Ensembling RGB + XYZ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Jun  6 09:45:52 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 460.106.00   Driver Version: 460.106.00   CUDA Version: 11.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  A30                 On   | 00000000:03:00.0 Off |                    0 |\n",
      "| N/A   43C    P0    53W / 165W |      0MiB / 24258MiB |     62%      Default |\n",
      "|                               |                      |             Disabled |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "gpu_info = !nvidia-smi\n",
    "gpu_info = '\\n'.join(gpu_info)\n",
    "if gpu_info.find('failed') >= 0:\n",
    "  print('Not connected to a GPU')\n",
    "else:\n",
    "  print(gpu_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import tensorflow_io as tfio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import PIL\n",
    "import pathlib\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model():\n",
    "  data_dir = pathlib.Path('content/img_RGB')\n",
    "\n",
    "  batch_size = 64\n",
    "  img_height = 128\n",
    "  img_width = 128\n",
    "\n",
    "  IMG_SHAPE = (img_height, img_width) + (3,)\n",
    "\n",
    "  train_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "    pathlib.Path('content/img_RGB'),\n",
    "    validation_split=0.2,\n",
    "    subset=\"training\",\n",
    "    seed=123,\n",
    "    image_size=(img_height, img_width),\n",
    "    batch_size=batch_size)\n",
    "  \n",
    "  val_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "    pathlib.Path('content/img_RGB'),\n",
    "    validation_split=0.2,\n",
    "    subset=\"validation\",\n",
    "    seed=123,\n",
    "    image_size=(img_height, img_width),\n",
    "    batch_size=batch_size)\n",
    "\n",
    "  class_names = train_ds.class_names\n",
    "\n",
    "  AUTOTUNE = tf.data.AUTOTUNE\n",
    "  num_classes = len(class_names)\n",
    "\n",
    "  train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n",
    "  val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
    "\n",
    "  data_augmentation = Sequential([\n",
    "    tf.keras.layers.RandomFlip('horizontal'),\n",
    "    tf.keras.layers.RandomRotation(0.2),\n",
    "  ])\n",
    "\n",
    "  # Create the base model from the pre-trained model\n",
    "  base_model_1 = tf.keras.applications.DenseNet201(input_shape=IMG_SHAPE,\n",
    "                                              include_top=False,\n",
    "                                              weights='imagenet')\n",
    "  base_model_1.trainable = False\n",
    "    \n",
    "    # Create the base model from the pre-trained model\n",
    "  base_model_xception = tf.keras.applications.xception.Xception(input_shape=IMG_SHAPE,\n",
    "                                              include_top=False,\n",
    "                                              weights='imagenet')\n",
    "  base_model_xception.trainable = False  \n",
    "\n",
    "  inputs = tf.keras.Input(shape=(128, 128, 3))\n",
    "  x = data_augmentation(inputs)\n",
    "\n",
    "  x1 = tf.keras.layers.Rescaling(scale=1./255)(x)\n",
    "  x1 = tfio.experimental.color.rgb_to_xyz(x1)\n",
    "  x1 = base_model_1(x1)\n",
    "  x1 = tf.keras.layers.MaxPooling2D()(x1)\n",
    "  x1 = tf.keras.layers.Dropout(0.2)(x1)\n",
    "  x1 = tf.keras.layers.Flatten()(x1)\n",
    "    \n",
    "  x2 = tf.keras.applications.densenet.preprocess_input(x)  \n",
    "  x2 = base_model_1(x2)  \n",
    "  x2 = tf.keras.layers.MaxPooling2D()(x2)\n",
    "  x2 = tf.keras.layers.Dropout(0.2)(x2)\n",
    "  x2 = tf.keras.layers.Flatten()(x2)\n",
    "\n",
    "  x3 = tf.keras.layers.Rescaling(scale=1./255)(x)\n",
    "  x3 = tfio.experimental.color.rgb_to_xyz(x3)\n",
    "  x3 = base_model_xception(x3)\n",
    "  x3 = tf.keras.layers.MaxPooling2D()(x3)\n",
    "  x3 = tf.keras.layers.Dropout(0.2)(x3)\n",
    "  x3 = tf.keras.layers.Flatten()(x3)\n",
    "    \n",
    "  x4 = tf.keras.applications.xception.preprocess_input(x)  \n",
    "  x4 = base_model_xception(x4)  \n",
    "  x4 = tf.keras.layers.MaxPooling2D()(x4)\n",
    "  x4 = tf.keras.layers.Dropout(0.2)(x4)\n",
    "  x4 = tf.keras.layers.Flatten()(x4)\n",
    "    \n",
    "  concatted = tf.keras.layers.Concatenate()([x1, x2, x3, x4])\n",
    "\n",
    "  concatted = tf.keras.layers.Dense(512, activation='relu')(concatted)\n",
    "  concatted = tf.keras.layers.Dropout(0.2)(concatted)\n",
    "  concatted = tf.keras.layers.Dense(128, activation='relu')(concatted)\n",
    "  outputs = tf.keras.layers.Dense(5)(concatted)\n",
    "  model = tf.keras.Model(inputs, outputs)\n",
    "\n",
    "  base_learning_rate = 0.0005\n",
    "\n",
    "  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "  epochs=100\n",
    "  history = model.fit(\n",
    "    train_ds,\n",
    "    validation_data=val_ds,\n",
    "    epochs=epochs\n",
    "  )\n",
    "\n",
    "  acc = history.history['accuracy']\n",
    "  val_acc = history.history['val_accuracy']\n",
    "\n",
    "  loss = history.history['loss']\n",
    "  val_loss = history.history['val_loss']\n",
    "\n",
    "  epochs_range = range(epochs)\n",
    "\n",
    "  plt.figure(figsize=(16, 6))\n",
    "  plt.subplot(1, 2, 1)\n",
    "  plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "  plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "  plt.legend(loc='lower right')\n",
    "  plt.title('Training and Validation Accuracy')\n",
    "\n",
    "  plt.subplot(1, 2, 2)\n",
    "  plt.plot(epochs_range, loss, label='Training Loss')\n",
    "  plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "  plt.legend(loc='upper right')\n",
    "  plt.title('Training and Validation Loss')\n",
    "  plt.show()\n",
    "    \n",
    "  return {'acc': acc, 'val_acc': val_acc, 'loss': loss, 'val_loss': val_loss}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 28793 files belonging to 5 classes.\n",
      "Using 23035 files for training.\n",
      "Found 28793 files belonging to 5 classes.\n",
      "Using 5758 files for validation.\n",
      "Epoch 1/100\n",
      "360/360 [==============================] - 72s 138ms/step - loss: 0.5438 - accuracy: 0.7804 - val_loss: 0.3852 - val_accuracy: 0.8517\n",
      "Epoch 2/100\n",
      "360/360 [==============================] - 39s 108ms/step - loss: 0.4074 - accuracy: 0.8312 - val_loss: 0.3683 - val_accuracy: 0.8491\n",
      "Epoch 3/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.3756 - accuracy: 0.8424 - val_loss: 0.3398 - val_accuracy: 0.8560\n",
      "Epoch 4/100\n",
      "360/360 [==============================] - 39s 107ms/step - loss: 0.3572 - accuracy: 0.8535 - val_loss: 0.2970 - val_accuracy: 0.8854\n",
      "Epoch 5/100\n",
      "360/360 [==============================] - 39s 108ms/step - loss: 0.3434 - accuracy: 0.8580 - val_loss: 0.3293 - val_accuracy: 0.8750\n",
      "Epoch 6/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.3362 - accuracy: 0.8646 - val_loss: 0.3168 - val_accuracy: 0.8744\n",
      "Epoch 7/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.3197 - accuracy: 0.8721 - val_loss: 0.2736 - val_accuracy: 0.8944\n",
      "Epoch 8/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.3072 - accuracy: 0.8755 - val_loss: 0.2780 - val_accuracy: 0.8951\n",
      "Epoch 9/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.3086 - accuracy: 0.8780 - val_loss: 0.2745 - val_accuracy: 0.8922\n",
      "Epoch 10/100\n",
      "360/360 [==============================] - 37s 104ms/step - loss: 0.2902 - accuracy: 0.8840 - val_loss: 0.2683 - val_accuracy: 0.8986\n",
      "Epoch 11/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2925 - accuracy: 0.8843 - val_loss: 0.2611 - val_accuracy: 0.8967\n",
      "Epoch 12/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2844 - accuracy: 0.8842 - val_loss: 0.2774 - val_accuracy: 0.8944\n",
      "Epoch 13/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2803 - accuracy: 0.8891 - val_loss: 0.2523 - val_accuracy: 0.9014\n",
      "Epoch 14/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2758 - accuracy: 0.8898 - val_loss: 0.2580 - val_accuracy: 0.8984\n",
      "Epoch 15/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2784 - accuracy: 0.8891 - val_loss: 0.2517 - val_accuracy: 0.9059\n",
      "Epoch 16/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.2731 - accuracy: 0.8927 - val_loss: 0.2634 - val_accuracy: 0.8972\n",
      "Epoch 17/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2740 - accuracy: 0.8895 - val_loss: 0.2509 - val_accuracy: 0.9081\n",
      "Epoch 18/100\n",
      "360/360 [==============================] - 39s 107ms/step - loss: 0.2680 - accuracy: 0.8931 - val_loss: 0.2661 - val_accuracy: 0.8923\n",
      "Epoch 19/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2677 - accuracy: 0.8948 - val_loss: 0.2699 - val_accuracy: 0.9010\n",
      "Epoch 20/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.2660 - accuracy: 0.8948 - val_loss: 0.2516 - val_accuracy: 0.9055\n",
      "Epoch 21/100\n",
      "360/360 [==============================] - 38s 104ms/step - loss: 0.2553 - accuracy: 0.8985 - val_loss: 0.2609 - val_accuracy: 0.8954\n",
      "Epoch 22/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.2565 - accuracy: 0.8978 - val_loss: 0.2495 - val_accuracy: 0.9000\n",
      "Epoch 23/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2509 - accuracy: 0.9000 - val_loss: 0.2480 - val_accuracy: 0.9036\n",
      "Epoch 24/100\n",
      "360/360 [==============================] - 39s 107ms/step - loss: 0.2543 - accuracy: 0.8991 - val_loss: 0.2500 - val_accuracy: 0.8994\n",
      "Epoch 25/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2546 - accuracy: 0.9002 - val_loss: 0.2631 - val_accuracy: 0.8939\n",
      "Epoch 26/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2476 - accuracy: 0.9015 - val_loss: 0.2412 - val_accuracy: 0.9067\n",
      "Epoch 27/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2515 - accuracy: 0.9005 - val_loss: 0.2606 - val_accuracy: 0.8965\n",
      "Epoch 28/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2460 - accuracy: 0.9028 - val_loss: 0.2651 - val_accuracy: 0.8984\n",
      "Epoch 29/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2433 - accuracy: 0.9048 - val_loss: 0.2467 - val_accuracy: 0.9015\n",
      "Epoch 30/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2463 - accuracy: 0.9027 - val_loss: 0.2675 - val_accuracy: 0.8982\n",
      "Epoch 31/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2393 - accuracy: 0.9066 - val_loss: 0.2412 - val_accuracy: 0.9064\n",
      "Epoch 32/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2394 - accuracy: 0.9032 - val_loss: 0.2733 - val_accuracy: 0.8923\n",
      "Epoch 33/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2385 - accuracy: 0.9070 - val_loss: 0.2592 - val_accuracy: 0.8972\n",
      "Epoch 34/100\n",
      "360/360 [==============================] - 37s 104ms/step - loss: 0.2424 - accuracy: 0.9049 - val_loss: 0.2327 - val_accuracy: 0.9093\n",
      "Epoch 35/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2327 - accuracy: 0.9086 - val_loss: 0.2376 - val_accuracy: 0.9118\n",
      "Epoch 36/100\n",
      "360/360 [==============================] - 37s 104ms/step - loss: 0.2335 - accuracy: 0.9074 - val_loss: 0.2406 - val_accuracy: 0.9071\n",
      "Epoch 37/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2337 - accuracy: 0.9074 - val_loss: 0.2492 - val_accuracy: 0.9010\n",
      "Epoch 38/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2304 - accuracy: 0.9104 - val_loss: 0.2449 - val_accuracy: 0.9074\n",
      "Epoch 39/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2337 - accuracy: 0.9074 - val_loss: 0.2331 - val_accuracy: 0.9114\n",
      "Epoch 40/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2315 - accuracy: 0.9094 - val_loss: 0.2494 - val_accuracy: 0.9048\n",
      "Epoch 41/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2266 - accuracy: 0.9097 - val_loss: 0.2435 - val_accuracy: 0.9057\n",
      "Epoch 42/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2280 - accuracy: 0.9120 - val_loss: 0.2237 - val_accuracy: 0.9149\n",
      "Epoch 43/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2245 - accuracy: 0.9122 - val_loss: 0.2461 - val_accuracy: 0.9069\n",
      "Epoch 44/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2292 - accuracy: 0.9114 - val_loss: 0.2498 - val_accuracy: 0.9048\n",
      "Epoch 45/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2200 - accuracy: 0.9144 - val_loss: 0.2386 - val_accuracy: 0.9080\n",
      "Epoch 46/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2262 - accuracy: 0.9092 - val_loss: 0.2355 - val_accuracy: 0.9074\n",
      "Epoch 47/100\n",
      "360/360 [==============================] - 37s 104ms/step - loss: 0.2194 - accuracy: 0.9135 - val_loss: 0.2229 - val_accuracy: 0.9147\n",
      "Epoch 48/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2197 - accuracy: 0.9118 - val_loss: 0.2313 - val_accuracy: 0.9133\n",
      "Epoch 49/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2222 - accuracy: 0.9141 - val_loss: 0.2403 - val_accuracy: 0.9074\n",
      "Epoch 50/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2224 - accuracy: 0.9140 - val_loss: 0.2303 - val_accuracy: 0.9106\n",
      "Epoch 51/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.2220 - accuracy: 0.9120 - val_loss: 0.2265 - val_accuracy: 0.9139\n",
      "Epoch 52/100\n",
      "360/360 [==============================] - 37s 104ms/step - loss: 0.2173 - accuracy: 0.9130 - val_loss: 0.2268 - val_accuracy: 0.9133\n",
      "Epoch 53/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2157 - accuracy: 0.9154 - val_loss: 0.2220 - val_accuracy: 0.9166\n",
      "Epoch 54/100\n",
      "360/360 [==============================] - 37s 104ms/step - loss: 0.2174 - accuracy: 0.9131 - val_loss: 0.2293 - val_accuracy: 0.9121\n",
      "Epoch 55/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2141 - accuracy: 0.9148 - val_loss: 0.2413 - val_accuracy: 0.9067\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 56/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2164 - accuracy: 0.9132 - val_loss: 0.2450 - val_accuracy: 0.9064\n",
      "Epoch 57/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2122 - accuracy: 0.9165 - val_loss: 0.2468 - val_accuracy: 0.9024\n",
      "Epoch 58/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2124 - accuracy: 0.9166 - val_loss: 0.2269 - val_accuracy: 0.9149\n",
      "Epoch 59/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2162 - accuracy: 0.9153 - val_loss: 0.2211 - val_accuracy: 0.9139\n",
      "Epoch 60/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2088 - accuracy: 0.9189 - val_loss: 0.2380 - val_accuracy: 0.9090\n",
      "Epoch 61/100\n",
      "360/360 [==============================] - 38s 104ms/step - loss: 0.2134 - accuracy: 0.9148 - val_loss: 0.2250 - val_accuracy: 0.9146\n",
      "Epoch 62/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.2060 - accuracy: 0.9187 - val_loss: 0.2366 - val_accuracy: 0.9086\n",
      "Epoch 63/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2073 - accuracy: 0.9182 - val_loss: 0.2486 - val_accuracy: 0.9050\n",
      "Epoch 64/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2096 - accuracy: 0.9182 - val_loss: 0.2374 - val_accuracy: 0.9111\n",
      "Epoch 65/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2057 - accuracy: 0.9206 - val_loss: 0.2320 - val_accuracy: 0.9109\n",
      "Epoch 66/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2105 - accuracy: 0.9193 - val_loss: 0.2392 - val_accuracy: 0.9090\n",
      "Epoch 67/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2061 - accuracy: 0.9212 - val_loss: 0.2260 - val_accuracy: 0.9163\n",
      "Epoch 68/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2038 - accuracy: 0.9178 - val_loss: 0.2514 - val_accuracy: 0.9066\n",
      "Epoch 69/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.2066 - accuracy: 0.9199 - val_loss: 0.2474 - val_accuracy: 0.9081\n",
      "Epoch 70/100\n",
      "360/360 [==============================] - 38s 104ms/step - loss: 0.2110 - accuracy: 0.9146 - val_loss: 0.2239 - val_accuracy: 0.9139\n",
      "Epoch 71/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.2026 - accuracy: 0.9199 - val_loss: 0.2387 - val_accuracy: 0.9111\n",
      "Epoch 72/100\n",
      "360/360 [==============================] - 37s 104ms/step - loss: 0.2012 - accuracy: 0.9192 - val_loss: 0.2353 - val_accuracy: 0.9118\n",
      "Epoch 73/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2024 - accuracy: 0.9199 - val_loss: 0.2277 - val_accuracy: 0.9125\n",
      "Epoch 74/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2055 - accuracy: 0.9216 - val_loss: 0.2420 - val_accuracy: 0.9074\n",
      "Epoch 75/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.2031 - accuracy: 0.9184 - val_loss: 0.2308 - val_accuracy: 0.9113\n",
      "Epoch 76/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.2011 - accuracy: 0.9219 - val_loss: 0.2271 - val_accuracy: 0.9132\n",
      "Epoch 77/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2000 - accuracy: 0.9207 - val_loss: 0.2368 - val_accuracy: 0.9097\n",
      "Epoch 78/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1967 - accuracy: 0.9209 - val_loss: 0.2301 - val_accuracy: 0.9123\n",
      "Epoch 79/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1997 - accuracy: 0.9209 - val_loss: 0.2324 - val_accuracy: 0.9135\n",
      "Epoch 80/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.2036 - accuracy: 0.9191 - val_loss: 0.2427 - val_accuracy: 0.9114\n",
      "Epoch 81/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.1991 - accuracy: 0.9238 - val_loss: 0.2292 - val_accuracy: 0.9151\n",
      "Epoch 82/100\n",
      "360/360 [==============================] - 38s 107ms/step - loss: 0.1887 - accuracy: 0.9266 - val_loss: 0.2267 - val_accuracy: 0.9147\n",
      "Epoch 83/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.1995 - accuracy: 0.9236 - val_loss: 0.2314 - val_accuracy: 0.9121\n",
      "Epoch 84/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1913 - accuracy: 0.9235 - val_loss: 0.2294 - val_accuracy: 0.9144\n",
      "Epoch 85/100\n",
      "360/360 [==============================] - 37s 104ms/step - loss: 0.1966 - accuracy: 0.9224 - val_loss: 0.2287 - val_accuracy: 0.9113\n",
      "Epoch 86/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1945 - accuracy: 0.9232 - val_loss: 0.2270 - val_accuracy: 0.9144\n",
      "Epoch 87/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.1913 - accuracy: 0.9240 - val_loss: 0.2446 - val_accuracy: 0.9064\n",
      "Epoch 88/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1908 - accuracy: 0.9232 - val_loss: 0.2393 - val_accuracy: 0.9067\n",
      "Epoch 89/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1985 - accuracy: 0.9229 - val_loss: 0.2289 - val_accuracy: 0.9168\n",
      "Epoch 90/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1901 - accuracy: 0.9236 - val_loss: 0.2488 - val_accuracy: 0.9076\n",
      "Epoch 91/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1949 - accuracy: 0.9241 - val_loss: 0.2252 - val_accuracy: 0.9151\n",
      "Epoch 92/100\n",
      "360/360 [==============================] - 38s 104ms/step - loss: 0.1941 - accuracy: 0.9239 - val_loss: 0.2252 - val_accuracy: 0.9121\n",
      "Epoch 93/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1926 - accuracy: 0.9226 - val_loss: 0.2256 - val_accuracy: 0.9172\n",
      "Epoch 94/100\n",
      "360/360 [==============================] - 37s 104ms/step - loss: 0.1931 - accuracy: 0.9235 - val_loss: 0.2574 - val_accuracy: 0.9050\n",
      "Epoch 95/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1881 - accuracy: 0.9246 - val_loss: 0.2403 - val_accuracy: 0.9059\n",
      "Epoch 96/100\n",
      "360/360 [==============================] - 38s 104ms/step - loss: 0.1882 - accuracy: 0.9271 - val_loss: 0.2435 - val_accuracy: 0.9055\n",
      "Epoch 97/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1920 - accuracy: 0.9237 - val_loss: 0.2340 - val_accuracy: 0.9128\n",
      "Epoch 98/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.1861 - accuracy: 0.9262 - val_loss: 0.2443 - val_accuracy: 0.9092\n",
      "Epoch 99/100\n",
      "360/360 [==============================] - 38s 105ms/step - loss: 0.1882 - accuracy: 0.9255 - val_loss: 0.2338 - val_accuracy: 0.9111\n",
      "Epoch 100/100\n",
      "360/360 [==============================] - 38s 106ms/step - loss: 0.1851 - accuracy: 0.9248 - val_loss: 0.2426 - val_accuracy: 0.9102\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DenseNet201 XYZ* + DenseNet201 RGB\n",
    "conf1_mix = train_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9171587228775024"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(conf1_mix['val_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 28793 files belonging to 5 classes.\n",
      "Using 23035 files for training.\n",
      "Found 28793 files belonging to 5 classes.\n",
      "Using 5758 files for validation.\n",
      "Epoch 1/100\n",
      "360/360 [==============================] - 87s 180ms/step - loss: 0.5204 - accuracy: 0.8044 - val_loss: 0.3375 - val_accuracy: 0.8753\n",
      "Epoch 2/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.3740 - accuracy: 0.8498 - val_loss: 0.3515 - val_accuracy: 0.8515\n",
      "Epoch 3/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.3458 - accuracy: 0.8615 - val_loss: 0.3143 - val_accuracy: 0.8718\n",
      "Epoch 4/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.3272 - accuracy: 0.8669 - val_loss: 0.2887 - val_accuracy: 0.8843\n",
      "Epoch 5/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.3107 - accuracy: 0.8751 - val_loss: 0.2712 - val_accuracy: 0.8923\n",
      "Epoch 6/100\n",
      "360/360 [==============================] - 59s 163ms/step - loss: 0.2995 - accuracy: 0.8810 - val_loss: 0.2747 - val_accuracy: 0.8925\n",
      "Epoch 7/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2862 - accuracy: 0.8872 - val_loss: 0.2625 - val_accuracy: 0.9041\n",
      "Epoch 8/100\n",
      "360/360 [==============================] - 59s 163ms/step - loss: 0.2717 - accuracy: 0.8924 - val_loss: 0.2576 - val_accuracy: 0.9019\n",
      "Epoch 9/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2764 - accuracy: 0.8909 - val_loss: 0.2517 - val_accuracy: 0.9043\n",
      "Epoch 10/100\n",
      "360/360 [==============================] - 59s 163ms/step - loss: 0.2566 - accuracy: 0.8998 - val_loss: 0.2394 - val_accuracy: 0.9100\n",
      "Epoch 11/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2575 - accuracy: 0.8979 - val_loss: 0.2456 - val_accuracy: 0.9066\n",
      "Epoch 12/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2601 - accuracy: 0.8979 - val_loss: 0.2363 - val_accuracy: 0.9126\n",
      "Epoch 13/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.2528 - accuracy: 0.9004 - val_loss: 0.2641 - val_accuracy: 0.8970\n",
      "Epoch 14/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2506 - accuracy: 0.9022 - val_loss: 0.2408 - val_accuracy: 0.9118\n",
      "Epoch 15/100\n",
      "360/360 [==============================] - 59s 163ms/step - loss: 0.2446 - accuracy: 0.9040 - val_loss: 0.2467 - val_accuracy: 0.9069\n",
      "Epoch 16/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2355 - accuracy: 0.9081 - val_loss: 0.2475 - val_accuracy: 0.9095\n",
      "Epoch 17/100\n",
      "360/360 [==============================] - 59s 163ms/step - loss: 0.2363 - accuracy: 0.9090 - val_loss: 0.2587 - val_accuracy: 0.9066\n",
      "Epoch 18/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2312 - accuracy: 0.9083 - val_loss: 0.2363 - val_accuracy: 0.9144\n",
      "Epoch 19/100\n",
      "360/360 [==============================] - 59s 163ms/step - loss: 0.2292 - accuracy: 0.9098 - val_loss: 0.2482 - val_accuracy: 0.9066\n",
      "Epoch 20/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2231 - accuracy: 0.9123 - val_loss: 0.2536 - val_accuracy: 0.9086\n",
      "Epoch 21/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2228 - accuracy: 0.9123 - val_loss: 0.2491 - val_accuracy: 0.9050\n",
      "Epoch 22/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.2254 - accuracy: 0.9114 - val_loss: 0.2285 - val_accuracy: 0.9158\n",
      "Epoch 23/100\n",
      "360/360 [==============================] - 59s 163ms/step - loss: 0.2228 - accuracy: 0.9117 - val_loss: 0.2395 - val_accuracy: 0.9121\n",
      "Epoch 24/100\n",
      "360/360 [==============================] - 58s 163ms/step - loss: 0.2217 - accuracy: 0.9151 - val_loss: 0.2227 - val_accuracy: 0.9151\n",
      "Epoch 25/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2150 - accuracy: 0.9153 - val_loss: 0.2251 - val_accuracy: 0.9149\n",
      "Epoch 26/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2156 - accuracy: 0.9154 - val_loss: 0.2506 - val_accuracy: 0.9055\n",
      "Epoch 27/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2115 - accuracy: 0.9170 - val_loss: 0.2376 - val_accuracy: 0.9067\n",
      "Epoch 28/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2110 - accuracy: 0.9175 - val_loss: 0.2235 - val_accuracy: 0.9156\n",
      "Epoch 29/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2080 - accuracy: 0.9172 - val_loss: 0.2277 - val_accuracy: 0.9166\n",
      "Epoch 30/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2032 - accuracy: 0.9197 - val_loss: 0.2296 - val_accuracy: 0.9161\n",
      "Epoch 31/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2041 - accuracy: 0.9199 - val_loss: 0.2426 - val_accuracy: 0.9088\n",
      "Epoch 32/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2073 - accuracy: 0.9204 - val_loss: 0.2283 - val_accuracy: 0.9146\n",
      "Epoch 33/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.2011 - accuracy: 0.9203 - val_loss: 0.2262 - val_accuracy: 0.9173\n",
      "Epoch 34/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2019 - accuracy: 0.9210 - val_loss: 0.2267 - val_accuracy: 0.9175\n",
      "Epoch 35/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.2006 - accuracy: 0.9215 - val_loss: 0.2171 - val_accuracy: 0.9156\n",
      "Epoch 36/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1975 - accuracy: 0.9228 - val_loss: 0.2284 - val_accuracy: 0.9154\n",
      "Epoch 37/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1971 - accuracy: 0.9219 - val_loss: 0.2213 - val_accuracy: 0.9123\n",
      "Epoch 38/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1944 - accuracy: 0.9233 - val_loss: 0.2273 - val_accuracy: 0.9135\n",
      "Epoch 39/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1936 - accuracy: 0.9253 - val_loss: 0.2242 - val_accuracy: 0.9139\n",
      "Epoch 40/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1895 - accuracy: 0.9262 - val_loss: 0.2235 - val_accuracy: 0.9161\n",
      "Epoch 41/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1927 - accuracy: 0.9250 - val_loss: 0.2307 - val_accuracy: 0.9137\n",
      "Epoch 42/100\n",
      "360/360 [==============================] - 59s 164ms/step - loss: 0.1904 - accuracy: 0.9245 - val_loss: 0.2359 - val_accuracy: 0.9165\n",
      "Epoch 43/100\n",
      "360/360 [==============================] - 58s 163ms/step - loss: 0.1888 - accuracy: 0.9255 - val_loss: 0.2204 - val_accuracy: 0.9170\n",
      "Epoch 44/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1862 - accuracy: 0.9275 - val_loss: 0.2360 - val_accuracy: 0.9123\n",
      "Epoch 45/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1821 - accuracy: 0.9287 - val_loss: 0.2281 - val_accuracy: 0.9172\n",
      "Epoch 46/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1841 - accuracy: 0.9282 - val_loss: 0.2298 - val_accuracy: 0.9121\n",
      "Epoch 47/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1832 - accuracy: 0.9278 - val_loss: 0.2288 - val_accuracy: 0.9104\n",
      "Epoch 48/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1842 - accuracy: 0.9259 - val_loss: 0.2277 - val_accuracy: 0.9194\n",
      "Epoch 49/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1824 - accuracy: 0.9265 - val_loss: 0.2170 - val_accuracy: 0.9185\n",
      "Epoch 50/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1821 - accuracy: 0.9271 - val_loss: 0.2415 - val_accuracy: 0.9093\n",
      "Epoch 51/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1806 - accuracy: 0.9282 - val_loss: 0.2290 - val_accuracy: 0.9132\n",
      "Epoch 52/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1803 - accuracy: 0.9282 - val_loss: 0.2284 - val_accuracy: 0.9177\n",
      "Epoch 53/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1785 - accuracy: 0.9313 - val_loss: 0.2385 - val_accuracy: 0.9125\n",
      "Epoch 54/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1748 - accuracy: 0.9315 - val_loss: 0.2157 - val_accuracy: 0.9206\n",
      "Epoch 55/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1740 - accuracy: 0.9325 - val_loss: 0.2220 - val_accuracy: 0.9137\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 56/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1753 - accuracy: 0.9328 - val_loss: 0.2319 - val_accuracy: 0.9126\n",
      "Epoch 57/100\n",
      "360/360 [==============================] - 59s 163ms/step - loss: 0.1725 - accuracy: 0.9311 - val_loss: 0.2285 - val_accuracy: 0.9149\n",
      "Epoch 58/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1765 - accuracy: 0.9295 - val_loss: 0.2305 - val_accuracy: 0.9144\n",
      "Epoch 59/100\n",
      "360/360 [==============================] - 59s 163ms/step - loss: 0.1714 - accuracy: 0.9318 - val_loss: 0.2254 - val_accuracy: 0.9161\n",
      "Epoch 60/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1724 - accuracy: 0.9325 - val_loss: 0.2156 - val_accuracy: 0.9185\n",
      "Epoch 61/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1711 - accuracy: 0.9319 - val_loss: 0.2168 - val_accuracy: 0.9213\n",
      "Epoch 62/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1742 - accuracy: 0.9312 - val_loss: 0.2252 - val_accuracy: 0.9168\n",
      "Epoch 63/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1734 - accuracy: 0.9318 - val_loss: 0.2302 - val_accuracy: 0.9151\n",
      "Epoch 64/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1678 - accuracy: 0.9338 - val_loss: 0.2139 - val_accuracy: 0.9213\n",
      "Epoch 65/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1651 - accuracy: 0.9347 - val_loss: 0.2235 - val_accuracy: 0.9173\n",
      "Epoch 66/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.1656 - accuracy: 0.9348 - val_loss: 0.2242 - val_accuracy: 0.9180\n",
      "Epoch 67/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1654 - accuracy: 0.9335 - val_loss: 0.2293 - val_accuracy: 0.9165\n",
      "Epoch 68/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1668 - accuracy: 0.9341 - val_loss: 0.2141 - val_accuracy: 0.9184\n",
      "Epoch 69/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1699 - accuracy: 0.9336 - val_loss: 0.2169 - val_accuracy: 0.9187\n",
      "Epoch 70/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1640 - accuracy: 0.9344 - val_loss: 0.2144 - val_accuracy: 0.9199\n",
      "Epoch 71/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1625 - accuracy: 0.9343 - val_loss: 0.2227 - val_accuracy: 0.9179\n",
      "Epoch 72/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1588 - accuracy: 0.9378 - val_loss: 0.2207 - val_accuracy: 0.9199\n",
      "Epoch 73/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.1627 - accuracy: 0.9373 - val_loss: 0.2213 - val_accuracy: 0.9182\n",
      "Epoch 74/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.1631 - accuracy: 0.9373 - val_loss: 0.2241 - val_accuracy: 0.9206\n",
      "Epoch 75/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1628 - accuracy: 0.9367 - val_loss: 0.2295 - val_accuracy: 0.9121\n",
      "Epoch 76/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.1593 - accuracy: 0.9375 - val_loss: 0.2330 - val_accuracy: 0.9184\n",
      "Epoch 77/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1596 - accuracy: 0.9396 - val_loss: 0.2351 - val_accuracy: 0.9165\n",
      "Epoch 78/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1603 - accuracy: 0.9364 - val_loss: 0.2245 - val_accuracy: 0.9198\n",
      "Epoch 79/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.1621 - accuracy: 0.9372 - val_loss: 0.2371 - val_accuracy: 0.9161\n",
      "Epoch 80/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1572 - accuracy: 0.9382 - val_loss: 0.2087 - val_accuracy: 0.9241\n",
      "Epoch 81/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1573 - accuracy: 0.9387 - val_loss: 0.2206 - val_accuracy: 0.9210\n",
      "Epoch 82/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1538 - accuracy: 0.9395 - val_loss: 0.2233 - val_accuracy: 0.9215\n",
      "Epoch 83/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1590 - accuracy: 0.9371 - val_loss: 0.2230 - val_accuracy: 0.9168\n",
      "Epoch 84/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1569 - accuracy: 0.9381 - val_loss: 0.2369 - val_accuracy: 0.9166\n",
      "Epoch 85/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1557 - accuracy: 0.9384 - val_loss: 0.2096 - val_accuracy: 0.9220\n",
      "Epoch 86/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1563 - accuracy: 0.9381 - val_loss: 0.2366 - val_accuracy: 0.9126\n",
      "Epoch 87/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1567 - accuracy: 0.9379 - val_loss: 0.2334 - val_accuracy: 0.9161\n",
      "Epoch 88/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1520 - accuracy: 0.9413 - val_loss: 0.2205 - val_accuracy: 0.9224\n",
      "Epoch 89/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1518 - accuracy: 0.9401 - val_loss: 0.2457 - val_accuracy: 0.9109\n",
      "Epoch 90/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1551 - accuracy: 0.9398 - val_loss: 0.2280 - val_accuracy: 0.9151\n",
      "Epoch 91/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1517 - accuracy: 0.9404 - val_loss: 0.2364 - val_accuracy: 0.9172\n",
      "Epoch 92/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1490 - accuracy: 0.9420 - val_loss: 0.2231 - val_accuracy: 0.9182\n",
      "Epoch 93/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1500 - accuracy: 0.9415 - val_loss: 0.2323 - val_accuracy: 0.9106\n",
      "Epoch 94/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1521 - accuracy: 0.9401 - val_loss: 0.2208 - val_accuracy: 0.9203\n",
      "Epoch 95/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1551 - accuracy: 0.9403 - val_loss: 0.2401 - val_accuracy: 0.9185\n",
      "Epoch 96/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1514 - accuracy: 0.9397 - val_loss: 0.2248 - val_accuracy: 0.9210\n",
      "Epoch 97/100\n",
      "360/360 [==============================] - 58s 161ms/step - loss: 0.1494 - accuracy: 0.9403 - val_loss: 0.2248 - val_accuracy: 0.9194\n",
      "Epoch 98/100\n",
      "360/360 [==============================] - 58s 162ms/step - loss: 0.1450 - accuracy: 0.9437 - val_loss: 0.2353 - val_accuracy: 0.9180\n",
      "Epoch 99/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.1503 - accuracy: 0.9406 - val_loss: 0.2287 - val_accuracy: 0.9180\n",
      "Epoch 100/100\n",
      "360/360 [==============================] - 58s 160ms/step - loss: 0.1432 - accuracy: 0.9431 - val_loss: 0.2174 - val_accuracy: 0.9179\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf2_mix = train_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9241055846214294"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(conf2_mix['val_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir ensembles_mix\n",
    "pd.DataFrame(conf1_mix).to_csv(\"ensembles_mix/conf1_mix.csv\", index=False)\n",
    "pd.DataFrame(conf2_mix).to_csv(\"ensembles_mix/conf2_mix.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}