val_loss: 0.5138 - val_accuracy: 0.7574 - val_auc: 0.8312\n",
      "Epoch 30/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5326 - accuracy: 0.7251 - auc: 0.7909 - val_loss: 0.5465 - val_accuracy: 0.7279 - val_auc: 0.8077\n",
      "Epoch 31/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5527 - accuracy: 0.7288 - auc: 0.7837 - val_loss: 0.4959 - val_accuracy: 0.7574 - val_auc: 0.8542\n",
      "Epoch 32/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5503 - accuracy: 0.7251 - auc: 0.7807 - val_loss: 0.5131 - val_accuracy: 0.7132 - val_auc: 0.8345\n",
      "Epoch 33/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5063 - accuracy: 0.7694 - auc: 0.8251 - val_loss: 0.5138 - val_accuracy: 0.7206 - val_auc: 0.8239\n",
      "Epoch 34/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.5319 - accuracy: 0.7380 - auc: 0.8009 - val_loss: 0.5106 - val_accuracy: 0.7500 - val_auc: 0.8293\n",
      "Epoch 35/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5467 - accuracy: 0.7417 - auc: 0.7874 - val_loss: 0.5558 - val_accuracy: 0.7206 - val_auc: 0.8011\n",
      "Epoch 36/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.5127 - accuracy: 0.7583 - auc: 0.8154 - val_loss: 0.4962 - val_accuracy: 0.7647 - val_auc: 0.8440\n",
      "Epoch 37/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.5514 - accuracy: 0.7177 - auc: 0.7767 - val_loss: 0.5155 - val_accuracy: 0.7279 - val_auc: 0.8220\n",
      "Epoch 38/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5241 - accuracy: 0.7491 - auc: 0.7927 - val_loss: 0.5351 - val_accuracy: 0.7353 - val_auc: 0.8091\n",
      "Epoch 39/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5245 - accuracy: 0.7232 - auc: 0.7941 - val_loss: 0.5334 - val_accuracy: 0.7426 - val_auc: 0.8178\n",
      "Epoch 40/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5203 - accuracy: 0.7435 - auc: 0.8037 - val_loss: 0.5348 - val_accuracy: 0.6691 - val_auc: 0.7988\n",
      "Epoch 41/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.5238 - accuracy: 0.7509 - auc: 0.7940 - val_loss: 0.4969 - val_accuracy: 0.7353 - val_auc: 0.8266\n",
      "Epoch 42/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.5061 - accuracy: 0.7454 - auc: 0.8120 - val_loss: 0.4984 - val_accuracy: 0.7500 - val_auc: 0.8352\n",
      "Epoch 43/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.5299 - accuracy: 0.7399 - auc: 0.8004 - val_loss: 0.4777 - val_accuracy: 0.7868 - val_auc: 0.8567\n",
      "Epoch 44/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.5025 - accuracy: 0.7343 - auc: 0.8255 - val_loss: 0.5201 - val_accuracy: 0.7059 - val_auc: 0.8156\n",
      "Epoch 45/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.5097 - accuracy: 0.7325 - auc: 0.8143 - val_loss: 0.4689 - val_accuracy: 0.7868 - val_auc: 0.8513\n",
      "Epoch 46/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.5059 - accuracy: 0.7472 - auc: 0.8146 - val_loss: 0.5293 - val_accuracy: 0.7059 - val_auc: 0.8100\n",
      "Epoch 47/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4906 - accuracy: 0.7694 - auc: 0.8328 - val_loss: 0.4775 - val_accuracy: 0.7574 - val_auc: 0.8397\n",
      "Epoch 48/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.5051 - accuracy: 0.7509 - auc: 0.8179 - val_loss: 0.5288 - val_accuracy: 0.7132 - val_auc: 0.8074\n",
      "Epoch 49/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4820 - accuracy: 0.7528 - auc: 0.8386 - val_loss: 0.4641 - val_accuracy: 0.7647 - val_auc: 0.8554\n",
      "Epoch 50/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.5163 - accuracy: 0.7546 - auc: 0.8152 - val_loss: 0.5060 - val_accuracy: 0.7059 - val_auc: 0.8200\n",
      "Epoch 51/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5114 - accuracy: 0.7491 - auc: 0.8123 - val_loss: 0.5002 - val_accuracy: 0.7500 - val_auc: 0.8356\n",
      "Epoch 52/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5072 - accuracy: 0.7601 - auc: 0.8148 - val_loss: 0.4739 - val_accuracy: 0.7426 - val_auc: 0.8502\n",
      "Epoch 53/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5029 - accuracy: 0.7454 - auc: 0.8227 - val_loss: 0.4934 - val_accuracy: 0.7574 - val_auc: 0.8330\n",
      "Epoch 54/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5193 - accuracy: 0.7399 - auc: 0.8011 - val_loss: 0.5104 - val_accuracy: 0.7279 - val_auc: 0.8253\n",
      "Epoch 55/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4955 - accuracy: 0.7528 - auc: 0.8307 - val_loss: 0.4887 - val_accuracy: 0.7574 - val_auc: 0.8435\n",
      "Epoch 56/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4981 - accuracy: 0.7546 - auc: 0.8193 - val_loss: 0.4886 - val_accuracy: 0.7426 - val_auc: 0.8467\n",
      "Epoch 57/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4873 - accuracy: 0.7454 - auc: 0.8280 - val_loss: 0.4819 - val_accuracy: 0.7500 - val_auc: 0.8384\n",
      "Epoch 58/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4880 - accuracy: 0.7583 - auc: 0.8395 - val_loss: 0.5241 - val_accuracy: 0.7206 - val_auc: 0.8157\n",
      "Epoch 59/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4809 - accuracy: 0.7657 - auc: 0.8305 - val_loss: 0.4864 - val_accuracy: 0.7353 - val_auc: 0.8434\n",
      "Epoch 60/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5037 - accuracy: 0.7491 - auc: 0.8109 - val_loss: 0.5028 - val_accuracy: 0.7426 - val_auc: 0.8327\n",
      "Epoch 61/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4887 - accuracy: 0.7675 - auc: 0.8377 - val_loss: 0.4992 - val_accuracy: 0.7426 - val_auc: 0.8324\n",
      "Epoch 62/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4799 - accuracy: 0.7768 - auc: 0.8446 - val_loss: 0.5183 - val_accuracy: 0.7353 - val_auc: 0.8237\n",
      "Epoch 63/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.5031 - accuracy: 0.7694 - auc: 0.8298 - val_loss: 0.5436 - val_accuracy: 0.7353 - val_auc: 0.8262\n",
      "Epoch 64/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5092 - accuracy: 0.7454 - auc: 0.8181 - val_loss: 0.4983 - val_accuracy: 0.7279 - val_auc: 0.8336\n",
      "Epoch 65/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5129 - accuracy: 0.7288 - auc: 0.8079 - val_loss: 0.4737 - val_accuracy: 0.7721 - val_auc: 0.8550\n",
      "Epoch 66/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4777 - accuracy: 0.7638 - auc: 0.8456 - val_loss: 0.4838 - val_accuracy: 0.7279 - val_auc: 0.8361\n",
      "Epoch 67/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4872 - accuracy: 0.7749 - auc: 0.8387 - val_loss: 0.5127 - val_accuracy: 0.7426 - val_auc: 0.8341\n",
      "Epoch 68/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4988 - accuracy: 0.7694 - auc: 0.8212 - val_loss: 0.5254 - val_accuracy: 0.7279 - val_auc: 0.8267\n",
      "Epoch 69/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.5129 - accuracy: 0.7399 - auc: 0.8140 - val_loss: 0.5015 - val_accuracy: 0.7574 - val_auc: 0.8431\n",
      "Epoch 70/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4893 - accuracy: 0.7675 - auc: 0.8394 - val_loss: 0.4872 - val_accuracy: 0.7647 - val_auc: 0.8424\n",
      "Epoch 71/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4950 - accuracy: 0.7565 - auc: 0.8232 - val_loss: 0.5124 - val_accuracy: 0.7206 - val_auc: 0.8297\n",
      "Epoch 72/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5051 - accuracy: 0.7528 - auc: 0.8233 - val_loss: 0.5067 - val_accuracy: 0.7279 - val_auc: 0.8274\n",
      "Epoch 73/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4690 - accuracy: 0.7768 - auc: 0.8492 - val_loss: 0.4984 - val_accuracy: 0.7426 - val_auc: 0.8483\n",
      "Epoch 74/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4659 - accuracy: 0.7749 - auc: 0.8525 - val_loss: 0.5103 - val_accuracy: 0.7059 - val_auc: 0.8241\n",
      "Epoch 75/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4786 - accuracy: 0.7712 - auc: 0.8408 - val_loss: 0.5569 - val_accuracy: 0.7279 - val_auc: 0.8075\n",
      "Epoch 76/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4744 - accuracy: 0.7583 - auc: 0.8396 - val_loss: 0.4561 - val_accuracy: 0.7574 - val_auc: 0.8633\n",
      "Epoch 77/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4557 - accuracy: 0.7712 - auc: 0.8577 - val_loss: 0.4622 - val_accuracy: 0.7941 - val_auc: 0.8617\n",
      "Epoch 78/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.5186 - accuracy: 0.7325 - auc: 0.8117 - val_loss: 0.4560 - val_accuracy: 0.7794 - val_auc: 0.8672\n",
      "Epoch 79/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4737 - accuracy: 0.7786 - auc: 0.8470 - val_loss: 0.4672 - val_accuracy: 0.7500 - val_auc: 0.8556\n",
      "Epoch 80/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4639 - accuracy: 0.7768 - auc: 0.8558 - val_loss: 0.4909 - val_accuracy: 0.7647 - val_auc: 0.8467\n",
      "Epoch 81/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4645 - accuracy: 0.7768 - auc: 0.8464 - val_loss: 0.4743 - val_accuracy: 0.7500 - val_auc: 0.8526\n",
      "Epoch 82/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4890 - accuracy: 0.7749 - auc: 0.8316 - val_loss: 0.4300 - val_accuracy: 0.7868 - val_auc: 0.8820\n",
      "Epoch 83/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4755 - accuracy: 0.7638 - auc: 0.8466 - val_loss: 0.4762 - val_accuracy: 0.7426 - val_auc: 0.8535\n",
      "Epoch 84/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4712 - accuracy: 0.7841 - auc: 0.8494 - val_loss: 0.4942 - val_accuracy: 0.7574 - val_auc: 0.8462\n",
      "Epoch 85/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4596 - accuracy: 0.7804 - auc: 0.8492 - val_loss: 0.4760 - val_accuracy: 0.7721 - val_auc: 0.8569\n",
      "Epoch 86/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4666 - accuracy: 0.7878 - auc: 0.8528 - val_loss: 0.4526 - val_accuracy: 0.7721 - val_auc: 0.8660\n",
      "Epoch 87/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4747 - accuracy: 0.7565 - auc: 0.8406 - val_loss: 0.4947 - val_accuracy: 0.7279 - val_auc: 0.8410\n",
      "Epoch 88/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4815 - accuracy: 0.7601 - auc: 0.8351 - val_loss: 0.5085 - val_accuracy: 0.7353 - val_auc: 0.8410\n",
      "Epoch 89/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4951 - accuracy: 0.7583 - auc: 0.8262 - val_loss: 0.4643 - val_accuracy: 0.7647 - val_auc: 0.8801\n",
      "Epoch 90/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4550 - accuracy: 0.7952 - auc: 0.8663 - val_loss: 0.4737 - val_accuracy: 0.7500 - val_auc: 0.8511\n",
      "Epoch 91/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4540 - accuracy: 0.7804 - auc: 0.8591 - val_loss: 0.4743 - val_accuracy: 0.7721 - val_auc: 0.8676\n",
      "Epoch 92/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4788 - accuracy: 0.7712 - auc: 0.8359 - val_loss: 0.4631 - val_accuracy: 0.7721 - val_auc: 0.8593\n",
      "Epoch 93/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4685 - accuracy: 0.7804 - auc: 0.8526 - val_loss: 0.5446 - val_accuracy: 0.7500 - val_auc: 0.8360\n",
      "Epoch 94/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4889 - accuracy: 0.7934 - auc: 0.8395 - val_loss: 0.4756 - val_accuracy: 0.7647 - val_auc: 0.8540\n",
      "Epoch 95/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4792 - accuracy: 0.7638 - auc: 0.8404 - val_loss: 0.4805 - val_accuracy: 0.7794 - val_auc: 0.8587\n",
      "Epoch 96/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4596 - accuracy: 0.7712 - auc: 0.8539 - val_loss: 0.4823 - val_accuracy: 0.7574 - val_auc: 0.8477\n",
      "Epoch 97/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4736 - accuracy: 0.7638 - auc: 0.8478 - val_loss: 0.5693 - val_accuracy: 0.7279 - val_auc: 0.8349\n",
      "Epoch 98/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4470 - accuracy: 0.7970 - auc: 0.8616 - val_loss: 0.4886 - val_accuracy: 0.7868 - val_auc: 0.8530\n",
      "Epoch 99/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4768 - accuracy: 0.7583 - auc: 0.8401 - val_loss: 0.4816 - val_accuracy: 0.7721 - val_auc: 0.8576\n",
      "Epoch 100/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4629 - accuracy: 0.7731 - auc: 0.8522 - val_loss: 0.4588 - val_accuracy: 0.7794 - val_auc: 0.8690\n",
      "Epoch 101/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4779 - accuracy: 0.7657 - auc: 0.8404 - val_loss: 0.4613 - val_accuracy: 0.7647 - val_auc: 0.8716\n",
      "Epoch 102/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4628 - accuracy: 0.7878 - auc: 0.8529 - val_loss: 0.4584 - val_accuracy: 0.7574 - val_auc: 0.8608\n",
      "Epoch 103/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4626 - accuracy: 0.7731 - auc: 0.8513 - val_loss: 0.4539 - val_accuracy: 0.7647 - val_auc: 0.8600\n",
      "Epoch 104/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4639 - accuracy: 0.7694 - auc: 0.8440 - val_loss: 0.4616 - val_accuracy: 0.7647 - val_auc: 0.8589\n",
      "Epoch 105/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4655 - accuracy: 0.7768 - auc: 0.8511 - val_loss: 0.4647 - val_accuracy: 0.7721 - val_auc: 0.8713\n",
      "Epoch 106/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4191 - accuracy: 0.7989 - auc: 0.8830 - val_loss: 0.4221 - val_accuracy: 0.8235 - val_auc: 0.8774\n",
      "Epoch 107/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4463 - accuracy: 0.7915 - auc: 0.8642 - val_loss: 0.4747 - val_accuracy: 0.7426 - val_auc: 0.8510\n",
      "Epoch 108/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4571 - accuracy: 0.7638 - auc: 0.8491 - val_loss: 0.4587 - val_accuracy: 0.7500 - val_auc: 0.8590\n",
      "Epoch 109/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4349 - accuracy: 0.8247 - auc: 0.8726 - val_loss: 0.4565 - val_accuracy: 0.7500 - val_auc: 0.8560\n",
      "Epoch 110/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4500 - accuracy: 0.7915 - auc: 0.8611 - val_loss: 0.4955 - val_accuracy: 0.7353 - val_auc: 0.8469\n",
      "Epoch 111/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4298 - accuracy: 0.8063 - auc: 0.8807 - val_loss: 0.5003 - val_accuracy: 0.7574 - val_auc: 0.8549\n",
      "Epoch 112/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4578 - accuracy: 0.7786 - auc: 0.8486 - val_loss: 0.4360 - val_accuracy: 0.7721 - val_auc: 0.8945\n",
      "Epoch 113/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4621 - accuracy: 0.7768 - auc: 0.8511 - val_loss: 0.5192 - val_accuracy: 0.7500 - val_auc: 0.8379\n",
      "Epoch 114/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4621 - accuracy: 0.7786 - auc: 0.8568 - val_loss: 0.4568 - val_accuracy: 0.8015 - val_auc: 0.8804\n",
      "Epoch 115/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4753 - accuracy: 0.7823 - auc: 0.8366 - val_loss: 0.5110 - val_accuracy: 0.7721 - val_auc: 0.8508\n",
      "Epoch 116/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4352 - accuracy: 0.8137 - auc: 0.8666 - val_loss: 0.4171 - val_accuracy: 0.7868 - val_auc: 0.8851\n",
      "Epoch 117/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4633 - accuracy: 0.7675 - auc: 0.8493 - val_loss: 0.4727 - val_accuracy: 0.7500 - val_auc: 0.8517\n",
      "Epoch 118/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4845 - accuracy: 0.7712 - auc: 0.8346 - val_loss: 0.5468 - val_accuracy: 0.6838 - val_auc: 0.8637\n",
      "Epoch 119/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4556 - accuracy: 0.7638 - auc: 0.8538 - val_loss: 0.4823 - val_accuracy: 0.7721 - val_auc: 0.8691\n",
      "Epoch 120/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4562 - accuracy: 0.7860 - auc: 0.8585 - val_loss: 0.4400 - val_accuracy: 0.7574 - val_auc: 0.8824\n",
      "Epoch 121/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4456 - accuracy: 0.8007 - auc: 0.8657 - val_loss: 0.4949 - val_accuracy: 0.7500 - val_auc: 0.8574\n",
      "Epoch 122/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4762 - accuracy: 0.7749 - auc: 0.8417 - val_loss: 0.4872 - val_accuracy: 0.7721 - val_auc: 0.8703\n",
      "Epoch 123/200\n",
      "17/17 [==============================] - 0s 20ms/step - loss: 0.4548 - accuracy: 0.7823 - auc: 0.8647 - val_loss: 0.4276 - val_accuracy: 0.8015 - val_auc: 0.8862\n",
      "Epoch 124/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4310 - accuracy: 0.7786 - auc: 0.8690 - val_loss: 0.4645 - val_accuracy: 0.7574 - val_auc: 0.8637\n",
      "Epoch 125/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4557 - accuracy: 0.8118 - auc: 0.8540 - val_loss: 0.4359 - val_accuracy: 0.7868 - val_auc: 0.8810\n",
      "Epoch 126/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4425 - accuracy: 0.7804 - auc: 0.8667 - val_loss: 0.4392 - val_accuracy: 0.7794 - val_auc: 0.8703\n",
      "Epoch 127/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4583 - accuracy: 0.7952 - auc: 0.8554 - val_loss: 0.5010 - val_accuracy: 0.7721 - val_auc: 0.8608\n",
      "Epoch 128/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4442 - accuracy: 0.7860 - auc: 0.8608 - val_loss: 0.5242 - val_accuracy: 0.7279 - val_auc: 0.8701\n",
      "Epoch 129/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4527 - accuracy: 0.7804 - auc: 0.8590 - val_loss: 0.5076 - val_accuracy: 0.7353 - val_auc: 0.8429\n",
      "Epoch 130/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4240 - accuracy: 0.8100 - auc: 0.8814 - val_loss: 0.4232 - val_accuracy: 0.7721 - val_auc: 0.8815\n",
      "Epoch 131/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4578 - accuracy: 0.7878 - auc: 0.8612 - val_loss: 0.4302 - val_accuracy: 0.7794 - val_auc: 0.8828\n",
      "Epoch 132/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4281 - accuracy: 0.8137 - auc: 0.8743 - val_loss: 0.4563 - val_accuracy: 0.7794 - val_auc: 0.8737\n",
      "Epoch 133/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4346 - accuracy: 0.8026 - auc: 0.8719 - val_loss: 0.4833 - val_accuracy: 0.7868 - val_auc: 0.8637\n",
      "Epoch 134/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4243 - accuracy: 0.7970 - auc: 0.8763 - val_loss: 0.4559 - val_accuracy: 0.7721 - val_auc: 0.8751\n",
      "Epoch 135/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4251 - accuracy: 0.8044 - auc: 0.8803 - val_loss: 0.4158 - val_accuracy: 0.8015 - val_auc: 0.8872\n",
      "Epoch 136/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4505 - accuracy: 0.7897 - auc: 0.8655 - val_loss: 0.4828 - val_accuracy: 0.7500 - val_auc: 0.8443\n",
      "Epoch 137/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4606 - accuracy: 0.7786 - auc: 0.8558 - val_loss: 0.5191 - val_accuracy: 0.7279 - val_auc: 0.8635\n",
      "Epoch 138/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4619 - accuracy: 0.7823 - auc: 0.8487 - val_loss: 0.4404 - val_accuracy: 0.7794 - val_auc: 0.8826\n",
      "Epoch 139/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4100 - accuracy: 0.7860 - auc: 0.8874 - val_loss: 0.4487 - val_accuracy: 0.7868 - val_auc: 0.8580\n",
      "Epoch 140/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4573 - accuracy: 0.7583 - auc: 0.8586 - val_loss: 0.4418 - val_accuracy: 0.7868 - val_auc: 0.8835\n",
      "Epoch 141/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4238 - accuracy: 0.8044 - auc: 0.8849 - val_loss: 0.4869 - val_accuracy: 0.7574 - val_auc: 0.8706\n",
      "Epoch 142/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4608 - accuracy: 0.7989 - auc: 0.8573 - val_loss: 0.4595 - val_accuracy: 0.7647 - val_auc: 0.8652\n",
      "Epoch 143/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4246 - accuracy: 0.8026 - auc: 0.8769 - val_loss: 0.4846 - val_accuracy: 0.7868 - val_auc: 0.8690\n",
      "Epoch 144/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4030 - accuracy: 0.8173 - auc: 0.8900 - val_loss: 0.4906 - val_accuracy: 0.7647 - val_auc: 0.8696\n",
      "Epoch 145/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4336 - accuracy: 0.8026 - auc: 0.8690 - val_loss: 0.4278 - val_accuracy: 0.8088 - val_auc: 0.8978\n",
      "Epoch 146/200\n",
      "17/17 [==============================] - 0s 25ms/step - loss: 0.4589 - accuracy: 0.7970 - auc: 0.8584 - val_loss: 0.4509 - val_accuracy: 0.7574 - val_auc: 0.8626\n",
      "Epoch 147/200\n",
      "17/17 [==============================] - 0s 25ms/step - loss: 0.4117 - accuracy: 0.8100 - auc: 0.8904 - val_loss: 0.4451 - val_accuracy: 0.7647 - val_auc: 0.8653\n",
      "Epoch 148/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4191 - accuracy: 0.7952 - auc: 0.8786 - val_loss: 0.4561 - val_accuracy: 0.7868 - val_auc: 0.8787\n",
      "Epoch 149/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4204 - accuracy: 0.8026 - auc: 0.8796 - val_loss: 0.4592 - val_accuracy: 0.7721 - val_auc: 0.8641\n",
      "Epoch 150/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.4278 - accuracy: 0.8044 - auc: 0.8749 - val_loss: 0.4511 - val_accuracy: 0.7500 - val_auc: 0.8733\n",
      "Epoch 151/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4395 - accuracy: 0.7860 - auc: 0.8740 - val_loss: 0.5037 - val_accuracy: 0.7794 - val_auc: 0.8643\n",
      "Epoch 152/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4513 - accuracy: 0.7860 - auc: 0.8560 - val_loss: 0.4341 - val_accuracy: 0.7794 - val_auc: 0.8814\n",
      "Epoch 153/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4559 - accuracy: 0.7915 - auc: 0.8630 - val_loss: 0.4778 - val_accuracy: 0.7941 - val_auc: 0.8747\n",
      "Epoch 154/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4536 - accuracy: 0.7897 - auc: 0.8666 - val_loss: 0.4363 - val_accuracy: 0.7868 - val_auc: 0.8926\n",
      "Epoch 155/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4254 - accuracy: 0.7989 - auc: 0.8780 - val_loss: 0.4254 - val_accuracy: 0.7647 - val_auc: 0.8799\n",
      "Epoch 156/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4033 - accuracy: 0.8155 - auc: 0.8895 - val_loss: 0.4839 - val_accuracy: 0.7868 - val_auc: 0.8762\n",
      "Epoch 157/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4148 - accuracy: 0.8026 - auc: 0.8889 - val_loss: 0.4409 - val_accuracy: 0.7868 - val_auc: 0.8815\n",
      "Epoch 158/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4252 - accuracy: 0.8063 - auc: 0.8804 - val_loss: 0.4781 - val_accuracy: 0.7500 - val_auc: 0.8749\n",
      "Epoch 159/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4122 - accuracy: 0.8044 - auc: 0.8888 - val_loss: 0.4152 - val_accuracy: 0.7868 - val_auc: 0.8929\n",
      "Epoch 160/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.3908 - accuracy: 0.8210 - auc: 0.8980 - val_loss: 0.4613 - val_accuracy: 0.7721 - val_auc: 0.8844\n",
      "Epoch 161/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4121 - accuracy: 0.8007 - auc: 0.8866 - val_loss: 0.4537 - val_accuracy: 0.7868 - val_auc: 0.8781\n",
      "Epoch 162/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4338 - accuracy: 0.7952 - auc: 0.8745 - val_loss: 0.4790 - val_accuracy: 0.7500 - val_auc: 0.8666\n",
      "Epoch 163/200\n",
      "17/17 [==============================] - 0s 25ms/step - loss: 0.4042 - accuracy: 0.8081 - auc: 0.8912 - val_loss: 0.4291 - val_accuracy: 0.7868 - val_auc: 0.8825\n",
      "Epoch 164/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4301 - accuracy: 0.7915 - auc: 0.8771 - val_loss: 0.4698 - val_accuracy: 0.7868 - val_auc: 0.8758\n",
      "Epoch 165/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4345 - accuracy: 0.7915 - auc: 0.8739 - val_loss: 0.4569 - val_accuracy: 0.7868 - val_auc: 0.9066\n",
      "Epoch 166/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4171 - accuracy: 0.8210 - auc: 0.8845 - val_loss: 0.4462 - val_accuracy: 0.8015 - val_auc: 0.8818\n",
      "Epoch 167/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4190 - accuracy: 0.7970 - auc: 0.8837 - val_loss: 0.4180 - val_accuracy: 0.8088 - val_auc: 0.8994\n",
      "Epoch 168/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4183 - accuracy: 0.7934 - auc: 0.8800 - val_loss: 0.4360 - val_accuracy: 0.7794 - val_auc: 0.8769\n",
      "Epoch 169/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4311 - accuracy: 0.7712 - auc: 0.8692 - val_loss: 0.3880 - val_accuracy: 0.8015 - val_auc: 0.9057\n",
      "Epoch 170/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4155 - accuracy: 0.8026 - auc: 0.8812 - val_loss: 0.4720 - val_accuracy: 0.7647 - val_auc: 0.8799\n",
      "Epoch 171/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.3902 - accuracy: 0.8173 - auc: 0.9002 - val_loss: 0.3885 - val_accuracy: 0.8235 - val_auc: 0.8997\n",
      "Epoch 172/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4218 - accuracy: 0.8118 - auc: 0.8834 - val_loss: 0.4491 - val_accuracy: 0.7868 - val_auc: 0.8817\n",
      "Epoch 173/200\n",
      "17/17 [==============================] - 0s 26ms/step - loss: 0.4103 - accuracy: 0.8118 - auc: 0.8888 - val_loss: 0.4647 - val_accuracy: 0.7721 - val_auc: 0.8920\n",
      "Epoch 174/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4283 - accuracy: 0.8007 - auc: 0.8767 - val_loss: 0.4056 - val_accuracy: 0.8015 - val_auc: 0.8963\n",
      "Epoch 175/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.3881 - accuracy: 0.8266 - auc: 0.9018 - val_loss: 0.4641 - val_accuracy: 0.7647 - val_auc: 0.8817\n",
      "Epoch 176/200\n",
      "17/17 [==============================] - 0s 24ms/step - loss: 0.4081 - accuracy: 0.8210 - auc: 0.8872 - val_loss: 0.4311 - val_accuracy: 0.7794 - val_auc: 0.8919\n",
      "Epoch 177/200\n",
      "17/17 [==============================] - 0s 26ms/step - loss: 0.4206 - accuracy: 0.8063 - auc: 0.8850 - val_loss: 0.4274 - val_accuracy: 0.8088 - val_auc: 0.8817\n",
      "Epoch 178/200\n",
      "17/17 [==============================] - 0s 27ms/step - loss: 0.4118 - accuracy: 0.7860 - auc: 0.8848 - val_loss: 0.5185 - val_accuracy: 0.7353 - val_auc: 0.8906\n",
      "Epoch 179/200\n",
      "17/17 [==============================] - 0s 25ms/step - loss: 0.4205 - accuracy: 0.8081 - auc: 0.8862 - val_loss: 0.4464 - val_accuracy: 0.7721 - val_auc: 0.8764\n",
      "Epoch 180/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4197 - accuracy: 0.8137 - auc: 0.8788 - val_loss: 0.4075 - val_accuracy: 0.7941 - val_auc: 0.8927\n",
      "Epoch 181/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4262 - accuracy: 0.8063 - auc: 0.8781 - val_loss: 0.4517 - val_accuracy: 0.7868 - val_auc: 0.8866\n",
      "Epoch 182/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.3979 - accuracy: 0.8284 - auc: 0.8970 - val_loss: 0.4796 - val_accuracy: 0.7868 - val_auc: 0.9086\n",
      "Epoch 183/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4273 - accuracy: 0.8192 - auc: 0.8765 - val_loss: 0.4351 - val_accuracy: 0.7794 - val_auc: 0.8857\n",
      "Epoch 184/200\n",
      "17/17 [==============================] - 0s 23ms/step - loss: 0.3922 - accuracy: 0.8063 - auc: 0.8958 - val_loss: 0.4012 - val_accuracy: 0.8309 - val_auc: 0.9002\n",
      "Epoch 185/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4575 - accuracy: 0.7970 - auc: 0.8591 - val_loss: 0.4043 - val_accuracy: 0.7794 - val_auc: 0.8962\n",
      "Epoch 186/200\n",
      "17/17 [==============================] - 0s 25ms/step - loss: 0.4121 - accuracy: 0.8339 - auc: 0.8914 - val_loss: 0.4080 - val_accuracy: 0.7941 - val_auc: 0.8921\n",
      "Epoch 187/200\n",
      "17/17 [==============================] - 0s 22ms/step - loss: 0.4283 - accuracy: 0.8007 - auc: 0.8703 - val_loss: 0.4398 - val_accuracy: 0.7794 - val_auc: 0.8974\n",
      "Epoch 188/200\n",
      "17/17 [==============================] - 0s 21ms/step - loss: 0.4229 - accuracy: 0.8063 - auc: 0.8770 - val_loss: 0.4248 - val_accuracy: 0.7941 - val_auc: 0.8944\n",
      "Epoch 189/200\n",
      "17/17 [==============================] - 0s 26ms/step - loss: 0.3879 - accuracy: 0.8432 - auc: 0.9018 - val_loss: 0.4436 - val_accuracy: 0.8162 - val_auc: 0.9020\n",
      "Epoch 190/200\n",
      "17/17 [==============================] - 0s 26ms/step - loss: 0.3993 - accuracy: 0.8100 - auc: 0.8933 - val_loss: 0.4324 - val_accuracy: 0.7941 - val_auc: 0.8770\n",
      "Epoch 191/200\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4198 - accuracy: 0.8044 - auc: 0.8829 - val_loss: 0.4548 - val_accuracy: 0.7941 - val_auc: 0.8899\n",
      "Epoch 192/200\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.4298 - accuracy: 0.8007 - auc: 0.8738 - val_loss: 0.4962 - val_accuracy: 0.7279 - val_auc: 0.8708\n",
      "Epoch 193/200\n",
      "17/17 [==============================] - 1s 36ms/step - loss: 0.4298 - accuracy: 0.7970 - auc: 0.8723 - val_loss: 0.4878 - val_accuracy: 0.7426 - val_auc: 0.8569\n",
      "Epoch 194/200\n",
      "17/17 [==============================] - 0s 25ms/step - loss: 0.4207 - accuracy: 0.7915 - auc: 0.8785 - val_loss: 0.4208 - val_accuracy: 0.8015 - val_auc: 0.9003\n",
      "Epoch 195/200\n",
      "17/17 [==============================] - 1s 33ms/step - loss: 0.4236 - accuracy: 0.8081 - auc: 0.8807 - val_loss: 0.4301 - val_accuracy: 0.7941 - val_auc: 0.9051\n",
      "Epoch 196/200\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4413 - accuracy: 0.7749 - auc: 0.8654 - val_loss: 0.4202 - val_accuracy: 0.7941 - val_auc: 0.8996\n",
      "Epoch 197/200\n",
      "17/17 [==============================] - 0s 26ms/step - loss: 0.4151 - accuracy: 0.8210 - auc: 0.8859 - val_loss: 0.4209 - val_accuracy: 0.7868 - val_auc: 0.8869\n",
      "Epoch 198/200\n",
      "17/17 [==============================] - 1s 31ms/step - loss: 0.4008 - accuracy: 0.8173 - auc: 0.8955 - val_loss: 0.4213 - val_accuracy: 0.8015 - val_auc: 0.8951\n",
      "Epoch 199/200\n",
      "17/17 [==============================] - 1s 36ms/step - loss: 0.4041 - accuracy: 0.8173 - auc: 0.8913 - val_loss: 0.4768 - val_accuracy: 0.7721 - val_auc: 0.8805\n",
      "Epoch 200/200\n",
      "17/17 [==============================] - 1s 34ms/step - loss: 0.4202 - accuracy: 0.8173 - auc: 0.8844 - val_loss: 0.4932 - val_accuracy: 0.7574 - val_auc: 0.8759\n"
     ]
    }
   ],
   "source": [
    "# Compile and fit the model\n",
    "\n",
    "# If early stopping is required...\n",
    "# es = tf.keras.callbacks.EarlyStopping(monitor='val_auc', mode='max', verbose=1, patience=20)\n",
    "\n",
    "model.compile(tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
    "              metrics=['accuracy','AUC'])\n",
    "\n",
    "historyLN = model.fit(x_train_LN, y_train, batch_size = 32, epochs=200,\n",
    "                    validation_data=(x_test_LN, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7c5d36cf-b91a-41ee-b875-22efbff9c02e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the results of the train/val process\n",
    "\n",
    "fig, axs = plt.subplots(1,3, figsize=(11,3))\n",
    "\n",
    "axs[0].plot(historyLN.history['accuracy'], label='accuracy')\n",
    "axs[0].plot(historyLN.history['val_accuracy'], label = 'val_accuracy')\n",
    "axs[0].set_xlabel('Epoch')\n",
    "axs[0].set_ylim([0.5, 1])\n",
    "\n",
    "axs[1].plot(historyLN.history['loss'], label='loss')\n",
    "axs[1].plot(historyLN.history['val_loss'], label = 'val_loss')\n",
    "axs[1].set_xlabel('Epoch')\n",
    "axs[1].set_ylim([0.4, 1])\n",
    "\n",
    "axs[2].plot(historyLN.history['auc'], label='Training')\n",
    "axs[2].plot(historyLN.history['val_auc'], label = 'Validation')\n",
    "axs[2].set_xlabel('Epoch')\n",
    "axs[2].set_ylim([0.5, 1])\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "axs[0].set_title('Accuracy')\n",
    "axs[1].set_title('Loss')\n",
    "axs[2].set_title('ROC-AUC')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "476ecfab-79d3-4d0d-bc28-ca387363d944",
   "metadata": {},
   "source": [
    "## Test models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e3c03f-760e-4248-9eae-af0ee9ed914d",
   "metadata": {},
   "source": [
    "First, import and process the test images as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e64a2e91-f222-4c17-9ee5-71c1d949e75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test the models\n",
    "\n",
    "root_dir = 'C:/Users/chen/Desktop/Python_Projects/Zircon_Images/Test_Data2'\n",
    "y_testing = []\n",
    "x_testing = []\n",
    "for filename in glob.iglob(root_dir + '**/*.png', recursive=True):\n",
    "    img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)\n",
    "    # These images are too large we need to downsize\n",
    "    canny = cv2.Canny(img, 20, 255, 1)\n",
    "    kernel = np.ones((4,4),np.uint8)\n",
    "    dilate = cv2.dilate(canny, kernel, iterations=1)\n",
    "\n",
    "    # Code from https://stackoverflow.com/questions/56604151/python-extract-multiple-objects-from-image-opencv\n",
    "    # Find contours\n",
    "    cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    cnts = cnts[0] if len(cnts) == 2 else cnts[1]\n",
    "\n",
    "    image_number = 0\n",
    "    for c in cnts:\n",
    "        x,y,w,h = cv2.boundingRect(c)\n",
    "        # cv2.rectangle(img, (x, y), (x + w, y + h), (36,255,12), 2)\n",
    "        if w > 40 and h > 40 and w < 90 and h < 90:\n",
    "            ROI = img[y:y+h, x:x+w]\n",
    "            x_testing.append(ROI)\n",
    "            y_testing.append(filename[-10:-4])\n",
    "            # if image_number % 10 == 0:\n",
    "            #     cv2.imwrite(\"ROI_{}.png\".format(image_number), ROI)\n",
    "        image_number += 1\n",
    "\n",
    "    # cv2.imshow('canny', canny)\n",
    "    # cv2.imshow('image', img)\n",
    "    cv2.waitKey(0)\n",
    "y_testing_samples = y_testing\n",
    "y_test_zircon = [*map(samples_mapper.get, y_testing)]\n",
    "    \n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(x_testing[i])\n",
    "    # The CIFAR labels happen to be arrays, \n",
    "    # which is why you need the extra index\n",
    "    plt.xlabel(y_testing[i])\n",
    "plt.show()\n",
    "\n",
    "def convert_to_tensor(n):\n",
    "    out = tf.convert_to_tensor(n, dtype=None, dtype_hint=None, name=None)\n",
    "    return out\n",
    "\n",
    "out = []\n",
    "for i in x_testing:\n",
    "    i = tf.convert_to_tensor(i, dtype=None, dtype_hint=None, name=None)\n",
    "    i = tf.reshape(i, (i.shape[0],i.shape[1],1), name=None) # reshape to be 3D\n",
    "    i = tf.image.resize_with_crop_or_pad(i, 90,90) # Crop or pad if larger/smaller than 70 pixel long or wide\n",
    "    i = tf.image.resize(i, [32,32])\n",
    "    out.append(i)\n",
    "y_testing = tf.stack(y_test_zircon)\n",
    "x_test_zircon = tf.stack(out)\n",
    "\n",
    "# Transfer learning images\n",
    "out = []\n",
    "for i in x_testing:\n",
    "    i = tf.convert_to_tensor(i, dtype=None, dtype_hint=None, name=None)\n",
    "    i = tf.reshape(i, (i.shape[0],i.shape[1],1), name=None) # reshape to be 3D\n",
    "    i = tf.image.resize_with_crop_or_pad(i, 90,90) # Crop or pad if larger/smaller than 70 pixel long or wide\n",
    "    i = tf.image.resize(i, [224,224]) # all images to 32x32\n",
    "    i = tf.image.grayscale_to_rgb(i, name=None)\n",
    "    out.append(i)\n",
    "y_testing = tf.stack(y_test_zircon)\n",
    "x_test_zircon_TL = tf.stack(out)\n",
    "\n",
    "# AlexNet images\n",
    "\n",
    "out = []\n",
    "for i in x_testing:\n",
    "    i = tf.convert_to_tensor(i, dtype=None, dtype_hint=None, name=None)\n",
    "    i = tf.reshape(i, (i.shape[0],i.shape[1],1), name=None) # reshape to be 3D\n",
    "    i = tf.image.resize_with_crop_or_pad(i, 90,90) # Crop or pad if larger/smaller than 70 pixel long or wide\n",
    "    i = tf.image.resize(i, [224,224]) # all images to 32x32\n",
    "    out.append(i)\n",
    "y_testing = tf.stack(y_test_zircon)\n",
    "x_test_zircon_alex = tf.stack(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40f6feb2-9f9c-4c7a-947c-1075380f1d24",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a2e2c64f-7ba6-4c7c-8521-417f2989ae84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71/71 [==============================] - 10s 135ms/step - loss: 0.3983 - accuracy: 0.8592 - auc: 0.9309\n",
      "test loss, test acc: [0.3983076512813568, 0.8591549396514893, 0.9308695793151855]\n",
      "71/71 [==============================] - 11s 160ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transfer learning - import model\n",
    "TL_VGG_model = tf.keras.models.load_model('C:/Users/chen/Desktop/Python_Projects/Zircon_Images/VGG_TL/')\n",
    "\n",
    "from tensorflow.keras.applications.vgg16 import preprocess_input\n",
    "\n",
    "## Preprocessing input\n",
    "test_ds = preprocess_input(x_test_zircon_TL)\n",
    "results_vgg = TL_VGG_model.evaluate(test_ds, y_testing, batch_size=1)\n",
    "print(\"test loss, test acc:\", results_vgg)\n",
    "\n",
    "predictions =  TL_VGG_model.predict(test_ds, batch_size = 1)\n",
    "\n",
    "fig, axs = plt.subplots(1,1, figsize = (4,3))\n",
    "test_results = pd.concat([pd.Series(predictions.flatten()),pd.Series(y_testing)], axis = 1)\n",
    "test_results.columns = ['Prediction','Real']\n",
    "sb.histplot(test_results.groupby('Real').get_group(0)['Prediction'], bins = 25, ax  = axs, color = 'tab:blue')\n",
    "sb.histplot(test_results.groupby('Real').get_group(1)['Prediction'], bins = 25, ax  = axs, color = 'tab:orange')\n",
    "test_results_VGG = test_results\n",
    "axs.vlines(0.5, ymin = 10, ymax = 0, color = 'black', linestyle = 'dashed')\n",
    "\n",
    "axs.set_xlim([0,1])\n",
    "axs.set_ylim([0,10])\n",
    "axs.set_xlabel('Probability')\n",
    "axs.text(0.45,6,'Threshold',rotation=90)\n",
    "# plt.savefig('220808_VGGTestPredictions.svg', dpi = 1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9756aa07-4b1a-4e13-b34e-d6a7d5435fe7",
   "metadata": {},
   "source": [
    "## Feature extraction using UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f23b19dc-a170-47e4-a8b1-ce3fefbb03dd",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " sequential_input (InputLaye  [(None, 32, 32, 1)]      0         \n",
      " r)                                                              \n",
      "                                                                 \n",
      " sequential (Sequential)     (None, 32, 32, 1)         0         \n",
      "                                                                 \n",
      " rescaling (Rescaling)       (None, 32, 32, 1)         0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 32, 32, 6)         60        \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 16, 6)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 16, 16, 16)        880       \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 8, 8, 16)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 8, 8, 32)          4640      \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 4, 4, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 512)               0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 512)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                16416     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 21,996\n",
      "Trainable params: 21,996\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "17/17 [==============================] - 0s 6ms/step\n",
      "3/3 [==============================] - 0s 5ms/step\n",
      "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer-1.layer-0._random_generator._generator._state_var\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer-1.layer-1._random_generator._generator._state_var\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).layer-1.layer-2._random_generator._generator._state_var\n"
     ]
    }
   ],
   "source": [
    "# Try feature extraction\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "trim_model = Model(inputs=model.inputs, outputs=model.layers[-2].output)\n",
    "trim_model.summary()\n",
    "features = trim_model.predict(x_train_LN)\n",
    "features = pd.DataFrame(features)\n",
    "\n",
    "features_test = pd.DataFrame(trim_model.predict(x_test_zircon))\n",
    "\n",
    "\n",
    "import umap\n",
    "data_umap = umap.UMAP(random_state=1, n_components=3).fit_transform(features)\n",
    "trans = umap.UMAP(random_state=1, n_components=3).fit(pd.concat([features, features_test], axis = 0))\n",
    "data_umap_test = pd.DataFrame(data = trans.transform(features_test) , columns = ['UMAP1','UMAP2', 'UMAP3'])\n",
    "data_umap = pd.DataFrame(data =  trans.transform(features), columns = ['UMAP1','UMAP2', 'UMAP3'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c1a6d989-8f8a-4af9-b5cf-7c4ee0a8e54a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QVC055\n",
      "QVC075\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QVC055\n",
      "QVC075\n",
      "QVC055\n",
      "QVC075\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x496.8 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# UMAP1 = crystal shape\n",
    "# UMAP2 = frequency of zoning\n",
    "# UMAP3 = intensity of CL response\n",
    "\n",
    "colour_dict = {'QVC074':'cornflowerblue','QVC075':'cornflowerblue',\n",
    "               'QVC082':'cornflowerblue','QVC083':'cornflowerblue',\n",
    "              'QVC028':'coral','QVC058':'coral','QVC009':'coral',\n",
    "              'QVC034':'coral','QVC024':'coral','QVC060':'coral',\n",
    "              'QVC061':'coral','QVC055':'coral','QVC029':'coral',\n",
    "              'QVC041':'coral'}\n",
    "\n",
    "shape_dict = {'QVC074':'v','QVC075':'^',\n",
    "               'QVC082':'<','QVC083':'>',\n",
    "              'QVC028':'d','QVC058':'s','QVC009':'o',\n",
    "              'QVC034':'P','QVC024':'X','QVC060':'D',\n",
    "              'QVC061':'v','QVC055':'^','QVC029':'>',\n",
    "              'QVC041':'<'}\n",
    "\n",
    "fig = plt.figure(figsize = (6,6))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "data_umap2 = pd.concat([pd.Series(y_train_samples, name = 'Samples'), data_umap], axis = 1)\n",
    "data_umap_test2 = pd.concat([pd.Series(y_testing_samples, name = 'Samples'), data_umap_test], axis = 1)\n",
    "for name, group in data_umap2.groupby('Samples'):\n",
    "    ax.scatter(group['UMAP1'],group['UMAP2'], group['UMAP3'],color = colour_dict.get(name), marker = shape_dict.get(name),\n",
    "               label = name, edgecolor = 'black', linewidth = 0.7, s = 30, depthshade=True)\n",
    "for name, group in data_umap_test2.groupby('Samples'):\n",
    "    print(name)\n",
    "    ax.scatter(group['UMAP1'],group['UMAP2'], group['UMAP3'],color = colour_dict.get(name), marker = shape_dict.get(name),\n",
    "               label = name, edgecolor = 'red', linewidth = 1, s = 30, depthshade=True)\n",
    "\n",
    "ax.legend(loc = 2)\n",
    "\n",
    "\n",
    "ax.set_xlabel('UMAP1')\n",
    "ax.set_ylabel('UMAP2')\n",
    "ax.set_zlabel('UMAP3')\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "# plt.savefig('220801_3D_UMAP.svg', dpi = 1200)\n",
    "plt.show()\n",
    "\n",
    "fig, axs = plt.subplots(2,1, figsize = (3.5,6.9))\n",
    "\n",
    "    \n",
    "for name, group in data_umap2.groupby('Samples'):\n",
    "    axs[0].scatter(group['UMAP1'],group['UMAP2'],color = colour_dict.get(name), marker = shape_dict.get(name),\n",
    "               label = name, edgecolor = 'black', linewidth = 0.7, s = 30)\n",
    "for name, group in data_umap_test2.groupby('Samples'):\n",
    "    print(name)\n",
    "    axs[0].scatter(group['UMAP1'],group['UMAP2'],color = colour_dict.get(name), marker = shape_dict.get(name),\n",
    "               label = name, edgecolor = 'red', linewidth = 1, s = 30)\n",
    "\n",
    "for name, group in data_umap2.groupby('Samples'):\n",
    "    axs[1].scatter(group['UMAP1'],group['UMAP3'],color = colour_dict.get(name), marker = shape_dict.get(name),\n",
    "               label = name, edgecolor = 'black', linewidth = 1, s = 30)\n",
    "\n",
    "for name, group in data_umap_test2.groupby('Samples'):\n",
    "    print(name)\n",
    "    axs[1].scatter(group['UMAP1'],group['UMAP3'],color = colour_dict.get(name), marker = shape_dict.get(name),\n",
    "               label = name, edgecolor = 'red', linewidth = 0.7, s = 30)\n",
    "\n",
    "axs[0].set_xlabel('UMAP1')\n",
    "axs[0].set_ylabel('UMAP2')\n",
    "\n",
    "axs[1].set_xlabel('UMAP1')\n",
    "axs[1].set_ylabel('UMAP3')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a95456-56fb-4593-be01-2029eeedeab2",
   "metadata": {},
   "source": [
    "## Transfer learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d535540-ce52-4dc0-bf7c-a0b77714710d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Augmentation\n",
    "data_augmentation = tf.keras.Sequential([\n",
    "  tf.keras.layers.experimental.preprocessing.RandomContrast(0.2),\n",
    "    tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),\n",
    "    tf.keras.layers.experimental.preprocessing.RandomZoom(height_factor = (-0.4,0.4)),\n",
    "])\n",
    "\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "\n",
    "# Import VGG16 from keras\n",
    "vgg_model = VGG16(include_top=False, input_shape =(224,224,3),  weights = 'imagenet')\n",
    "\n",
    "#Get the dictionary of config for vgg16\n",
    "vgg_config = vgg_model.get_config()\n",
    "\n",
    "# Freeze layers\n",
    "for layers in (vgg_model.layers)[:13]:\n",
    "    print(layers)\n",
    "    layers.trainable = False\n",
    "\n",
    "inputs = tf.keras.Input(shape=x_train_TL[0].shape)\n",
    "\n",
    "preprocess_input = tf.keras.applications.vgg16.preprocess_input\n",
    "prediction_layer = tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
    "\n",
    "x = tf.keras.layers.experimental.preprocessing.Rescaling(1./127.5, offset=-1)(inputs)\n",
    "x = data_augmentation(inputs)\n",
    "x = vgg_model(x, training=True)\n",
    "x = global_average_layer(x)\n",
    "x = tf.keras.layers.Flatten()(x)  \n",
    "x = tf.keras.layers.Dropout(0.5)(x)\n",
    "x = tf.keras.layers.Dense(32, activation='relu')(x)\n",
    "x = tf.keras.layers.Dropout(0.5)(x)\n",
    "outputs = prediction_layer(x)\n",
    "VGG_model = tf.keras.Model(inputs, outputs)\n",
    "print(VGG_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f4efe04-a0f3-46d4-b9e7-9b24db498d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code for early stopping based on threshold\n",
    "import time\n",
    "class SOMT(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, model,  train_thold, valid_thold):\n",
    "        super(SOMT, self).__init__()\n",
    "        self.model=model        \n",
    "        self.train_thold=train_thold\n",
    "        self.valid_thold=valid_thold\n",
    "        \n",
    "    def on_train_begin(self, logs=None):\n",
    "        print('Starting Training - training will halt if training accuracy achieves or exceeds ', self.train_thold)\n",
    "        print ('and validation accuracy meets or exceeds ', self.valid_thold) \n",
    "        msg='{0:^8s}{1:^12s}{2:^12s}{3:^12s}{4:^12s}{5:^12s}'.format('Epoch', 'Train Acc', 'Train Loss','Valid Acc','Valid_Loss','Duration')\n",
    "        print (msg)                                                                                    \n",
    "            \n",
    "    def on_train_batch_end(self, batch, logs=None):\n",
    "        acc=logs.get('accuracy')* 100  # get training accuracy \n",
    "        loss=logs.get('loss')\n",
    "        msg='{0:1s}processed batch {1:4s}  training accuracy= {2:8.3f}  loss: {3:8.5f}'.format(' ', str(batch),  acc, loss)\n",
    "        print(msg, '\\r', end='') # prints over on the same line to show running batch count \n",
    "        \n",
    "    def on_epoch_begin(self,epoch, logs=None):\n",
    "        self.now= time.time()\n",
    "        \n",
    "    def on_epoch_end(self,epoch, logs=None): \n",
    "        later=time.time()\n",
    "        duration=later-self.now \n",
    "        tacc=logs.get('accuracy')           \n",
    "        vacc=logs.get('val_accuracy')\n",
    "        tr_loss=logs.get('loss')\n",
    "        v_loss=logs.get('val_loss')\n",
    "        ep=epoch+1\n",
    "        print(f'{ep:^8.0f} {tacc:^12.2f}{tr_loss:^12.4f}{vacc:^12.2f}{v_loss:^12.4f}{duration:^12.2f}')\n",
    "        if tacc>= self.train_thold and vacc>= self.valid_thold:\n",
    "            print( f'\\ntraining accuracy and validation accuracy reached the thresholds on epoch {epoch + 1}' )\n",
    "            self.model.stop_training = True # stop training\n",
    "\n",
    "train_thold= .95\n",
    "valid_thold=95\n",
    "callbacks=[SOMT(VGG_model, train_thold, valid_thold)]\n",
    "\n",
    "\n",
    "# Train first for 10 epochs with higher learning rate\n",
    "VGG_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
    "              metrics=['accuracy','AUC'])\n",
    "history_VGG = VGG_model.fit(x_train_TL, y_train_TL, epochs=200, batch_size = 32,callbacks = callbacks,validation_data=(x_test_TL, y_test_TL))\n",
    "\n",
    "vgg_model.trainable = True\n",
    "\n",
    "# Unfreeze last few layers\n",
    "for layer in vgg_model.layers[:-2]:\n",
    "    layer.trainable = False\n",
    "\n",
    "\n",
    "for i,layer in enumerate(VGG_model.get_layer('vgg16').layers):\n",
    "    print(i,layer.name,layer.trainable)\n",
    "\n",
    "VGG_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
    "              metrics=['accuracy','AUC'])\n",
    "\n",
    "VGG_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9166cbd-6df8-4457-8e6b-e9cb127323a0",
   "metadata": {},
   "source": [
    "## Visualise outputs of individual convolutional filters\n",
    "\n",
    "Code from Francois Chollet - Deep Learning with Python (2nd edition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "14193903-70de-45d8-ad8e-3a2ecc820fb2",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_13 (Conv2D)          (None, 32, 32, 6)         60        \n",
      "                                                                 \n",
      " max_pooling2d_12 (MaxPoolin  (None, 16, 16, 6)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_14 (Conv2D)          (None, 16, 16, 18)        990       \n",
      "                                                                 \n",
      " max_pooling2d_13 (MaxPoolin  (None, 8, 8, 18)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_15 (Conv2D)          (None, 8, 8, 32)          5216      \n",
      "                                                                 \n",
      " max_pooling2d_14 (MaxPoolin  (None, 4, 4, 32)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_4 (Flatten)         (None, 512)               0         \n",
      "                                                                 \n",
      " dropout_8 (Dropout)         (None, 512)               0         \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 32)                16416     \n",
      "                                                                 \n",
      " dropout_9 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 22,715\n",
      "Trainable params: 22,715\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/100\n",
      "17/17 [==============================] - 2s 40ms/step - loss: 7.2204 - accuracy: 0.5166 - auc: 0.4902 - val_loss: 1.3427 - val_accuracy: 0.5956 - val_auc: 0.5653\n",
      "Epoch 2/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 1.9545 - accuracy: 0.5277 - auc: 0.5027 - val_loss: 0.7026 - val_accuracy: 0.4485 - val_auc: 0.6470\n",
      "Epoch 3/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.9037 - accuracy: 0.5074 - auc: 0.5425 - val_loss: 0.6926 - val_accuracy: 0.4044 - val_auc: 0.5318\n",
      "Epoch 4/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.7329 - accuracy: 0.5664 - auc: 0.5367 - val_loss: 0.6925 - val_accuracy: 0.5882 - val_auc: 0.4597\n",
      "Epoch 5/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.6790 - accuracy: 0.6384 - auc: 0.5910 - val_loss: 0.6917 - val_accuracy: 0.5882 - val_auc: 0.4724\n",
      "Epoch 6/100\n",
      "17/17 [==============================] - 0s 16ms/step - loss: 0.7246 - accuracy: 0.5996 - auc: 0.5845 - val_loss: 0.6830 - val_accuracy: 0.5956 - val_auc: 0.5048\n",
      "Epoch 7/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.6972 - accuracy: 0.5996 - auc: 0.5450 - val_loss: 0.6818 - val_accuracy: 0.5956 - val_auc: 0.5461\n",
      "Epoch 8/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.6748 - accuracy: 0.6218 - auc: 0.5778 - val_loss: 0.6793 - val_accuracy: 0.5956 - val_auc: 0.5694\n",
      "Epoch 9/100\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.6942 - accuracy: 0.6089 - auc: 0.5278 - val_loss: 0.6784 - val_accuracy: 0.6103 - val_auc: 0.6047\n",
      "Epoch 10/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.6857 - accuracy: 0.6181 - auc: 0.5947 - val_loss: 0.6738 - val_accuracy: 0.5956 - val_auc: 0.6138\n",
      "Epoch 11/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.6707 - accuracy: 0.6273 - auc: 0.6134 - val_loss: 0.6755 - val_accuracy: 0.6029 - val_auc: 0.6349\n",
      "Epoch 12/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.6667 - accuracy: 0.6199 - auc: 0.6240 - val_loss: 0.6659 - val_accuracy: 0.5956 - val_auc: 0.6375\n",
      "Epoch 13/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.6533 - accuracy: 0.5959 - auc: 0.6035 - val_loss: 0.6676 - val_accuracy: 0.5956 - val_auc: 0.6559\n",
      "Epoch 14/100\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.6446 - accuracy: 0.6125 - auc: 0.6291 - val_loss: 0.6601 - val_accuracy: 0.6176 - val_auc: 0.6722\n",
      "Epoch 15/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.6467 - accuracy: 0.6070 - auc: 0.6302 - val_loss: 0.6593 - val_accuracy: 0.6029 - val_auc: 0.6770\n",
      "Epoch 16/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.6509 - accuracy: 0.6125 - auc: 0.6222 - val_loss: 0.6525 - val_accuracy: 0.6103 - val_auc: 0.6699\n",
      "Epoch 17/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.6312 - accuracy: 0.6439 - auc: 0.6590 - val_loss: 0.6533 - val_accuracy: 0.5956 - val_auc: 0.6415\n",
      "Epoch 18/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.6145 - accuracy: 0.6218 - auc: 0.6766 - val_loss: 0.6461 - val_accuracy: 0.5956 - val_auc: 0.6629\n",
      "Epoch 19/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.6460 - accuracy: 0.6347 - auc: 0.6538 - val_loss: 0.6458 - val_accuracy: 0.5956 - val_auc: 0.6660\n",
      "Epoch 20/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.6347 - accuracy: 0.6292 - auc: 0.6467 - val_loss: 0.6514 - val_accuracy: 0.6029 - val_auc: 0.6470\n",
      "Epoch 21/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.6198 - accuracy: 0.6365 - auc: 0.6865 - val_loss: 0.6373 - val_accuracy: 0.6103 - val_auc: 0.6700\n",
      "Epoch 22/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.5931 - accuracy: 0.6347 - auc: 0.7068 - val_loss: 0.6363 - val_accuracy: 0.5956 - val_auc: 0.6898\n",
      "Epoch 23/100\n",
      "17/17 [==============================] - 0s 16ms/step - loss: 0.5927 - accuracy: 0.6255 - auc: 0.7356 - val_loss: 0.6361 - val_accuracy: 0.6029 - val_auc: 0.6869\n",
      "Epoch 24/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.5958 - accuracy: 0.6310 - auc: 0.7191 - val_loss: 0.6368 - val_accuracy: 0.6176 - val_auc: 0.6972\n",
      "Epoch 25/100\n",
      "17/17 [==============================] - 0s 15ms/step - loss: 0.5742 - accuracy: 0.6494 - auc: 0.7424 - val_loss: 0.6300 - val_accuracy: 0.6176 - val_auc: 0.6752\n",
      "Epoch 26/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.6010 - accuracy: 0.6292 - auc: 0.7053 - val_loss: 0.6243 - val_accuracy: 0.6471 - val_auc: 0.7232\n",
      "Epoch 27/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.5968 - accuracy: 0.6292 - auc: 0.7187 - val_loss: 0.6256 - val_accuracy: 0.6691 - val_auc: 0.7331\n",
      "Epoch 28/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5943 - accuracy: 0.6679 - auc: 0.7297 - val_loss: 0.6311 - val_accuracy: 0.6250 - val_auc: 0.7009\n",
      "Epoch 29/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.6068 - accuracy: 0.6531 - auc: 0.7183 - val_loss: 0.6193 - val_accuracy: 0.6618 - val_auc: 0.6996\n",
      "Epoch 30/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.5720 - accuracy: 0.6605 - auc: 0.7538 - val_loss: 0.6177 - val_accuracy: 0.6471 - val_auc: 0.7113\n",
      "Epoch 31/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.5732 - accuracy: 0.6716 - auc: 0.7416 - val_loss: 0.6283 - val_accuracy: 0.6471 - val_auc: 0.7028\n",
      "Epoch 32/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.5746 - accuracy: 0.6716 - auc: 0.7399 - val_loss: 0.6221 - val_accuracy: 0.6765 - val_auc: 0.7388\n",
      "Epoch 33/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5499 - accuracy: 0.7011 - auc: 0.7743 - val_loss: 0.6176 - val_accuracy: 0.6691 - val_auc: 0.7147\n",
      "Epoch 34/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5415 - accuracy: 0.6661 - auc: 0.7668 - val_loss: 0.6147 - val_accuracy: 0.6397 - val_auc: 0.7230\n",
      "Epoch 35/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.5636 - accuracy: 0.6734 - auc: 0.7628 - val_loss: 0.6119 - val_accuracy: 0.7059 - val_auc: 0.7475\n",
      "Epoch 36/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5543 - accuracy: 0.6900 - auc: 0.7725 - val_loss: 0.6019 - val_accuracy: 0.6912 - val_auc: 0.7565\n",
      "Epoch 37/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5461 - accuracy: 0.6993 - auc: 0.7825 - val_loss: 0.5946 - val_accuracy: 0.6912 - val_auc: 0.7779\n",
      "Epoch 38/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5650 - accuracy: 0.6790 - auc: 0.7561 - val_loss: 0.6036 - val_accuracy: 0.7059 - val_auc: 0.7476\n",
      "Epoch 39/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5423 - accuracy: 0.7030 - auc: 0.7851 - val_loss: 0.5908 - val_accuracy: 0.7132 - val_auc: 0.7698\n",
      "Epoch 40/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.5565 - accuracy: 0.7030 - auc: 0.7703 - val_loss: 0.5892 - val_accuracy: 0.7426 - val_auc: 0.7759\n",
      "Epoch 41/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5442 - accuracy: 0.6771 - auc: 0.7820 - val_loss: 0.5915 - val_accuracy: 0.7279 - val_auc: 0.7783\n",
      "Epoch 42/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5379 - accuracy: 0.7066 - auc: 0.7951 - val_loss: 0.5895 - val_accuracy: 0.7279 - val_auc: 0.7760\n",
      "Epoch 43/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5188 - accuracy: 0.7048 - auc: 0.7987 - val_loss: 0.5882 - val_accuracy: 0.7206 - val_auc: 0.7777\n",
      "Epoch 44/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5236 - accuracy: 0.7306 - auc: 0.8153 - val_loss: 0.5795 - val_accuracy: 0.7206 - val_auc: 0.7834\n",
      "Epoch 45/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5051 - accuracy: 0.7380 - auc: 0.8201 - val_loss: 0.5695 - val_accuracy: 0.7500 - val_auc: 0.7978\n",
      "Epoch 46/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.5226 - accuracy: 0.7306 - auc: 0.8103 - val_loss: 0.5841 - val_accuracy: 0.6985 - val_auc: 0.8034\n",
      "Epoch 47/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5289 - accuracy: 0.6771 - auc: 0.7896 - val_loss: 0.5862 - val_accuracy: 0.7279 - val_auc: 0.7819\n",
      "Epoch 48/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5234 - accuracy: 0.7103 - auc: 0.7922 - val_loss: 0.5655 - val_accuracy: 0.7647 - val_auc: 0.7962\n",
      "Epoch 49/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.5107 - accuracy: 0.7417 - auc: 0.8181 - val_loss: 0.5658 - val_accuracy: 0.7500 - val_auc: 0.7958\n",
      "Epoch 50/100\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.5118 - accuracy: 0.7140 - auc: 0.8049 - val_loss: 0.5655 - val_accuracy: 0.6691 - val_auc: 0.8062\n",
      "Epoch 51/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4731 - accuracy: 0.7694 - auc: 0.8551 - val_loss: 0.5682 - val_accuracy: 0.7206 - val_auc: 0.7774\n",
      "Epoch 52/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.5068 - accuracy: 0.7325 - auc: 0.8167 - val_loss: 0.5804 - val_accuracy: 0.7206 - val_auc: 0.7694\n",
      "Epoch 53/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4958 - accuracy: 0.7454 - auc: 0.8291 - val_loss: 0.5671 - val_accuracy: 0.7059 - val_auc: 0.7835\n",
      "Epoch 54/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4972 - accuracy: 0.7638 - auc: 0.8397 - val_loss: 0.5719 - val_accuracy: 0.7206 - val_auc: 0.7773\n",
      "Epoch 55/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4938 - accuracy: 0.7657 - auc: 0.8345 - val_loss: 0.5540 - val_accuracy: 0.7059 - val_auc: 0.7985\n",
      "Epoch 56/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4659 - accuracy: 0.7435 - auc: 0.8503 - val_loss: 0.5544 - val_accuracy: 0.7206 - val_auc: 0.8043\n",
      "Epoch 57/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4393 - accuracy: 0.7454 - auc: 0.8560 - val_loss: 0.5494 - val_accuracy: 0.7353 - val_auc: 0.8120\n",
      "Epoch 58/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4460 - accuracy: 0.7731 - auc: 0.8597 - val_loss: 0.5462 - val_accuracy: 0.7206 - val_auc: 0.8058\n",
      "Epoch 59/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4638 - accuracy: 0.7601 - auc: 0.8520 - val_loss: 0.5488 - val_accuracy: 0.7132 - val_auc: 0.8223\n",
      "Epoch 60/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4406 - accuracy: 0.7804 - auc: 0.8670 - val_loss: 0.5599 - val_accuracy: 0.6471 - val_auc: 0.8253\n",
      "Epoch 61/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4383 - accuracy: 0.7878 - auc: 0.8721 - val_loss: 0.5606 - val_accuracy: 0.6691 - val_auc: 0.8166\n",
      "Epoch 62/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4557 - accuracy: 0.7675 - auc: 0.8586 - val_loss: 0.5471 - val_accuracy: 0.6912 - val_auc: 0.8075\n",
      "Epoch 63/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.4448 - accuracy: 0.7970 - auc: 0.8681 - val_loss: 0.5417 - val_accuracy: 0.6765 - val_auc: 0.8204\n",
      "Epoch 64/100\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.4289 - accuracy: 0.7823 - auc: 0.8706 - val_loss: 0.5499 - val_accuracy: 0.6618 - val_auc: 0.8262\n",
      "Epoch 65/100\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.4300 - accuracy: 0.8081 - auc: 0.8838 - val_loss: 0.5441 - val_accuracy: 0.7132 - val_auc: 0.8086\n",
      "Epoch 66/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.4370 - accuracy: 0.7731 - auc: 0.8651 - val_loss: 0.5806 - val_accuracy: 0.6324 - val_auc: 0.8175\n",
      "Epoch 67/100\n",
      "17/17 [==============================] - 0s 15ms/step - loss: 0.4372 - accuracy: 0.7952 - auc: 0.8673 - val_loss: 0.5469 - val_accuracy: 0.7206 - val_auc: 0.8022\n",
      "Epoch 68/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.4349 - accuracy: 0.7970 - auc: 0.8755 - val_loss: 0.5362 - val_accuracy: 0.7426 - val_auc: 0.8221\n",
      "Epoch 69/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.3695 - accuracy: 0.8210 - auc: 0.9127 - val_loss: 0.5502 - val_accuracy: 0.6912 - val_auc: 0.8212\n",
      "Epoch 70/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.4019 - accuracy: 0.8100 - auc: 0.8915 - val_loss: 0.5307 - val_accuracy: 0.7059 - val_auc: 0.8304\n",
      "Epoch 71/100\n",
      "17/17 [==============================] - 0s 18ms/step - loss: 0.3985 - accuracy: 0.8044 - auc: 0.8951 - val_loss: 0.5963 - val_accuracy: 0.6397 - val_auc: 0.8273\n",
      "Epoch 72/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.4298 - accuracy: 0.7804 - auc: 0.8736 - val_loss: 0.5513 - val_accuracy: 0.6765 - val_auc: 0.8290\n",
      "Epoch 73/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.4076 - accuracy: 0.7897 - auc: 0.8854 - val_loss: 0.5366 - val_accuracy: 0.6838 - val_auc: 0.8302\n",
      "Epoch 74/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.3880 - accuracy: 0.8063 - auc: 0.8976 - val_loss: 0.5325 - val_accuracy: 0.7059 - val_auc: 0.8297\n",
      "Epoch 75/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.3722 - accuracy: 0.8100 - auc: 0.9056 - val_loss: 0.5166 - val_accuracy: 0.7353 - val_auc: 0.8318\n",
      "Epoch 76/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.3705 - accuracy: 0.8100 - auc: 0.9046 - val_loss: 0.5610 - val_accuracy: 0.7647 - val_auc: 0.8117\n",
      "Epoch 77/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.3688 - accuracy: 0.8100 - auc: 0.9076 - val_loss: 0.5591 - val_accuracy: 0.7353 - val_auc: 0.8038\n",
      "Epoch 78/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.3663 - accuracy: 0.8303 - auc: 0.9072 - val_loss: 0.5175 - val_accuracy: 0.6985 - val_auc: 0.8424\n",
      "Epoch 79/100\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.3599 - accuracy: 0.8284 - auc: 0.9193 - val_loss: 0.5347 - val_accuracy: 0.6838 - val_auc: 0.8470\n",
      "Epoch 80/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.3411 - accuracy: 0.8395 - auc: 0.9262 - val_loss: 0.5838 - val_accuracy: 0.7279 - val_auc: 0.8171\n",
      "Epoch 81/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.3490 - accuracy: 0.8266 - auc: 0.9162 - val_loss: 0.5414 - val_accuracy: 0.7206 - val_auc: 0.8134\n",
      "Epoch 82/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.3629 - accuracy: 0.8284 - auc: 0.9162 - val_loss: 0.5526 - val_accuracy: 0.7721 - val_auc: 0.8107\n",
      "Epoch 83/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.3613 - accuracy: 0.8413 - auc: 0.9155 - val_loss: 0.5292 - val_accuracy: 0.6838 - val_auc: 0.8040\n",
      "Epoch 84/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.3678 - accuracy: 0.8376 - auc: 0.9155 - val_loss: 0.5372 - val_accuracy: 0.6838 - val_auc: 0.8340\n",
      "Epoch 85/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.3275 - accuracy: 0.8672 - auc: 0.9306 - val_loss: 0.5652 - val_accuracy: 0.6838 - val_auc: 0.8316\n",
      "Epoch 86/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.3217 - accuracy: 0.8469 - auc: 0.9340 - val_loss: 0.5439 - val_accuracy: 0.6985 - val_auc: 0.8135\n",
      "Epoch 87/100\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.2993 - accuracy: 0.8542 - auc: 0.9415 - val_loss: 0.5924 - val_accuracy: 0.7574 - val_auc: 0.7884\n",
      "Epoch 88/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.3297 - accuracy: 0.8487 - auc: 0.9273 - val_loss: 0.5954 - val_accuracy: 0.7353 - val_auc: 0.7809\n",
      "Epoch 89/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.3308 - accuracy: 0.8579 - auc: 0.9287 - val_loss: 0.5815 - val_accuracy: 0.7426 - val_auc: 0.7981\n",
      "Epoch 90/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.3241 - accuracy: 0.8395 - auc: 0.9310 - val_loss: 0.5941 - val_accuracy: 0.6765 - val_auc: 0.8022\n",
      "Epoch 91/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.2885 - accuracy: 0.8616 - auc: 0.9465 - val_loss: 0.5840 - val_accuracy: 0.7059 - val_auc: 0.8237\n",
      "Epoch 92/100\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.3123 - accuracy: 0.8524 - auc: 0.9383 - val_loss: 0.5336 - val_accuracy: 0.7353 - val_auc: 0.8268\n",
      "Epoch 93/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.2759 - accuracy: 0.8727 - auc: 0.9522 - val_loss: 0.5642 - val_accuracy: 0.7279 - val_auc: 0.8395\n",
      "Epoch 94/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.2681 - accuracy: 0.8838 - auc: 0.9535 - val_loss: 0.5219 - val_accuracy: 0.7941 - val_auc: 0.8501\n",
      "Epoch 95/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.2585 - accuracy: 0.8911 - auc: 0.9565 - val_loss: 0.5395 - val_accuracy: 0.7868 - val_auc: 0.8363\n",
      "Epoch 96/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.2887 - accuracy: 0.8801 - auc: 0.9462 - val_loss: 0.5366 - val_accuracy: 0.7279 - val_auc: 0.8297\n",
      "Epoch 97/100\n",
      "17/17 [==============================] - 0s 13ms/step - loss: 0.2793 - accuracy: 0.8727 - auc: 0.9527 - val_loss: 0.5662 - val_accuracy: 0.7059 - val_auc: 0.8375\n",
      "Epoch 98/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.2434 - accuracy: 0.8875 - auc: 0.9614 - val_loss: 0.5634 - val_accuracy: 0.7500 - val_auc: 0.8290\n",
      "Epoch 99/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.2796 - accuracy: 0.8690 - auc: 0.9488 - val_loss: 0.5740 - val_accuracy: 0.7353 - val_auc: 0.8219\n",
      "Epoch 100/100\n",
      "17/17 [==============================] - 0s 12ms/step - loss: 0.2789 - accuracy: 0.8727 - auc: 0.9482 - val_loss: 0.5379 - val_accuracy: 0.7574 - val_auc: 0.8424\n"
     ]
    }
   ],
   "source": [
    "# Fit a LN model without data augmentation as this appears to cause issues with the code below\n",
    "\n",
    "model = tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Conv2D(6, 3, activation='relu',strides = (1,1), padding = 'same', input_shape=(32, 32, 1)))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(18, 3,strides = (1,1), padding = 'same', activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(32,3,strides = (1,1), padding = 'same',activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2),strides=(2, 2)))\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(tf.keras.layers.Dropout(0.5)) # Drop out significantly reduces overfitting\n",
    "model.add(tf.keras.layers.Dense(32, activation='relu'))\n",
    "model.add(tf.keras.layers.Dropout(0.5))\n",
    "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
    "model.build(input_shape=(None, 32, 32, 1))\n",
    "model.summary()\n",
    "\n",
    "model.compile(tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
    "              metrics=['accuracy','AUC'])\n",
    "\n",
    "\n",
    "history = model.fit(x_train_LN, y_train, epochs=100, \n",
    "                    validation_data=(x_test_LN, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "80aaf6de-f0bc-4e0e-8a5d-2764235bf20f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv_0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAU2UlEQVR4nO3dfYxcZ3XH8e+Z2V2v7V2/xY6zsk3tBAuKKHHQkkYKRWnDS0CIwB8gogrljwhTlUiggqoorSD9L61KKKVSkAMRBvEWCRARSinBgiZpkcGB4DgYQuI4jpON7Th+i+19mzn9Y27Ujbnn2fG8evP8PtJqZ+8z996zd+bMnblnnucxd0dEXv0q/Q5ARHpDyS6SCSW7SCaU7CKZULKLZELJLpKJgXZWNrPrgC8AVeDL7n576v5DtsiHWdrOLkUkYZLTTPuUlbVZq3V2M6sCjwPvAA4CvwRucPffRusss1X+53ZtS/sTkfnt9B2c9BdLk72dt/FXAk+4+z53nwa+DVzfxvZEpIvaSfZ1wDNz/j5YLBORC1A7n9nL3ir80WcCM9sKbAUYZkkbuxORdrRzZj8IbJjz93rguXPv5O7b3H3c3ccHWdTG7kSkHe0k+y+BzWa2ycyGgA8D93YmLBHptJbfxrv7rJndDPwXjdLb3e7+WMciE5GOaqvO7u73Afd1KBYR6SJ9g04kE0p2kUwo2UUyoWQXyYSSXSQTSnaRTCjZRTKhZBfJhJJdJBNKdpFMKNlFMqFkF8mEkl0kE0p2kUwo2UUyoWQXyYSSXSQTSnaRTLQ1LJVkwEonF5l/tWq1fPlA4ilXSZx76vW4LdgXAMGMRz4zG69SqyXiSLRd4HRmF8mEkl0kE0p2kUwo2UUyoWQXyYSSXSQTbZXezGw/cAqoAbPuPt6JoJre/+BQ3OiJUk3LO2zhtbEbcaQEZajKyNJwlfprxsK22tLBeFdnZ8K2mRXDpcuPbIkn99z4vn1h272bfxS2pXzpePks4l+68/pwnYsfPh22Dfx2f9hWfyleL/Xc8ZnpeL0O6kSd/S/d/YUObEdEukhv40Uy0W6yO/BjM3vYzLZ2IiAR6Y5238Zf7e7PmdnFwP1m9jt3f2DuHYoXga0Awyxpc3ci0qq2zuzu/lzx+zDwfeDKkvtsc/dxdx8fJL44IyLd1XKym9lSMxt9+TbwTmBPpwITkc5q5238WuD71ugVNQB8091bq4+0KNk7KSHqkdXYaKJU1kIZzevlva4ArNJij7JEz7HK2jWly6cuLV8OMHFVeZkM4Mxr47LQR9/y87Bt1/HXlC5/6qkN4TqP/Xpj2LZpb3xJqLosjrFaLX/MaledDddZcnhx2Lby+ZVhm01OhW1e63EJtkTLye7u+4DLOxiLiHSRSm8imVCyi2RCyS6SCSW7SCaU7CKZePUOOJksobVW8mql15tV4jhSZbnKUNzbzNbHvdROXH5x6fJ3fObBcJ3/feHSsO3xpy8J27784DVhm82UH2NLPSyL4uPBYLxi7UTc+7FeK4/DF8dl2+ffHffmW3R8ddi2+OSpsM1PxG29ojO7SCaU7CKZULKLZELJLpIJJbtIJhb01fh0h5bEFfdWxpKDjo8nZ4OJDi2rLwrbjl9RfsUd4O3/+FDp8u0P/UUcSOrfGoyvkLsl2kbOv5PS4NL4KrhVEvtKXMSfOVN+pd5eSjx3lsVTQx0aj6/8b3xqRdhmZ+KON70ag05ndpFMKNlFMqFkF8mEkl0kE0p2kUwo2UUysaBLb8lSWKq81up6rZTsEuPMVcfWhm1nXheX1y77u71h2zf/822ly31lXE5KldeYTPzPi+LjaNXybVYSHVrq9Xhf9en4OPrs+T8uPpT6n+Oy3NTr4xLa1PoVYduio8fi/Z05E7d1kM7sIplQsotkQskukgklu0gmlOwimVCyi2Ri3tKbmd0NvBc47O5vLJatAr4DbAT2Ax9y90Rt4cKSnpLp/MtyVo1fMysXrQrbpjbF45ldf8dPwrZ/3/GueH8bJkuXLxmOe1ZNBj3DAHwgMYZeouTlQfmqlihrRePWAXiiPGjBOHONHQbbS5QNk47Ek5MevDaO47IDy+NtHn2xtVjOUzNn9q8C152z7BZgh7tvBnYUf4vIBWzeZC/mWz/3ped6YHtxezvw/s6GJSKd1upn9rXuPgFQ/I6/7iUiF4Suf13WzLYCWwGGWdLt3YlIoNUz+yEzGwMofh+O7uju29x93N3HB4kvbohId7Wa7PcCNxa3bwR+0JlwRKRbmim9fQu4BlhtZgeBzwK3A/eY2U3AAeCD3QyyJYmebcmBKlOCHmw2sjRcZXZdPHDku77432HbF+8/twDy/3w07sH2+nXPly4/OTUcrnO4Fr/mTx9JfPRK9GBrhQ/E5bWB4/FjVhuN46hMBf9bosxXW5p47iT+5ZmRxKCYw/1/Vztvsrv7DUHTtR2ORUS6SN+gE8mEkl0kE0p2kUwo2UUyoWQXycTCHnCyGxIDRFaWLStdXl8ff1vYbo97NN316FvDtvpwXOPZsP5o2HZ2drB0+Yun4xLa1PG4LFedTJSoqoneZsGAkyR6qNls3DZwNm4bOhGX5aaXBwNfTiUe55l4e/VE1XboRNxWOVPeGxHSU+11ks7sIplQsotkQskukgklu0gmlOwimVCyi2RCpbdzVBI92Gy0vO2JT8cDNg4fXxG21Z5fHLa97vJn4vU8fo1+5mj5/qZOJnpdpcZrTM0RlyiVhSU2S8yx5vH2JtfGcVRPn38vxkqipDhwMl6vljiMqdIbZ+PSW6/ozC6SCSW7SCaU7CKZULKLZELJLpKJhX01PpiOaT6VZSPxJhNX43//t5eULh/f+Hi4zs7drw3bUlfcK4mr1vsOxVNKzZwKKgOJC+ckLpAnr7jXU23B8kRHI18SzNUEMJ2YairqdENiaqhU75NEW2043tfgs/F6Ph1Pv9UrOrOLZELJLpIJJbtIJpTsIplQsotkQskukolmpn+6G3gvcNjd31gsuw34KHCkuNut7n5ft4IMJaZ4qixJTFu0akXYdOLyNWHbX7/jwdLlX38gHkvuz970dNg2XYs7cPz+4NqwrX428bBFla0WO6AkS3apbUZTZSXGoPPUuafF01Il6D9jiSrfbFx9ZWZZ/JwbPRB31vHJqXijPdLMIfwqUDbx2OfdfUvx0/tEF5HzMm+yu/sDQDxEqogsCO18Zr/ZzHab2d1mtrJjEYlIV7Sa7HcClwFbgAngc9EdzWyrme0ys10z9P9zi0iuWkp2dz/k7jV3rwN3AVcm7rvN3cfdfXyQ/s9RLZKrlpLdzMbm/PkBYE9nwhGRbmmm9PYt4BpgtZkdBD4LXGNmW2j0l9oPfKx7ISZiG4jDt0R57eymuNfYN+8IP5HwN09+qHT5xZed/3RMAAdeiC911CcTD02qt1koVV5LlNBSq80keqItLq9tJctrCZUz8XrV6dSUUuXLU73XPFXZTJQOF++Lnwe16Zl4oz0yb7K7+w0li7/ShVhEpIv0DTqRTCjZRTKhZBfJhJJdJBNKdpFMLOgBJ235srBtatPqsO3ftv1H2Pb3z7wvbDs2WT5dUz3Ra+ypQxeFbfVE6So5CGSqVDZU3ivLEiU0T8RhiYEekz3ipsrXs1QPu1QFMFHySg0QWQnWm070Xkvta/1P4vX8hbgLidcS3ex6RGd2kUwo2UUyoWQXyYSSXSQTSnaRTCjZRTKxoEtvtU3lc68BHPnE2bDtU09+MGx79tjytmI61+BgXHKZbGXgSGD42bgn3eRYeTcvD0pyME95LTHXmy+NB1gkKOd5Ja6vDZyIB+BMSQ0eObUmaEyU+cZ+Fjcu/fkTYVvt1Kl4o56qpfaGzuwimVCyi2RCyS6SCSW7SCaU7CKZWNBX46dWxqPVDlQnw7Ynn7k4bLOBRK+K4ILqwFB8Obg229oV5tTV4qHjifWs/CGdWhXHmOr4UV+cOB6pzikD5f+ABR1kACzVoSUxztz0ikTnlODxXHd/HMeyB58K22pHF+58KTqzi2RCyS6SCSW7SCaU7CKZULKLZELJLpKJZqZ/2gB8DbiExmhf29z9C2a2CvgOsJHGFFAfcvdj3Qv1j9UXxeWYsdG4U8KxYyPxNqfjUllUlqvX4tfMeqo8lVBZEncyOfmmRF0u6miSGPvNE51dUnFUEp1aasfKy6IDL8XHauB0HMfMaGK6psG49Lb+R+X7G92xN1yndvJk2LaQNXNmnwU+5e5/ClwFfNzM3gDcAuxw983AjuJvEblAzZvs7j7h7r8qbp8C9gLrgOuB7cXdtgPv71KMItIB5/WZ3cw2AlcAO4G17j4BjRcEIP5amoj0XdPJbmYjwHeBT7p70x9qzGyrme0ys10zTLUSo4h0QFPJbmaDNBL9G+7+vWLxITMbK9rHgMNl67r7Nncfd/fxQeLvsotId82b7GZmNOZj3+vud8xpuhe4sbh9I/CDzocnIp3STK+3q4GPAI+a2SPFsluB24F7zOwm4AAQD+zWJQNn4pLL2dl4nDY/m+iJlhirLeqJZonpmFJtqVHJqtXEmHFBWQugNlreu21oRfwRqpYoHXo90SPu0HDYVp0pX68yHa6CJ56Ngy/Fcax+JI5/9ME/lC7veXmtknjO1XszNdS8ye7uDxEPf3htZ8MRkW7RN+hEMqFkF8mEkl0kE0p2kUwo2UUysbAHnFwRhz+26Ey8YjAYIpAeRDHo5VWbTfR6S0zxZMEUSQAzqY5ta+MyWrTF6dND8QYT/3NqaqhKokppQWe5elwRZclE3Lb2Fy/FcTwWDxBZP1s+DZgNxI+L1xMH3xP/dIJVEr0OW9vkedOZXSQTSnaRTCjZRTKhZBfJhJJdJBNKdpFMLOjSW2IMRfb8dHPceEk8iCKJXl4elKg89ZJZTZRxEj3Aqsfjh6Y+mCgbRYMvtvqynqpSLk8cxxPlMa7anei99j9x7c2fOxS31c6/11ir5TWrxr3Xktu8AOjMLpIJJbtIJpTsIplQsotkQskukokFfTV+5UMHwraRA2vCthOvXRK2Hd2SmNJoaXCVtsUhxDxxpb62LHFFeDje4UAwRZWnShepcfIS48yt2Rn3alm+r7wDyuCT8RX3+vETcRze4pVuKz+fWaoXD4nx4oLtzb/N/tOZXSQTSnaRTCjZRTKhZBfJhJJdJBNKdpFMzFt6M7MNwNeAS4A6sM3dv2BmtwEfBY4Ud73V3e/rVqBlai8cDduqiTLORfuWhm0r98QluyNvWVa6/PjrE6WrRakOF3HT0JG4/DMzErcNT5S/flcTE+guezru0DLyxLF4xWefD5vqZydLl9da7SySGqgtUQ5rSWp7vRowrguaqbPPAp9y91+Z2SjwsJndX7R93t3/tXvhiUinNDPX2wQwUdw+ZWZ7gXXdDkxEOuu83v+Y2UbgCmBnsehmM9ttZneb2cpOBycindN0spvZCPBd4JPufhK4E7gM2ELjzP+5YL2tZrbLzHbNkPjgKCJd1VSym9kgjUT/hrt/D8DdD7l7zd3rwF3AlWXruvs2dx939/FB4nnFRaS75k12MzPgK8Bed79jzvKxOXf7ALCn8+GJSKc0czX+auAjwKNm9kix7FbgBjPbQqOAtB/4WBfiS0uUcXw2MT7aqXgqIXv8dNh2yeHlpctX714drlMfiF9Pq2dnwrbKmXiAOjsVT23lM8E2p+KPUD4dx+H1FktNrZTYWi1rJdaLxoVLTcfUahypMeiS++uRZq7GPwSURdrTmrqItEffoBPJhJJdJBNKdpFMKNlFMqFkF8nEgh5w0mcS8yel1mtxf/UzQclrIp6aqJIqCyX21eIYlrFWB2x8lep157ULobOczuwimVCyi2RCyS6SCSW7SCaU7CKZULKLZGJBl956LipfeccLZSIdpzO7SCaU7CKZULKLZELJLpIJJbtIJpTsIplQsotkQskukgklu0gmlOwimVCyi2RCyS6SiWbmehs2s1+Y2W/M7DEz+6di+Sozu9/M/lD81pTNIhewZs7sU8BfufvlNKZnvs7MrgJuAXa4+2ZgR/G3iFyg5k12b3h5JsTB4seB64HtxfLtwPu7EaCIdEaz87NXixlcDwP3u/tOYK27TwAUvy/uWpQi0ramkt3da+6+BVgPXGlmb2x2B2a21cx2mdmuGeJpg0Wku87rary7Hwd+BlwHHDKzMYDi9+FgnW3uPu7u44Msai9aEWlZM1fj15jZiuL2YuDtwO+Ae4Ebi7vdCPygSzGKSAc0MwbdGLDdzKo0XhzucfcfmtnPgXvM7CbgAPDBLsYpIm2aN9ndfTdwRcnyo8C13QhKRDpP36ATyYSSXSQTSnaRTCjZRTKhZBfJhHk0pVE3dmZ2BHi6+HM18ELPdh5THK+kOF5pocXxJ+6+pqyhp8n+ih2b7XL38b7sXHEojgzj0Nt4kUwo2UUy0c9k39bHfc+lOF5JcbzSqyaOvn1mF5He0tt4kUz0JdnN7Doz+72ZPWFmfRu7zsz2m9mjZvaIme3q4X7vNrPDZrZnzrKeD+AZxHGbmT1bHJNHzOw9PYhjg5n91Mz2FoOafqJY3tNjkoijp8eka4O8untPf4Aq8CRwKTAE/AZ4Q6/jKGLZD6zuw37fBrwZ2DNn2b8AtxS3bwH+uU9x3AZ8usfHYwx4c3F7FHgceEOvj0kijp4eE8CAkeL2ILATuKrd49GPM/uVwBPuvs/dp4Fv0xi8Mhvu/gDw4jmLez6AZxBHz7n7hLv/qrh9CtgLrKPHxyQRR095Q8cHee1Hsq8Dnpnz90H6cEALDvzYzB42s619iuFlF9IAnjeb2e7ibX5P5wMws400xk/o66Cm58QBPT4m3RjktR/JbiXL+lUSuNrd3wy8G/i4mb2tT3FcSO4ELqMxR8AE8Lle7djMRoDvAp9095O92m8TcfT8mHgbg7xG+pHsB4ENc/5eDzzXhzhw9+eK34eB79P4iNEvTQ3g2W3ufqh4otWBu+jRMTGzQRoJ9g13/16xuOfHpCyOfh2TYt/HOc9BXiP9SPZfApvNbJOZDQEfpjF4ZU+Z2VIzG335NvBOYE96ra66IAbwfPnJVPgAPTgmZmbAV4C97n7HnKaeHpMojl4fk64N8tqrK4znXG18D40rnU8C/9CnGC6lUQn4DfBYL+MAvkXj7eAMjXc6NwEX0ZhG6w/F71V9iuPrwKPA7uLJNdaDON5K46PcbuCR4uc9vT4miTh6ekyANwG/Lva3B/hMsbyt46Fv0IlkQt+gE8mEkl0kE0p2kUwo2UUyoWQXyYSSXSQTSnaRTCjZRTLxf+9O7hFl+TrBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 40ms/step\n",
      "(1, 32, 32, 6)\n",
      "1/1 [==============================] - 0s 38ms/step\n",
      "(1, 16, 16, 6)\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "(1, 16, 16, 18)\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "(1, 8, 8, 18)\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "(1, 8, 8, 32)\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "(1, 4, 4, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_conv_layer(layer_name,image):\n",
    "  \n",
    "  layer_output=model.get_layer(layer_name).output\n",
    "  intermediate_model=tf.keras.models.Model(inputs=model.input,outputs=layer_output)\n",
    "  intermediate_model.build(input_shape=(None, 32, 32, 1))\n",
    "\n",
    "  intermediate_prediction=intermediate_model.predict(tf.reshape(image, (1,32,32,1)))\n",
    "  \n",
    "  row_size=3\n",
    "  col_size=2\n",
    "  \n",
    "  img_index=0\n",
    "\n",
    "  print(np.shape(intermediate_prediction))\n",
    "  return intermediate_prediction\n",
    "\n",
    "\n",
    "zircon_to_visualise = x_train[1]\n",
    "model.layers[0]._name='conv_0'\n",
    "print(model.layers[0].name)\n",
    "plt.imshow(zircon_to_visualise)\n",
    "plt.show()\n",
    "int_1 = visualize_conv_layer(model.layers[0].name, zircon_to_visualise)\n",
    "int_2 = visualize_conv_layer(model.layers[1].name, zircon_to_visualise)\n",
    "int_3 = visualize_conv_layer(model.layers[2].name, zircon_to_visualise)\n",
    "int_4 = visualize_conv_layer(model.layers[3].name, zircon_to_visualise)\n",
    "int_5 = visualize_conv_layer(model.layers[4].name, zircon_to_visualise)\n",
    "int_6 = visualize_conv_layer(model.layers[5].name, zircon_to_visualise)\n",
    "\n",
    "fig,axs=plt.subplots(5,6,figsize=(10,8))\n",
    "\n",
    "axs[0,0].imshow(int_1[0, :, :, 0], cmap='viridis')\n",
    "axs[1,0].imshow(int_1[0, :, :, 1], cmap='viridis')\n",
    "axs[2,0].imshow(int_1[0, :, :, 2], cmap='viridis')\n",
    "axs[3,0].imshow(int_1[0, :, :, 3], cmap='viridis')\n",
    "axs[4,0].imshow(int_1[0, :, :, 5], cmap='viridis')\n",
    "\n",
    "axs[0,1].imshow(int_2[0, :, :, 0], cmap='viridis')\n",
    "axs[1,1].imshow(int_2[0, :, :, 1], cmap='viridis')\n",
    "axs[2,1].imshow(int_2[0, :, :, 2], cmap='viridis')\n",
    "axs[3,1].imshow(int_2[0, :, :, 3], cmap='viridis')\n",
    "axs[4,1].imshow(int_2[0, :, :, 5], cmap='viridis')\n",
    "\n",
    "axs[0,2].imshow(int_3[0, :, :, 0], cmap='viridis')\n",
    "axs[1,2].imshow(int_3[0, :, :, 1], cmap='viridis')\n",
    "axs[2,2].imshow(int_3[0, :, :, 2], cmap='viridis')\n",
    "axs[3,2].imshow(int_3[0, :, :, 3], cmap='viridis')\n",
    "axs[4,2].imshow(int_3[0, :, :, 5], cmap='viridis')\n",
    "\n",
    "axs[0,3].imshow(int_4[0, :, :, 0], cmap='viridis')\n",
    "axs[1,3].imshow(int_4[0, :, :, 1], cmap='viridis')\n",
    "axs[2,3].imshow(int_4[0, :, :, 2], cmap='viridis')\n",
    "axs[3,3].imshow(int_4[0, :, :, 3], cmap='viridis')\n",
    "axs[4,3].imshow(int_4[0, :, :, 5], cmap='viridis')\n",
    "\n",
    "axs[0,4].imshow(int_5[0, :, :, 0], cmap='viridis')\n",
    "axs[1,4].imshow(int_5[0, :, :, 1], cmap='viridis')\n",
    "axs[2,4].imshow(int_5[0, :, :, 2], cmap='viridis')\n",
    "axs[3,4].imshow(int_5[0, :, :, 3], cmap='viridis')\n",
    "axs[4,4].imshow(int_5[0, :, :, 5], cmap='viridis')\n",
    "\n",
    "axs[0,5].imshow(int_6[0, :, :, 0], cmap='viridis')\n",
    "axs[1,5].imshow(int_6[0, :, :, 1], cmap='viridis')\n",
    "axs[2,5].imshow(int_6[0, :, :, 2], cmap='viridis')\n",
    "axs[3,5].imshow(int_6[0, :, :, 3], cmap='viridis')\n",
    "axs[4,5].imshow(int_6[0, :, :, 5], cmap='viridis')\n",
    "\n",
    "axr = axs.flatten()\n",
    "for ax in axr:\n",
    "    ax.set_axis_off()\n",
    "\n",
    "axs[0,0].set_title('C1')\n",
    "axs[0,1].set_title('S2')\n",
    "axs[0,2].set_title('C3')\n",
    "axs[0,3].set_title('S4')\n",
    "axs[0,4].set_title('C5')\n",
    "axs[0,5].set_title('S6')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1aa6dc99-52f4-4478-a400-7b69c2237eb4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}