Efficientnet finetuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "575a8832-a319-44e1-b085-453838d1aa87",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os \n",
    "import glob\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage.io import imread,imsave\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import save_model, load_model, Model\n",
    "from tensorflow.keras.layers import Input,InputLayer, Dropout, Flatten, Dense, Activation, MaxPooling2D, Conv2D\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98e25180-d7af-479a-a429-252bc5961c74",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def efficientnet_row(optimizer, loss, metrics, shape):\n",
    "    image_input = tf.keras.applications.EfficientNetB0(include_top=False, weights='imagenet', input_shape=shape)  # Change model to EfficientNet\n",
    "\n",
    "    for layer in image_input.layers:\n",
    "        layer.trainable = False\n",
    "        layer._name = layer._name + str('_img')\n",
    "\n",
    "    y = Flatten()(image_input.output)\n",
    "    c = Dropout(0.4)(y)\n",
    "    c = Dense(512)(c)\n",
    "    c = Dense(256)(c)\n",
    "    c = Dense(128)(c)\n",
    "    output_layer = Dense(3, activation='softmax')(c)\n",
    "\n",
    "    model = Model(inputs=image_input.input, outputs=output_layer)\n",
    "    model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f693961-efa8-4543-b99d-36ce488b0534",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "root='/blue/oguzhan/swetha.lenkala/AFFINE_REG_SKS_MASK_2D_IMAGES_DIR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dff2a2d1-bf8d-44c1-bbaf-f5791b36c1d6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(true_lab,predict_lab,classes,path):\n",
    "  matrix_confusion = tf.math.confusion_matrix(labels=true_lab, predictions=predicted_lab, num_classes=len(classes))\n",
    "  ax = plt.subplot()\n",
    "  sns.heatmap(matrix_confusion, square=True, annot=True, cmap='Blues', fmt='d', cbar=True)\n",
    "  ax.set_xlabel('Predicted labels')\n",
    "  ax.set_ylabel('True labels')\n",
    "  ax.set_title('Confusion Matrix')\n",
    "  ax.xaxis.set_ticklabels(classes)\n",
    "  ax.yaxis.set_ticklabels(classes)\n",
    "  plt.savefig(f'{path}confusion_matrix.png')\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb202cf1-6e1f-432b-9e47-aae36aa64436",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def dump_text(text,path):\n",
    "  text_file = open(path, \"w\")\n",
    "  text_file.write(text)\n",
    "  text_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0adfd47f-db8b-45c7-81fa-3c14ddef7a97",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1901 images belonging to 3 classes.\n",
      "Found 478 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "image_Shape=(436,364,3)\n",
    "training_image_datagen = ImageDataGenerator(horizontal_flip=True).flow_from_directory(\n",
    "                                                  directory=f'{root}/training/',\n",
    "                                                  target_size=(image_Shape[0], image_Shape[1]),\n",
    "                                                  class_mode='categorical',\n",
    "                                                  batch_size=4\n",
    "                                                  )\n",
    "\n",
    "testing_image_datagen = ImageDataGenerator().flow_from_directory(\n",
    "                                                  directory = f'{root}/testing/' ,\n",
    "                                                  target_size = (image_Shape[0] ,image_Shape[1]) ,\n",
    "                                                  class_mode = 'categorical' ,\n",
    "                                                  batch_size=1 ,\n",
    "                                                  shuffle = False\n",
    "                                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d491be16-972b-4f20-bca9-952a0a562640",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-14 19:32:33.198195: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-04-14 19:32:34.045277: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9629 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:1b:00.0, compute capability: 7.5\n"
     ]
    }
   ],
   "source": [
    "EPOCH = 100\n",
    "METRICS = [\n",
    "    tf.keras.metrics.CategoricalAccuracy(name='Accuracy'),\n",
    "    tf.keras.metrics.Precision(name='AD_Precision',class_id=0),\n",
    "    tf.keras.metrics.Precision(name='CN_Precision',class_id=1),\n",
    "    tf.keras.metrics.Precision(name='MCI_Precision',class_id=2),\n",
    "    tf.keras.metrics.Recall(name='AD_Recall',class_id=0),\n",
    "    tf.keras.metrics.Recall(name='CN_Recall',class_id=1),\n",
    "    tf.keras.metrics.Recall(name='MCI_Recall',class_id=2),\n",
    "    ]\n",
    "LOOS = 'categorical_crossentropy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bcccdac6-63b0-4071-8f2c-bd00a0436215",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-19 19:30:05.772348: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "476/476 [==============================] - 49s 89ms/step - loss: 7.0454 - Accuracy: 0.4766 - AD_Precision: 0.2148 - CN_Precision: 0.3656 - MCI_Precision: 0.5954 - AD_Recall: 0.1747 - CN_Recall: 0.3223 - MCI_Recall: 0.5949 - val_loss: 1.2436 - val_Accuracy: 0.5335 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.5833 - val_MCI_Precision: 0.5300 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.1892 - val_MCI_Recall: 0.9247\n",
      "Epoch 2/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 1.3585 - Accuracy: 0.5076 - AD_Precision: 0.2780 - CN_Precision: 0.4111 - MCI_Precision: 0.6055 - AD_Recall: 0.2018 - CN_Recall: 0.3320 - MCI_Recall: 0.6319 - val_loss: 1.3921 - val_Accuracy: 0.5272 - val_AD_Precision: 0.4545 - val_CN_Precision: 0.8000 - val_MCI_Precision: 0.5394 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.0811 - val_MCI_Recall: 0.9163\n",
      "Epoch 3/50\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 1.5293 - Accuracy: 0.5018 - AD_Precision: 0.3015 - CN_Precision: 0.4135 - MCI_Precision: 0.6060 - AD_Recall: 0.2470 - CN_Recall: 0.3573 - MCI_Recall: 0.6129 - val_loss: 2.1008 - val_Accuracy: 0.5105 - val_AD_Precision: 0.6111 - val_CN_Precision: 0.0000e+00 - val_MCI_Precision: 0.5087 - val_AD_Recall: 0.1209 - val_CN_Recall: 0.0000e+00 - val_MCI_Recall: 0.9749\n",
      "Epoch 4/50\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 1.8245 - Accuracy: 0.4903 - AD_Precision: 0.2517 - CN_Precision: 0.3956 - MCI_Precision: 0.6073 - AD_Recall: 0.2199 - CN_Recall: 0.3495 - MCI_Recall: 0.6148 - val_loss: 1.3192 - val_Accuracy: 0.5523 - val_AD_Precision: 0.5417 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5487 - val_AD_Recall: 0.1429 - val_CN_Recall: 0.0405 - val_MCI_Recall: 0.9665\n",
      "Epoch 5/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 1.4084 - Accuracy: 0.5460 - AD_Precision: 0.3308 - CN_Precision: 0.4739 - MCI_Precision: 0.6473 - AD_Recall: 0.2651 - CN_Recall: 0.4058 - MCI_Recall: 0.6651 - val_loss: 1.1589 - val_Accuracy: 0.4937 - val_AD_Precision: 0.3385 - val_CN_Precision: 0.6484 - val_MCI_Precision: 0.7229 - val_AD_Recall: 0.7143 - val_CN_Recall: 0.5608 - val_MCI_Recall: 0.2510\n",
      "Epoch 6/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 1.3430 - Accuracy: 0.5665 - AD_Precision: 0.3755 - CN_Precision: 0.5275 - MCI_Precision: 0.6601 - AD_Recall: 0.3133 - CN_Recall: 0.4660 - MCI_Recall: 0.6727 - val_loss: 1.7253 - val_Accuracy: 0.4226 - val_AD_Precision: 0.2543 - val_CN_Precision: 0.7692 - val_MCI_Precision: 0.7544 - val_AD_Recall: 0.8132 - val_CN_Recall: 0.2027 - val_MCI_Recall: 0.3598\n",
      "Epoch 7/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 2.1877 - Accuracy: 0.5387 - AD_Precision: 0.3411 - CN_Precision: 0.4588 - MCI_Precision: 0.6433 - AD_Recall: 0.3072 - CN_Recall: 0.4330 - MCI_Recall: 0.6433 - val_loss: 1.0523 - val_Accuracy: 0.5607 - val_AD_Precision: 0.4337 - val_CN_Precision: 0.6161 - val_MCI_Precision: 0.6033 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.4662 - val_MCI_Recall: 0.6109\n",
      "Epoch 8/50\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 1.3183 - Accuracy: 0.5892 - AD_Precision: 0.4296 - CN_Precision: 0.5124 - MCI_Precision: 0.6818 - AD_Recall: 0.3675 - CN_Recall: 0.4816 - MCI_Recall: 0.6831 - val_loss: 1.6333 - val_Accuracy: 0.4644 - val_AD_Precision: 0.3611 - val_CN_Precision: 0.4416 - val_MCI_Precision: 0.7727 - val_AD_Recall: 0.2857 - val_CN_Recall: 0.9459 - val_MCI_Recall: 0.2134\n",
      "Epoch 9/50\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 1.6088 - Accuracy: 0.5802 - AD_Precision: 0.3899 - CN_Precision: 0.5225 - MCI_Precision: 0.6731 - AD_Recall: 0.3735 - CN_Recall: 0.4951 - MCI_Recall: 0.6660 - val_loss: 0.8796 - val_Accuracy: 0.7050 - val_AD_Precision: 0.7857 - val_CN_Precision: 0.7538 - val_MCI_Precision: 0.6936 - val_AD_Recall: 0.2418 - val_CN_Recall: 0.6622 - val_MCI_Recall: 0.8619\n",
      "Epoch 10/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 1.8229 - Accuracy: 0.5976 - AD_Precision: 0.4076 - CN_Precision: 0.5445 - MCI_Precision: 0.6879 - AD_Recall: 0.3855 - CN_Recall: 0.5107 - MCI_Recall: 0.6879 - val_loss: 2.7513 - val_Accuracy: 0.5941 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.7647 - val_MCI_Precision: 0.5659 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.3514 - val_MCI_Recall: 0.9707\n",
      "Epoch 11/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 1.2953 - Accuracy: 0.6297 - AD_Precision: 0.4515 - CN_Precision: 0.5980 - MCI_Precision: 0.7097 - AD_Recall: 0.4066 - CN_Recall: 0.5806 - MCI_Recall: 0.7144 - val_loss: 0.9044 - val_Accuracy: 0.6255 - val_AD_Precision: 0.5968 - val_CN_Precision: 0.5600 - val_MCI_Precision: 0.7826 - val_AD_Recall: 0.4066 - val_CN_Recall: 0.8514 - val_MCI_Recall: 0.5272\n",
      "Epoch 12/50\n",
      "476/476 [==============================] - 32s 68ms/step - loss: 1.3082 - Accuracy: 0.6423 - AD_Precision: 0.5049 - CN_Precision: 0.5884 - MCI_Precision: 0.7241 - AD_Recall: 0.4699 - CN_Recall: 0.5495 - MCI_Recall: 0.7296 - val_loss: 3.7952 - val_Accuracy: 0.5230 - val_AD_Precision: 0.6923 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5141 - val_AD_Recall: 0.0989 - val_CN_Recall: 0.0270 - val_MCI_Recall: 0.9916\n",
      "Epoch 13/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 1.5475 - Accuracy: 0.6281 - AD_Precision: 0.4246 - CN_Precision: 0.5908 - MCI_Precision: 0.7100 - AD_Recall: 0.4157 - CN_Recall: 0.5495 - MCI_Recall: 0.7201 - val_loss: 2.0833 - val_Accuracy: 0.6695 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.7333 - val_MCI_Precision: 0.6462 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.6689 - val_MCI_Recall: 0.9247\n",
      "Epoch 14/50\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 1.6231 - Accuracy: 0.6391 - AD_Precision: 0.4859 - CN_Precision: 0.5905 - MCI_Precision: 0.7146 - AD_Recall: 0.4669 - CN_Recall: 0.5573 - MCI_Recall: 0.7220 - val_loss: 1.3550 - val_Accuracy: 0.5272 - val_AD_Precision: 0.3529 - val_CN_Precision: 0.6020 - val_MCI_Precision: 0.8235 - val_AD_Recall: 0.7253 - val_CN_Recall: 0.8176 - val_MCI_Recall: 0.2343\n",
      "Epoch 15/50\n",
      "476/476 [==============================] - 32s 68ms/step - loss: 1.1152 - Accuracy: 0.6902 - AD_Precision: 0.5608 - CN_Precision: 0.6579 - MCI_Precision: 0.7535 - AD_Recall: 0.5000 - CN_Recall: 0.6350 - MCI_Recall: 0.7628 - val_loss: 1.1014 - val_Accuracy: 0.6276 - val_AD_Precision: 0.4435 - val_CN_Precision: 0.6287 - val_MCI_Precision: 0.8134 - val_AD_Recall: 0.6044 - val_CN_Recall: 0.8581 - val_MCI_Recall: 0.4561\n",
      "Epoch 16/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 1.6466 - Accuracy: 0.6370 - AD_Precision: 0.4717 - CN_Precision: 0.5952 - MCI_Precision: 0.7193 - AD_Recall: 0.4518 - CN_Recall: 0.5825 - MCI_Recall: 0.7125 - val_loss: 2.3076 - val_Accuracy: 0.4812 - val_AD_Precision: 0.3261 - val_CN_Precision: 0.9091 - val_MCI_Precision: 0.5694 - val_AD_Recall: 0.6593 - val_CN_Recall: 0.0676 - val_MCI_Recall: 0.6695\n",
      "Epoch 17/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 1.1127 - Accuracy: 0.6965 - AD_Precision: 0.5942 - CN_Precision: 0.6460 - MCI_Precision: 0.7618 - AD_Recall: 0.5512 - CN_Recall: 0.6272 - MCI_Recall: 0.7676 - val_loss: 2.2488 - val_Accuracy: 0.6109 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.4982 - val_MCI_Precision: 0.7713 - val_AD_Recall: 0.0549 - val_CN_Recall: 0.9527 - val_MCI_Recall: 0.6067\n",
      "Epoch 18/50\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 1.3715 - Accuracy: 0.6912 - AD_Precision: 0.5432 - CN_Precision: 0.6538 - MCI_Precision: 0.7573 - AD_Recall: 0.5301 - CN_Recall: 0.6272 - MCI_Recall: 0.7666 - val_loss: 1.4382 - val_Accuracy: 0.6904 - val_AD_Precision: 0.8519 - val_CN_Precision: 0.6621 - val_MCI_Precision: 0.6890 - val_AD_Recall: 0.2527 - val_CN_Recall: 0.6486 - val_MCI_Recall: 0.8619\n",
      "Epoch 19/50\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 1.1351 - Accuracy: 0.6996 - AD_Precision: 0.5732 - CN_Precision: 0.6633 - MCI_Precision: 0.7671 - AD_Recall: 0.5542 - CN_Recall: 0.6427 - MCI_Recall: 0.7657 - val_loss: 1.0518 - val_Accuracy: 0.6715 - val_AD_Precision: 0.4545 - val_CN_Precision: 0.7278 - val_MCI_Precision: 0.8511 - val_AD_Recall: 0.7143 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.5021\n",
      "Epoch 20/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 1.0330 - Accuracy: 0.7212 - AD_Precision: 0.5850 - CN_Precision: 0.6881 - MCI_Precision: 0.7842 - AD_Recall: 0.5392 - CN_Recall: 0.6854 - MCI_Recall: 0.7827 - val_loss: 1.9015 - val_Accuracy: 0.6151 - val_AD_Precision: 0.8158 - val_CN_Precision: 0.8857 - val_MCI_Precision: 0.5771 - val_AD_Recall: 0.3407 - val_CN_Recall: 0.2095 - val_MCI_Recall: 0.9707\n",
      "Epoch 21/50\n",
      "476/476 [==============================] - 34s 73ms/step - loss: 1.0820 - Accuracy: 0.7170 - AD_Precision: 0.6065 - CN_Precision: 0.6934 - MCI_Precision: 0.7743 - AD_Recall: 0.5663 - CN_Recall: 0.6718 - MCI_Recall: 0.7780 - val_loss: 1.5084 - val_Accuracy: 0.6046 - val_AD_Precision: 0.3974 - val_CN_Precision: 0.9302 - val_MCI_Precision: 0.6852 - val_AD_Recall: 0.6593 - val_CN_Recall: 0.2703 - val_MCI_Recall: 0.7741\n",
      "Epoch 22/50\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 1.2108 - Accuracy: 0.6954 - AD_Precision: 0.5463 - CN_Precision: 0.6686 - MCI_Precision: 0.7629 - AD_Recall: 0.5151 - CN_Recall: 0.6621 - MCI_Recall: 0.7600 - val_loss: 1.1071 - val_Accuracy: 0.6653 - val_AD_Precision: 0.4726 - val_CN_Precision: 0.6742 - val_MCI_Precision: 0.8639 - val_AD_Recall: 0.7582 - val_CN_Recall: 0.8108 - val_MCI_Recall: 0.5314\n",
      "Epoch 23/50\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.9650 - Accuracy: 0.7280 - AD_Precision: 0.5897 - CN_Precision: 0.7069 - MCI_Precision: 0.7867 - AD_Recall: 0.5542 - CN_Recall: 0.6932 - MCI_Recall: 0.7941 - val_loss: 1.0432 - val_Accuracy: 0.7406 - val_AD_Precision: 0.8750 - val_CN_Precision: 0.8099 - val_MCI_Precision: 0.6987 - val_AD_Recall: 0.2308 - val_CN_Recall: 0.7770 - val_MCI_Recall: 0.9121\n",
      "Epoch 24/50\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 1.0219 - Accuracy: 0.7338 - AD_Precision: 0.5962 - CN_Precision: 0.7101 - MCI_Precision: 0.7878 - AD_Recall: 0.5602 - CN_Recall: 0.6990 - MCI_Recall: 0.7960 - val_loss: 0.8668 - val_Accuracy: 0.7720 - val_AD_Precision: 0.7377 - val_CN_Precision: 0.8248 - val_MCI_Precision: 0.7609 - val_AD_Recall: 0.4945 - val_CN_Recall: 0.7635 - val_MCI_Recall: 0.8787\n",
      "Epoch 25/50\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 1.2784 - Accuracy: 0.7149 - AD_Precision: 0.5728 - CN_Precision: 0.6818 - MCI_Precision: 0.7759 - AD_Recall: 0.5572 - CN_Recall: 0.6699 - MCI_Recall: 0.7818 - val_loss: 3.1781 - val_Accuracy: 0.4331 - val_AD_Precision: 0.2640 - val_CN_Precision: 0.7843 - val_MCI_Precision: 0.8085 - val_AD_Recall: 0.9341 - val_CN_Recall: 0.5405 - val_MCI_Recall: 0.1590\n",
      "Epoch 26/50\n",
      "476/476 [==============================] - 38s 79ms/step - loss: 1.0002 - Accuracy: 0.7507 - AD_Precision: 0.6656 - CN_Precision: 0.7149 - MCI_Precision: 0.8011 - AD_Recall: 0.6416 - CN_Recall: 0.6913 - MCI_Recall: 0.8065 - val_loss: 1.5100 - val_Accuracy: 0.6736 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6959 - val_MCI_Precision: 0.6553 - val_AD_Recall: 0.0769 - val_CN_Recall: 0.6959 - val_MCI_Recall: 0.8828\n",
      "Epoch 27/50\n",
      "476/476 [==============================] - 36s 76ms/step - loss: 0.9905 - Accuracy: 0.7480 - AD_Precision: 0.6321 - CN_Precision: 0.7253 - MCI_Precision: 0.7977 - AD_Recall: 0.6054 - CN_Recall: 0.7126 - MCI_Recall: 0.8008 - val_loss: 1.0232 - val_Accuracy: 0.7259 - val_AD_Precision: 0.8478 - val_CN_Precision: 0.6418 - val_MCI_Precision: 0.7812 - val_AD_Recall: 0.4286 - val_CN_Recall: 0.8716 - val_MCI_Recall: 0.7322\n",
      "Epoch 28/50\n",
      "476/476 [==============================] - 28s 60ms/step - loss: 0.8859 - Accuracy: 0.7654 - AD_Precision: 0.6780 - CN_Precision: 0.7223 - MCI_Precision: 0.8172 - AD_Recall: 0.6596 - CN_Recall: 0.7223 - MCI_Recall: 0.8102 - val_loss: 3.3217 - val_Accuracy: 0.5837 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9487 - val_MCI_Precision: 0.5461 - val_AD_Recall: 0.0549 - val_CN_Recall: 0.2500 - val_MCI_Recall: 0.9916\n",
      "Epoch 29/50\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.9119 - Accuracy: 0.7564 - AD_Precision: 0.6667 - CN_Precision: 0.7275 - MCI_Precision: 0.8019 - AD_Recall: 0.6506 - CN_Recall: 0.7049 - MCI_Recall: 0.8065 - val_loss: 1.8607 - val_Accuracy: 0.6004 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.4932 - val_MCI_Precision: 0.7514 - val_AD_Recall: 0.1319 - val_CN_Recall: 0.9730 - val_MCI_Recall: 0.5439\n",
      "Epoch 30/50\n",
      "476/476 [==============================] - 25s 54ms/step - loss: 0.9528 - Accuracy: 0.7617 - AD_Precision: 0.6485 - CN_Precision: 0.7435 - MCI_Precision: 0.8152 - AD_Recall: 0.6446 - CN_Recall: 0.7262 - MCI_Recall: 0.8121 - val_loss: 2.5201 - val_Accuracy: 0.5837 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9355 - val_MCI_Precision: 0.5461 - val_AD_Recall: 0.1429 - val_CN_Recall: 0.1959 - val_MCI_Recall: 0.9916\n",
      "Epoch 31/50\n",
      "476/476 [==============================] - 26s 56ms/step - loss: 0.7368 - Accuracy: 0.8017 - AD_Precision: 0.6832 - CN_Precision: 0.8012 - MCI_Precision: 0.8472 - AD_Recall: 0.6627 - CN_Recall: 0.7825 - MCI_Recall: 0.8472 - val_loss: 2.0082 - val_Accuracy: 0.6883 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8034 - val_MCI_Precision: 0.6461 - val_AD_Recall: 0.0549 - val_CN_Recall: 0.6351 - val_MCI_Recall: 0.9623\n",
      "Epoch 32/50\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.9671 - Accuracy: 0.7685 - AD_Precision: 0.7013 - CN_Precision: 0.7343 - MCI_Precision: 0.8056 - AD_Recall: 0.6717 - CN_Recall: 0.7243 - MCI_Recall: 0.8140 - val_loss: 2.1120 - val_Accuracy: 0.6067 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9512 - val_MCI_Precision: 0.5616 - val_AD_Recall: 0.1538 - val_CN_Recall: 0.2635 - val_MCI_Recall: 0.9916\n",
      "Epoch 33/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 1.0112 - Accuracy: 0.7706 - AD_Precision: 0.6563 - CN_Precision: 0.7623 - MCI_Precision: 0.8174 - AD_Recall: 0.6386 - CN_Recall: 0.7534 - MCI_Recall: 0.8112 - val_loss: 1.3809 - val_Accuracy: 0.6925 - val_AD_Precision: 0.9143 - val_CN_Precision: 0.8571 - val_MCI_Precision: 0.6409 - val_AD_Recall: 0.3516 - val_CN_Recall: 0.4459 - val_MCI_Recall: 0.9707\n",
      "Epoch 34/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.8297 - Accuracy: 0.7796 - AD_Precision: 0.7063 - CN_Precision: 0.7539 - MCI_Precision: 0.8190 - AD_Recall: 0.6807 - CN_Recall: 0.7437 - MCI_Recall: 0.8245 - val_loss: 1.5043 - val_Accuracy: 0.6695 - val_AD_Precision: 0.7091 - val_CN_Precision: 0.9434 - val_MCI_Precision: 0.6336 - val_AD_Recall: 0.4286 - val_CN_Recall: 0.3378 - val_MCI_Recall: 0.9623\n",
      "Epoch 35/50\n",
      "476/476 [==============================] - 42s 87ms/step - loss: 0.9229 - Accuracy: 0.7780 - AD_Precision: 0.6906 - CN_Precision: 0.7549 - MCI_Precision: 0.8217 - AD_Recall: 0.6657 - CN_Recall: 0.7417 - MCI_Recall: 0.8264 - val_loss: 0.9120 - val_Accuracy: 0.7762 - val_AD_Precision: 0.8723 - val_CN_Precision: 0.8929 - val_MCI_Precision: 0.7247 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.6757 - val_MCI_Recall: 0.9582\n",
      "Epoch 36/50\n",
      "476/476 [==============================] - 33s 68ms/step - loss: 1.2181 - Accuracy: 0.7638 - AD_Precision: 0.6794 - CN_Precision: 0.7246 - MCI_Precision: 0.8142 - AD_Recall: 0.6446 - CN_Recall: 0.7204 - MCI_Recall: 0.8188 - val_loss: 2.1034 - val_Accuracy: 0.6360 - val_AD_Precision: 0.8125 - val_CN_Precision: 0.5235 - val_MCI_Precision: 0.7939 - val_AD_Recall: 0.2857 - val_CN_Recall: 0.9797 - val_MCI_Recall: 0.5481\n",
      "Epoch 37/50\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 0.8341 - Accuracy: 0.7927 - AD_Precision: 0.7085 - CN_Precision: 0.7481 - MCI_Precision: 0.8415 - AD_Recall: 0.6807 - CN_Recall: 0.7670 - MCI_Recall: 0.8359 - val_loss: 1.7124 - val_Accuracy: 0.6987 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6222 - val_MCI_Precision: 0.7511 - val_AD_Recall: 0.1758 - val_CN_Recall: 0.9459 - val_MCI_Recall: 0.7448\n",
      "Epoch 38/50\n",
      "476/476 [==============================] - 33s 70ms/step - loss: 0.8038 - Accuracy: 0.8022 - AD_Precision: 0.7329 - CN_Precision: 0.7833 - MCI_Precision: 0.8368 - AD_Recall: 0.7108 - CN_Recall: 0.7650 - MCI_Recall: 0.8463 - val_loss: 0.9739 - val_Accuracy: 0.6757 - val_AD_Precision: 0.4545 - val_CN_Precision: 0.7158 - val_MCI_Precision: 0.8955 - val_AD_Recall: 0.7692 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.5021\n",
      "Epoch 39/50\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.7778 - Accuracy: 0.8017 - AD_Precision: 0.6930 - CN_Precision: 0.7797 - MCI_Precision: 0.8475 - AD_Recall: 0.6596 - CN_Recall: 0.7767 - MCI_Recall: 0.8491 - val_loss: 1.2652 - val_Accuracy: 0.6632 - val_AD_Precision: 0.4093 - val_CN_Precision: 0.8226 - val_MCI_Precision: 0.8710 - val_AD_Recall: 0.8681 - val_CN_Recall: 0.6892 - val_MCI_Recall: 0.5649\n",
      "Epoch 40/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 0.8540 - Accuracy: 0.7817 - AD_Precision: 0.6759 - CN_Precision: 0.7585 - MCI_Precision: 0.8324 - AD_Recall: 0.6596 - CN_Recall: 0.7379 - MCI_Recall: 0.8340 - val_loss: 1.5689 - val_Accuracy: 0.6485 - val_AD_Precision: 0.4021 - val_CN_Precision: 0.9455 - val_MCI_Precision: 0.7885 - val_AD_Recall: 0.8352 - val_CN_Recall: 0.3514 - val_MCI_Recall: 0.7490\n",
      "Epoch 41/50\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.8190 - Accuracy: 0.7991 - AD_Precision: 0.6988 - CN_Precision: 0.7881 - MCI_Precision: 0.8430 - AD_Recall: 0.6988 - CN_Recall: 0.7942 - MCI_Recall: 0.8302 - val_loss: 1.6849 - val_Accuracy: 0.6778 - val_AD_Precision: 0.9429 - val_CN_Precision: 0.9344 - val_MCI_Precision: 0.6142 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.3851 - val_MCI_Recall: 0.9791\n",
      "Epoch 42/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.7458 - Accuracy: 0.8027 - AD_Precision: 0.7035 - CN_Precision: 0.8008 - MCI_Precision: 0.8383 - AD_Recall: 0.6717 - CN_Recall: 0.7883 - MCI_Recall: 0.8463 - val_loss: 0.8946 - val_Accuracy: 0.7782 - val_AD_Precision: 0.6782 - val_CN_Precision: 0.8879 - val_MCI_Precision: 0.7668 - val_AD_Recall: 0.6484 - val_CN_Recall: 0.6419 - val_MCI_Recall: 0.9079\n",
      "Epoch 43/50\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.7034 - Accuracy: 0.8185 - AD_Precision: 0.7317 - CN_Precision: 0.7938 - MCI_Precision: 0.8607 - AD_Recall: 0.7229 - CN_Recall: 0.7922 - MCI_Recall: 0.8558 - val_loss: 1.1670 - val_Accuracy: 0.7929 - val_AD_Precision: 0.9487 - val_CN_Precision: 0.8231 - val_MCI_Precision: 0.7578 - val_AD_Recall: 0.4066 - val_CN_Recall: 0.8176 - val_MCI_Recall: 0.9163\n",
      "Epoch 44/50\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.7449 - Accuracy: 0.8138 - AD_Precision: 0.7344 - CN_Precision: 0.7972 - MCI_Precision: 0.8523 - AD_Recall: 0.7078 - CN_Recall: 0.7864 - MCI_Recall: 0.8539 - val_loss: 1.2956 - val_Accuracy: 0.7490 - val_AD_Precision: 0.6629 - val_CN_Precision: 0.9375 - val_MCI_Precision: 0.7222 - val_AD_Recall: 0.6484 - val_CN_Recall: 0.5068 - val_MCI_Recall: 0.9247\n",
      "Epoch 45/50\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 0.8727 - Accuracy: 0.8027 - AD_Precision: 0.7059 - CN_Precision: 0.7850 - MCI_Precision: 0.8494 - AD_Recall: 0.6867 - CN_Recall: 0.7728 - MCI_Recall: 0.8510 - val_loss: 0.7728 - val_Accuracy: 0.8033 - val_AD_Precision: 0.6410 - val_CN_Precision: 0.8301 - val_MCI_Precision: 0.8905 - val_AD_Recall: 0.8242 - val_CN_Recall: 0.8581 - val_MCI_Recall: 0.7490\n",
      "Epoch 46/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.6100 - Accuracy: 0.8375 - AD_Precision: 0.7508 - CN_Precision: 0.8182 - MCI_Precision: 0.8764 - AD_Recall: 0.7349 - CN_Recall: 0.8214 - MCI_Recall: 0.8748 - val_loss: 1.3730 - val_Accuracy: 0.7469 - val_AD_Precision: 0.9444 - val_CN_Precision: 0.8990 - val_MCI_Precision: 0.6824 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.6014 - val_MCI_Recall: 0.9707\n",
      "Epoch 47/50\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.8711 - Accuracy: 0.8017 - AD_Precision: 0.7021 - CN_Precision: 0.7824 - MCI_Precision: 0.8431 - AD_Recall: 0.6958 - CN_Recall: 0.7748 - MCI_Recall: 0.8463 - val_loss: 1.1471 - val_Accuracy: 0.7929 - val_AD_Precision: 0.9714 - val_CN_Precision: 0.8705 - val_MCI_Precision: 0.7384 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.8176 - val_MCI_Recall: 0.9331\n",
      "Epoch 48/50\n",
      "476/476 [==============================] - 28s 58ms/step - loss: 0.7415 - Accuracy: 0.8159 - AD_Precision: 0.7531 - CN_Precision: 0.7961 - MCI_Precision: 0.8475 - AD_Recall: 0.7259 - CN_Recall: 0.7961 - MCI_Recall: 0.8491 - val_loss: 1.2423 - val_Accuracy: 0.7490 - val_AD_Precision: 0.6786 - val_CN_Precision: 0.9186 - val_MCI_Precision: 0.7279 - val_AD_Recall: 0.6264 - val_CN_Recall: 0.5338 - val_MCI_Recall: 0.9289\n",
      "Epoch 49/50\n",
      "476/476 [==============================] - 34s 72ms/step - loss: 0.7449 - Accuracy: 0.8090 - AD_Precision: 0.7394 - CN_Precision: 0.7718 - MCI_Precision: 0.8539 - AD_Recall: 0.7349 - CN_Recall: 0.7748 - MCI_Recall: 0.8482 - val_loss: 1.3493 - val_Accuracy: 0.7301 - val_AD_Precision: 0.8167 - val_CN_Precision: 0.9577 - val_MCI_Precision: 0.6705 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.4595 - val_MCI_Recall: 0.9707\n",
      "Epoch 50/50\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.6809 - Accuracy: 0.8196 - AD_Precision: 0.7485 - CN_Precision: 0.7881 - MCI_Precision: 0.8592 - AD_Recall: 0.7440 - CN_Recall: 0.7728 - MCI_Recall: 0.8624 - val_loss: 1.4672 - val_Accuracy: 0.7406 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8447 - val_MCI_Precision: 0.6862 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.5878 - val_MCI_Recall: 0.9791\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/apps/tensorflow/2.7.0/lib/python3.9/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  layer_config = serialize_layer_fn(layer)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'model05' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m model04\u001b[38;5;241m.\u001b[39mload_weights(glob\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msavepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mefficientnet_row_10E-5_weights-epoch:*-val_Accuracy:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mmax\u001b[39m(history04\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_Accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     15\u001b[0m model04\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msavepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/efficientnet_50_0.0001.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 16\u001b[0m p2 \u001b[38;5;241m=\u001b[39m \u001b[43mmodel05\u001b[49m\u001b[38;5;241m.\u001b[39mpredict(testing_image_datagen)\n\u001b[1;32m     17\u001b[0m predicted_lab \u001b[38;5;241m=\u001b[39m p2\u001b[38;5;241m.\u001b[39margmax(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     18\u001b[0m true_lab \u001b[38;5;241m=\u001b[39m testing_image_datagen\u001b[38;5;241m.\u001b[39mclasses\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model05' is not defined"
     ]
    }
   ],
   "source": [
    "savepath = 'models'\n",
    "lr=0.0001\n",
    "if not os.path.isdir(savepath):\n",
    "      os.makedirs(savepath)\n",
    "csvloger_callback = tf.keras.callbacks.CSVLogger(f'{savepath}history.csv', separator=',', append=False)\n",
    "\n",
    "modelcheckpoint_callback = tf.keras.callbacks.ModelCheckpoint(savepath+'efficientnet_row_10E-5_weights-epoch:{epoch:03d}-val_Accuracy:{val_Accuracy:.4f}.h5',monitor='val_Accuracy'\n",
    "    ,verbose=0,save_best_only=True,save_weights_only=True,mode='max',save_freq='epoch' , initial_value_threshold=0.85)\n",
    "\n",
    "OPT = tf.keras.optimizers.Adam(learning_rate=lr)\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "history04 = model04.fit(training_image_datagen, epochs=EPOCH, validation_data=testing_image_datagen,callbacks=[csvloger_callback,modelcheckpoint_callback])\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "model04.load_weights(glob.glob(f\"{savepath}efficientnet_row_10E-5_weights-epoch:*-val_Accuracy:{max(history04.history['val_Accuracy']):.4f}.h5\")[0])\n",
    "model04.save(f\"{savepath}/efficientnet_50_0.0001.h5\")\n",
    "p2 = model05.predict(testing_image_datagen)\n",
    "predicted_lab = p2.argmax(axis=-1)\n",
    "true_lab = testing_image_datagen.classes\n",
    "plot_confusion_matrix(true_lab, predicted_lab, testing_image_datagen.class_indices.keys(),savepath)\n",
    "report = classification_report(true_lab, predicted_lab, target_names=testing_image_datagen.class_indices.keys())\n",
    "print(report)\n",
    "dump_text(report,f'{savepath}classification_report.txt')\n",
    "np.save(\"efficientnet_50_0.0001.npy\",p2 )   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "350d3d63-67d3-4b66-ad9f-ce0e53480d2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          AD       0.64      0.82      0.72        91\n",
      "          CN       0.82      0.87      0.84       148\n",
      "         MCI       0.89      0.75      0.81       239\n",
      "\n",
      "    accuracy                           0.80       478\n",
      "   macro avg       0.78      0.82      0.79       478\n",
      "weighted avg       0.82      0.80      0.81       478\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p2 = model04.predict(testing_image_datagen)\n",
    "predicted_lab = p2.argmax(axis=-1)\n",
    "true_lab = testing_image_datagen.classes\n",
    "plot_confusion_matrix(true_lab, predicted_lab, testing_image_datagen.class_indices.keys(),savepath)\n",
    "report = classification_report(true_lab, predicted_lab, target_names=testing_image_datagen.class_indices.keys())\n",
    "print(report)\n",
    "dump_text(report,f'{savepath}classification_report.txt')\n",
    "np.save(\"efficientnet_50_0.0001.npy\",p2 )   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f7dbc671-663a-4902-9ffc-d7935eaf74f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/apps/tensorflow/2.7.0/lib/python3.9/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  layer_config = serialize_layer_fn(layer)\n"
     ]
    }
   ],
   "source": [
    "model04.save(f\"{savepath}/efficientnet_50_0.0001.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0291de09-a331-421a-816d-f337d198b58e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "476/476 [==============================] - 32s 58ms/step - loss: 1.8129 - Accuracy: 0.4513 - AD_Precision: 0.2109 - CN_Precision: 0.3193 - MCI_Precision: 0.5746 - AD_Recall: 0.1747 - CN_Recall: 0.2796 - MCI_Recall: 0.5920 - val_loss: 1.1389 - val_Accuracy: 0.4895 - val_AD_Precision: 0.5455 - val_CN_Precision: 0.4217 - val_MCI_Precision: 0.5979 - val_AD_Recall: 0.0659 - val_CN_Recall: 0.6554 - val_MCI_Recall: 0.4728\n",
      "Epoch 2/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.6071 - Accuracy: 0.4918 - AD_Precision: 0.2575 - CN_Precision: 0.3904 - MCI_Precision: 0.6037 - AD_Recall: 0.2078 - CN_Recall: 0.3456 - MCI_Recall: 0.6129 - val_loss: 1.7513 - val_Accuracy: 0.5188 - val_AD_Precision: 0.7500 - val_CN_Precision: 0.8333 - val_MCI_Precision: 0.5130 - val_AD_Recall: 0.0659 - val_CN_Recall: 0.0338 - val_MCI_Recall: 0.9916\n",
      "Epoch 3/50\n",
      "476/476 [==============================] - 28s 58ms/step - loss: 1.4468 - Accuracy: 0.5218 - AD_Precision: 0.2930 - CN_Precision: 0.4317 - MCI_Precision: 0.6324 - AD_Recall: 0.2410 - CN_Recall: 0.3864 - MCI_Recall: 0.6414 - val_loss: 0.9841 - val_Accuracy: 0.5962 - val_AD_Precision: 0.5909 - val_CN_Precision: 0.5389 - val_MCI_Precision: 0.6552 - val_AD_Recall: 0.1429 - val_CN_Recall: 0.7027 - val_MCI_Recall: 0.6360\n",
      "Epoch 4/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 1.3445 - Accuracy: 0.5618 - AD_Precision: 0.3939 - CN_Precision: 0.4935 - MCI_Precision: 0.6511 - AD_Recall: 0.3133 - CN_Recall: 0.4427 - MCI_Recall: 0.6603 - val_loss: 1.1700 - val_Accuracy: 0.5753 - val_AD_Precision: 0.7857 - val_CN_Precision: 0.7083 - val_MCI_Precision: 0.5577 - val_AD_Recall: 0.1209 - val_CN_Recall: 0.2297 - val_MCI_Recall: 0.9498\n",
      "Epoch 5/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 1.2456 - Accuracy: 0.5502 - AD_Precision: 0.3419 - CN_Precision: 0.4781 - MCI_Precision: 0.6472 - AD_Recall: 0.2801 - CN_Recall: 0.4233 - MCI_Recall: 0.6613 - val_loss: 0.8705 - val_Accuracy: 0.6234 - val_AD_Precision: 0.4808 - val_CN_Precision: 0.8070 - val_MCI_Precision: 0.6783 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.3108 - val_MCI_Recall: 0.7322\n",
      "Epoch 6/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 1.1602 - Accuracy: 0.5881 - AD_Precision: 0.4170 - CN_Precision: 0.5288 - MCI_Precision: 0.6716 - AD_Recall: 0.3404 - CN_Recall: 0.4641 - MCI_Recall: 0.6869 - val_loss: 1.6130 - val_Accuracy: 0.5272 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9091 - val_MCI_Precision: 0.5163 - val_AD_Recall: 0.0440 - val_CN_Recall: 0.0676 - val_MCI_Recall: 0.9958\n",
      "Epoch 7/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 1.1452 - Accuracy: 0.6039 - AD_Precision: 0.4412 - CN_Precision: 0.5408 - MCI_Precision: 0.6801 - AD_Recall: 0.3614 - CN_Recall: 0.4757 - MCI_Recall: 0.7021 - val_loss: 0.9919 - val_Accuracy: 0.5565 - val_AD_Precision: 0.3782 - val_CN_Precision: 0.6047 - val_MCI_Precision: 0.7830 - val_AD_Recall: 0.6484 - val_CN_Recall: 0.7027 - val_MCI_Recall: 0.3473\n",
      "Epoch 8/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 1.0422 - Accuracy: 0.6181 - AD_Precision: 0.4816 - CN_Precision: 0.5590 - MCI_Precision: 0.6981 - AD_Recall: 0.3946 - CN_Recall: 0.4971 - MCI_Recall: 0.7173 - val_loss: 1.1544 - val_Accuracy: 0.5690 - val_AD_Precision: 0.5714 - val_CN_Precision: 0.9167 - val_MCI_Precision: 0.5689 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.0743 - val_MCI_Recall: 0.9331\n",
      "Epoch 9/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 0.9898 - Accuracy: 0.6386 - AD_Precision: 0.4845 - CN_Precision: 0.6123 - MCI_Precision: 0.7105 - AD_Recall: 0.4247 - CN_Recall: 0.5398 - MCI_Recall: 0.7334 - val_loss: 1.0192 - val_Accuracy: 0.6255 - val_AD_Precision: 0.8077 - val_CN_Precision: 0.8627 - val_MCI_Precision: 0.5848 - val_AD_Recall: 0.2308 - val_CN_Recall: 0.2973 - val_MCI_Recall: 0.9665\n",
      "Epoch 10/50\n",
      "476/476 [==============================] - 27s 58ms/step - loss: 1.0008 - Accuracy: 0.6465 - AD_Precision: 0.5263 - CN_Precision: 0.5941 - MCI_Precision: 0.7147 - AD_Recall: 0.4217 - CN_Recall: 0.5456 - MCI_Recall: 0.7391 - val_loss: 0.6980 - val_Accuracy: 0.7259 - val_AD_Precision: 0.6613 - val_CN_Precision: 0.7578 - val_MCI_Precision: 0.7368 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.6554 - val_MCI_Recall: 0.8201\n",
      "Epoch 11/50\n",
      "476/476 [==============================] - 24s 51ms/step - loss: 0.9579 - Accuracy: 0.6544 - AD_Precision: 0.5122 - CN_Precision: 0.6151 - MCI_Precision: 0.7220 - AD_Recall: 0.4428 - CN_Recall: 0.5553 - MCI_Recall: 0.7391 - val_loss: 0.7153 - val_Accuracy: 0.7238 - val_AD_Precision: 0.8049 - val_CN_Precision: 0.8351 - val_MCI_Precision: 0.6850 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.5473 - val_MCI_Recall: 0.9372\n",
      "Epoch 12/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 0.8860 - Accuracy: 0.6865 - AD_Precision: 0.5979 - CN_Precision: 0.6245 - MCI_Precision: 0.7592 - AD_Recall: 0.5241 - CN_Recall: 0.5748 - MCI_Recall: 0.7628 - val_loss: 1.2338 - val_Accuracy: 0.6213 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7971 - val_MCI_Precision: 0.5804 - val_AD_Recall: 0.0769 - val_CN_Recall: 0.3716 - val_MCI_Recall: 0.9665\n",
      "Epoch 13/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.8987 - Accuracy: 0.6591 - AD_Precision: 0.5468 - CN_Precision: 0.6124 - MCI_Precision: 0.7274 - AD_Recall: 0.4578 - CN_Recall: 0.5553 - MCI_Recall: 0.7543 - val_loss: 0.7129 - val_Accuracy: 0.7197 - val_AD_Precision: 0.7561 - val_CN_Precision: 0.8333 - val_MCI_Precision: 0.6945 - val_AD_Recall: 0.3407 - val_CN_Recall: 0.6081 - val_MCI_Recall: 0.9038\n",
      "Epoch 14/50\n",
      "476/476 [==============================] - 28s 58ms/step - loss: 0.8570 - Accuracy: 0.6896 - AD_Precision: 0.6241 - CN_Precision: 0.6391 - MCI_Precision: 0.7399 - AD_Recall: 0.5452 - CN_Recall: 0.5709 - MCI_Recall: 0.7638 - val_loss: 0.8792 - val_Accuracy: 0.6820 - val_AD_Precision: 0.8750 - val_CN_Precision: 0.5900 - val_MCI_Precision: 0.8105 - val_AD_Recall: 0.2308 - val_CN_Recall: 0.9527 - val_MCI_Recall: 0.6444\n",
      "Epoch 15/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.7628 - Accuracy: 0.7112 - AD_Precision: 0.6057 - CN_Precision: 0.6823 - MCI_Precision: 0.7747 - AD_Recall: 0.5090 - CN_Recall: 0.6214 - MCI_Recall: 0.7894 - val_loss: 0.6856 - val_Accuracy: 0.7029 - val_AD_Precision: 0.6364 - val_CN_Precision: 0.6862 - val_MCI_Precision: 0.8354 - val_AD_Recall: 0.6154 - val_CN_Recall: 0.8716 - val_MCI_Recall: 0.5732\n",
      "Epoch 16/50\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.8121 - Accuracy: 0.6954 - AD_Precision: 0.6281 - CN_Precision: 0.6562 - MCI_Precision: 0.7523 - AD_Recall: 0.5392 - CN_Recall: 0.6078 - MCI_Recall: 0.7638 - val_loss: 0.8902 - val_Accuracy: 0.7113 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8349 - val_MCI_Precision: 0.6657 - val_AD_Recall: 0.1868 - val_CN_Recall: 0.6149 - val_MCI_Recall: 0.9498\n",
      "Epoch 17/50\n",
      "476/476 [==============================] - 24s 50ms/step - loss: 0.7349 - Accuracy: 0.7291 - AD_Precision: 0.6479 - CN_Precision: 0.7119 - MCI_Precision: 0.7769 - AD_Recall: 0.5542 - CN_Recall: 0.6621 - MCI_Recall: 0.7960 - val_loss: 1.0739 - val_Accuracy: 0.6569 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9167 - val_MCI_Precision: 0.6005 - val_AD_Recall: 0.1538 - val_CN_Recall: 0.4459 - val_MCI_Recall: 0.9749\n",
      "Epoch 18/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.7692 - Accuracy: 0.7175 - AD_Precision: 0.5993 - CN_Precision: 0.7002 - MCI_Precision: 0.7660 - AD_Recall: 0.5181 - CN_Recall: 0.6621 - MCI_Recall: 0.7856 - val_loss: 0.9195 - val_Accuracy: 0.7343 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8085 - val_MCI_Precision: 0.6891 - val_AD_Recall: 0.1868 - val_CN_Recall: 0.7703 - val_MCI_Recall: 0.8996\n",
      "Epoch 19/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.7013 - Accuracy: 0.7386 - AD_Precision: 0.6859 - CN_Precision: 0.7032 - MCI_Precision: 0.7907 - AD_Recall: 0.5723 - CN_Recall: 0.6485 - MCI_Recall: 0.8027 - val_loss: 0.8378 - val_Accuracy: 0.6925 - val_AD_Precision: 0.8947 - val_CN_Precision: 0.5885 - val_MCI_Precision: 0.8239 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.9662 - val_MCI_Recall: 0.6067\n",
      "Epoch 20/50\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.7164 - Accuracy: 0.7275 - AD_Precision: 0.6416 - CN_Precision: 0.7025 - MCI_Precision: 0.7813 - AD_Recall: 0.5392 - CN_Recall: 0.6602 - MCI_Recall: 0.7932 - val_loss: 0.8318 - val_Accuracy: 0.7259 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8374 - val_MCI_Precision: 0.6809 - val_AD_Recall: 0.2198 - val_CN_Recall: 0.6959 - val_MCI_Recall: 0.9372\n",
      "Epoch 21/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.7119 - Accuracy: 0.7464 - AD_Precision: 0.6574 - CN_Precision: 0.7230 - MCI_Precision: 0.7993 - AD_Recall: 0.5723 - CN_Recall: 0.6893 - MCI_Recall: 0.8121 - val_loss: 1.0051 - val_Accuracy: 0.6381 - val_AD_Precision: 0.7193 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.6089 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.1959 - val_MCI_Recall: 0.9707\n",
      "Epoch 22/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.6376 - Accuracy: 0.7591 - AD_Precision: 0.6818 - CN_Precision: 0.7421 - MCI_Precision: 0.8030 - AD_Recall: 0.5873 - CN_Recall: 0.6874 - MCI_Recall: 0.8235 - val_loss: 0.7183 - val_Accuracy: 0.7657 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7500 - val_MCI_Precision: 0.7761 - val_AD_Recall: 0.2857 - val_CN_Recall: 0.8919 - val_MCI_Recall: 0.8410\n",
      "Epoch 23/50\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.6395 - Accuracy: 0.7459 - AD_Precision: 0.6879 - CN_Precision: 0.7093 - MCI_Precision: 0.7992 - AD_Recall: 0.5843 - CN_Recall: 0.6777 - MCI_Recall: 0.8046 - val_loss: 0.8639 - val_Accuracy: 0.6674 - val_AD_Precision: 0.6190 - val_CN_Precision: 0.9545 - val_MCI_Precision: 0.6597 - val_AD_Recall: 0.5714 - val_CN_Recall: 0.2838 - val_MCI_Recall: 0.9247\n",
      "Epoch 24/50\n",
      "476/476 [==============================] - 28s 60ms/step - loss: 0.6032 - Accuracy: 0.7659 - AD_Precision: 0.7208 - CN_Precision: 0.7390 - MCI_Precision: 0.8112 - AD_Recall: 0.6145 - CN_Recall: 0.6874 - MCI_Recall: 0.8273 - val_loss: 0.6077 - val_Accuracy: 0.7594 - val_AD_Precision: 0.7083 - val_CN_Precision: 0.7374 - val_MCI_Precision: 0.8450 - val_AD_Recall: 0.5604 - val_CN_Recall: 0.8919 - val_MCI_Recall: 0.7071\n",
      "Epoch 25/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.5996 - Accuracy: 0.7664 - AD_Precision: 0.7089 - CN_Precision: 0.7459 - MCI_Precision: 0.8056 - AD_Recall: 0.6235 - CN_Recall: 0.7010 - MCI_Recall: 0.8216 - val_loss: 0.5331 - val_Accuracy: 0.7887 - val_AD_Precision: 0.7571 - val_CN_Precision: 0.7987 - val_MCI_Precision: 0.8186 - val_AD_Recall: 0.5824 - val_CN_Recall: 0.8311 - val_MCI_Recall: 0.8117\n",
      "Epoch 26/50\n",
      "476/476 [==============================] - 28s 58ms/step - loss: 0.5738 - Accuracy: 0.7785 - AD_Precision: 0.7013 - CN_Precision: 0.7597 - MCI_Precision: 0.8247 - AD_Recall: 0.6295 - CN_Recall: 0.7243 - MCI_Recall: 0.8302 - val_loss: 0.5885 - val_Accuracy: 0.7762 - val_AD_Precision: 0.7121 - val_CN_Precision: 0.8718 - val_MCI_Precision: 0.7624 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.6892 - val_MCI_Recall: 0.8996\n",
      "Epoch 27/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.5605 - Accuracy: 0.7833 - AD_Precision: 0.7233 - CN_Precision: 0.7686 - MCI_Precision: 0.8250 - AD_Recall: 0.6536 - CN_Recall: 0.7223 - MCI_Recall: 0.8406 - val_loss: 0.5798 - val_Accuracy: 0.7908 - val_AD_Precision: 0.7500 - val_CN_Precision: 0.8917 - val_MCI_Precision: 0.7655 - val_AD_Recall: 0.4945 - val_CN_Recall: 0.7230 - val_MCI_Recall: 0.9289\n",
      "Epoch 28/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 0.5625 - Accuracy: 0.7859 - AD_Precision: 0.7383 - CN_Precision: 0.7766 - MCI_Precision: 0.8213 - AD_Recall: 0.6627 - CN_Recall: 0.7223 - MCI_Recall: 0.8416 - val_loss: 0.6461 - val_Accuracy: 0.7866 - val_AD_Precision: 0.9706 - val_CN_Precision: 0.8333 - val_MCI_Precision: 0.7606 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.8446 - val_MCI_Recall: 0.9038\n",
      "Epoch 29/50\n",
      "476/476 [==============================] - 28s 58ms/step - loss: 0.5201 - Accuracy: 0.7980 - AD_Precision: 0.7568 - CN_Precision: 0.7748 - MCI_Precision: 0.8358 - AD_Recall: 0.6657 - CN_Recall: 0.7417 - MCI_Recall: 0.8406 - val_loss: 0.6322 - val_Accuracy: 0.7762 - val_AD_Precision: 0.8511 - val_CN_Precision: 0.8870 - val_MCI_Precision: 0.7314 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.6892 - val_MCI_Recall: 0.9456\n",
      "Epoch 30/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 0.5242 - Accuracy: 0.7985 - AD_Precision: 0.7300 - CN_Precision: 0.7934 - MCI_Precision: 0.8340 - AD_Recall: 0.6596 - CN_Recall: 0.7456 - MCI_Recall: 0.8482 - val_loss: 0.6305 - val_Accuracy: 0.7531 - val_AD_Precision: 0.7843 - val_CN_Precision: 0.8942 - val_MCI_Precision: 0.7203 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.6284 - val_MCI_Recall: 0.9372\n",
      "Epoch 31/50\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.5105 - Accuracy: 0.8006 - AD_Precision: 0.7128 - CN_Precision: 0.8041 - MCI_Precision: 0.8432 - AD_Recall: 0.6355 - CN_Recall: 0.7650 - MCI_Recall: 0.8520 - val_loss: 0.6637 - val_Accuracy: 0.7824 - val_AD_Precision: 0.9231 - val_CN_Precision: 0.8026 - val_MCI_Precision: 0.7589 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.8243 - val_MCI_Recall: 0.8954\n",
      "Epoch 32/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 0.5229 - Accuracy: 0.8075 - AD_Precision: 0.7533 - CN_Precision: 0.7967 - MCI_Precision: 0.8415 - AD_Recall: 0.6807 - CN_Recall: 0.7612 - MCI_Recall: 0.8510 - val_loss: 0.6241 - val_Accuracy: 0.7824 - val_AD_Precision: 0.6354 - val_CN_Precision: 0.9074 - val_MCI_Precision: 0.7915 - val_AD_Recall: 0.6703 - val_CN_Recall: 0.6622 - val_MCI_Recall: 0.8577\n",
      "Epoch 33/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 0.4621 - Accuracy: 0.8185 - AD_Precision: 0.7848 - CN_Precision: 0.8116 - MCI_Precision: 0.8519 - AD_Recall: 0.7139 - CN_Recall: 0.7612 - MCI_Recall: 0.8624 - val_loss: 0.6334 - val_Accuracy: 0.7678 - val_AD_Precision: 0.8723 - val_CN_Precision: 0.7308 - val_MCI_Precision: 0.7967 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.8033\n",
      "Epoch 34/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 0.4753 - Accuracy: 0.8122 - AD_Precision: 0.7828 - CN_Precision: 0.8058 - MCI_Precision: 0.8444 - AD_Recall: 0.6837 - CN_Recall: 0.7573 - MCI_Recall: 0.8653 - val_loss: 0.7122 - val_Accuracy: 0.7343 - val_AD_Precision: 0.7667 - val_CN_Precision: 0.9367 - val_MCI_Precision: 0.6921 - val_AD_Recall: 0.5055 - val_CN_Recall: 0.5000 - val_MCI_Recall: 0.9498\n",
      "Epoch 35/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.4557 - Accuracy: 0.8196 - AD_Precision: 0.7993 - CN_Precision: 0.7971 - MCI_Precision: 0.8476 - AD_Recall: 0.7319 - CN_Recall: 0.7553 - MCI_Recall: 0.8653 - val_loss: 0.6301 - val_Accuracy: 0.7720 - val_AD_Precision: 0.8070 - val_CN_Precision: 0.7268 - val_MCI_Precision: 0.8479 - val_AD_Recall: 0.5055 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.7699\n",
      "Epoch 36/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 0.4901 - Accuracy: 0.8138 - AD_Precision: 0.7702 - CN_Precision: 0.7867 - MCI_Precision: 0.8543 - AD_Recall: 0.7169 - CN_Recall: 0.7592 - MCI_Recall: 0.8510 - val_loss: 1.4517 - val_Accuracy: 0.5837 - val_AD_Precision: 1.0000 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5469 - val_AD_Recall: 0.1868 - val_CN_Recall: 0.1486 - val_MCI_Recall: 1.0000\n",
      "Epoch 37/50\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.4437 - Accuracy: 0.8343 - AD_Precision: 0.7905 - CN_Precision: 0.8344 - MCI_Precision: 0.8622 - AD_Recall: 0.7048 - CN_Recall: 0.7825 - MCI_Recall: 0.8843 - val_loss: 0.8335 - val_Accuracy: 0.7176 - val_AD_Precision: 0.6105 - val_CN_Precision: 0.9483 - val_MCI_Precision: 0.7261 - val_AD_Recall: 0.6374 - val_CN_Recall: 0.3716 - val_MCI_Recall: 0.9205\n",
      "Epoch 38/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.4293 - Accuracy: 0.8290 - AD_Precision: 0.7638 - CN_Precision: 0.8357 - MCI_Precision: 0.8601 - AD_Recall: 0.7108 - CN_Recall: 0.7903 - MCI_Recall: 0.8634 - val_loss: 0.7159 - val_Accuracy: 0.7824 - val_AD_Precision: 0.9744 - val_CN_Precision: 0.9196 - val_MCI_Precision: 0.7152 - val_AD_Recall: 0.4176 - val_CN_Recall: 0.6959 - val_MCI_Recall: 0.9665\n",
      "Epoch 39/50\n",
      "476/476 [==============================] - 27s 58ms/step - loss: 0.4571 - Accuracy: 0.8159 - AD_Precision: 0.7819 - CN_Precision: 0.8135 - MCI_Precision: 0.8437 - AD_Recall: 0.7018 - CN_Recall: 0.7709 - MCI_Recall: 0.8605 - val_loss: 0.6641 - val_Accuracy: 0.7720 - val_AD_Precision: 0.8837 - val_CN_Precision: 0.9524 - val_MCI_Precision: 0.7050 - val_AD_Recall: 0.4176 - val_CN_Recall: 0.6757 - val_MCI_Recall: 0.9498\n",
      "Epoch 40/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 0.4440 - Accuracy: 0.8353 - AD_Precision: 0.7966 - CN_Precision: 0.8256 - MCI_Precision: 0.8626 - AD_Recall: 0.7078 - CN_Recall: 0.7903 - MCI_Recall: 0.8757 - val_loss: 0.6055 - val_Accuracy: 0.7929 - val_AD_Precision: 0.9167 - val_CN_Precision: 0.9076 - val_MCI_Precision: 0.7368 - val_AD_Recall: 0.4835 - val_CN_Recall: 0.7297 - val_MCI_Recall: 0.9372\n",
      "Epoch 41/50\n",
      "476/476 [==============================] - 24s 51ms/step - loss: 0.4365 - Accuracy: 0.8290 - AD_Precision: 0.8067 - CN_Precision: 0.8268 - MCI_Precision: 0.8525 - AD_Recall: 0.7289 - CN_Recall: 0.7786 - MCI_Recall: 0.8719 - val_loss: 0.6789 - val_Accuracy: 0.7678 - val_AD_Precision: 0.6204 - val_CN_Precision: 0.9326 - val_MCI_Precision: 0.7860 - val_AD_Recall: 0.7363 - val_CN_Recall: 0.5608 - val_MCI_Recall: 0.8912\n",
      "Epoch 42/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 0.3941 - Accuracy: 0.8438 - AD_Precision: 0.7910 - CN_Precision: 0.8449 - MCI_Precision: 0.8784 - AD_Recall: 0.7410 - CN_Recall: 0.8039 - MCI_Recall: 0.8843 - val_loss: 0.6229 - val_Accuracy: 0.7929 - val_AD_Precision: 0.6330 - val_CN_Precision: 0.9307 - val_MCI_Precision: 0.8294 - val_AD_Recall: 0.7582 - val_CN_Recall: 0.6351 - val_MCI_Recall: 0.8745\n",
      "Epoch 43/50\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3318 - Accuracy: 0.8690 - AD_Precision: 0.8590 - CN_Precision: 0.8583 - MCI_Precision: 0.8922 - AD_Recall: 0.7892 - CN_Recall: 0.8117 - MCI_Recall: 0.9032 - val_loss: 0.5288 - val_Accuracy: 0.8117 - val_AD_Precision: 0.7353 - val_CN_Precision: 0.8828 - val_MCI_Precision: 0.8081 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.7635 - val_MCI_Recall: 0.9163\n",
      "Epoch 44/50\n",
      "476/476 [==============================] - 25s 54ms/step - loss: 0.4108 - Accuracy: 0.8443 - AD_Precision: 0.8361 - CN_Precision: 0.8368 - MCI_Precision: 0.8637 - AD_Recall: 0.7530 - CN_Recall: 0.7864 - MCI_Recall: 0.8899 - val_loss: 0.5568 - val_Accuracy: 0.7929 - val_AD_Precision: 0.7833 - val_CN_Precision: 0.8647 - val_MCI_Precision: 0.7742 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.7770 - val_MCI_Recall: 0.9038\n",
      "Epoch 45/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 0.3910 - Accuracy: 0.8511 - AD_Precision: 0.8158 - CN_Precision: 0.8551 - MCI_Precision: 0.8690 - AD_Recall: 0.7470 - CN_Recall: 0.8252 - MCI_Recall: 0.8871 - val_loss: 0.5554 - val_Accuracy: 0.7992 - val_AD_Precision: 0.7903 - val_CN_Precision: 0.8403 - val_MCI_Precision: 0.8053 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.8176 - val_MCI_Recall: 0.8828\n",
      "Epoch 46/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 0.4114 - Accuracy: 0.8469 - AD_Precision: 0.7700 - CN_Precision: 0.8569 - MCI_Precision: 0.8744 - AD_Recall: 0.7259 - CN_Recall: 0.8136 - MCI_Recall: 0.8786 - val_loss: 0.8396 - val_Accuracy: 0.7741 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8968 - val_MCI_Precision: 0.7103 - val_AD_Recall: 0.2857 - val_CN_Recall: 0.7635 - val_MCI_Recall: 0.9540\n",
      "Epoch 47/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 0.3816 - Accuracy: 0.8574 - AD_Precision: 0.7981 - CN_Precision: 0.8714 - MCI_Precision: 0.8802 - AD_Recall: 0.7500 - CN_Recall: 0.8291 - MCI_Recall: 0.8852 - val_loss: 0.5996 - val_Accuracy: 0.7636 - val_AD_Precision: 0.5564 - val_CN_Precision: 0.8540 - val_MCI_Precision: 0.8723 - val_AD_Recall: 0.8132 - val_CN_Recall: 0.7905 - val_MCI_Recall: 0.6862\n",
      "Epoch 48/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 0.3924 - Accuracy: 0.8569 - AD_Precision: 0.8173 - CN_Precision: 0.8612 - MCI_Precision: 0.8797 - AD_Recall: 0.7681 - CN_Recall: 0.8194 - MCI_Recall: 0.8947 - val_loss: 0.6498 - val_Accuracy: 0.7950 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7446 - val_MCI_Precision: 0.8032 - val_AD_Recall: 0.4615 - val_CN_Recall: 0.9257 - val_MCI_Recall: 0.8368\n",
      "Epoch 49/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 0.3553 - Accuracy: 0.8538 - AD_Precision: 0.8167 - CN_Precision: 0.8580 - MCI_Precision: 0.8781 - AD_Recall: 0.7651 - CN_Recall: 0.8330 - MCI_Recall: 0.8814 - val_loss: 0.5494 - val_Accuracy: 0.8117 - val_AD_Precision: 0.9000 - val_CN_Precision: 0.8592 - val_MCI_Precision: 0.7865 - val_AD_Recall: 0.4945 - val_CN_Recall: 0.8243 - val_MCI_Recall: 0.9247\n",
      "Epoch 50/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.3333 - Accuracy: 0.8738 - AD_Precision: 0.8548 - CN_Precision: 0.8452 - MCI_Precision: 0.8996 - AD_Recall: 0.7801 - CN_Recall: 0.8272 - MCI_Recall: 0.9099 - val_loss: 0.6997 - val_Accuracy: 0.8033 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8160 - val_MCI_Precision: 0.7814 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.9121\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          AD       0.75      0.58      0.65        91\n",
      "          CN       0.88      0.77      0.82       148\n",
      "         MCI       0.80      0.92      0.86       239\n",
      "\n",
      "    accuracy                           0.81       478\n",
      "   macro avg       0.81      0.76      0.78       478\n",
      "weighted avg       0.81      0.81      0.81       478\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "savepath = 'models'\n",
    "lr=0.000001\n",
    "if not os.path.isdir(savepath):\n",
    "      os.makedirs(savepath)\n",
    "csvloger_callback = tf.keras.callbacks.CSVLogger(f'{savepath}history.csv', separator=',', append=False)\n",
    "\n",
    "modelcheckpoint_callback = tf.keras.callbacks.ModelCheckpoint(savepath+'efficientnet_row_10E-5_weights-epoch:{epoch:03d}-val_Accuracy:{val_Accuracy:.4f}.h5',monitor='val_Accuracy'\n",
    "    ,verbose=0,save_best_only=True,save_weights_only=True,mode='max',save_freq='epoch' , initial_value_threshold=0.85)\n",
    "\n",
    "OPT = tf.keras.optimizers.Adam(learning_rate=lr)\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "history04 = model04.fit(training_image_datagen, epochs=EPOCH, validation_data=testing_image_datagen,callbacks=[csvloger_callback,modelcheckpoint_callback])\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "model04.load_weights(glob.glob(f\"{savepath}efficientnet_row_10E-5_weights-epoch:*-val_Accuracy:{max(history04.history['val_Accuracy']):.4f}.h5\")[0])\n",
    "model04.save(f\"{savepath}/efficientnet_50_0.000001.h5\")\n",
    "p2 = model04.predict(testing_image_datagen)\n",
    "predicted_lab = p2.argmax(axis=-1)\n",
    "true_lab = testing_image_datagen.classes\n",
    "plot_confusion_matrix(true_lab, predicted_lab, testing_image_datagen.class_indices.keys(),savepath)\n",
    "report = classification_report(true_lab, predicted_lab, target_names=testing_image_datagen.class_indices.keys())\n",
    "print(report)\n",
    "dump_text(report,f'{savepath}classification_report.txt')\n",
    "np.save(\"efficientnet_50_0.000001.npy\",p2 )   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b46f5ea-c7bc-4c73-9b80-5c2d4361b745",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-20 11:00:15.260116: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "476/476 [==============================] - 40s 61ms/step - loss: 13.7586 - Accuracy: 0.4129 - AD_Precision: 0.1843 - CN_Precision: 0.2837 - MCI_Precision: 0.5491 - AD_Recall: 0.1837 - CN_Recall: 0.2699 - MCI_Recall: 0.5408 - val_loss: 1.6368 - val_Accuracy: 0.4540 - val_AD_Precision: 0.1860 - val_CN_Precision: 0.0000e+00 - val_MCI_Precision: 0.5184 - val_AD_Recall: 0.1758 - val_CN_Recall: 0.0000e+00 - val_MCI_Recall: 0.8243\n",
      "Epoch 2/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 3.0362 - Accuracy: 0.4308 - AD_Precision: 0.2054 - CN_Precision: 0.3111 - MCI_Precision: 0.5646 - AD_Recall: 0.2078 - CN_Recall: 0.2893 - MCI_Recall: 0.5474 - val_loss: 3.9092 - val_Accuracy: 0.3096 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.3082 - val_MCI_Precision: 0.0000e+00 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.9932 - val_MCI_Recall: 0.0000e+00\n",
      "Epoch 3/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 2.8283 - Accuracy: 0.4482 - AD_Precision: 0.2143 - CN_Precision: 0.3260 - MCI_Precision: 0.5771 - AD_Recall: 0.1898 - CN_Recall: 0.3184 - MCI_Recall: 0.5645 - val_loss: 3.9568 - val_Accuracy: 0.2490 - val_AD_Precision: 0.1949 - val_CN_Precision: 0.6923 - val_MCI_Precision: 0.4615 - val_AD_Recall: 0.8462 - val_CN_Recall: 0.1216 - val_MCI_Recall: 0.0502\n",
      "Epoch 4/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 2.7831 - Accuracy: 0.4487 - AD_Precision: 0.1988 - CN_Precision: 0.3581 - MCI_Precision: 0.5783 - AD_Recall: 0.1958 - CN_Recall: 0.3456 - MCI_Recall: 0.5607 - val_loss: 3.9002 - val_Accuracy: 0.5000 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.0000e+00 - val_MCI_Precision: 0.5000 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.0000e+00 - val_MCI_Recall: 1.0000\n",
      "Epoch 5/50\n",
      "476/476 [==============================] - 24s 50ms/step - loss: 2.7413 - Accuracy: 0.4629 - AD_Precision: 0.2300 - CN_Precision: 0.3678 - MCI_Precision: 0.5821 - AD_Recall: 0.2169 - CN_Recall: 0.3456 - MCI_Recall: 0.5816 - val_loss: 3.2007 - val_Accuracy: 0.5000 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.0000e+00 - val_MCI_Precision: 0.5000 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.0000e+00 - val_MCI_Recall: 1.0000\n",
      "Epoch 6/50\n",
      "476/476 [==============================] - 24s 51ms/step - loss: 2.6428 - Accuracy: 0.4629 - AD_Precision: 0.2532 - CN_Precision: 0.3516 - MCI_Precision: 0.5821 - AD_Recall: 0.2410 - CN_Recall: 0.3359 - MCI_Recall: 0.5787 - val_loss: 8.6728 - val_Accuracy: 0.3096 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.3096 - val_MCI_Precision: 0.0000e+00 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 1.0000 - val_MCI_Recall: 0.0000e+00\n",
      "Epoch 7/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 2.5934 - Accuracy: 0.4629 - AD_Precision: 0.2469 - CN_Precision: 0.3644 - MCI_Precision: 0.5807 - AD_Recall: 0.2410 - CN_Recall: 0.3573 - MCI_Recall: 0.5702 - val_loss: 1.8207 - val_Accuracy: 0.5481 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8148 - val_MCI_Precision: 0.5282 - val_AD_Recall: 0.0549 - val_CN_Recall: 0.1486 - val_MCI_Recall: 0.9791\n",
      "Epoch 8/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 2.4597 - Accuracy: 0.5008 - AD_Precision: 0.3148 - CN_Precision: 0.4000 - MCI_Precision: 0.6157 - AD_Recall: 0.3072 - CN_Recall: 0.3883 - MCI_Recall: 0.6034 - val_loss: 3.2945 - val_Accuracy: 0.5042 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5021 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.0135 - val_MCI_Recall: 1.0000\n",
      "Epoch 9/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 2.2500 - Accuracy: 0.5208 - AD_Precision: 0.3377 - CN_Precision: 0.4320 - MCI_Precision: 0.6199 - AD_Recall: 0.3133 - CN_Recall: 0.4136 - MCI_Recall: 0.6252 - val_loss: 1.4243 - val_Accuracy: 0.4142 - val_AD_Precision: 0.2991 - val_CN_Precision: 0.5215 - val_MCI_Precision: 0.6216 - val_AD_Recall: 0.7363 - val_CN_Recall: 0.5743 - val_MCI_Recall: 0.0962\n",
      "Epoch 10/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 2.2413 - Accuracy: 0.5266 - AD_Precision: 0.3098 - CN_Precision: 0.4498 - MCI_Precision: 0.6368 - AD_Recall: 0.3042 - CN_Recall: 0.4350 - MCI_Recall: 0.6338 - val_loss: 1.4832 - val_Accuracy: 0.5628 - val_AD_Precision: 0.3913 - val_CN_Precision: 0.6852 - val_MCI_Precision: 0.5703 - val_AD_Recall: 0.1978 - val_CN_Recall: 0.2500 - val_MCI_Recall: 0.8828\n",
      "Epoch 11/50\n",
      "476/476 [==============================] - 24s 51ms/step - loss: 2.1919 - Accuracy: 0.4934 - AD_Precision: 0.3067 - CN_Precision: 0.4029 - MCI_Precision: 0.5972 - AD_Recall: 0.2892 - CN_Recall: 0.3786 - MCI_Recall: 0.6006 - val_loss: 2.3671 - val_Accuracy: 0.5167 - val_AD_Precision: 0.7500 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5086 - val_AD_Recall: 0.0989 - val_CN_Recall: 0.0135 - val_MCI_Recall: 0.9874\n",
      "Epoch 12/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 1.9785 - Accuracy: 0.5287 - AD_Precision: 0.3302 - CN_Precision: 0.4489 - MCI_Precision: 0.6319 - AD_Recall: 0.3163 - CN_Recall: 0.4350 - MCI_Recall: 0.6319 - val_loss: 1.7882 - val_Accuracy: 0.4686 - val_AD_Precision: 0.6364 - val_CN_Precision: 0.3949 - val_MCI_Precision: 0.7363 - val_AD_Recall: 0.1538 - val_CN_Recall: 0.9392 - val_MCI_Recall: 0.2803\n",
      "Epoch 13/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.9660 - Accuracy: 0.5576 - AD_Precision: 0.3531 - CN_Precision: 0.4969 - MCI_Precision: 0.6523 - AD_Recall: 0.3404 - CN_Recall: 0.4641 - MCI_Recall: 0.6584 - val_loss: 1.6638 - val_Accuracy: 0.5377 - val_AD_Precision: 0.5789 - val_CN_Precision: 0.4493 - val_MCI_Precision: 0.7292 - val_AD_Recall: 0.1209 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.4393\n",
      "Epoch 14/50\n",
      "476/476 [==============================] - 24s 51ms/step - loss: 1.8887 - Accuracy: 0.5618 - AD_Precision: 0.3950 - CN_Precision: 0.4881 - MCI_Precision: 0.6530 - AD_Recall: 0.3795 - CN_Recall: 0.4777 - MCI_Recall: 0.6499 - val_loss: 1.4229 - val_Accuracy: 0.5042 - val_AD_Precision: 0.3810 - val_CN_Precision: 0.4806 - val_MCI_Precision: 0.7614 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.2803\n",
      "Epoch 15/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.8642 - Accuracy: 0.5581 - AD_Precision: 0.3586 - CN_Precision: 0.5030 - MCI_Precision: 0.6480 - AD_Recall: 0.3283 - CN_Recall: 0.4874 - MCI_Recall: 0.6480 - val_loss: 3.5323 - val_Accuracy: 0.5356 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.6190 - val_MCI_Precision: 0.5275 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.1757 - val_MCI_Recall: 0.9623\n",
      "Epoch 16/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 1.7904 - Accuracy: 0.5676 - AD_Precision: 0.3636 - CN_Precision: 0.5189 - MCI_Precision: 0.6613 - AD_Recall: 0.3373 - CN_Recall: 0.5068 - MCI_Recall: 0.6613 - val_loss: 1.0704 - val_Accuracy: 0.5983 - val_AD_Precision: 0.5273 - val_CN_Precision: 0.5684 - val_MCI_Precision: 0.7205 - val_AD_Recall: 0.3187 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.4854\n",
      "Epoch 17/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.6744 - Accuracy: 0.5829 - AD_Precision: 0.4326 - CN_Precision: 0.5327 - MCI_Precision: 0.6560 - AD_Recall: 0.4157 - CN_Recall: 0.5068 - MCI_Recall: 0.6603 - val_loss: 2.5317 - val_Accuracy: 0.4477 - val_AD_Precision: 0.8333 - val_CN_Precision: 0.3808 - val_MCI_Precision: 0.7273 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.9932 - val_MCI_Recall: 0.2008\n",
      "Epoch 18/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 1.6152 - Accuracy: 0.5981 - AD_Precision: 0.4295 - CN_Precision: 0.5271 - MCI_Precision: 0.6906 - AD_Recall: 0.4127 - CN_Recall: 0.5107 - MCI_Recall: 0.6841 - val_loss: 1.0512 - val_Accuracy: 0.6674 - val_AD_Precision: 0.7500 - val_CN_Precision: 0.7176 - val_MCI_Precision: 0.6497 - val_AD_Recall: 0.1978 - val_CN_Recall: 0.6351 - val_MCI_Recall: 0.8536\n",
      "Epoch 19/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.5897 - Accuracy: 0.6128 - AD_Precision: 0.4221 - CN_Precision: 0.5729 - MCI_Precision: 0.6969 - AD_Recall: 0.3916 - CN_Recall: 0.5573 - MCI_Recall: 0.7002 - val_loss: 1.1684 - val_Accuracy: 0.6339 - val_AD_Precision: 0.5000 - val_CN_Precision: 0.8226 - val_MCI_Precision: 0.6369 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.3446 - val_MCI_Recall: 0.8661\n",
      "Epoch 20/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 1.5184 - Accuracy: 0.6007 - AD_Precision: 0.4072 - CN_Precision: 0.5650 - MCI_Precision: 0.6863 - AD_Recall: 0.3765 - CN_Recall: 0.5573 - MCI_Recall: 0.6746 - val_loss: 2.2986 - val_Accuracy: 0.5188 - val_AD_Precision: 0.3578 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5640 - val_AD_Recall: 0.4286 - val_CN_Recall: 0.0135 - val_MCI_Recall: 0.8661\n",
      "Epoch 21/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.4776 - Accuracy: 0.6318 - AD_Precision: 0.4748 - CN_Precision: 0.5902 - MCI_Precision: 0.7071 - AD_Recall: 0.4548 - CN_Recall: 0.5592 - MCI_Recall: 0.7078 - val_loss: 1.4916 - val_Accuracy: 0.4937 - val_AD_Precision: 0.3019 - val_CN_Precision: 0.7789 - val_MCI_Precision: 0.8000 - val_AD_Recall: 0.8791 - val_CN_Recall: 0.5000 - val_MCI_Recall: 0.3013\n",
      "Epoch 22/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 1.4923 - Accuracy: 0.6155 - AD_Precision: 0.4236 - CN_Precision: 0.5734 - MCI_Precision: 0.6966 - AD_Recall: 0.4006 - CN_Recall: 0.5534 - MCI_Recall: 0.6992 - val_loss: 3.8018 - val_Accuracy: 0.2657 - val_AD_Precision: 0.2111 - val_CN_Precision: 0.8788 - val_MCI_Precision: 0.7500 - val_AD_Recall: 1.0000 - val_CN_Recall: 0.1959 - val_MCI_Recall: 0.0251\n",
      "Epoch 23/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 1.4438 - Accuracy: 0.6102 - AD_Precision: 0.4105 - CN_Precision: 0.5889 - MCI_Precision: 0.6925 - AD_Recall: 0.4006 - CN_Recall: 0.5786 - MCI_Recall: 0.6860 - val_loss: 2.7687 - val_Accuracy: 0.5167 - val_AD_Precision: 1.0000 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5085 - val_AD_Recall: 0.0330 - val_CN_Recall: 0.0338 - val_MCI_Recall: 1.0000\n",
      "Epoch 24/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.3803 - Accuracy: 0.6276 - AD_Precision: 0.4554 - CN_Precision: 0.6121 - MCI_Precision: 0.6945 - AD_Recall: 0.4307 - CN_Recall: 0.5883 - MCI_Recall: 0.6945 - val_loss: 1.4538 - val_Accuracy: 0.5565 - val_AD_Precision: 0.3902 - val_CN_Precision: 0.8919 - val_MCI_Precision: 0.5922 - val_AD_Recall: 0.5275 - val_CN_Recall: 0.2230 - val_MCI_Recall: 0.7657\n",
      "Epoch 25/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 1.3798 - Accuracy: 0.6318 - AD_Precision: 0.4448 - CN_Precision: 0.5984 - MCI_Precision: 0.7085 - AD_Recall: 0.4127 - CN_Recall: 0.5845 - MCI_Recall: 0.7078 - val_loss: 3.0671 - val_Accuracy: 0.4079 - val_AD_Precision: 0.2909 - val_CN_Precision: 0.5722 - val_MCI_Precision: 0.8000 - val_AD_Recall: 0.8791 - val_CN_Recall: 0.7500 - val_MCI_Recall: 0.0167\n",
      "Epoch 26/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 1.2673 - Accuracy: 0.6665 - AD_Precision: 0.5321 - CN_Precision: 0.6198 - MCI_Precision: 0.7359 - AD_Recall: 0.5000 - CN_Recall: 0.6078 - MCI_Recall: 0.7324 - val_loss: 1.2979 - val_Accuracy: 0.6297 - val_AD_Precision: 0.5522 - val_CN_Precision: 0.9487 - val_MCI_Precision: 0.6150 - val_AD_Recall: 0.4066 - val_CN_Recall: 0.2500 - val_MCI_Recall: 0.9289\n",
      "Epoch 27/50\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 1.2950 - Accuracy: 0.6639 - AD_Precision: 0.5065 - CN_Precision: 0.6538 - MCI_Precision: 0.7192 - AD_Recall: 0.4729 - CN_Recall: 0.6233 - MCI_Recall: 0.7315 - val_loss: 1.5785 - val_Accuracy: 0.6841 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7170 - val_MCI_Precision: 0.6635 - val_AD_Recall: 0.0549 - val_CN_Recall: 0.7703 - val_MCI_Recall: 0.8661\n",
      "Epoch 28/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 1.2280 - Accuracy: 0.6696 - AD_Precision: 0.5188 - CN_Precision: 0.6541 - MCI_Precision: 0.7328 - AD_Recall: 0.5000 - CN_Recall: 0.6388 - MCI_Recall: 0.7287 - val_loss: 1.5351 - val_Accuracy: 0.6276 - val_AD_Precision: 0.8750 - val_CN_Precision: 0.9730 - val_MCI_Precision: 0.5770 - val_AD_Recall: 0.3077 - val_CN_Recall: 0.2432 - val_MCI_Recall: 0.9874\n",
      "Epoch 29/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.2377 - Accuracy: 0.6749 - AD_Precision: 0.5158 - CN_Precision: 0.6514 - MCI_Precision: 0.7446 - AD_Recall: 0.4910 - CN_Recall: 0.6350 - MCI_Recall: 0.7467 - val_loss: 1.0578 - val_Accuracy: 0.6151 - val_AD_Precision: 0.3631 - val_CN_Precision: 0.8108 - val_MCI_Precision: 0.7598 - val_AD_Recall: 0.6703 - val_CN_Recall: 0.6081 - val_MCI_Recall: 0.5690\n",
      "Epoch 30/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 1.1785 - Accuracy: 0.6902 - AD_Precision: 0.5513 - CN_Precision: 0.6582 - MCI_Precision: 0.7531 - AD_Recall: 0.5181 - CN_Recall: 0.6544 - MCI_Recall: 0.7524 - val_loss: 1.1150 - val_Accuracy: 0.7259 - val_AD_Precision: 0.9231 - val_CN_Precision: 0.8211 - val_MCI_Precision: 0.6759 - val_AD_Recall: 0.2637 - val_CN_Recall: 0.6824 - val_MCI_Recall: 0.9163\n",
      "Epoch 31/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 1.1926 - Accuracy: 0.6833 - AD_Precision: 0.5338 - CN_Precision: 0.6561 - MCI_Precision: 0.7452 - AD_Recall: 0.5000 - CN_Recall: 0.6447 - MCI_Recall: 0.7438 - val_loss: 1.6520 - val_Accuracy: 0.6172 - val_AD_Precision: 0.4086 - val_CN_Precision: 0.9070 - val_MCI_Precision: 0.6420 - val_AD_Recall: 0.4176 - val_CN_Recall: 0.2635 - val_MCI_Recall: 0.9079\n",
      "Epoch 32/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 1.1299 - Accuracy: 0.6917 - AD_Precision: 0.5503 - CN_Precision: 0.6680 - MCI_Precision: 0.7500 - AD_Recall: 0.5271 - CN_Recall: 0.6524 - MCI_Recall: 0.7571 - val_loss: 1.2781 - val_Accuracy: 0.6904 - val_AD_Precision: 0.8400 - val_CN_Precision: 0.9167 - val_MCI_Precision: 0.6304 - val_AD_Recall: 0.2308 - val_CN_Recall: 0.5203 - val_MCI_Recall: 0.9707\n",
      "Epoch 33/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 1.1261 - Accuracy: 0.7049 - AD_Precision: 0.5654 - CN_Precision: 0.6819 - MCI_Precision: 0.7629 - AD_Recall: 0.5211 - CN_Recall: 0.6660 - MCI_Recall: 0.7723 - val_loss: 1.4500 - val_Accuracy: 0.5439 - val_AD_Precision: 0.3178 - val_CN_Precision: 0.9189 - val_MCI_Precision: 0.7368 - val_AD_Recall: 0.8242 - val_CN_Recall: 0.4595 - val_MCI_Recall: 0.4686\n",
      "Epoch 34/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 1.0941 - Accuracy: 0.7217 - AD_Precision: 0.6094 - CN_Precision: 0.6865 - MCI_Precision: 0.7825 - AD_Recall: 0.5873 - CN_Recall: 0.6718 - MCI_Recall: 0.7818 - val_loss: 2.6265 - val_Accuracy: 0.5481 - val_AD_Precision: 0.8333 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5277 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.0608 - val_MCI_Recall: 0.9958\n",
      "Epoch 35/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 1.0560 - Accuracy: 0.7238 - AD_Precision: 0.6057 - CN_Precision: 0.7101 - MCI_Precision: 0.7732 - AD_Recall: 0.5783 - CN_Recall: 0.6990 - MCI_Recall: 0.7732 - val_loss: 1.0360 - val_Accuracy: 0.6611 - val_AD_Precision: 0.4296 - val_CN_Precision: 0.8654 - val_MCI_Precision: 0.7318 - val_AD_Recall: 0.6703 - val_CN_Recall: 0.6081 - val_MCI_Recall: 0.6736\n",
      "Epoch 36/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 1.0548 - Accuracy: 0.7154 - AD_Precision: 0.5542 - CN_Precision: 0.7131 - MCI_Precision: 0.7752 - AD_Recall: 0.5392 - CN_Recall: 0.6951 - MCI_Recall: 0.7723 - val_loss: 0.8936 - val_Accuracy: 0.7134 - val_AD_Precision: 0.5833 - val_CN_Precision: 0.8505 - val_MCI_Precision: 0.7138 - val_AD_Recall: 0.6154 - val_CN_Recall: 0.6149 - val_MCI_Recall: 0.8033\n",
      "Epoch 37/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.0466 - Accuracy: 0.7265 - AD_Precision: 0.6019 - CN_Precision: 0.7052 - MCI_Precision: 0.7802 - AD_Recall: 0.5783 - CN_Recall: 0.6874 - MCI_Recall: 0.7846 - val_loss: 5.5578 - val_Accuracy: 0.3556 - val_AD_Precision: 0.7895 - val_CN_Precision: 0.3288 - val_MCI_Precision: 0.6429 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.9865 - val_MCI_Recall: 0.0377\n",
      "Epoch 38/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 1.0007 - Accuracy: 0.7307 - AD_Precision: 0.5904 - CN_Precision: 0.7151 - MCI_Precision: 0.7870 - AD_Recall: 0.5904 - CN_Recall: 0.7068 - MCI_Recall: 0.7713 - val_loss: 1.8556 - val_Accuracy: 0.6172 - val_AD_Precision: 0.9412 - val_CN_Precision: 0.5160 - val_MCI_Precision: 0.7472 - val_AD_Recall: 0.1758 - val_CN_Recall: 0.9797 - val_MCI_Recall: 0.5565\n",
      "Epoch 39/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 1.0611 - Accuracy: 0.7217 - AD_Precision: 0.6302 - CN_Precision: 0.6840 - MCI_Precision: 0.7736 - AD_Recall: 0.5904 - CN_Recall: 0.6893 - MCI_Recall: 0.7685 - val_loss: 1.0767 - val_Accuracy: 0.6485 - val_AD_Precision: 0.4277 - val_CN_Precision: 0.7267 - val_MCI_Precision: 0.8571 - val_AD_Recall: 0.8132 - val_CN_Recall: 0.8446 - val_MCI_Recall: 0.4519\n",
      "Epoch 40/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 1.0018 - Accuracy: 0.7312 - AD_Precision: 0.6019 - CN_Precision: 0.7174 - MCI_Precision: 0.7858 - AD_Recall: 0.5783 - CN_Recall: 0.6951 - MCI_Recall: 0.7903 - val_loss: 1.4096 - val_Accuracy: 0.6569 - val_AD_Precision: 0.8293 - val_CN_Precision: 0.5396 - val_MCI_Precision: 0.8098 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.9662 - val_MCI_Recall: 0.5523\n",
      "Epoch 41/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 0.9875 - Accuracy: 0.7307 - AD_Precision: 0.6324 - CN_Precision: 0.6955 - MCI_Precision: 0.7854 - AD_Recall: 0.6114 - CN_Recall: 0.6874 - MCI_Recall: 0.7846 - val_loss: 3.0986 - val_Accuracy: 0.3808 - val_AD_Precision: 0.2368 - val_CN_Precision: 0.8906 - val_MCI_Precision: 0.8125 - val_AD_Recall: 0.9341 - val_CN_Recall: 0.3851 - val_MCI_Recall: 0.1632\n",
      "Epoch 42/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 1.0010 - Accuracy: 0.7407 - AD_Precision: 0.6080 - CN_Precision: 0.7323 - MCI_Precision: 0.7981 - AD_Recall: 0.5934 - CN_Recall: 0.7223 - MCI_Recall: 0.7913 - val_loss: 1.5246 - val_Accuracy: 0.6611 - val_AD_Precision: 0.8387 - val_CN_Precision: 0.9492 - val_MCI_Precision: 0.6047 - val_AD_Recall: 0.2857 - val_CN_Recall: 0.3784 - val_MCI_Recall: 0.9791\n",
      "Epoch 43/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 0.9921 - Accuracy: 0.7270 - AD_Precision: 0.5918 - CN_Precision: 0.7166 - MCI_Precision: 0.7758 - AD_Recall: 0.5633 - CN_Recall: 0.6971 - MCI_Recall: 0.7780 - val_loss: 0.9695 - val_Accuracy: 0.6820 - val_AD_Precision: 0.5041 - val_CN_Precision: 0.8200 - val_MCI_Precision: 0.7347 - val_AD_Recall: 0.6703 - val_CN_Recall: 0.5541 - val_MCI_Recall: 0.7531\n",
      "Epoch 44/50\n",
      "476/476 [==============================] - 24s 51ms/step - loss: 0.9399 - Accuracy: 0.7464 - AD_Precision: 0.6469 - CN_Precision: 0.7269 - MCI_Precision: 0.7964 - AD_Recall: 0.6235 - CN_Recall: 0.7184 - MCI_Recall: 0.7903 - val_loss: 1.7579 - val_Accuracy: 0.6213 - val_AD_Precision: 0.8846 - val_CN_Precision: 0.9512 - val_MCI_Precision: 0.5718 - val_AD_Recall: 0.2527 - val_CN_Recall: 0.2635 - val_MCI_Recall: 0.9833\n",
      "Epoch 45/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.9651 - Accuracy: 0.7475 - AD_Precision: 0.6460 - CN_Precision: 0.7208 - MCI_Precision: 0.8006 - AD_Recall: 0.6265 - CN_Recall: 0.7068 - MCI_Recall: 0.7960 - val_loss: 0.8763 - val_Accuracy: 0.7259 - val_AD_Precision: 0.4889 - val_CN_Precision: 0.8417 - val_MCI_Precision: 0.8141 - val_AD_Recall: 0.7253 - val_CN_Recall: 0.7905 - val_MCI_Recall: 0.6778\n",
      "Epoch 46/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.9469 - Accuracy: 0.7412 - AD_Precision: 0.6106 - CN_Precision: 0.7312 - MCI_Precision: 0.7937 - AD_Recall: 0.5904 - CN_Recall: 0.7184 - MCI_Recall: 0.7922 - val_loss: 1.0190 - val_Accuracy: 0.7657 - val_AD_Precision: 0.8085 - val_CN_Precision: 0.8571 - val_MCI_Precision: 0.7353 - val_AD_Recall: 0.4176 - val_CN_Recall: 0.6892 - val_MCI_Recall: 0.9414\n",
      "Epoch 47/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 0.9201 - Accuracy: 0.7664 - AD_Precision: 0.6456 - CN_Precision: 0.7548 - MCI_Precision: 0.8153 - AD_Recall: 0.6145 - CN_Recall: 0.7650 - MCI_Recall: 0.8083 - val_loss: 1.0818 - val_Accuracy: 0.6423 - val_AD_Precision: 0.4403 - val_CN_Precision: 0.7086 - val_MCI_Precision: 0.8397 - val_AD_Recall: 0.7692 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.4603\n",
      "Epoch 48/50\n",
      "476/476 [==============================] - 24s 51ms/step - loss: 0.9305 - Accuracy: 0.7464 - AD_Precision: 0.6304 - CN_Precision: 0.7210 - MCI_Precision: 0.7994 - AD_Recall: 0.6114 - CN_Recall: 0.7126 - MCI_Recall: 0.7979 - val_loss: 1.3030 - val_Accuracy: 0.7490 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7949 - val_MCI_Precision: 0.7114 - val_AD_Recall: 0.2308 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.8870\n",
      "Epoch 49/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 0.9009 - Accuracy: 0.7533 - AD_Precision: 0.6196 - CN_Precision: 0.7535 - MCI_Precision: 0.8000 - AD_Recall: 0.6084 - CN_Recall: 0.7301 - MCI_Recall: 0.8046 - val_loss: 1.6233 - val_Accuracy: 0.6674 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9420 - val_MCI_Precision: 0.6026 - val_AD_Recall: 0.2088 - val_CN_Recall: 0.4392 - val_MCI_Recall: 0.9833\n",
      "Epoch 50/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 0.8731 - Accuracy: 0.7812 - AD_Precision: 0.6942 - CN_Precision: 0.7614 - MCI_Precision: 0.8226 - AD_Recall: 0.6837 - CN_Recall: 0.7437 - MCI_Recall: 0.8226 - val_loss: 2.2339 - val_Accuracy: 0.4812 - val_AD_Precision: 0.5340 - val_CN_Precision: 0.4360 - val_MCI_Precision: 0.8158 - val_AD_Recall: 0.6044 - val_CN_Recall: 0.9662 - val_MCI_Recall: 0.1297\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/apps/tensorflow/2.7.0/lib/python3.9/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  layer_config = serialize_layer_fn(layer)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          AD       0.79      0.42      0.55        91\n",
      "          CN       0.86      0.70      0.77       148\n",
      "         MCI       0.73      0.94      0.82       239\n",
      "\n",
      "    accuracy                           0.77       478\n",
      "   macro avg       0.79      0.68      0.71       478\n",
      "weighted avg       0.78      0.77      0.75       478\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "savepath = 'models'\n",
    "lr=0.0001\n",
    "if not os.path.isdir(savepath):\n",
    "      os.makedirs(savepath)\n",
    "csvloger_callback = tf.keras.callbacks.CSVLogger(f'{savepath}history.csv', separator=',', append=False)\n",
    "\n",
    "modelcheckpoint_callback = tf.keras.callbacks.ModelCheckpoint(savepath+'efficientnet_row_10E-5_weights-epoch:{epoch:03d}-val_Accuracy:{val_Accuracy:.4f}.h5',monitor='val_Accuracy'\n",
    "    ,verbose=0,save_best_only=True,save_weights_only=True,mode='max',save_freq='epoch' , initial_value_threshold=0.85)\n",
    "\n",
    "OPT = tf.keras.optimizers.RMSprop(learning_rate=lr)\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "history04 = model04.fit(training_image_datagen, epochs=EPOCH, validation_data=testing_image_datagen,callbacks=[csvloger_callback,modelcheckpoint_callback])\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "model04.load_weights(glob.glob(f\"{savepath}efficientnet_row_10E-5_weights-epoch:*-val_Accuracy:{max(history04.history['val_Accuracy']):.4f}.h5\")[0])\n",
    "model04.save(f\"{savepath}/efficientnet_50_0.0001.h5\")\n",
    "p2 = model04.predict(testing_image_datagen)\n",
    "predicted_lab = p2.argmax(axis=-1)\n",
    "true_lab = testing_image_datagen.classes\n",
    "plot_confusion_matrix(true_lab, predicted_lab, testing_image_datagen.class_indices.keys(),savepath)\n",
    "report = classification_report(true_lab, predicted_lab, target_names=testing_image_datagen.class_indices.keys())\n",
    "print(report)\n",
    "dump_text(report,f'{savepath}classification_report.txt')\n",
    "np.save(\"efficientnet_50_0.0001.npy\",p2 )   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "874a44c0-365f-4e12-8bde-eee51cc2a398",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "476/476 [==============================] - 55s 106ms/step - loss: 6.4294 - Accuracy: 0.4472 - AD_Precision: 0.3095 - CN_Precision: 0.3569 - MCI_Precision: 0.5787 - AD_Recall: 0.3168 - CN_Recall: 0.4570 - MCI_Recall: 0.4834 - val_loss: 2.3632 - val_Accuracy: 0.5586 - val_AD_Precision: 0.2778 - val_CN_Precision: 0.5270 - val_MCI_Precision: 0.5935 - val_AD_Recall: 0.0549 - val_CN_Recall: 0.5270 - val_MCI_Recall: 0.7699\n",
      "Epoch 2/50\n",
      "476/476 [==============================] - 32s 66ms/step - loss: 4.0351 - Accuracy: 0.5008 - AD_Precision: 0.2888 - CN_Precision: 0.4039 - MCI_Precision: 0.6163 - AD_Recall: 0.2861 - CN_Recall: 0.4000 - MCI_Recall: 0.6110 - val_loss: 4.2188 - val_Accuracy: 0.3096 - val_AD_Precision: 0.2193 - val_CN_Precision: 0.5229 - val_MCI_Precision: 0.6364 - val_AD_Recall: 0.8242 - val_CN_Recall: 0.3851 - val_MCI_Recall: 0.0586\n",
      "Epoch 3/50\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 2.9299 - Accuracy: 0.5423 - AD_Precision: 0.3486 - CN_Precision: 0.4624 - MCI_Precision: 0.6471 - AD_Recall: 0.3434 - CN_Recall: 0.4660 - MCI_Recall: 0.6385 - val_loss: 3.8432 - val_Accuracy: 0.3661 - val_AD_Precision: 0.2358 - val_CN_Precision: 0.6765 - val_MCI_Precision: 0.7143 - val_AD_Recall: 0.8681 - val_CN_Recall: 0.4662 - val_MCI_Recall: 0.1046\n",
      "Epoch 4/50\n",
      "476/476 [==============================] - 36s 75ms/step - loss: 2.3534 - Accuracy: 0.5592 - AD_Precision: 0.3692 - CN_Precision: 0.4754 - MCI_Precision: 0.6628 - AD_Recall: 0.3614 - CN_Recall: 0.4699 - MCI_Recall: 0.6565 - val_loss: 1.1704 - val_Accuracy: 0.6464 - val_AD_Precision: 0.5522 - val_CN_Precision: 0.7273 - val_MCI_Precision: 0.6445 - val_AD_Recall: 0.4066 - val_CN_Recall: 0.4865 - val_MCI_Recall: 0.8117\n",
      "Epoch 5/50\n",
      "476/476 [==============================] - 35s 74ms/step - loss: 1.8477 - Accuracy: 0.5928 - AD_Precision: 0.3841 - CN_Precision: 0.5440 - MCI_Precision: 0.6886 - AD_Recall: 0.3795 - CN_Recall: 0.5282 - MCI_Recall: 0.6860 - val_loss: 1.3709 - val_Accuracy: 0.5879 - val_AD_Precision: 0.6522 - val_CN_Precision: 0.5117 - val_MCI_Precision: 0.7198 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.5481\n",
      "Epoch 6/50\n",
      "476/476 [==============================] - 40s 84ms/step - loss: 1.6392 - Accuracy: 0.6076 - AD_Precision: 0.4583 - CN_Precision: 0.5416 - MCI_Precision: 0.6918 - AD_Recall: 0.4307 - CN_Recall: 0.5184 - MCI_Recall: 0.6964 - val_loss: 1.0908 - val_Accuracy: 0.6736 - val_AD_Precision: 0.7000 - val_CN_Precision: 0.7658 - val_MCI_Precision: 0.6405 - val_AD_Recall: 0.2308 - val_CN_Recall: 0.5743 - val_MCI_Recall: 0.8870\n",
      "Epoch 7/50\n",
      "476/476 [==============================] - 42s 89ms/step - loss: 1.3432 - Accuracy: 0.6191 - AD_Precision: 0.4739 - CN_Precision: 0.5702 - MCI_Precision: 0.6965 - AD_Recall: 0.4367 - CN_Recall: 0.5282 - MCI_Recall: 0.7097 - val_loss: 2.3105 - val_Accuracy: 0.5209 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.4404 - val_MCI_Precision: 0.6986 - val_AD_Recall: 0.0330 - val_CN_Recall: 0.9730 - val_MCI_Recall: 0.4268\n",
      "Epoch 8/50\n",
      "476/476 [==============================] - 46s 97ms/step - loss: 1.1674 - Accuracy: 0.6491 - AD_Precision: 0.5000 - CN_Precision: 0.6020 - MCI_Precision: 0.7247 - AD_Recall: 0.4428 - CN_Recall: 0.5786 - MCI_Recall: 0.7343 - val_loss: 1.5962 - val_Accuracy: 0.5439 - val_AD_Precision: 0.4127 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5976 - val_AD_Recall: 0.5714 - val_CN_Recall: 0.0473 - val_MCI_Recall: 0.8326\n",
      "Epoch 9/50\n",
      "476/476 [==============================] - 38s 81ms/step - loss: 1.0260 - Accuracy: 0.6628 - AD_Precision: 0.5323 - CN_Precision: 0.6242 - MCI_Precision: 0.7292 - AD_Recall: 0.4970 - CN_Recall: 0.6000 - MCI_Recall: 0.7306 - val_loss: 0.7349 - val_Accuracy: 0.6925 - val_AD_Precision: 0.5000 - val_CN_Precision: 0.7817 - val_MCI_Precision: 0.7476 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.7500 - val_MCI_Recall: 0.6569\n",
      "Epoch 10/50\n",
      "476/476 [==============================] - 50s 104ms/step - loss: 0.9141 - Accuracy: 0.6670 - AD_Precision: 0.5272 - CN_Precision: 0.6452 - MCI_Precision: 0.7339 - AD_Recall: 0.4669 - CN_Recall: 0.5825 - MCI_Recall: 0.7562 - val_loss: 1.4497 - val_Accuracy: 0.4247 - val_AD_Precision: 0.2680 - val_CN_Precision: 0.8438 - val_MCI_Precision: 0.7500 - val_AD_Recall: 0.9011 - val_CN_Recall: 0.3649 - val_MCI_Recall: 0.2510\n",
      "Epoch 11/50\n",
      "476/476 [==============================] - 37s 77ms/step - loss: 0.8479 - Accuracy: 0.6844 - AD_Precision: 0.5750 - CN_Precision: 0.6624 - MCI_Precision: 0.7395 - AD_Recall: 0.4849 - CN_Recall: 0.5981 - MCI_Recall: 0.7676 - val_loss: 0.9810 - val_Accuracy: 0.6360 - val_AD_Precision: 0.6471 - val_CN_Precision: 0.5583 - val_MCI_Precision: 0.8030 - val_AD_Recall: 0.4835 - val_CN_Recall: 0.9054 - val_MCI_Recall: 0.4435\n",
      "Epoch 12/50\n",
      "476/476 [==============================] - 35s 73ms/step - loss: 0.7797 - Accuracy: 0.7054 - AD_Precision: 0.6123 - CN_Precision: 0.6878 - MCI_Precision: 0.7575 - AD_Recall: 0.5090 - CN_Recall: 0.6117 - MCI_Recall: 0.7913 - val_loss: 0.9644 - val_Accuracy: 0.6987 - val_AD_Precision: 0.8889 - val_CN_Precision: 0.6616 - val_MCI_Precision: 0.7227 - val_AD_Recall: 0.0879 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.7741\n",
      "Epoch 13/50\n",
      "476/476 [==============================] - 32s 68ms/step - loss: 0.7290 - Accuracy: 0.7154 - AD_Precision: 0.6075 - CN_Precision: 0.6805 - MCI_Precision: 0.7808 - AD_Recall: 0.4849 - CN_Recall: 0.6369 - MCI_Recall: 0.7941 - val_loss: 0.9469 - val_Accuracy: 0.5732 - val_AD_Precision: 0.3550 - val_CN_Precision: 0.8537 - val_MCI_Precision: 0.7651 - val_AD_Recall: 0.7802 - val_CN_Recall: 0.4730 - val_MCI_Recall: 0.4770\n",
      "Epoch 14/50\n",
      "476/476 [==============================] - 37s 79ms/step - loss: 0.6902 - Accuracy: 0.7154 - AD_Precision: 0.6553 - CN_Precision: 0.6920 - MCI_Precision: 0.7554 - AD_Recall: 0.5211 - CN_Recall: 0.6194 - MCI_Recall: 0.7913 - val_loss: 0.8147 - val_Accuracy: 0.6381 - val_AD_Precision: 0.5045 - val_CN_Precision: 0.6492 - val_MCI_Precision: 0.8407 - val_AD_Recall: 0.6154 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.3975\n",
      "Epoch 15/50\n",
      "476/476 [==============================] - 33s 70ms/step - loss: 0.6734 - Accuracy: 0.7201 - AD_Precision: 0.6169 - CN_Precision: 0.7301 - MCI_Precision: 0.7637 - AD_Recall: 0.4849 - CN_Recall: 0.6408 - MCI_Recall: 0.7941 - val_loss: 0.6561 - val_Accuracy: 0.7280 - val_AD_Precision: 0.6167 - val_CN_Precision: 0.7730 - val_MCI_Precision: 0.7664 - val_AD_Recall: 0.4066 - val_CN_Recall: 0.7365 - val_MCI_Recall: 0.7824\n",
      "Epoch 16/50\n",
      "476/476 [==============================] - 43s 90ms/step - loss: 0.6598 - Accuracy: 0.7291 - AD_Precision: 0.6038 - CN_Precision: 0.7457 - MCI_Precision: 0.7797 - AD_Recall: 0.4729 - CN_Recall: 0.6718 - MCI_Recall: 0.8027 - val_loss: 0.6731 - val_Accuracy: 0.7364 - val_AD_Precision: 0.8621 - val_CN_Precision: 0.8374 - val_MCI_Precision: 0.7026 - val_AD_Recall: 0.2747 - val_CN_Recall: 0.6959 - val_MCI_Recall: 0.8996\n",
      "Epoch 17/50\n",
      "476/476 [==============================] - 47s 98ms/step - loss: 0.6248 - Accuracy: 0.7422 - AD_Precision: 0.6944 - CN_Precision: 0.7230 - MCI_Precision: 0.7891 - AD_Recall: 0.5271 - CN_Recall: 0.6233 - MCI_Recall: 0.8273 - val_loss: 0.6580 - val_Accuracy: 0.7322 - val_AD_Precision: 0.7302 - val_CN_Precision: 0.8646 - val_MCI_Precision: 0.7244 - val_AD_Recall: 0.5055 - val_CN_Recall: 0.5608 - val_MCI_Recall: 0.8577\n",
      "Epoch 18/50\n",
      "476/476 [==============================] - 45s 95ms/step - loss: 0.6185 - Accuracy: 0.7459 - AD_Precision: 0.7101 - CN_Precision: 0.7500 - MCI_Precision: 0.7866 - AD_Recall: 0.5090 - CN_Recall: 0.6583 - MCI_Recall: 0.8216 - val_loss: 0.8118 - val_Accuracy: 0.6339 - val_AD_Precision: 0.3988 - val_CN_Precision: 0.9125 - val_MCI_Precision: 0.7680 - val_AD_Recall: 0.7143 - val_CN_Recall: 0.4932 - val_MCI_Recall: 0.6234\n",
      "Epoch 19/50\n",
      "476/476 [==============================] - 35s 74ms/step - loss: 0.5968 - Accuracy: 0.7438 - AD_Precision: 0.6692 - CN_Precision: 0.7544 - MCI_Precision: 0.7863 - AD_Recall: 0.5301 - CN_Recall: 0.6621 - MCI_Recall: 0.8065 - val_loss: 0.7725 - val_Accuracy: 0.7301 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8425 - val_MCI_Precision: 0.7013 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.7230 - val_MCI_Recall: 0.9331\n",
      "Epoch 20/50\n",
      "476/476 [==============================] - 35s 73ms/step - loss: 0.5578 - Accuracy: 0.7685 - AD_Precision: 0.7088 - CN_Precision: 0.7841 - MCI_Precision: 0.8088 - AD_Recall: 0.5572 - CN_Recall: 0.6913 - MCI_Recall: 0.8349 - val_loss: 1.1013 - val_Accuracy: 0.6318 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9423 - val_MCI_Precision: 0.5784 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.3311 - val_MCI_Recall: 0.9874\n",
      "Epoch 21/50\n",
      "476/476 [==============================] - 35s 73ms/step - loss: 0.5699 - Accuracy: 0.7743 - AD_Precision: 0.7396 - CN_Precision: 0.7700 - MCI_Precision: 0.8123 - AD_Recall: 0.5904 - CN_Recall: 0.7087 - MCI_Recall: 0.8292 - val_loss: 0.6710 - val_Accuracy: 0.7531 - val_AD_Precision: 0.9565 - val_CN_Precision: 0.7647 - val_MCI_Precision: 0.7640 - val_AD_Recall: 0.2418 - val_CN_Recall: 0.8784 - val_MCI_Recall: 0.8536\n",
      "Epoch 22/50\n",
      "476/476 [==============================] - 39s 81ms/step - loss: 0.5538 - Accuracy: 0.7754 - AD_Precision: 0.7014 - CN_Precision: 0.7804 - MCI_Precision: 0.8118 - AD_Recall: 0.5873 - CN_Recall: 0.7107 - MCI_Recall: 0.8387 - val_loss: 0.7884 - val_Accuracy: 0.7280 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6884 - val_MCI_Precision: 0.7828 - val_AD_Recall: 0.1868 - val_CN_Recall: 0.9257 - val_MCI_Recall: 0.7992\n",
      "Epoch 23/50\n",
      "476/476 [==============================] - 33s 69ms/step - loss: 0.5387 - Accuracy: 0.7870 - AD_Precision: 0.7396 - CN_Precision: 0.7920 - MCI_Precision: 0.8137 - AD_Recall: 0.5904 - CN_Recall: 0.7320 - MCI_Recall: 0.8368 - val_loss: 0.7508 - val_Accuracy: 0.6611 - val_AD_Precision: 0.4128 - val_CN_Precision: 0.8774 - val_MCI_Precision: 0.8148 - val_AD_Recall: 0.7802 - val_CN_Recall: 0.6284 - val_MCI_Recall: 0.5523\n",
      "Epoch 24/50\n",
      "476/476 [==============================] - 35s 73ms/step - loss: 0.5014 - Accuracy: 0.8048 - AD_Precision: 0.7707 - CN_Precision: 0.7992 - MCI_Precision: 0.8342 - AD_Recall: 0.6175 - CN_Recall: 0.7340 - MCI_Recall: 0.8643 - val_loss: 0.5945 - val_Accuracy: 0.7594 - val_AD_Precision: 0.7826 - val_CN_Precision: 0.8833 - val_MCI_Precision: 0.7315 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.7162 - val_MCI_Recall: 0.9121\n",
      "Epoch 25/50\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 0.5017 - Accuracy: 0.8090 - AD_Precision: 0.7729 - CN_Precision: 0.8186 - MCI_Precision: 0.8335 - AD_Recall: 0.6355 - CN_Recall: 0.7534 - MCI_Recall: 0.8596 - val_loss: 0.5844 - val_Accuracy: 0.7615 - val_AD_Precision: 0.6769 - val_CN_Precision: 0.7975 - val_MCI_Precision: 0.8235 - val_AD_Recall: 0.4835 - val_CN_Recall: 0.8514 - val_MCI_Recall: 0.7615\n",
      "Epoch 26/50\n",
      "476/476 [==============================] - 34s 72ms/step - loss: 0.5120 - Accuracy: 0.7943 - AD_Precision: 0.7173 - CN_Precision: 0.8084 - MCI_Precision: 0.8349 - AD_Recall: 0.6114 - CN_Recall: 0.7456 - MCI_Recall: 0.8539 - val_loss: 1.2497 - val_Accuracy: 0.5753 - val_AD_Precision: 0.8571 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5459 - val_AD_Recall: 0.1978 - val_CN_Recall: 0.1284 - val_MCI_Recall: 0.9958\n",
      "Epoch 27/50\n",
      "476/476 [==============================] - 42s 87ms/step - loss: 0.4830 - Accuracy: 0.8169 - AD_Precision: 0.7615 - CN_Precision: 0.8340 - MCI_Precision: 0.8410 - AD_Recall: 0.5964 - CN_Recall: 0.7806 - MCI_Recall: 0.8729 - val_loss: 1.4621 - val_Accuracy: 0.5209 - val_AD_Precision: 0.8824 - val_CN_Precision: 0.4269 - val_MCI_Precision: 0.8000 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.9865 - val_MCI_Recall: 0.3515\n",
      "Epoch 28/50\n",
      "476/476 [==============================] - 40s 84ms/step - loss: 0.4863 - Accuracy: 0.7943 - AD_Precision: 0.7153 - CN_Precision: 0.7964 - MCI_Precision: 0.8330 - AD_Recall: 0.6054 - CN_Recall: 0.7748 - MCI_Recall: 0.8472 - val_loss: 0.6789 - val_Accuracy: 0.7573 - val_AD_Precision: 0.8108 - val_CN_Precision: 0.8480 - val_MCI_Precision: 0.7303 - val_AD_Recall: 0.3297 - val_CN_Recall: 0.7162 - val_MCI_Recall: 0.9289\n",
      "Epoch 29/50\n",
      "476/476 [==============================] - 37s 77ms/step - loss: 0.4850 - Accuracy: 0.8054 - AD_Precision: 0.7604 - CN_Precision: 0.8017 - MCI_Precision: 0.8341 - AD_Recall: 0.6596 - CN_Recall: 0.7379 - MCI_Recall: 0.8586 - val_loss: 0.8262 - val_Accuracy: 0.6862 - val_AD_Precision: 0.8710 - val_CN_Precision: 0.5779 - val_MCI_Precision: 0.8118 - val_AD_Recall: 0.2967 - val_CN_Recall: 0.9527 - val_MCI_Recall: 0.6318\n",
      "Epoch 30/50\n",
      "476/476 [==============================] - 37s 77ms/step - loss: 0.4706 - Accuracy: 0.8127 - AD_Precision: 0.7770 - CN_Precision: 0.8075 - MCI_Precision: 0.8471 - AD_Recall: 0.6717 - CN_Recall: 0.7573 - MCI_Recall: 0.8672 - val_loss: 1.0249 - val_Accuracy: 0.6192 - val_AD_Precision: 0.6410 - val_CN_Precision: 0.5364 - val_MCI_Precision: 0.8505 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.9459 - val_MCI_Recall: 0.3808\n",
      "Epoch 31/50\n",
      "476/476 [==============================] - 45s 95ms/step - loss: 0.4617 - Accuracy: 0.8222 - AD_Precision: 0.7814 - CN_Precision: 0.8386 - MCI_Precision: 0.8467 - AD_Recall: 0.6566 - CN_Recall: 0.7767 - MCI_Recall: 0.8700 - val_loss: 0.6206 - val_Accuracy: 0.7720 - val_AD_Precision: 0.8039 - val_CN_Precision: 0.8595 - val_MCI_Precision: 0.7474 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.7027 - val_MCI_Recall: 0.9038\n",
      "Epoch 32/50\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.4555 - Accuracy: 0.8175 - AD_Precision: 0.7614 - CN_Precision: 0.8097 - MCI_Precision: 0.8566 - AD_Recall: 0.6536 - CN_Recall: 0.7767 - MCI_Recall: 0.8672 - val_loss: 0.6868 - val_Accuracy: 0.7678 - val_AD_Precision: 0.9333 - val_CN_Precision: 0.8540 - val_MCI_Precision: 0.7349 - val_AD_Recall: 0.3077 - val_CN_Recall: 0.7905 - val_MCI_Recall: 0.9163\n",
      "Epoch 33/50\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 0.4455 - Accuracy: 0.8233 - AD_Precision: 0.7914 - CN_Precision: 0.8396 - MCI_Precision: 0.8496 - AD_Recall: 0.6627 - CN_Recall: 0.7825 - MCI_Recall: 0.8738 - val_loss: 0.5823 - val_Accuracy: 0.7908 - val_AD_Precision: 0.7619 - val_CN_Precision: 0.8025 - val_MCI_Precision: 0.8174 - val_AD_Recall: 0.5275 - val_CN_Recall: 0.8784 - val_MCI_Recall: 0.7866\n",
      "Epoch 34/50\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.4268 - Accuracy: 0.8364 - AD_Precision: 0.8129 - CN_Precision: 0.8285 - MCI_Precision: 0.8605 - AD_Recall: 0.7199 - CN_Recall: 0.7786 - MCI_Recall: 0.8719 - val_loss: 0.6003 - val_Accuracy: 0.7929 - val_AD_Precision: 0.8889 - val_CN_Precision: 0.8311 - val_MCI_Precision: 0.7651 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.8311 - val_MCI_Recall: 0.8996\n",
      "Epoch 35/50\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 0.4281 - Accuracy: 0.8369 - AD_Precision: 0.7869 - CN_Precision: 0.8474 - MCI_Precision: 0.8644 - AD_Recall: 0.7229 - CN_Recall: 0.7981 - MCI_Recall: 0.8767 - val_loss: 0.7128 - val_Accuracy: 0.7657 - val_AD_Precision: 0.9355 - val_CN_Precision: 0.7391 - val_MCI_Precision: 0.7866 - val_AD_Recall: 0.3187 - val_CN_Recall: 0.9189 - val_MCI_Recall: 0.8326\n",
      "Epoch 36/50\n",
      "476/476 [==============================] - 36s 76ms/step - loss: 0.4234 - Accuracy: 0.8332 - AD_Precision: 0.7895 - CN_Precision: 0.8340 - MCI_Precision: 0.8594 - AD_Recall: 0.7229 - CN_Recall: 0.7806 - MCI_Recall: 0.8700 - val_loss: 0.6315 - val_Accuracy: 0.7845 - val_AD_Precision: 0.8776 - val_CN_Precision: 0.7848 - val_MCI_Precision: 0.7954 - val_AD_Recall: 0.4725 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.8619\n",
      "Epoch 37/50\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 0.4549 - Accuracy: 0.8248 - AD_Precision: 0.7847 - CN_Precision: 0.8065 - MCI_Precision: 0.8555 - AD_Recall: 0.6807 - CN_Recall: 0.7767 - MCI_Recall: 0.8710 - val_loss: 0.6446 - val_Accuracy: 0.7678 - val_AD_Precision: 0.8462 - val_CN_Precision: 0.9091 - val_MCI_Precision: 0.7355 - val_AD_Recall: 0.4835 - val_CN_Recall: 0.6081 - val_MCI_Recall: 0.9540\n",
      "Epoch 38/50\n",
      "476/476 [==============================] - 45s 94ms/step - loss: 0.4004 - Accuracy: 0.8432 - AD_Precision: 0.7669 - CN_Precision: 0.8725 - MCI_Precision: 0.8664 - AD_Recall: 0.6837 - CN_Recall: 0.8369 - MCI_Recall: 0.8795 - val_loss: 0.6373 - val_Accuracy: 0.7803 - val_AD_Precision: 0.8780 - val_CN_Precision: 0.7866 - val_MCI_Precision: 0.7915 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.8716 - val_MCI_Recall: 0.8577\n",
      "Epoch 39/50\n",
      "476/476 [==============================] - 45s 94ms/step - loss: 0.4022 - Accuracy: 0.8538 - AD_Precision: 0.8233 - CN_Precision: 0.8494 - MCI_Precision: 0.8768 - AD_Recall: 0.7440 - CN_Recall: 0.8214 - MCI_Recall: 0.8843 - val_loss: 0.9057 - val_Accuracy: 0.6967 - val_AD_Precision: 0.5625 - val_CN_Precision: 0.9661 - val_MCI_Precision: 0.7089 - val_AD_Recall: 0.6923 - val_CN_Recall: 0.3851 - val_MCI_Recall: 0.8661\n",
      "Epoch 40/50\n",
      "476/476 [==============================] - 90s 190ms/step - loss: 0.4138 - Accuracy: 0.8359 - AD_Precision: 0.7889 - CN_Precision: 0.8409 - MCI_Precision: 0.8589 - AD_Recall: 0.6867 - CN_Recall: 0.7903 - MCI_Recall: 0.8776 - val_loss: 0.5750 - val_Accuracy: 0.8180 - val_AD_Precision: 0.8824 - val_CN_Precision: 0.7952 - val_MCI_Precision: 0.8293 - val_AD_Recall: 0.4945 - val_CN_Recall: 0.8919 - val_MCI_Recall: 0.8536\n",
      "Epoch 41/50\n",
      "476/476 [==============================] - 54s 112ms/step - loss: 0.4062 - Accuracy: 0.8459 - AD_Precision: 0.7960 - CN_Precision: 0.8516 - MCI_Precision: 0.8701 - AD_Recall: 0.7169 - CN_Recall: 0.8136 - MCI_Recall: 0.8833 - val_loss: 0.6014 - val_Accuracy: 0.7741 - val_AD_Precision: 0.6667 - val_CN_Precision: 0.8084 - val_MCI_Precision: 0.8571 - val_AD_Recall: 0.6374 - val_CN_Recall: 0.9122 - val_MCI_Recall: 0.7029\n",
      "Epoch 42/50\n",
      "476/476 [==============================] - 34s 72ms/step - loss: 0.3796 - Accuracy: 0.8501 - AD_Precision: 0.8271 - CN_Precision: 0.8449 - MCI_Precision: 0.8716 - AD_Recall: 0.7349 - CN_Recall: 0.8252 - MCI_Recall: 0.8824 - val_loss: 0.5759 - val_Accuracy: 0.7762 - val_AD_Precision: 0.6170 - val_CN_Precision: 0.8378 - val_MCI_Precision: 0.8462 - val_AD_Recall: 0.6374 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.7364\n",
      "Epoch 43/50\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 0.3937 - Accuracy: 0.8432 - AD_Precision: 0.8103 - CN_Precision: 0.8463 - MCI_Precision: 0.8719 - AD_Recall: 0.7078 - CN_Recall: 0.8233 - MCI_Recall: 0.8786 - val_loss: 1.0058 - val_Accuracy: 0.6485 - val_AD_Precision: 0.4581 - val_CN_Precision: 0.9804 - val_MCI_Precision: 0.7148 - val_AD_Recall: 0.7802 - val_CN_Recall: 0.3378 - val_MCI_Recall: 0.7657\n",
      "Epoch 44/50\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.3839 - Accuracy: 0.8543 - AD_Precision: 0.7980 - CN_Precision: 0.8625 - MCI_Precision: 0.8779 - AD_Recall: 0.7380 - CN_Recall: 0.8408 - MCI_Recall: 0.8871 - val_loss: 0.9848 - val_Accuracy: 0.6967 - val_AD_Precision: 0.9394 - val_CN_Precision: 0.9565 - val_MCI_Precision: 0.6283 - val_AD_Recall: 0.3407 - val_CN_Recall: 0.4459 - val_MCI_Recall: 0.9833\n",
      "Epoch 45/50\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.3764 - Accuracy: 0.8485 - AD_Precision: 0.7934 - CN_Precision: 0.8595 - MCI_Precision: 0.8705 - AD_Recall: 0.7289 - CN_Recall: 0.8194 - MCI_Recall: 0.8928 - val_loss: 0.6143 - val_Accuracy: 0.7678 - val_AD_Precision: 0.6019 - val_CN_Precision: 0.7975 - val_MCI_Precision: 0.8783 - val_AD_Recall: 0.7143 - val_CN_Recall: 0.8784 - val_MCI_Recall: 0.6946\n",
      "Epoch 46/50\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3689 - Accuracy: 0.8574 - AD_Precision: 0.8361 - CN_Precision: 0.8495 - MCI_Precision: 0.8806 - AD_Recall: 0.7530 - CN_Recall: 0.8330 - MCI_Recall: 0.8890 - val_loss: 0.5992 - val_Accuracy: 0.7929 - val_AD_Precision: 0.8000 - val_CN_Precision: 0.7627 - val_MCI_Precision: 0.8468 - val_AD_Recall: 0.5714 - val_CN_Recall: 0.9122 - val_MCI_Recall: 0.7866\n",
      "Epoch 47/50\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 0.3744 - Accuracy: 0.8569 - AD_Precision: 0.8339 - CN_Precision: 0.8623 - MCI_Precision: 0.8785 - AD_Recall: 0.7410 - CN_Recall: 0.8272 - MCI_Recall: 0.8985 - val_loss: 0.7817 - val_Accuracy: 0.7803 - val_AD_Precision: 0.9512 - val_CN_Precision: 0.7000 - val_MCI_Precision: 0.8197 - val_AD_Recall: 0.4286 - val_CN_Recall: 0.9459 - val_MCI_Recall: 0.7992\n",
      "Epoch 48/50\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.3788 - Accuracy: 0.8559 - AD_Precision: 0.8046 - CN_Precision: 0.8583 - MCI_Precision: 0.8792 - AD_Recall: 0.7319 - CN_Recall: 0.8233 - MCI_Recall: 0.8909 - val_loss: 0.6868 - val_Accuracy: 0.7364 - val_AD_Precision: 0.5575 - val_CN_Precision: 0.7706 - val_MCI_Precision: 0.8869 - val_AD_Recall: 0.6923 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.6234\n",
      "Epoch 49/50\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.3607 - Accuracy: 0.8711 - AD_Precision: 0.8269 - CN_Precision: 0.8707 - MCI_Precision: 0.8953 - AD_Recall: 0.7771 - CN_Recall: 0.8369 - MCI_Recall: 0.9004 - val_loss: 0.5963 - val_Accuracy: 0.7803 - val_AD_Precision: 0.6304 - val_CN_Precision: 0.8276 - val_MCI_Precision: 0.8225 - val_AD_Recall: 0.6374 - val_CN_Recall: 0.8108 - val_MCI_Recall: 0.7950\n",
      "Epoch 50/50\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.3576 - Accuracy: 0.8632 - AD_Precision: 0.8356 - CN_Precision: 0.8617 - MCI_Precision: 0.8842 - AD_Recall: 0.7500 - CN_Recall: 0.8466 - MCI_Recall: 0.8985 - val_loss: 0.5728 - val_Accuracy: 0.8117 - val_AD_Precision: 0.8226 - val_CN_Precision: 0.8779 - val_MCI_Precision: 0.7906 - val_AD_Recall: 0.5604 - val_CN_Recall: 0.7770 - val_MCI_Recall: 0.9163\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/apps/tensorflow/2.7.0/lib/python3.9/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  layer_config = serialize_layer_fn(layer)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          AD       0.89      0.53      0.66        91\n",
      "          CN       0.79      0.91      0.84       148\n",
      "         MCI       0.82      0.87      0.85       239\n",
      "\n",
      "    accuracy                           0.82       478\n",
      "   macro avg       0.83      0.77      0.78       478\n",
      "weighted avg       0.82      0.82      0.81       478\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "savepath = 'Finetuning/Efficientnet/RMSprop/Efficientnet_50_0.00001'\n",
    "lr=0.00001\n",
    "if not os.path.isdir(savepath):\n",
    "      os.makedirs(savepath)\n",
    "csvloger_callback = tf.keras.callbacks.CSVLogger(f'{savepath}history.csv', separator=',', append=False)\n",
    "\n",
    "modelcheckpoint_callback = tf.keras.callbacks.ModelCheckpoint(savepath+'efficientnet_row_10E-5_weights-epoch:{epoch:03d}-val_Accuracy:{val_Accuracy:.4f}.h5',monitor='val_Accuracy'\n",
    "    ,verbose=0,save_best_only=True,save_weights_only=True,mode='max',save_freq='epoch' , initial_value_threshold=0.85)\n",
    "\n",
    "OPT = tf.keras.optimizers.RMSprop(learning_rate=lr)\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "history04 = model04.fit(training_image_datagen, epochs=EPOCH, validation_data=testing_image_datagen,callbacks=[csvloger_callback,modelcheckpoint_callback])\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "model04.load_weights(glob.glob(f\"{savepath}efficientnet_row_10E-5_weights-epoch:*-val_Accuracy:{max(history04.history['val_Accuracy']):.4f}.h5\")[0])\n",
    "model04.save(f\"{savepath}/efficientnet_50_0.00001.h5\")\n",
    "p2 = model04.predict(testing_image_datagen)\n",
    "predicted_lab = p2.argmax(axis=-1)\n",
    "true_lab = testing_image_datagen.classes\n",
    "plot_confusion_matrix(true_lab, predicted_lab, testing_image_datagen.class_indices.keys(),savepath)\n",
    "report = classification_report(true_lab, predicted_lab, target_names=testing_image_datagen.class_indices.keys())\n",
    "print(report)\n",
    "dump_text(report,f'{savepath}classification_report.txt')\n",
    "np.save(\"efficientnet_50_0.00001.npy\",p2 )   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "622e270d-a08e-4227-b63f-483d42c84792",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "476/476 [==============================] - 43s 81ms/step - loss: 1.9167 - Accuracy: 0.5183 - AD_Precision: 0.3272 - CN_Precision: 0.4281 - MCI_Precision: 0.6156 - AD_Recall: 0.2506 - CN_Recall: 0.3725 - MCI_Recall: 0.6404 - val_loss: 1.6397 - val_Accuracy: 0.5021 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.5000 - val_MCI_Precision: 0.5101 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.0541 - val_MCI_Recall: 0.9498\n",
      "Epoch 2/50\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 1.8270 - Accuracy: 0.4640 - AD_Precision: 0.2399 - CN_Precision: 0.3232 - MCI_Precision: 0.5943 - AD_Recall: 0.1958 - CN_Recall: 0.2893 - MCI_Recall: 0.5977 - val_loss: 2.7004 - val_Accuracy: 0.5000 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.0000e+00 - val_MCI_Precision: 0.5000 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.0000e+00 - val_MCI_Recall: 1.0000\n",
      "Epoch 3/50\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 1.6372 - Accuracy: 0.5118 - AD_Precision: 0.2816 - CN_Precision: 0.4156 - MCI_Precision: 0.6220 - AD_Recall: 0.2349 - CN_Recall: 0.3728 - MCI_Recall: 0.6338 - val_loss: 1.6017 - val_Accuracy: 0.4874 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.4037 - val_MCI_Precision: 0.6783 - val_AD_Recall: 0.0220 - val_CN_Recall: 0.8919 - val_MCI_Recall: 0.4059\n",
      "Epoch 4/50\n",
      "476/476 [==============================] - 34s 72ms/step - loss: 1.6003 - Accuracy: 0.4971 - AD_Precision: 0.2905 - CN_Precision: 0.3978 - MCI_Precision: 0.6126 - AD_Recall: 0.2590 - CN_Recall: 0.3553 - MCI_Recall: 0.6091 - val_loss: 1.3013 - val_Accuracy: 0.5941 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.5909 - val_MCI_Precision: 0.6024 - val_AD_Recall: 0.0220 - val_CN_Recall: 0.5270 - val_MCI_Recall: 0.8494\n",
      "Epoch 5/50\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 1.4525 - Accuracy: 0.5529 - AD_Precision: 0.3613 - CN_Precision: 0.4688 - MCI_Precision: 0.6516 - AD_Recall: 0.2982 - CN_Recall: 0.4369 - MCI_Recall: 0.6565 - val_loss: 0.9892 - val_Accuracy: 0.5858 - val_AD_Precision: 0.3868 - val_CN_Precision: 0.7160 - val_MCI_Precision: 0.6792 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.3919 - val_MCI_Recall: 0.6820\n",
      "Epoch 6/50\n",
      "476/476 [==============================] - 33s 68ms/step - loss: 1.3595 - Accuracy: 0.5797 - AD_Precision: 0.3861 - CN_Precision: 0.5211 - MCI_Precision: 0.6757 - AD_Recall: 0.3524 - CN_Recall: 0.4563 - MCI_Recall: 0.6898 - val_loss: 2.1292 - val_Accuracy: 0.3933 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.3467 - val_MCI_Precision: 0.7708 - val_AD_Recall: 0.0220 - val_CN_Recall: 0.9932 - val_MCI_Recall: 0.1548\n",
      "Epoch 7/50\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 1.3228 - Accuracy: 0.5823 - AD_Precision: 0.3921 - CN_Precision: 0.5106 - MCI_Precision: 0.6767 - AD_Recall: 0.3283 - CN_Recall: 0.4680 - MCI_Recall: 0.6793 - val_loss: 0.9636 - val_Accuracy: 0.6213 - val_AD_Precision: 0.5000 - val_CN_Precision: 0.5865 - val_MCI_Precision: 0.7366 - val_AD_Recall: 0.2527 - val_CN_Recall: 0.8243 - val_MCI_Recall: 0.5732\n",
      "Epoch 8/50\n",
      "476/476 [==============================] - 35s 74ms/step - loss: 1.2227 - Accuracy: 0.6023 - AD_Precision: 0.4399 - CN_Precision: 0.5496 - MCI_Precision: 0.6888 - AD_Recall: 0.3855 - CN_Recall: 0.4951 - MCI_Recall: 0.6992 - val_loss: 0.9555 - val_Accuracy: 0.6778 - val_AD_Precision: 0.8125 - val_CN_Precision: 0.6957 - val_MCI_Precision: 0.6859 - val_AD_Recall: 0.1429 - val_CN_Recall: 0.7568 - val_MCI_Recall: 0.7950\n",
      "Epoch 9/50\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 1.2104 - Accuracy: 0.6049 - AD_Precision: 0.4800 - CN_Precision: 0.5279 - MCI_Precision: 0.6793 - AD_Recall: 0.3976 - CN_Recall: 0.4777 - MCI_Recall: 0.6973 - val_loss: 1.0665 - val_Accuracy: 0.6109 - val_AD_Precision: 0.5333 - val_CN_Precision: 0.8163 - val_MCI_Precision: 0.6158 - val_AD_Recall: 0.2637 - val_CN_Recall: 0.2703 - val_MCI_Recall: 0.9121\n",
      "Epoch 10/50\n",
      "476/476 [==============================] - 39s 81ms/step - loss: 1.1127 - Accuracy: 0.6255 - AD_Precision: 0.4390 - CN_Precision: 0.5864 - MCI_Precision: 0.7078 - AD_Recall: 0.3795 - CN_Recall: 0.5534 - MCI_Recall: 0.7125 - val_loss: 0.8156 - val_Accuracy: 0.6841 - val_AD_Precision: 0.5000 - val_CN_Precision: 0.7059 - val_MCI_Precision: 0.7137 - val_AD_Recall: 0.3297 - val_CN_Recall: 0.6486 - val_MCI_Recall: 0.7615\n",
      "Epoch 11/50\n",
      "476/476 [==============================] - 35s 73ms/step - loss: 1.1005 - Accuracy: 0.6297 - AD_Precision: 0.4765 - CN_Precision: 0.5730 - MCI_Precision: 0.7091 - AD_Recall: 0.3976 - CN_Recall: 0.5107 - MCI_Recall: 0.7287 - val_loss: 0.8817 - val_Accuracy: 0.6820 - val_AD_Precision: 0.6452 - val_CN_Precision: 0.7807 - val_MCI_Precision: 0.6699 - val_AD_Recall: 0.2198 - val_CN_Recall: 0.6014 - val_MCI_Recall: 0.8577\n",
      "Epoch 12/50\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 1.1122 - Accuracy: 0.6433 - AD_Precision: 0.5037 - CN_Precision: 0.5789 - MCI_Precision: 0.7224 - AD_Recall: 0.4096 - CN_Recall: 0.5340 - MCI_Recall: 0.7457 - val_loss: 0.9206 - val_Accuracy: 0.6381 - val_AD_Precision: 0.4400 - val_CN_Precision: 0.7313 - val_MCI_Precision: 0.7121 - val_AD_Recall: 0.4835 - val_CN_Recall: 0.6622 - val_MCI_Recall: 0.5900\n",
      "Epoch 13/50\n",
      "476/476 [==============================] - 34s 72ms/step - loss: 1.0931 - Accuracy: 0.6460 - AD_Precision: 0.4433 - CN_Precision: 0.6288 - MCI_Precision: 0.7237 - AD_Recall: 0.3886 - CN_Recall: 0.5592 - MCI_Recall: 0.7381 - val_loss: 0.9057 - val_Accuracy: 0.6757 - val_AD_Precision: 0.6939 - val_CN_Precision: 0.8767 - val_MCI_Precision: 0.6486 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.4324 - val_MCI_Recall: 0.9038\n",
      "Epoch 14/50\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 0.9729 - Accuracy: 0.6696 - AD_Precision: 0.5679 - CN_Precision: 0.6042 - MCI_Precision: 0.7415 - AD_Recall: 0.4789 - CN_Recall: 0.5573 - MCI_Recall: 0.7619 - val_loss: 0.8405 - val_Accuracy: 0.6946 - val_AD_Precision: 0.6977 - val_CN_Precision: 0.6939 - val_MCI_Precision: 0.7228 - val_AD_Recall: 0.3297 - val_CN_Recall: 0.6892 - val_MCI_Recall: 0.8075\n",
      "Epoch 15/50\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 0.9699 - Accuracy: 0.6833 - AD_Precision: 0.5575 - CN_Precision: 0.6520 - MCI_Precision: 0.7486 - AD_Recall: 0.4819 - CN_Recall: 0.6039 - MCI_Recall: 0.7628 - val_loss: 0.8665 - val_Accuracy: 0.6862 - val_AD_Precision: 0.5256 - val_CN_Precision: 0.6757 - val_MCI_Precision: 0.7784 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.8446 - val_MCI_Recall: 0.5732\n",
      "Epoch 16/50\n",
      "476/476 [==============================] - 34s 72ms/step - loss: 0.9635 - Accuracy: 0.6696 - AD_Precision: 0.5791 - CN_Precision: 0.6232 - MCI_Precision: 0.7355 - AD_Recall: 0.4849 - CN_Recall: 0.5942 - MCI_Recall: 0.7438 - val_loss: 0.9915 - val_Accuracy: 0.6925 - val_AD_Precision: 0.8235 - val_CN_Precision: 0.6294 - val_MCI_Precision: 0.7371 - val_AD_Recall: 0.1538 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.7741\n",
      "Epoch 17/50\n",
      "476/476 [==============================] - 32s 68ms/step - loss: 0.9302 - Accuracy: 0.6886 - AD_Precision: 0.5804 - CN_Precision: 0.6542 - MCI_Precision: 0.7517 - AD_Recall: 0.5000 - CN_Recall: 0.6136 - MCI_Recall: 0.7552 - val_loss: 0.7877 - val_Accuracy: 0.7259 - val_AD_Precision: 0.6296 - val_CN_Precision: 0.7484 - val_MCI_Precision: 0.7627 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.8041 - val_MCI_Recall: 0.7531\n",
      "Epoch 18/50\n",
      "476/476 [==============================] - 33s 70ms/step - loss: 0.9247 - Accuracy: 0.6959 - AD_Precision: 0.5623 - CN_Precision: 0.6701 - MCI_Precision: 0.7637 - AD_Recall: 0.4759 - CN_Recall: 0.6272 - MCI_Recall: 0.7666 - val_loss: 0.7870 - val_Accuracy: 0.7238 - val_AD_Precision: 0.6557 - val_CN_Precision: 0.7453 - val_MCI_Precision: 0.7727 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.8108 - val_MCI_Recall: 0.7113\n",
      "Epoch 19/50\n",
      "476/476 [==============================] - 37s 78ms/step - loss: 0.9072 - Accuracy: 0.6839 - AD_Precision: 0.5581 - CN_Precision: 0.6584 - MCI_Precision: 0.7526 - AD_Recall: 0.5060 - CN_Recall: 0.6175 - MCI_Recall: 0.7533 - val_loss: 1.0178 - val_Accuracy: 0.6987 - val_AD_Precision: 0.9091 - val_CN_Precision: 0.8142 - val_MCI_Precision: 0.6607 - val_AD_Recall: 0.2198 - val_CN_Recall: 0.6216 - val_MCI_Recall: 0.9205\n",
      "Epoch 20/50\n",
      "476/476 [==============================] - 39s 82ms/step - loss: 0.8188 - Accuracy: 0.7170 - AD_Precision: 0.5952 - CN_Precision: 0.6905 - MCI_Precision: 0.7761 - AD_Recall: 0.5181 - CN_Recall: 0.6369 - MCI_Recall: 0.7894 - val_loss: 0.8440 - val_Accuracy: 0.6841 - val_AD_Precision: 0.4409 - val_CN_Precision: 0.8443 - val_MCI_Precision: 0.7673 - val_AD_Recall: 0.6154 - val_CN_Recall: 0.6959 - val_MCI_Recall: 0.6485\n",
      "Epoch 21/50\n",
      "476/476 [==============================] - 38s 81ms/step - loss: 0.8319 - Accuracy: 0.7075 - AD_Precision: 0.5925 - CN_Precision: 0.6915 - MCI_Precision: 0.7626 - AD_Recall: 0.5211 - CN_Recall: 0.6311 - MCI_Recall: 0.7770 - val_loss: 1.0431 - val_Accuracy: 0.6715 - val_AD_Precision: 0.7838 - val_CN_Precision: 0.5766 - val_MCI_Precision: 0.8150 - val_AD_Recall: 0.3187 - val_CN_Recall: 0.9662 - val_MCI_Recall: 0.5900\n",
      "Epoch 22/50\n",
      "476/476 [==============================] - 35s 74ms/step - loss: 0.7462 - Accuracy: 0.7344 - AD_Precision: 0.6298 - CN_Precision: 0.7116 - MCI_Precision: 0.7833 - AD_Recall: 0.5482 - CN_Recall: 0.6660 - MCI_Recall: 0.8093 - val_loss: 0.7837 - val_Accuracy: 0.7134 - val_AD_Precision: 0.5616 - val_CN_Precision: 0.8348 - val_MCI_Precision: 0.7226 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.6486 - val_MCI_Recall: 0.8285\n",
      "Epoch 23/50\n",
      "476/476 [==============================] - 35s 73ms/step - loss: 0.7770 - Accuracy: 0.7412 - AD_Precision: 0.6575 - CN_Precision: 0.7029 - MCI_Precision: 0.7970 - AD_Recall: 0.5783 - CN_Recall: 0.6524 - MCI_Recall: 0.8083 - val_loss: 0.8958 - val_Accuracy: 0.6695 - val_AD_Precision: 0.4952 - val_CN_Precision: 0.6791 - val_MCI_Precision: 0.8176 - val_AD_Recall: 0.5714 - val_CN_Recall: 0.8581 - val_MCI_Recall: 0.5063\n",
      "Epoch 24/50\n",
      "476/476 [==============================] - 36s 75ms/step - loss: 0.7327 - Accuracy: 0.7286 - AD_Precision: 0.6498 - CN_Precision: 0.7092 - MCI_Precision: 0.7876 - AD_Recall: 0.5813 - CN_Recall: 0.6583 - MCI_Recall: 0.7951 - val_loss: 0.9895 - val_Accuracy: 0.6297 - val_AD_Precision: 0.3826 - val_CN_Precision: 0.9077 - val_MCI_Precision: 0.7154 - val_AD_Recall: 0.6264 - val_CN_Recall: 0.3986 - val_MCI_Recall: 0.7364\n",
      "Epoch 25/50\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.7573 - Accuracy: 0.7307 - AD_Precision: 0.6508 - CN_Precision: 0.7023 - MCI_Precision: 0.7826 - AD_Recall: 0.5783 - CN_Recall: 0.6641 - MCI_Recall: 0.7922 - val_loss: 0.7901 - val_Accuracy: 0.7406 - val_AD_Precision: 0.7143 - val_CN_Precision: 0.8800 - val_MCI_Precision: 0.7134 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.5946 - val_MCI_Recall: 0.9163\n",
      "Epoch 26/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 0.7292 - Accuracy: 0.7517 - AD_Precision: 0.6532 - CN_Precision: 0.7349 - MCI_Precision: 0.8026 - AD_Recall: 0.5843 - CN_Recall: 0.6835 - MCI_Recall: 0.8140 - val_loss: 0.7767 - val_Accuracy: 0.7406 - val_AD_Precision: 0.7500 - val_CN_Precision: 0.8062 - val_MCI_Precision: 0.7285 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.7027 - val_MCI_Recall: 0.8870\n",
      "Epoch 27/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 0.7220 - Accuracy: 0.7359 - AD_Precision: 0.6529 - CN_Precision: 0.7128 - MCI_Precision: 0.7837 - AD_Recall: 0.5723 - CN_Recall: 0.6602 - MCI_Recall: 0.8008 - val_loss: 0.9934 - val_Accuracy: 0.6820 - val_AD_Precision: 0.7674 - val_CN_Precision: 0.5755 - val_MCI_Precision: 0.8375 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.9527 - val_MCI_Recall: 0.5607\n",
      "Epoch 28/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 0.7640 - Accuracy: 0.7407 - AD_Precision: 0.6469 - CN_Precision: 0.7241 - MCI_Precision: 0.7858 - AD_Recall: 0.5572 - CN_Recall: 0.6777 - MCI_Recall: 0.8008 - val_loss: 1.0376 - val_Accuracy: 0.6297 - val_AD_Precision: 0.5106 - val_CN_Precision: 0.6136 - val_MCI_Precision: 0.8217 - val_AD_Recall: 0.5275 - val_CN_Recall: 0.9122 - val_MCI_Recall: 0.4435\n",
      "Epoch 29/50\n",
      "476/476 [==============================] - 26s 56ms/step - loss: 0.6953 - Accuracy: 0.7633 - AD_Precision: 0.6613 - CN_Precision: 0.7603 - MCI_Precision: 0.8086 - AD_Recall: 0.6175 - CN_Recall: 0.7146 - MCI_Recall: 0.8216 - val_loss: 0.7438 - val_Accuracy: 0.7615 - val_AD_Precision: 0.6774 - val_CN_Precision: 0.7558 - val_MCI_Precision: 0.8133 - val_AD_Recall: 0.4615 - val_CN_Recall: 0.8784 - val_MCI_Recall: 0.7657\n",
      "Epoch 30/50\n",
      "476/476 [==============================] - 28s 58ms/step - loss: 0.7098 - Accuracy: 0.7585 - AD_Precision: 0.7014 - CN_Precision: 0.7310 - MCI_Precision: 0.8015 - AD_Recall: 0.6084 - CN_Recall: 0.6913 - MCI_Recall: 0.8159 - val_loss: 0.7771 - val_Accuracy: 0.7238 - val_AD_Precision: 0.5625 - val_CN_Precision: 0.9010 - val_MCI_Precision: 0.7260 - val_AD_Recall: 0.4945 - val_CN_Recall: 0.6149 - val_MCI_Recall: 0.8536\n",
      "Epoch 31/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 0.6304 - Accuracy: 0.7717 - AD_Precision: 0.7041 - CN_Precision: 0.7465 - MCI_Precision: 0.8219 - AD_Recall: 0.6235 - CN_Recall: 0.7146 - MCI_Recall: 0.8321 - val_loss: 0.7549 - val_Accuracy: 0.7406 - val_AD_Precision: 0.6667 - val_CN_Precision: 0.8065 - val_MCI_Precision: 0.7455 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.6757 - val_MCI_Recall: 0.8703\n",
      "Epoch 32/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.6477 - Accuracy: 0.7696 - AD_Precision: 0.6892 - CN_Precision: 0.7531 - MCI_Precision: 0.8199 - AD_Recall: 0.6145 - CN_Recall: 0.7107 - MCI_Recall: 0.8292 - val_loss: 0.7876 - val_Accuracy: 0.7092 - val_AD_Precision: 0.4872 - val_CN_Precision: 0.7895 - val_MCI_Precision: 0.8211 - val_AD_Recall: 0.6264 - val_CN_Recall: 0.8108 - val_MCI_Recall: 0.6527\n",
      "Epoch 33/50\n",
      "476/476 [==============================] - 25s 52ms/step - loss: 0.6604 - Accuracy: 0.7622 - AD_Precision: 0.6599 - CN_Precision: 0.7505 - MCI_Precision: 0.8130 - AD_Recall: 0.5843 - CN_Recall: 0.7184 - MCI_Recall: 0.8292 - val_loss: 0.7727 - val_Accuracy: 0.7218 - val_AD_Precision: 0.6400 - val_CN_Precision: 0.7268 - val_MCI_Precision: 0.8220 - val_AD_Recall: 0.5275 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.6569\n",
      "Epoch 34/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.5956 - Accuracy: 0.7854 - AD_Precision: 0.7201 - CN_Precision: 0.7594 - MCI_Precision: 0.8316 - AD_Recall: 0.6355 - CN_Recall: 0.7417 - MCI_Recall: 0.8292 - val_loss: 0.8932 - val_Accuracy: 0.6987 - val_AD_Precision: 0.6761 - val_CN_Precision: 0.6618 - val_MCI_Precision: 0.8182 - val_AD_Recall: 0.5275 - val_CN_Recall: 0.9257 - val_MCI_Recall: 0.5649\n",
      "Epoch 35/50\n",
      "476/476 [==============================] - 27s 57ms/step - loss: 0.6425 - Accuracy: 0.7722 - AD_Precision: 0.7017 - CN_Precision: 0.7593 - MCI_Precision: 0.8155 - AD_Recall: 0.6235 - CN_Recall: 0.7165 - MCI_Recall: 0.8264 - val_loss: 0.7975 - val_Accuracy: 0.7678 - val_AD_Precision: 0.7872 - val_CN_Precision: 0.7486 - val_MCI_Precision: 0.7854 - val_AD_Recall: 0.4066 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.8117\n",
      "Epoch 36/50\n",
      "476/476 [==============================] - 25s 54ms/step - loss: 0.6333 - Accuracy: 0.7796 - AD_Precision: 0.6980 - CN_Precision: 0.7787 - MCI_Precision: 0.8261 - AD_Recall: 0.6265 - CN_Recall: 0.7379 - MCI_Recall: 0.8340 - val_loss: 0.7682 - val_Accuracy: 0.7615 - val_AD_Precision: 0.8293 - val_CN_Precision: 0.8027 - val_MCI_Precision: 0.7518 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.7973 - val_MCI_Recall: 0.8745\n",
      "Epoch 37/50\n",
      "476/476 [==============================] - 28s 58ms/step - loss: 0.6204 - Accuracy: 0.7833 - AD_Precision: 0.7003 - CN_Precision: 0.7739 - MCI_Precision: 0.8259 - AD_Recall: 0.6476 - CN_Recall: 0.7379 - MCI_Recall: 0.8283 - val_loss: 0.7831 - val_Accuracy: 0.7155 - val_AD_Precision: 0.5109 - val_CN_Precision: 0.8654 - val_MCI_Precision: 0.7453 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.6081 - val_MCI_Recall: 0.8326\n",
      "Epoch 38/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 0.5808 - Accuracy: 0.7938 - AD_Precision: 0.7261 - CN_Precision: 0.7748 - MCI_Precision: 0.8355 - AD_Recall: 0.6627 - CN_Recall: 0.7417 - MCI_Recall: 0.8482 - val_loss: 0.8921 - val_Accuracy: 0.7343 - val_AD_Precision: 0.8372 - val_CN_Precision: 0.6798 - val_MCI_Precision: 0.7818 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.9324 - val_MCI_Recall: 0.7197\n",
      "Epoch 39/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.6082 - Accuracy: 0.7938 - AD_Precision: 0.7176 - CN_Precision: 0.7848 - MCI_Precision: 0.8380 - AD_Recall: 0.6506 - CN_Recall: 0.7437 - MCI_Recall: 0.8491 - val_loss: 0.7382 - val_Accuracy: 0.7803 - val_AD_Precision: 0.8444 - val_CN_Precision: 0.7630 - val_MCI_Precision: 0.8033 - val_AD_Recall: 0.4176 - val_CN_Recall: 0.8919 - val_MCI_Recall: 0.8201\n",
      "Epoch 40/50\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.5652 - Accuracy: 0.7938 - AD_Precision: 0.7204 - CN_Precision: 0.7812 - MCI_Precision: 0.8315 - AD_Recall: 0.6596 - CN_Recall: 0.7417 - MCI_Recall: 0.8425 - val_loss: 0.8677 - val_Accuracy: 0.7448 - val_AD_Precision: 0.9189 - val_CN_Precision: 0.6731 - val_MCI_Precision: 0.8000 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.9459 - val_MCI_Recall: 0.7531\n",
      "Epoch 41/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 0.5449 - Accuracy: 0.7954 - AD_Precision: 0.7258 - CN_Precision: 0.7818 - MCI_Precision: 0.8368 - AD_Recall: 0.6536 - CN_Recall: 0.7515 - MCI_Recall: 0.8463 - val_loss: 0.9423 - val_Accuracy: 0.7343 - val_AD_Precision: 0.9333 - val_CN_Precision: 0.6512 - val_MCI_Precision: 0.8026 - val_AD_Recall: 0.3077 - val_CN_Recall: 0.9459 - val_MCI_Recall: 0.7657\n",
      "Epoch 42/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 0.5474 - Accuracy: 0.8075 - AD_Precision: 0.7419 - CN_Precision: 0.7971 - MCI_Precision: 0.8437 - AD_Recall: 0.6928 - CN_Recall: 0.7553 - MCI_Recall: 0.8501 - val_loss: 0.7060 - val_Accuracy: 0.7573 - val_AD_Precision: 0.7083 - val_CN_Precision: 0.7371 - val_MCI_Precision: 0.8279 - val_AD_Recall: 0.5604 - val_CN_Recall: 0.8716 - val_MCI_Recall: 0.7448\n",
      "Epoch 43/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 0.5521 - Accuracy: 0.7980 - AD_Precision: 0.7020 - CN_Precision: 0.7861 - MCI_Precision: 0.8478 - AD_Recall: 0.6386 - CN_Recall: 0.7709 - MCI_Recall: 0.8510 - val_loss: 1.0593 - val_Accuracy: 0.6130 - val_AD_Precision: 0.3590 - val_CN_Precision: 0.8966 - val_MCI_Precision: 0.7966 - val_AD_Recall: 0.7692 - val_CN_Recall: 0.5270 - val_MCI_Recall: 0.5900\n",
      "Epoch 44/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 0.5058 - Accuracy: 0.8122 - AD_Precision: 0.7659 - CN_Precision: 0.8028 - MCI_Precision: 0.8433 - AD_Recall: 0.6898 - CN_Recall: 0.7670 - MCI_Recall: 0.8577 - val_loss: 0.9306 - val_Accuracy: 0.7699 - val_AD_Precision: 0.9655 - val_CN_Precision: 0.8310 - val_MCI_Precision: 0.7243 - val_AD_Recall: 0.3077 - val_CN_Recall: 0.7973 - val_MCI_Recall: 0.9121\n",
      "Epoch 45/50\n",
      "476/476 [==============================] - 26s 55ms/step - loss: 0.5476 - Accuracy: 0.7959 - AD_Precision: 0.7148 - CN_Precision: 0.7914 - MCI_Precision: 0.8355 - AD_Recall: 0.6416 - CN_Recall: 0.7515 - MCI_Recall: 0.8529 - val_loss: 0.8063 - val_Accuracy: 0.7406 - val_AD_Precision: 0.6494 - val_CN_Precision: 0.9022 - val_MCI_Precision: 0.7381 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.5608 - val_MCI_Recall: 0.9079\n",
      "Epoch 46/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 0.5154 - Accuracy: 0.8164 - AD_Precision: 0.7592 - CN_Precision: 0.8024 - MCI_Precision: 0.8517 - AD_Recall: 0.6837 - CN_Recall: 0.7728 - MCI_Recall: 0.8662 - val_loss: 0.6904 - val_Accuracy: 0.7636 - val_AD_Precision: 0.6500 - val_CN_Precision: 0.8069 - val_MCI_Precision: 0.8170 - val_AD_Recall: 0.5714 - val_CN_Recall: 0.7905 - val_MCI_Recall: 0.8033\n",
      "Epoch 47/50\n",
      "476/476 [==============================] - 25s 53ms/step - loss: 0.5016 - Accuracy: 0.8143 - AD_Precision: 0.7492 - CN_Precision: 0.8120 - MCI_Precision: 0.8517 - AD_Recall: 0.7018 - CN_Recall: 0.7883 - MCI_Recall: 0.8558 - val_loss: 0.9809 - val_Accuracy: 0.6402 - val_AD_Precision: 0.3770 - val_CN_Precision: 0.8397 - val_MCI_Precision: 0.8403 - val_AD_Recall: 0.7912 - val_CN_Recall: 0.7432 - val_MCI_Recall: 0.5063\n",
      "Epoch 48/50\n",
      "476/476 [==============================] - 27s 56ms/step - loss: 0.5301 - Accuracy: 0.8101 - AD_Precision: 0.7410 - CN_Precision: 0.8089 - MCI_Precision: 0.8446 - AD_Recall: 0.6807 - CN_Recall: 0.7806 - MCI_Recall: 0.8510 - val_loss: 0.8277 - val_Accuracy: 0.6904 - val_AD_Precision: 0.4444 - val_CN_Precision: 0.8750 - val_MCI_Precision: 0.7753 - val_AD_Recall: 0.6593 - val_CN_Recall: 0.6149 - val_MCI_Recall: 0.7364\n",
      "Epoch 49/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 0.4872 - Accuracy: 0.8222 - AD_Precision: 0.7770 - CN_Precision: 0.8215 - MCI_Precision: 0.8537 - AD_Recall: 0.6928 - CN_Recall: 0.7864 - MCI_Recall: 0.8634 - val_loss: 0.7424 - val_Accuracy: 0.7699 - val_AD_Precision: 0.7188 - val_CN_Precision: 0.8655 - val_MCI_Precision: 0.7553 - val_AD_Recall: 0.5055 - val_CN_Recall: 0.6959 - val_MCI_Recall: 0.8912\n",
      "Epoch 50/50\n",
      "476/476 [==============================] - 26s 54ms/step - loss: 0.5065 - Accuracy: 0.8290 - AD_Precision: 0.7685 - CN_Precision: 0.8368 - MCI_Precision: 0.8571 - AD_Recall: 0.6898 - CN_Recall: 0.7864 - MCI_Recall: 0.8767 - val_loss: 0.9471 - val_Accuracy: 0.6569 - val_AD_Precision: 0.4080 - val_CN_Precision: 0.8475 - val_MCI_Precision: 0.8274 - val_AD_Recall: 0.7802 - val_CN_Recall: 0.6757 - val_MCI_Recall: 0.5816\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/apps/tensorflow/2.7.0/lib/python3.9/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  layer_config = serialize_layer_fn(layer)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          AD       0.80      0.44      0.57        91\n",
      "          CN       0.76      0.90      0.82       148\n",
      "         MCI       0.79      0.84      0.81       239\n",
      "\n",
      "    accuracy                           0.78       478\n",
      "   macro avg       0.78      0.73      0.73       478\n",
      "weighted avg       0.78      0.78      0.77       478\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "savepath = 'Finetuning/Efficientnet/RMSprop/Efficientnet_50_0.00001/'\n",
    "lr=0.000001\n",
    "if not os.path.isdir(savepath):\n",
    "      os.makedirs(savepath)\n",
    "csvloger_callback = tf.keras.callbacks.CSVLogger(f'{savepath}history.csv', separator=',', append=False)\n",
    "\n",
    "modelcheckpoint_callback = tf.keras.callbacks.ModelCheckpoint(savepath+'efficientnet_row_10E-5_weights-epoch:{epoch:03d}-val_Accuracy:{val_Accuracy:.4f}.h5',monitor='val_Accuracy'\n",
    "    ,verbose=0,save_best_only=True,save_weights_only=True,mode='max',save_freq='epoch' , initial_value_threshold=0.85)\n",
    "\n",
    "OPT = tf.keras.optimizers.RMSprop(learning_rate=lr)\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "history04 = model04.fit(training_image_datagen, epochs=EPOCH, validation_data=testing_image_datagen,callbacks=[csvloger_callback,modelcheckpoint_callback])\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "model04.load_weights(glob.glob(f\"{savepath}efficientnet_row_10E-5_weights-epoch:*-val_Accuracy:{max(history04.history['val_Accuracy']):.4f}.h5\")[0])\n",
    "model04.save(f\"{savepath}/efficientnet_50_0.000001.h5\")\n",
    "p2 = model04.predict(testing_image_datagen)\n",
    "predicted_lab = p2.argmax(axis=-1)\n",
    "true_lab = testing_image_datagen.classes\n",
    "plot_confusion_matrix(true_lab, predicted_lab, testing_image_datagen.class_indices.keys(),savepath)\n",
    "report = classification_report(true_lab, predicted_lab, target_names=testing_image_datagen.class_indices.keys())\n",
    "print(report)\n",
    "dump_text(report,f'{savepath}classification_report.txt')\n",
    "np.save(\"efficientnet_50_0.000001.npy\",p2 )  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53e6e5ad-d64e-4a01-866a-87a53e893aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-14 17:46:38.353722: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "476/476 [==============================] - 45s 79ms/step - loss: 4.2733 - Accuracy: 0.4671 - AD_Precision: 0.2484 - CN_Precision: 0.3458 - MCI_Precision: 0.5912 - AD_Recall: 0.2380 - CN_Recall: 0.3398 - MCI_Recall: 0.5968 - val_loss: 3.2821 - val_Accuracy: 0.3724 - val_AD_Precision: 0.4324 - val_CN_Precision: 0.3487 - val_MCI_Precision: 0.7273 - val_AD_Recall: 0.1758 - val_CN_Recall: 0.9730 - val_MCI_Recall: 0.0669\n",
      "Epoch 2/70\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 3.0595 - Accuracy: 0.5339 - AD_Precision: 0.3222 - CN_Precision: 0.4370 - MCI_Precision: 0.6504 - AD_Recall: 0.3193 - CN_Recall: 0.4311 - MCI_Recall: 0.6442 - val_loss: 3.0318 - val_Accuracy: 0.5523 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.7308 - val_MCI_Precision: 0.5305 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.2568 - val_MCI_Recall: 0.9456\n",
      "Epoch 3/70\n",
      "476/476 [==============================] - 32s 66ms/step - loss: 1.8329 - Accuracy: 0.5718 - AD_Precision: 0.3933 - CN_Precision: 0.5102 - MCI_Precision: 0.6622 - AD_Recall: 0.3886 - CN_Recall: 0.4854 - MCI_Recall: 0.6622 - val_loss: 1.0918 - val_Accuracy: 0.6339 - val_AD_Precision: 0.4658 - val_CN_Precision: 0.8182 - val_MCI_Precision: 0.6454 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.4257 - val_MCI_Recall: 0.8452\n",
      "Epoch 4/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 1.4280 - Accuracy: 0.6281 - AD_Precision: 0.4572 - CN_Precision: 0.5717 - MCI_Precision: 0.7147 - AD_Recall: 0.4187 - CN_Recall: 0.5417 - MCI_Recall: 0.7201 - val_loss: 1.5329 - val_Accuracy: 0.6046 - val_AD_Precision: 0.7143 - val_CN_Precision: 0.5094 - val_MCI_Precision: 0.7382 - val_AD_Recall: 0.1099 - val_CN_Recall: 0.9122 - val_MCI_Recall: 0.5900\n",
      "Epoch 5/70\n",
      "476/476 [==============================] - 34s 71ms/step - loss: 1.0496 - Accuracy: 0.6691 - AD_Precision: 0.5493 - CN_Precision: 0.6215 - MCI_Precision: 0.7392 - AD_Recall: 0.4699 - CN_Recall: 0.5961 - MCI_Recall: 0.7476 - val_loss: 0.8876 - val_Accuracy: 0.6736 - val_AD_Precision: 0.6226 - val_CN_Precision: 0.7714 - val_MCI_Precision: 0.6689 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.5473 - val_MCI_Recall: 0.8536\n",
      "Epoch 6/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 1.0058 - Accuracy: 0.6575 - AD_Precision: 0.5217 - CN_Precision: 0.6203 - MCI_Precision: 0.7201 - AD_Recall: 0.4337 - CN_Recall: 0.5709 - MCI_Recall: 0.7419 - val_loss: 2.1229 - val_Accuracy: 0.5418 - val_AD_Precision: 1.0000 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5218 - val_AD_Recall: 0.0330 - val_CN_Recall: 0.1149 - val_MCI_Recall: 1.0000\n",
      "Epoch 7/70\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.8465 - Accuracy: 0.7112 - AD_Precision: 0.5917 - CN_Precision: 0.6860 - MCI_Precision: 0.7679 - AD_Recall: 0.5151 - CN_Recall: 0.6447 - MCI_Recall: 0.7751 - val_loss: 0.8823 - val_Accuracy: 0.7008 - val_AD_Precision: 0.8621 - val_CN_Precision: 0.6042 - val_MCI_Precision: 0.8214 - val_AD_Recall: 0.2747 - val_CN_Recall: 0.9797 - val_MCI_Recall: 0.6736\n",
      "Epoch 8/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.7552 - Accuracy: 0.6975 - AD_Precision: 0.6007 - CN_Precision: 0.6717 - MCI_Precision: 0.7551 - AD_Recall: 0.5211 - CN_Recall: 0.6078 - MCI_Recall: 0.7694 - val_loss: 0.9479 - val_Accuracy: 0.6946 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6618 - val_MCI_Precision: 0.7237 - val_AD_Recall: 0.0879 - val_CN_Recall: 0.9122 - val_MCI_Recall: 0.7782\n",
      "Epoch 9/70\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 0.7682 - Accuracy: 0.7159 - AD_Precision: 0.6219 - CN_Precision: 0.7076 - MCI_Precision: 0.7717 - AD_Recall: 0.5301 - CN_Recall: 0.6485 - MCI_Recall: 0.7827 - val_loss: 0.6553 - val_Accuracy: 0.7406 - val_AD_Precision: 0.8485 - val_CN_Precision: 0.8475 - val_MCI_Precision: 0.7019 - val_AD_Recall: 0.3077 - val_CN_Recall: 0.6757 - val_MCI_Recall: 0.9163\n",
      "Epoch 10/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.6786 - Accuracy: 0.7286 - AD_Precision: 0.6553 - CN_Precision: 0.7118 - MCI_Precision: 0.7785 - AD_Recall: 0.5211 - CN_Recall: 0.6427 - MCI_Recall: 0.7970 - val_loss: 0.7078 - val_Accuracy: 0.7008 - val_AD_Precision: 0.5000 - val_CN_Precision: 0.8614 - val_MCI_Precision: 0.7532 - val_AD_Recall: 0.6264 - val_CN_Recall: 0.5878 - val_MCI_Recall: 0.7280\n",
      "Epoch 11/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.5971 - Accuracy: 0.7612 - AD_Precision: 0.7444 - CN_Precision: 0.7522 - MCI_Precision: 0.7940 - AD_Recall: 0.6054 - CN_Recall: 0.6718 - MCI_Recall: 0.8264 - val_loss: 0.7593 - val_Accuracy: 0.7071 - val_AD_Precision: 0.7917 - val_CN_Precision: 0.8125 - val_MCI_Precision: 0.6883 - val_AD_Recall: 0.2088 - val_CN_Recall: 0.6149 - val_MCI_Recall: 0.9331\n",
      "Epoch 12/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.5833 - Accuracy: 0.7691 - AD_Precision: 0.7181 - CN_Precision: 0.7538 - MCI_Precision: 0.8129 - AD_Recall: 0.5602 - CN_Recall: 0.6660 - MCI_Recall: 0.8491 - val_loss: 0.9501 - val_Accuracy: 0.6548 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9296 - val_MCI_Precision: 0.5980 - val_AD_Recall: 0.0879 - val_CN_Recall: 0.4459 - val_MCI_Recall: 0.9833\n",
      "Epoch 13/70\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.6224 - Accuracy: 0.7459 - AD_Precision: 0.7355 - CN_Precision: 0.7082 - MCI_Precision: 0.7924 - AD_Recall: 0.5361 - CN_Recall: 0.6505 - MCI_Recall: 0.8112 - val_loss: 0.6092 - val_Accuracy: 0.7762 - val_AD_Precision: 0.7586 - val_CN_Precision: 0.8451 - val_MCI_Precision: 0.8058 - val_AD_Recall: 0.4835 - val_CN_Recall: 0.8108 - val_MCI_Recall: 0.8159\n",
      "Epoch 14/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.5428 - Accuracy: 0.7775 - AD_Precision: 0.7636 - CN_Precision: 0.7691 - MCI_Precision: 0.8088 - AD_Recall: 0.5934 - CN_Recall: 0.6854 - MCI_Recall: 0.8349 - val_loss: 0.6740 - val_Accuracy: 0.7176 - val_AD_Precision: 0.6316 - val_CN_Precision: 0.8861 - val_MCI_Precision: 0.7264 - val_AD_Recall: 0.5275 - val_CN_Recall: 0.4730 - val_MCI_Recall: 0.8996\n",
      "Epoch 15/70\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.5697 - Accuracy: 0.7654 - AD_Precision: 0.7165 - CN_Precision: 0.7618 - MCI_Precision: 0.8084 - AD_Recall: 0.5482 - CN_Recall: 0.6893 - MCI_Recall: 0.8368 - val_loss: 0.5917 - val_Accuracy: 0.7615 - val_AD_Precision: 0.7917 - val_CN_Precision: 0.8679 - val_MCI_Precision: 0.7315 - val_AD_Recall: 0.4176 - val_CN_Recall: 0.6216 - val_MCI_Recall: 0.9121\n",
      "Epoch 16/70\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 0.5236 - Accuracy: 0.7833 - AD_Precision: 0.7472 - CN_Precision: 0.7991 - MCI_Precision: 0.8197 - AD_Recall: 0.5964 - CN_Recall: 0.7262 - MCI_Recall: 0.8368 - val_loss: 0.6037 - val_Accuracy: 0.7741 - val_AD_Precision: 0.7302 - val_CN_Precision: 0.7733 - val_MCI_Precision: 0.8317 - val_AD_Recall: 0.5055 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.7238\n",
      "Epoch 17/70\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.5583 - Accuracy: 0.7722 - AD_Precision: 0.7159 - CN_Precision: 0.7749 - MCI_Precision: 0.8100 - AD_Recall: 0.5843 - CN_Recall: 0.7087 - MCI_Recall: 0.8292 - val_loss: 0.6631 - val_Accuracy: 0.7469 - val_AD_Precision: 0.6833 - val_CN_Precision: 0.8673 - val_MCI_Precision: 0.7465 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.5743 - val_MCI_Recall: 0.8996\n",
      "Epoch 18/70\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.5430 - Accuracy: 0.7738 - AD_Precision: 0.7518 - CN_Precision: 0.7738 - MCI_Precision: 0.8087 - AD_Recall: 0.6295 - CN_Recall: 0.7107 - MCI_Recall: 0.8302 - val_loss: 0.7183 - val_Accuracy: 0.7029 - val_AD_Precision: 0.8462 - val_CN_Precision: 0.9059 - val_MCI_Precision: 0.6540 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.5203 - val_MCI_Recall: 0.9331\n",
      "Epoch 19/70\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.5074 - Accuracy: 0.7906 - AD_Precision: 0.7617 - CN_Precision: 0.7720 - MCI_Precision: 0.8298 - AD_Recall: 0.5873 - CN_Recall: 0.7165 - MCI_Recall: 0.8463 - val_loss: 0.6636 - val_Accuracy: 0.7259 - val_AD_Precision: 0.6447 - val_CN_Precision: 0.9277 - val_MCI_Precision: 0.7292 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.5203 - val_MCI_Recall: 0.8787\n",
      "Epoch 20/70\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.5227 - Accuracy: 0.7927 - AD_Precision: 0.8083 - CN_Precision: 0.7934 - MCI_Precision: 0.8183 - AD_Recall: 0.6476 - CN_Recall: 0.7010 - MCI_Recall: 0.8501 - val_loss: 0.7827 - val_Accuracy: 0.6946 - val_AD_Precision: 0.8824 - val_CN_Precision: 0.8788 - val_MCI_Precision: 0.6397 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.5878 - val_MCI_Recall: 0.9582\n",
      "Epoch 21/70\n",
      "476/476 [==============================] - 33s 68ms/step - loss: 0.5215 - Accuracy: 0.7927 - AD_Precision: 0.7671 - CN_Precision: 0.8017 - MCI_Precision: 0.8222 - AD_Recall: 0.5753 - CN_Recall: 0.7223 - MCI_Recall: 0.8558 - val_loss: 0.5726 - val_Accuracy: 0.7950 - val_AD_Precision: 0.6778 - val_CN_Precision: 0.8188 - val_MCI_Precision: 0.8618 - val_AD_Recall: 0.6703 - val_CN_Recall: 0.8243 - val_MCI_Recall: 0.7824\n",
      "Epoch 22/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.4774 - Accuracy: 0.8106 - AD_Precision: 0.7757 - CN_Precision: 0.8082 - MCI_Precision: 0.8479 - AD_Recall: 0.6355 - CN_Recall: 0.7282 - MCI_Recall: 0.8729 - val_loss: 0.7688 - val_Accuracy: 0.6925 - val_AD_Precision: 0.7778 - val_CN_Precision: 0.9054 - val_MCI_Precision: 0.6453 - val_AD_Recall: 0.3077 - val_CN_Recall: 0.4527 - val_MCI_Recall: 0.9665\n",
      "Epoch 23/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.4649 - Accuracy: 0.8243 - AD_Precision: 0.7915 - CN_Precision: 0.8255 - MCI_Precision: 0.8502 - AD_Recall: 0.6747 - CN_Recall: 0.7534 - MCI_Recall: 0.8672 - val_loss: 0.6259 - val_Accuracy: 0.7741 - val_AD_Precision: 0.9429 - val_CN_Precision: 0.7683 - val_MCI_Precision: 0.7854 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.8514 - val_MCI_Recall: 0.8577\n",
      "Epoch 24/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.4433 - Accuracy: 0.8175 - AD_Precision: 0.8014 - CN_Precision: 0.8067 - MCI_Precision: 0.8415 - AD_Recall: 0.6807 - CN_Recall: 0.7534 - MCI_Recall: 0.8615 - val_loss: 0.5814 - val_Accuracy: 0.7824 - val_AD_Precision: 0.8511 - val_CN_Precision: 0.7840 - val_MCI_Precision: 0.8039 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.8581 - val_MCI_Recall: 0.8577\n",
      "Epoch 25/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.4449 - Accuracy: 0.8133 - AD_Precision: 0.7736 - CN_Precision: 0.8037 - MCI_Precision: 0.8555 - AD_Recall: 0.6898 - CN_Recall: 0.7631 - MCI_Recall: 0.8539 - val_loss: 0.6434 - val_Accuracy: 0.7510 - val_AD_Precision: 0.8367 - val_CN_Precision: 0.9231 - val_MCI_Precision: 0.7006 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.5676 - val_MCI_Recall: 0.9498\n",
      "Epoch 26/70\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.4435 - Accuracy: 0.8159 - AD_Precision: 0.8078 - CN_Precision: 0.8273 - MCI_Precision: 0.8388 - AD_Recall: 0.6837 - CN_Recall: 0.7534 - MCI_Recall: 0.8691 - val_loss: 0.5657 - val_Accuracy: 0.7741 - val_AD_Precision: 0.8649 - val_CN_Precision: 0.8489 - val_MCI_Precision: 0.7448 - val_AD_Recall: 0.3516 - val_CN_Recall: 0.7973 - val_MCI_Recall: 0.8912\n",
      "Epoch 27/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.4352 - Accuracy: 0.8206 - AD_Precision: 0.7917 - CN_Precision: 0.8111 - MCI_Precision: 0.8539 - AD_Recall: 0.6867 - CN_Recall: 0.7670 - MCI_Recall: 0.8653 - val_loss: 0.8391 - val_Accuracy: 0.7343 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8571 - val_MCI_Precision: 0.6786 - val_AD_Recall: 0.1648 - val_CN_Recall: 0.7297 - val_MCI_Recall: 0.9540\n",
      "Epoch 28/70\n",
      "476/476 [==============================] - 33s 69ms/step - loss: 0.4481 - Accuracy: 0.8248 - AD_Precision: 0.7993 - CN_Precision: 0.8102 - MCI_Precision: 0.8554 - AD_Recall: 0.6596 - CN_Recall: 0.7709 - MCI_Recall: 0.8700 - val_loss: 0.5091 - val_Accuracy: 0.8117 - val_AD_Precision: 0.8689 - val_CN_Precision: 0.9060 - val_MCI_Precision: 0.7900 - val_AD_Recall: 0.5824 - val_CN_Recall: 0.7162 - val_MCI_Recall: 0.9289\n",
      "Epoch 29/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.4025 - Accuracy: 0.8401 - AD_Precision: 0.7915 - CN_Precision: 0.8583 - MCI_Precision: 0.8642 - AD_Recall: 0.6747 - CN_Recall: 0.8233 - MCI_Recall: 0.8814 - val_loss: 0.8837 - val_Accuracy: 0.6820 - val_AD_Precision: 0.7843 - val_CN_Precision: 0.9792 - val_MCI_Precision: 0.6322 - val_AD_Recall: 0.4396 - val_CN_Recall: 0.3176 - val_MCI_Recall: 0.9707\n",
      "Epoch 30/70\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.3786 - Accuracy: 0.8459 - AD_Precision: 0.8007 - CN_Precision: 0.8663 - MCI_Precision: 0.8665 - AD_Recall: 0.6777 - CN_Recall: 0.8175 - MCI_Recall: 0.8871 - val_loss: 0.8669 - val_Accuracy: 0.6757 - val_AD_Precision: 0.6292 - val_CN_Precision: 0.9268 - val_MCI_Precision: 0.6897 - val_AD_Recall: 0.6154 - val_CN_Recall: 0.2568 - val_MCI_Recall: 0.9205\n",
      "Epoch 31/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.4148 - Accuracy: 0.8485 - AD_Precision: 0.8504 - CN_Precision: 0.8443 - MCI_Precision: 0.8678 - AD_Recall: 0.7018 - CN_Recall: 0.8000 - MCI_Recall: 0.8966 - val_loss: 0.6712 - val_Accuracy: 0.7343 - val_AD_Precision: 0.7937 - val_CN_Precision: 0.9437 - val_MCI_Precision: 0.6933 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.4527 - val_MCI_Recall: 0.9456\n",
      "Epoch 32/70\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.3679 - Accuracy: 0.8590 - AD_Precision: 0.8714 - CN_Precision: 0.8563 - MCI_Precision: 0.8731 - AD_Recall: 0.7349 - CN_Recall: 0.8097 - MCI_Recall: 0.9070 - val_loss: 0.5928 - val_Accuracy: 0.7845 - val_AD_Precision: 0.8723 - val_CN_Precision: 0.7433 - val_MCI_Precision: 0.8438 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.9392 - val_MCI_Recall: 0.7908\n",
      "Epoch 33/70\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.3746 - Accuracy: 0.8543 - AD_Precision: 0.8200 - CN_Precision: 0.8545 - MCI_Precision: 0.8771 - AD_Recall: 0.7410 - CN_Recall: 0.8097 - MCI_Recall: 0.8937 - val_loss: 0.7152 - val_Accuracy: 0.7531 - val_AD_Precision: 0.9630 - val_CN_Precision: 0.8879 - val_MCI_Precision: 0.7043 - val_AD_Recall: 0.2857 - val_CN_Recall: 0.6959 - val_MCI_Recall: 0.9665\n",
      "Epoch 34/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3736 - Accuracy: 0.8585 - AD_Precision: 0.8414 - CN_Precision: 0.8687 - MCI_Precision: 0.8790 - AD_Recall: 0.7349 - CN_Recall: 0.8350 - MCI_Recall: 0.8956 - val_loss: 0.8720 - val_Accuracy: 0.7364 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9286 - val_MCI_Precision: 0.6657 - val_AD_Recall: 0.1758 - val_CN_Recall: 0.7027 - val_MCI_Recall: 0.9665\n",
      "Epoch 35/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3643 - Accuracy: 0.8532 - AD_Precision: 0.8357 - CN_Precision: 0.8473 - MCI_Precision: 0.8788 - AD_Recall: 0.7199 - CN_Recall: 0.8078 - MCI_Recall: 0.8947 - val_loss: 0.7856 - val_Accuracy: 0.7741 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7939 - val_MCI_Precision: 0.7563 - val_AD_Recall: 0.2747 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.8828\n",
      "Epoch 36/70\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.3397 - Accuracy: 0.8669 - AD_Precision: 0.8255 - CN_Precision: 0.8760 - MCI_Precision: 0.8877 - AD_Recall: 0.7410 - CN_Recall: 0.8505 - MCI_Recall: 0.9004 - val_loss: 0.6036 - val_Accuracy: 0.7782 - val_AD_Precision: 0.6538 - val_CN_Precision: 0.7798 - val_MCI_Precision: 0.8817 - val_AD_Recall: 0.7473 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.6862\n",
      "Epoch 37/70\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.3489 - Accuracy: 0.8648 - AD_Precision: 0.8361 - CN_Precision: 0.8740 - MCI_Precision: 0.8852 - AD_Recall: 0.7530 - CN_Recall: 0.8214 - MCI_Recall: 0.8994 - val_loss: 0.9409 - val_Accuracy: 0.7218 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6715 - val_MCI_Precision: 0.7500 - val_AD_Recall: 0.2088 - val_CN_Recall: 0.9392 - val_MCI_Recall: 0.7782\n",
      "Epoch 38/70\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.3403 - Accuracy: 0.8659 - AD_Precision: 0.8600 - CN_Precision: 0.8694 - MCI_Precision: 0.8838 - AD_Recall: 0.7771 - CN_Recall: 0.8272 - MCI_Recall: 0.9023 - val_loss: 0.5992 - val_Accuracy: 0.8013 - val_AD_Precision: 0.9459 - val_CN_Precision: 0.7917 - val_MCI_Precision: 0.7962 - val_AD_Recall: 0.3846 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.8828\n",
      "Epoch 39/70\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.3139 - Accuracy: 0.8869 - AD_Precision: 0.8645 - CN_Precision: 0.8785 - MCI_Precision: 0.9085 - AD_Recall: 0.8072 - CN_Recall: 0.8563 - MCI_Recall: 0.9137 - val_loss: 0.6669 - val_Accuracy: 0.7636 - val_AD_Precision: 0.8571 - val_CN_Precision: 0.9263 - val_MCI_Precision: 0.7081 - val_AD_Recall: 0.4615 - val_CN_Recall: 0.5946 - val_MCI_Recall: 0.9540\n",
      "Epoch 40/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3315 - Accuracy: 0.8748 - AD_Precision: 0.8758 - CN_Precision: 0.8623 - MCI_Precision: 0.8924 - AD_Recall: 0.7861 - CN_Recall: 0.8272 - MCI_Recall: 0.9127 - val_loss: 0.7268 - val_Accuracy: 0.7803 - val_AD_Precision: 0.9375 - val_CN_Precision: 0.8500 - val_MCI_Precision: 0.7475 - val_AD_Recall: 0.3297 - val_CN_Recall: 0.8041 - val_MCI_Recall: 0.9289\n",
      "Epoch 41/70\n",
      "476/476 [==============================] - 32s 68ms/step - loss: 0.3395 - Accuracy: 0.8732 - AD_Precision: 0.8317 - CN_Precision: 0.8873 - MCI_Precision: 0.8968 - AD_Recall: 0.7590 - CN_Recall: 0.8563 - MCI_Recall: 0.9070 - val_loss: 0.5166 - val_Accuracy: 0.8138 - val_AD_Precision: 0.8276 - val_CN_Precision: 0.8289 - val_MCI_Precision: 0.8221 - val_AD_Recall: 0.5275 - val_CN_Recall: 0.8514 - val_MCI_Recall: 0.8703\n",
      "Epoch 42/70\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 0.3405 - Accuracy: 0.8669 - AD_Precision: 0.8537 - CN_Precision: 0.8702 - MCI_Precision: 0.8837 - AD_Recall: 0.7560 - CN_Recall: 0.8330 - MCI_Recall: 0.9013 - val_loss: 0.5169 - val_Accuracy: 0.8180 - val_AD_Precision: 0.8305 - val_CN_Precision: 0.8110 - val_MCI_Precision: 0.8465 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.8536\n",
      "Epoch 43/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2795 - Accuracy: 0.8911 - AD_Precision: 0.8833 - CN_Precision: 0.8960 - MCI_Precision: 0.9099 - AD_Recall: 0.7982 - CN_Recall: 0.8699 - MCI_Recall: 0.9194 - val_loss: 0.5785 - val_Accuracy: 0.8096 - val_AD_Precision: 0.8679 - val_CN_Precision: 0.8662 - val_MCI_Precision: 0.7920 - val_AD_Recall: 0.5055 - val_CN_Recall: 0.8311 - val_MCI_Recall: 0.9079\n",
      "Epoch 44/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.3291 - Accuracy: 0.8638 - AD_Precision: 0.8581 - CN_Precision: 0.8554 - MCI_Precision: 0.8860 - AD_Recall: 0.7831 - CN_Recall: 0.8272 - MCI_Recall: 0.8918 - val_loss: 0.7178 - val_Accuracy: 0.7657 - val_AD_Precision: 0.8136 - val_CN_Precision: 0.9765 - val_MCI_Precision: 0.7121 - val_AD_Recall: 0.5275 - val_CN_Recall: 0.5608 - val_MCI_Recall: 0.9623\n",
      "Epoch 45/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.3692 - Accuracy: 0.8606 - AD_Precision: 0.8495 - CN_Precision: 0.8563 - MCI_Precision: 0.8851 - AD_Recall: 0.7651 - CN_Recall: 0.8214 - MCI_Recall: 0.8918 - val_loss: 0.5485 - val_Accuracy: 0.8013 - val_AD_Precision: 0.7349 - val_CN_Precision: 0.9091 - val_MCI_Precision: 0.7912 - val_AD_Recall: 0.6703 - val_CN_Recall: 0.6757 - val_MCI_Recall: 0.9038\n",
      "Epoch 46/70\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.2941 - Accuracy: 0.8869 - AD_Precision: 0.8980 - CN_Precision: 0.8775 - MCI_Precision: 0.9057 - AD_Recall: 0.8223 - CN_Recall: 0.8485 - MCI_Recall: 0.9108 - val_loss: 0.5507 - val_Accuracy: 0.8117 - val_AD_Precision: 0.8226 - val_CN_Precision: 0.8939 - val_MCI_Precision: 0.7891 - val_AD_Recall: 0.5604 - val_CN_Recall: 0.7973 - val_MCI_Recall: 0.9079\n",
      "Epoch 47/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2904 - Accuracy: 0.8911 - AD_Precision: 0.8779 - CN_Precision: 0.8798 - MCI_Precision: 0.9139 - AD_Recall: 0.8012 - CN_Recall: 0.8524 - MCI_Recall: 0.9260 - val_loss: 0.7907 - val_Accuracy: 0.7552 - val_AD_Precision: 0.7105 - val_CN_Precision: 0.9733 - val_MCI_Precision: 0.7197 - val_AD_Recall: 0.5934 - val_CN_Recall: 0.4932 - val_MCI_Recall: 0.9456\n",
      "Epoch 48/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2868 - Accuracy: 0.8864 - AD_Precision: 0.8677 - CN_Precision: 0.8929 - MCI_Precision: 0.9044 - AD_Recall: 0.8102 - CN_Recall: 0.8583 - MCI_Recall: 0.9156 - val_loss: 0.7015 - val_Accuracy: 0.8033 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9280 - val_MCI_Precision: 0.7492 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.7838 - val_MCI_Recall: 0.9623\n",
      "Epoch 49/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2875 - Accuracy: 0.8948 - AD_Precision: 0.8730 - CN_Precision: 0.9022 - MCI_Precision: 0.9065 - AD_Recall: 0.8072 - CN_Recall: 0.8777 - MCI_Recall: 0.9194 - val_loss: 0.6478 - val_Accuracy: 0.7573 - val_AD_Precision: 0.5241 - val_CN_Precision: 0.8601 - val_MCI_Precision: 0.8840 - val_AD_Recall: 0.8352 - val_CN_Recall: 0.8311 - val_MCI_Recall: 0.6695\n",
      "Epoch 50/70\n",
      "476/476 [==============================] - 33s 69ms/step - loss: 0.3020 - Accuracy: 0.8837 - AD_Precision: 0.8803 - CN_Precision: 0.8740 - MCI_Precision: 0.9039 - AD_Recall: 0.8193 - CN_Recall: 0.8485 - MCI_Recall: 0.9099 - val_loss: 0.5081 - val_Accuracy: 0.8222 - val_AD_Precision: 0.7917 - val_CN_Precision: 0.8551 - val_MCI_Precision: 0.8263 - val_AD_Recall: 0.6264 - val_CN_Recall: 0.7973 - val_MCI_Recall: 0.8954\n",
      "Epoch 51/70\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.2798 - Accuracy: 0.8990 - AD_Precision: 0.8951 - CN_Precision: 0.8924 - MCI_Precision: 0.9099 - AD_Recall: 0.8223 - CN_Recall: 0.8699 - MCI_Recall: 0.9203 - val_loss: 0.5560 - val_Accuracy: 0.8201 - val_AD_Precision: 0.8621 - val_CN_Precision: 0.8872 - val_MCI_Precision: 0.7880 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.7973 - val_MCI_Recall: 0.9331\n",
      "Epoch 52/70\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.2749 - Accuracy: 0.8906 - AD_Precision: 0.8849 - CN_Precision: 0.9006 - MCI_Precision: 0.9033 - AD_Recall: 0.8102 - CN_Recall: 0.8621 - MCI_Recall: 0.9213 - val_loss: 0.6307 - val_Accuracy: 0.8117 - val_AD_Precision: 0.9750 - val_CN_Precision: 0.8643 - val_MCI_Precision: 0.7698 - val_AD_Recall: 0.4286 - val_CN_Recall: 0.8176 - val_MCI_Recall: 0.9372\n",
      "Epoch 53/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.2889 - Accuracy: 0.8943 - AD_Precision: 0.8603 - CN_Precision: 0.8874 - MCI_Precision: 0.9150 - AD_Recall: 0.8163 - CN_Recall: 0.8718 - MCI_Recall: 0.9194 - val_loss: 0.6242 - val_Accuracy: 0.8075 - val_AD_Precision: 0.8500 - val_CN_Precision: 0.9369 - val_MCI_Precision: 0.7625 - val_AD_Recall: 0.5604 - val_CN_Recall: 0.7027 - val_MCI_Recall: 0.9540\n",
      "Epoch 54/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2628 - Accuracy: 0.9053 - AD_Precision: 0.8987 - CN_Precision: 0.9054 - MCI_Precision: 0.9229 - AD_Recall: 0.8283 - CN_Recall: 0.8738 - MCI_Recall: 0.9317 - val_loss: 0.6272 - val_Accuracy: 0.8180 - val_AD_Precision: 0.8548 - val_CN_Precision: 0.9237 - val_MCI_Precision: 0.7747 - val_AD_Recall: 0.5824 - val_CN_Recall: 0.7365 - val_MCI_Recall: 0.9498\n",
      "Epoch 55/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.2675 - Accuracy: 0.8995 - AD_Precision: 0.8921 - CN_Precision: 0.8832 - MCI_Precision: 0.9143 - AD_Recall: 0.8464 - CN_Recall: 0.8660 - MCI_Recall: 0.9213 - val_loss: 1.0460 - val_Accuracy: 0.7552 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8289 - val_MCI_Precision: 0.7157 - val_AD_Recall: 0.1099 - val_CN_Recall: 0.8514 - val_MCI_Recall: 0.9372\n",
      "Epoch 56/70\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.2893 - Accuracy: 0.8895 - AD_Precision: 0.8841 - CN_Precision: 0.8911 - MCI_Precision: 0.9071 - AD_Recall: 0.8042 - CN_Recall: 0.8583 - MCI_Recall: 0.9175 - val_loss: 0.5730 - val_Accuracy: 0.8138 - val_AD_Precision: 0.8868 - val_CN_Precision: 0.8025 - val_MCI_Precision: 0.8196 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.8784 - val_MCI_Recall: 0.8745\n",
      "Epoch 57/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2703 - Accuracy: 0.9048 - AD_Precision: 0.8656 - CN_Precision: 0.9145 - MCI_Precision: 0.9259 - AD_Recall: 0.8343 - CN_Recall: 0.8932 - MCI_Recall: 0.9250 - val_loss: 0.8517 - val_Accuracy: 0.7762 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7418 - val_MCI_Precision: 0.7895 - val_AD_Recall: 0.2527 - val_CN_Recall: 0.9122 - val_MCI_Recall: 0.8787\n",
      "Epoch 58/70\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.2903 - Accuracy: 0.8906 - AD_Precision: 0.8516 - CN_Precision: 0.9063 - MCI_Precision: 0.9058 - AD_Recall: 0.7952 - CN_Recall: 0.8641 - MCI_Recall: 0.9213 - val_loss: 0.7249 - val_Accuracy: 0.7762 - val_AD_Precision: 0.8868 - val_CN_Precision: 0.9200 - val_MCI_Precision: 0.7156 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.6216 - val_MCI_Recall: 0.9582\n",
      "Epoch 59/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2322 - Accuracy: 0.9206 - AD_Precision: 0.9164 - CN_Precision: 0.9312 - MCI_Precision: 0.9290 - AD_Recall: 0.8584 - CN_Recall: 0.8932 - MCI_Recall: 0.9431 - val_loss: 0.7212 - val_Accuracy: 0.7657 - val_AD_Precision: 0.7241 - val_CN_Precision: 0.9524 - val_MCI_Precision: 0.7367 - val_AD_Recall: 0.6923 - val_CN_Recall: 0.5405 - val_MCI_Recall: 0.9247\n",
      "Epoch 60/70\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2842 - Accuracy: 0.8985 - AD_Precision: 0.8698 - CN_Precision: 0.8963 - MCI_Precision: 0.9171 - AD_Recall: 0.8253 - CN_Recall: 0.8563 - MCI_Recall: 0.9336 - val_loss: 0.7500 - val_Accuracy: 0.7699 - val_AD_Precision: 0.7966 - val_CN_Precision: 0.9667 - val_MCI_Precision: 0.7099 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.5878 - val_MCI_Recall: 0.9623\n",
      "Epoch 61/70\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.2659 - Accuracy: 0.8964 - AD_Precision: 0.8794 - CN_Precision: 0.8900 - MCI_Precision: 0.9119 - AD_Recall: 0.8343 - CN_Recall: 0.8641 - MCI_Recall: 0.9231 - val_loss: 0.5610 - val_Accuracy: 0.8305 - val_AD_Precision: 0.8060 - val_CN_Precision: 0.8397 - val_MCI_Precision: 0.8440 - val_AD_Recall: 0.5934 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.8828\n",
      "Epoch 62/70\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2698 - Accuracy: 0.8958 - AD_Precision: 0.8762 - CN_Precision: 0.9022 - MCI_Precision: 0.9090 - AD_Recall: 0.8102 - CN_Recall: 0.8777 - MCI_Recall: 0.9194 - val_loss: 0.7855 - val_Accuracy: 0.7510 - val_AD_Precision: 0.9362 - val_CN_Precision: 0.6068 - val_MCI_Precision: 0.8796 - val_AD_Recall: 0.4835 - val_CN_Recall: 0.9595 - val_MCI_Recall: 0.7029\n",
      "Epoch 63/70\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.2897 - Accuracy: 0.8906 - AD_Precision: 0.8839 - CN_Precision: 0.8780 - MCI_Precision: 0.9122 - AD_Recall: 0.8253 - CN_Recall: 0.8388 - MCI_Recall: 0.9165 - val_loss: 0.6328 - val_Accuracy: 0.8285 - val_AD_Precision: 0.9556 - val_CN_Precision: 0.8681 - val_MCI_Precision: 0.7972 - val_AD_Recall: 0.4725 - val_CN_Recall: 0.8446 - val_MCI_Recall: 0.9372\n",
      "Epoch 64/70\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2644 - Accuracy: 0.9069 - AD_Precision: 0.9058 - CN_Precision: 0.9012 - MCI_Precision: 0.9196 - AD_Recall: 0.8404 - CN_Recall: 0.8854 - MCI_Recall: 0.9222 - val_loss: 0.6016 - val_Accuracy: 0.8159 - val_AD_Precision: 0.7532 - val_CN_Precision: 0.9469 - val_MCI_Precision: 0.7964 - val_AD_Recall: 0.6374 - val_CN_Recall: 0.7230 - val_MCI_Recall: 0.9331\n",
      "Epoch 65/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.2428 - Accuracy: 0.9069 - AD_Precision: 0.8959 - CN_Precision: 0.9042 - MCI_Precision: 0.9228 - AD_Recall: 0.8554 - CN_Recall: 0.8796 - MCI_Recall: 0.9298 - val_loss: 0.8614 - val_Accuracy: 0.7950 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8378 - val_MCI_Precision: 0.7585 - val_AD_Recall: 0.3407 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.9331\n",
      "Epoch 66/70\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2207 - Accuracy: 0.9137 - AD_Precision: 0.8913 - CN_Precision: 0.9016 - MCI_Precision: 0.9336 - AD_Recall: 0.8645 - CN_Recall: 0.8893 - MCI_Recall: 0.9336 - val_loss: 0.7666 - val_Accuracy: 0.8117 - val_AD_Precision: 0.9545 - val_CN_Precision: 0.9417 - val_MCI_Precision: 0.7484 - val_AD_Recall: 0.4615 - val_CN_Recall: 0.7635 - val_MCI_Recall: 0.9707\n",
      "Epoch 67/70\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2732 - Accuracy: 0.8911 - AD_Precision: 0.8585 - CN_Precision: 0.8858 - MCI_Precision: 0.9144 - AD_Recall: 0.8223 - CN_Recall: 0.8738 - MCI_Recall: 0.9127 - val_loss: 0.6085 - val_Accuracy: 0.8075 - val_AD_Precision: 0.6186 - val_CN_Precision: 0.8857 - val_MCI_Precision: 0.8726 - val_AD_Recall: 0.8022 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.7741\n",
      "Epoch 68/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.2202 - Accuracy: 0.9164 - AD_Precision: 0.9055 - CN_Precision: 0.9257 - MCI_Precision: 0.9230 - AD_Recall: 0.8373 - CN_Recall: 0.8951 - MCI_Recall: 0.9440 - val_loss: 0.7235 - val_Accuracy: 0.8054 - val_AD_Precision: 0.8545 - val_CN_Precision: 0.9316 - val_MCI_Precision: 0.7500 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.7365 - val_MCI_Recall: 0.9540\n",
      "Epoch 69/70\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.2424 - Accuracy: 0.9106 - AD_Precision: 0.8842 - CN_Precision: 0.9093 - MCI_Precision: 0.9241 - AD_Recall: 0.8283 - CN_Recall: 0.8951 - MCI_Recall: 0.9355 - val_loss: 0.5469 - val_Accuracy: 0.8389 - val_AD_Precision: 0.8209 - val_CN_Precision: 0.8905 - val_MCI_Precision: 0.8189 - val_AD_Recall: 0.6044 - val_CN_Recall: 0.8243 - val_MCI_Recall: 0.9079\n",
      "Epoch 70/70\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.2336 - Accuracy: 0.9116 - AD_Precision: 0.8984 - CN_Precision: 0.9061 - MCI_Precision: 0.9289 - AD_Recall: 0.8524 - CN_Recall: 0.8990 - MCI_Recall: 0.9298 - val_loss: 0.5597 - val_Accuracy: 0.8285 - val_AD_Precision: 0.8088 - val_CN_Precision: 0.9206 - val_MCI_Precision: 0.8014 - val_AD_Recall: 0.6044 - val_CN_Recall: 0.7838 - val_MCI_Recall: 0.9289\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/apps/tensorflow/2.7.0/lib/python3.9/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  layer_config = serialize_layer_fn(layer)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'model05' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m model04\u001b[38;5;241m.\u001b[39mload_weights(glob\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msavepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mefficientnet_row_10E-5_weights-epoch:*-val_Accuracy:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mmax\u001b[39m(history04\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_Accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     15\u001b[0m model04\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msavepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/efficientnet_70_0.00001.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 16\u001b[0m p2 \u001b[38;5;241m=\u001b[39m \u001b[43mmodel05\u001b[49m\u001b[38;5;241m.\u001b[39mpredict(testing_image_datagen)\n\u001b[1;32m     17\u001b[0m predicted_lab \u001b[38;5;241m=\u001b[39m p2\u001b[38;5;241m.\u001b[39margmax(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     18\u001b[0m true_lab \u001b[38;5;241m=\u001b[39m testing_image_datagen\u001b[38;5;241m.\u001b[39mclasses\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model05' is not defined"
     ]
    }
   ],
   "source": [
    "savepath = 'Finetuning/efficientnet/efficientnet_70_0.00001'\n",
    "lr=0.00001\n",
    "if not os.path.isdir(savepath):\n",
    "      os.makedirs(savepath)\n",
    "csvloger_callback = tf.keras.callbacks.CSVLogger(f'{savepath}history.csv', separator=',', append=False)\n",
    "\n",
    "modelcheckpoint_callback = tf.keras.callbacks.ModelCheckpoint(savepath+'efficientnet_row_10E-5_weights-epoch:{epoch:03d}-val_Accuracy:{val_Accuracy:.4f}.h5',monitor='val_Accuracy'\n",
    "    ,verbose=0,save_best_only=True,save_weights_only=True,mode='max',save_freq='epoch' , initial_value_threshold=0.85)\n",
    "\n",
    "OPT = tf.keras.optimizers.Adam(learning_rate=lr)\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "history04 = model04.fit(training_image_datagen, epochs=EPOCH, validation_data=testing_image_datagen,callbacks=[csvloger_callback,modelcheckpoint_callback])\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "model04.load_weights(glob.glob(f\"{savepath}efficientnet_row_10E-5_weights-epoch:*-val_Accuracy:{max(history04.history['val_Accuracy']):.4f}.h5\")[0])\n",
    "model04.save(f\"{savepath}/efficientnet_70_0.00001.h5\")\n",
    "p2 = model05.predict(testing_image_datagen)\n",
    "predicted_lab = p2.argmax(axis=-1)\n",
    "true_lab = testing_image_datagen.classes\n",
    "plot_confusion_matrix(true_lab, predicted_lab, testing_image_datagen.class_indices.keys(),savepath)\n",
    "report = classification_report(true_lab, predicted_lab, target_names=testing_image_datagen.class_indices.keys())\n",
    "print(report)\n",
    "dump_text(report,f'{savepath}classification_report.txt')\n",
    "np.save(\"efficientnet_70_0.00001.npy\",p2 )   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "726ca60a-5ea3-4544-849d-518171d12672",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          AD       0.81      0.63      0.71        91\n",
      "          CN       0.89      0.84      0.86       148\n",
      "         MCI       0.82      0.92      0.87       239\n",
      "\n",
      "    accuracy                           0.84       478\n",
      "   macro avg       0.84      0.79      0.81       478\n",
      "weighted avg       0.84      0.84      0.84       478\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p2 = model04.predict(testing_image_datagen)\n",
    "predicted_lab = p2.argmax(axis=-1)\n",
    "true_lab = testing_image_datagen.classes\n",
    "plot_confusion_matrix(true_lab, predicted_lab, testing_image_datagen.class_indices.keys(),savepath)\n",
    "report = classification_report(true_lab, predicted_lab, target_names=testing_image_datagen.class_indices.keys())\n",
    "print(report)\n",
    "dump_text(report,f'{savepath}classification_report.txt')\n",
    "np.save(\"efficientnet_70_0.00001.npy\",p2 )   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aeb5982c-d370-4ac9-87d8-b9d39af129aa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-14 19:32:48.750314: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "476/476 [==============================] - 42s 71ms/step - loss: 4.0244 - Accuracy: 0.4645 - AD_Precision: 0.2323 - CN_Precision: 0.3710 - MCI_Precision: 0.5834 - AD_Recall: 0.2169 - CN_Recall: 0.3631 - MCI_Recall: 0.5873 - val_loss: 6.5886 - val_Accuracy: 0.5084 - val_AD_Precision: 0.0000e+00 - val_CN_Precision: 0.5000 - val_MCI_Precision: 0.5085 - val_AD_Recall: 0.0000e+00 - val_CN_Recall: 0.0270 - val_MCI_Recall: 1.0000\n",
      "Epoch 2/100\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 3.0701 - Accuracy: 0.5124 - AD_Precision: 0.3127 - CN_Precision: 0.4127 - MCI_Precision: 0.6276 - AD_Recall: 0.3042 - CN_Recall: 0.4039 - MCI_Recall: 0.6300 - val_loss: 2.0052 - val_Accuracy: 0.6067 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6606 - val_MCI_Precision: 0.5912 - val_AD_Recall: 0.0330 - val_CN_Recall: 0.4865 - val_MCI_Recall: 0.8954\n",
      "Epoch 3/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 1.7895 - Accuracy: 0.5886 - AD_Precision: 0.4108 - CN_Precision: 0.5256 - MCI_Precision: 0.6806 - AD_Recall: 0.3886 - CN_Recall: 0.4990 - MCI_Recall: 0.6812 - val_loss: 1.7415 - val_Accuracy: 0.5481 - val_AD_Precision: 0.6000 - val_CN_Precision: 0.4606 - val_MCI_Precision: 0.6718 - val_AD_Recall: 0.1319 - val_CN_Recall: 0.7905 - val_MCI_Recall: 0.5481\n",
      "Epoch 4/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 1.6219 - Accuracy: 0.6234 - AD_Precision: 0.5000 - CN_Precision: 0.5470 - MCI_Precision: 0.6963 - AD_Recall: 0.4458 - CN_Recall: 0.5534 - MCI_Recall: 0.6983 - val_loss: 1.6398 - val_Accuracy: 0.5879 - val_AD_Precision: 0.6897 - val_CN_Precision: 0.8529 - val_MCI_Precision: 0.5590 - val_AD_Recall: 0.2198 - val_CN_Recall: 0.1959 - val_MCI_Recall: 0.9707\n",
      "Epoch 5/100\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 1.1149 - Accuracy: 0.6597 - AD_Precision: 0.5357 - CN_Precision: 0.6091 - MCI_Precision: 0.7357 - AD_Recall: 0.4970 - CN_Recall: 0.5689 - MCI_Recall: 0.7343 - val_loss: 0.8283 - val_Accuracy: 0.7238 - val_AD_Precision: 0.8049 - val_CN_Precision: 0.8365 - val_MCI_Precision: 0.6930 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.5878 - val_MCI_Recall: 0.9163\n",
      "Epoch 6/100\n",
      "476/476 [==============================] - 33s 69ms/step - loss: 1.0022 - Accuracy: 0.6702 - AD_Precision: 0.5168 - CN_Precision: 0.6457 - MCI_Precision: 0.7367 - AD_Recall: 0.4639 - CN_Recall: 0.5981 - MCI_Recall: 0.7486 - val_loss: 0.7150 - val_Accuracy: 0.7259 - val_AD_Precision: 0.6098 - val_CN_Precision: 0.7734 - val_MCI_Precision: 0.7724 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.6689 - val_MCI_Recall: 0.7950\n",
      "Epoch 7/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.9053 - Accuracy: 0.6833 - AD_Precision: 0.5629 - CN_Precision: 0.6595 - MCI_Precision: 0.7440 - AD_Recall: 0.4849 - CN_Recall: 0.5942 - MCI_Recall: 0.7666 - val_loss: 1.4459 - val_Accuracy: 0.4854 - val_AD_Precision: 0.3054 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.6887 - val_AD_Recall: 0.8022 - val_CN_Recall: 0.0338 - val_MCI_Recall: 0.6109\n",
      "Epoch 8/100\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.7883 - Accuracy: 0.6986 - AD_Precision: 0.5860 - CN_Precision: 0.6771 - MCI_Precision: 0.7592 - AD_Recall: 0.5030 - CN_Recall: 0.6272 - MCI_Recall: 0.7657 - val_loss: 0.8823 - val_Accuracy: 0.7176 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7801 - val_MCI_Precision: 0.7006 - val_AD_Recall: 0.0879 - val_CN_Recall: 0.7432 - val_MCI_Recall: 0.9205\n",
      "Epoch 9/100\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.7155 - Accuracy: 0.7322 - AD_Precision: 0.6950 - CN_Precision: 0.7097 - MCI_Precision: 0.7713 - AD_Recall: 0.5422 - CN_Recall: 0.6408 - MCI_Recall: 0.8065 - val_loss: 0.7060 - val_Accuracy: 0.7218 - val_AD_Precision: 0.8438 - val_CN_Precision: 0.7829 - val_MCI_Precision: 0.7114 - val_AD_Recall: 0.2967 - val_CN_Recall: 0.6824 - val_MCI_Recall: 0.8870\n",
      "Epoch 10/100\n",
      "476/476 [==============================] - 33s 70ms/step - loss: 0.6317 - Accuracy: 0.7486 - AD_Precision: 0.6731 - CN_Precision: 0.7418 - MCI_Precision: 0.7998 - AD_Recall: 0.5271 - CN_Recall: 0.6583 - MCI_Recall: 0.8302 - val_loss: 1.0104 - val_Accuracy: 0.7238 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8156 - val_MCI_Precision: 0.6850 - val_AD_Recall: 0.0659 - val_CN_Recall: 0.7770 - val_MCI_Recall: 0.9372\n",
      "Epoch 11/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.6096 - Accuracy: 0.7649 - AD_Precision: 0.7361 - CN_Precision: 0.7553 - MCI_Precision: 0.8056 - AD_Recall: 0.5964 - CN_Recall: 0.6893 - MCI_Recall: 0.8216 - val_loss: 0.7407 - val_Accuracy: 0.6904 - val_AD_Precision: 0.5462 - val_CN_Precision: 0.6865 - val_MCI_Precision: 0.9098 - val_AD_Recall: 0.7143 - val_CN_Recall: 0.8581 - val_MCI_Recall: 0.5063\n",
      "Epoch 12/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.5935 - Accuracy: 0.7564 - AD_Precision: 0.7176 - CN_Precision: 0.7259 - MCI_Precision: 0.7984 - AD_Recall: 0.5663 - CN_Recall: 0.6583 - MCI_Recall: 0.8264 - val_loss: 1.4341 - val_Accuracy: 0.4728 - val_AD_Precision: 0.3170 - val_CN_Precision: 0.6951 - val_MCI_Precision: 0.9474 - val_AD_Recall: 0.9231 - val_CN_Recall: 0.7703 - val_MCI_Recall: 0.0753\n",
      "Epoch 13/100\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.5934 - Accuracy: 0.7612 - AD_Precision: 0.7080 - CN_Precision: 0.7672 - MCI_Precision: 0.8032 - AD_Recall: 0.5843 - CN_Recall: 0.6913 - MCI_Recall: 0.8093 - val_loss: 0.6449 - val_Accuracy: 0.7322 - val_AD_Precision: 0.6719 - val_CN_Precision: 0.8889 - val_MCI_Precision: 0.7317 - val_AD_Recall: 0.4725 - val_CN_Recall: 0.5405 - val_MCI_Recall: 0.8787\n",
      "Epoch 14/100\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.5848 - Accuracy: 0.7612 - AD_Precision: 0.7137 - CN_Precision: 0.7560 - MCI_Precision: 0.8070 - AD_Recall: 0.5633 - CN_Recall: 0.6738 - MCI_Recall: 0.8292 - val_loss: 0.6547 - val_Accuracy: 0.7510 - val_AD_Precision: 0.8529 - val_CN_Precision: 0.7000 - val_MCI_Precision: 0.8089 - val_AD_Recall: 0.3187 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.7615\n",
      "Epoch 15/100\n",
      "476/476 [==============================] - 31s 66ms/step - loss: 0.5345 - Accuracy: 0.7938 - AD_Precision: 0.7396 - CN_Precision: 0.8230 - MCI_Precision: 0.8295 - AD_Recall: 0.5904 - CN_Recall: 0.7223 - MCI_Recall: 0.8539 - val_loss: 0.8241 - val_Accuracy: 0.7134 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7552 - val_MCI_Precision: 0.6997 - val_AD_Recall: 0.1209 - val_CN_Recall: 0.7297 - val_MCI_Recall: 0.9163\n",
      "Epoch 16/100\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.5433 - Accuracy: 0.7796 - AD_Precision: 0.7370 - CN_Precision: 0.7814 - MCI_Precision: 0.8134 - AD_Recall: 0.5994 - CN_Recall: 0.7010 - MCI_Recall: 0.8435 - val_loss: 0.8170 - val_Accuracy: 0.6757 - val_AD_Precision: 0.8800 - val_CN_Precision: 0.5820 - val_MCI_Precision: 0.8108 - val_AD_Recall: 0.2418 - val_CN_Recall: 0.9595 - val_MCI_Recall: 0.6276\n",
      "Epoch 17/100\n",
      "476/476 [==============================] - 32s 67ms/step - loss: 0.5671 - Accuracy: 0.7617 - AD_Precision: 0.7346 - CN_Precision: 0.7426 - MCI_Precision: 0.8110 - AD_Recall: 0.5753 - CN_Recall: 0.6777 - MCI_Recall: 0.8264 - val_loss: 0.6265 - val_Accuracy: 0.7845 - val_AD_Precision: 0.7400 - val_CN_Precision: 0.8444 - val_MCI_Precision: 0.7786 - val_AD_Recall: 0.4066 - val_CN_Recall: 0.7703 - val_MCI_Recall: 0.8828\n",
      "Epoch 18/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.5140 - Accuracy: 0.7927 - AD_Precision: 0.7704 - CN_Precision: 0.7857 - MCI_Precision: 0.8296 - AD_Recall: 0.6265 - CN_Recall: 0.7049 - MCI_Recall: 0.8501 - val_loss: 0.7850 - val_Accuracy: 0.6485 - val_AD_Precision: 0.7636 - val_CN_Precision: 0.5635 - val_MCI_Precision: 0.8640 - val_AD_Recall: 0.4615 - val_CN_Recall: 0.9595 - val_MCI_Recall: 0.4519\n",
      "Epoch 19/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.5235 - Accuracy: 0.7991 - AD_Precision: 0.7692 - CN_Precision: 0.8093 - MCI_Precision: 0.8234 - AD_Recall: 0.6325 - CN_Recall: 0.7417 - MCI_Recall: 0.8491 - val_loss: 0.8120 - val_Accuracy: 0.6967 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6130 - val_MCI_Precision: 0.8000 - val_AD_Recall: 0.1758 - val_CN_Recall: 0.9527 - val_MCI_Recall: 0.7197\n",
      "Epoch 20/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.4922 - Accuracy: 0.8059 - AD_Precision: 0.8206 - CN_Precision: 0.7928 - MCI_Precision: 0.8435 - AD_Recall: 0.6476 - CN_Recall: 0.7282 - MCI_Recall: 0.8643 - val_loss: 0.5714 - val_Accuracy: 0.7824 - val_AD_Precision: 0.7465 - val_CN_Precision: 0.8899 - val_MCI_Precision: 0.7782 - val_AD_Recall: 0.5824 - val_CN_Recall: 0.6554 - val_MCI_Recall: 0.8661\n",
      "Epoch 21/100\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.4767 - Accuracy: 0.8085 - AD_Precision: 0.8120 - CN_Precision: 0.8025 - MCI_Precision: 0.8329 - AD_Recall: 0.6506 - CN_Recall: 0.7417 - MCI_Recall: 0.8558 - val_loss: 0.7368 - val_Accuracy: 0.7092 - val_AD_Precision: 0.7826 - val_CN_Precision: 0.9412 - val_MCI_Precision: 0.6657 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.4324 - val_MCI_Recall: 0.9498\n",
      "Epoch 22/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.4671 - Accuracy: 0.8169 - AD_Precision: 0.8199 - CN_Precision: 0.8180 - MCI_Precision: 0.8486 - AD_Recall: 0.6717 - CN_Recall: 0.7417 - MCI_Recall: 0.8719 - val_loss: 0.6170 - val_Accuracy: 0.7657 - val_AD_Precision: 0.8919 - val_CN_Precision: 0.7472 - val_MCI_Precision: 0.7942 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.8075\n",
      "Epoch 23/100\n",
      "476/476 [==============================] - 32s 66ms/step - loss: 0.4417 - Accuracy: 0.8201 - AD_Precision: 0.7544 - CN_Precision: 0.8418 - MCI_Precision: 0.8437 - AD_Recall: 0.6476 - CN_Recall: 0.7748 - MCI_Recall: 0.8605 - val_loss: 0.5884 - val_Accuracy: 0.7866 - val_AD_Precision: 0.7895 - val_CN_Precision: 0.8050 - val_MCI_Precision: 0.8025 - val_AD_Recall: 0.4945 - val_CN_Recall: 0.8649 - val_MCI_Recall: 0.7992\n",
      "Epoch 24/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.4824 - Accuracy: 0.8033 - AD_Precision: 0.7782 - CN_Precision: 0.8136 - MCI_Precision: 0.8326 - AD_Recall: 0.6446 - CN_Recall: 0.7456 - MCI_Recall: 0.8491 - val_loss: 0.9069 - val_Accuracy: 0.7050 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6372 - val_MCI_Precision: 0.7913 - val_AD_Recall: 0.1758 - val_CN_Recall: 0.9257 - val_MCI_Recall: 0.7615\n",
      "Epoch 25/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.4535 - Accuracy: 0.8196 - AD_Precision: 0.8043 - CN_Precision: 0.8172 - MCI_Precision: 0.8459 - AD_Recall: 0.6687 - CN_Recall: 0.7553 - MCI_Recall: 0.8748 - val_loss: 0.6818 - val_Accuracy: 0.7427 - val_AD_Precision: 0.8261 - val_CN_Precision: 0.9425 - val_MCI_Precision: 0.6997 - val_AD_Recall: 0.4176 - val_CN_Recall: 0.5541 - val_MCI_Recall: 0.9456\n",
      "Epoch 26/100\n",
      "476/476 [==============================] - 32s 68ms/step - loss: 0.4636 - Accuracy: 0.8080 - AD_Precision: 0.7790 - CN_Precision: 0.8021 - MCI_Precision: 0.8481 - AD_Recall: 0.6476 - CN_Recall: 0.7553 - MCI_Recall: 0.8529 - val_loss: 0.6411 - val_Accuracy: 0.7427 - val_AD_Precision: 0.9744 - val_CN_Precision: 0.9043 - val_MCI_Precision: 0.6909 - val_AD_Recall: 0.4176 - val_CN_Recall: 0.5743 - val_MCI_Recall: 0.9540\n",
      "Epoch 27/100\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.4422 - Accuracy: 0.8348 - AD_Precision: 0.8339 - CN_Precision: 0.8316 - MCI_Precision: 0.8565 - AD_Recall: 0.6807 - CN_Recall: 0.7670 - MCI_Recall: 0.8776 - val_loss: 0.9018 - val_Accuracy: 0.7071 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9109 - val_MCI_Precision: 0.6391 - val_AD_Recall: 0.1209 - val_CN_Recall: 0.6216 - val_MCI_Recall: 0.9707\n",
      "Epoch 28/100\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.4438 - Accuracy: 0.8275 - AD_Precision: 0.8021 - CN_Precision: 0.8274 - MCI_Precision: 0.8527 - AD_Recall: 0.6958 - CN_Recall: 0.7728 - MCI_Recall: 0.8681 - val_loss: 0.5546 - val_Accuracy: 0.7845 - val_AD_Precision: 0.8571 - val_CN_Precision: 0.8440 - val_MCI_Precision: 0.7709 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.8041 - val_MCI_Recall: 0.8870\n",
      "Epoch 29/100\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.3881 - Accuracy: 0.8417 - AD_Precision: 0.8256 - CN_Precision: 0.8468 - MCI_Precision: 0.8607 - AD_Recall: 0.6988 - CN_Recall: 0.7942 - MCI_Recall: 0.8852 - val_loss: 0.5090 - val_Accuracy: 0.8264 - val_AD_Precision: 0.7639 - val_CN_Precision: 0.8630 - val_MCI_Precision: 0.8506 - val_AD_Recall: 0.6044 - val_CN_Recall: 0.8514 - val_MCI_Recall: 0.8577\n",
      "Epoch 30/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.3981 - Accuracy: 0.8438 - AD_Precision: 0.8280 - CN_Precision: 0.8574 - MCI_Precision: 0.8673 - AD_Recall: 0.6958 - CN_Recall: 0.8058 - MCI_Recall: 0.8805 - val_loss: 0.9384 - val_Accuracy: 0.7343 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8720 - val_MCI_Precision: 0.6855 - val_AD_Recall: 0.1099 - val_CN_Recall: 0.7365 - val_MCI_Recall: 0.9665\n",
      "Epoch 31/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.3542 - Accuracy: 0.8585 - AD_Precision: 0.8446 - CN_Precision: 0.8516 - MCI_Precision: 0.8836 - AD_Recall: 0.7530 - CN_Recall: 0.8136 - MCI_Recall: 0.8928 - val_loss: 0.5332 - val_Accuracy: 0.8013 - val_AD_Precision: 0.7833 - val_CN_Precision: 0.8960 - val_MCI_Precision: 0.7745 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.7568 - val_MCI_Recall: 0.8912\n",
      "Epoch 32/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.3938 - Accuracy: 0.8522 - AD_Precision: 0.8470 - CN_Precision: 0.8568 - MCI_Precision: 0.8689 - AD_Recall: 0.7169 - CN_Recall: 0.8019 - MCI_Recall: 0.8928 - val_loss: 0.6635 - val_Accuracy: 0.7782 - val_AD_Precision: 0.9474 - val_CN_Precision: 0.7308 - val_MCI_Precision: 0.8072 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.8410\n",
      "Epoch 33/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.3767 - Accuracy: 0.8511 - AD_Precision: 0.8407 - CN_Precision: 0.8510 - MCI_Precision: 0.8757 - AD_Recall: 0.7470 - CN_Recall: 0.8097 - MCI_Recall: 0.8890 - val_loss: 0.7040 - val_Accuracy: 0.7866 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8777 - val_MCI_Precision: 0.7311 - val_AD_Recall: 0.2967 - val_CN_Recall: 0.8243 - val_MCI_Recall: 0.9331\n",
      "Epoch 34/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3797 - Accuracy: 0.8485 - AD_Precision: 0.8445 - CN_Precision: 0.8290 - MCI_Precision: 0.8790 - AD_Recall: 0.7199 - CN_Recall: 0.8097 - MCI_Recall: 0.8824 - val_loss: 0.7728 - val_Accuracy: 0.7448 - val_AD_Precision: 0.9688 - val_CN_Precision: 0.9200 - val_MCI_Precision: 0.6804 - val_AD_Recall: 0.3407 - val_CN_Recall: 0.6216 - val_MCI_Recall: 0.9707\n",
      "Epoch 35/100\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.3425 - Accuracy: 0.8585 - AD_Precision: 0.8498 - CN_Precision: 0.8531 - MCI_Precision: 0.8797 - AD_Recall: 0.7500 - CN_Recall: 0.8117 - MCI_Recall: 0.8947 - val_loss: 0.5160 - val_Accuracy: 0.8326 - val_AD_Precision: 0.7671 - val_CN_Precision: 0.8732 - val_MCI_Precision: 0.8353 - val_AD_Recall: 0.6154 - val_CN_Recall: 0.8378 - val_MCI_Recall: 0.8703\n",
      "Epoch 36/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.3679 - Accuracy: 0.8632 - AD_Precision: 0.8386 - CN_Precision: 0.8691 - MCI_Precision: 0.8886 - AD_Recall: 0.7199 - CN_Recall: 0.8252 - MCI_Recall: 0.9080 - val_loss: 0.6702 - val_Accuracy: 0.7845 - val_AD_Precision: 0.9722 - val_CN_Precision: 0.7211 - val_MCI_Precision: 0.8182 - val_AD_Recall: 0.3846 - val_CN_Recall: 0.9257 - val_MCI_Recall: 0.8285\n",
      "Epoch 37/100\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.4226 - Accuracy: 0.8411 - AD_Precision: 0.8225 - CN_Precision: 0.8257 - MCI_Precision: 0.8676 - AD_Recall: 0.7259 - CN_Recall: 0.8000 - MCI_Recall: 0.8767 - val_loss: 0.7414 - val_Accuracy: 0.7573 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7326 - val_MCI_Precision: 0.7761 - val_AD_Recall: 0.2418 - val_CN_Recall: 0.9257 - val_MCI_Recall: 0.8410\n",
      "Epoch 38/100\n",
      "476/476 [==============================] - 28s 60ms/step - loss: 0.3305 - Accuracy: 0.8722 - AD_Precision: 0.8576 - CN_Precision: 0.8783 - MCI_Precision: 0.8882 - AD_Recall: 0.7801 - CN_Recall: 0.8408 - MCI_Recall: 0.9042 - val_loss: 0.6602 - val_Accuracy: 0.7699 - val_AD_Precision: 0.8679 - val_CN_Precision: 0.6900 - val_MCI_Precision: 0.8599 - val_AD_Recall: 0.5055 - val_CN_Recall: 0.9324 - val_MCI_Recall: 0.7448\n",
      "Epoch 39/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.3302 - Accuracy: 0.8685 - AD_Precision: 0.8630 - CN_Precision: 0.8735 - MCI_Precision: 0.8821 - AD_Recall: 0.7590 - CN_Recall: 0.8311 - MCI_Recall: 0.9013 - val_loss: 0.9494 - val_Accuracy: 0.7134 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9670 - val_MCI_Precision: 0.6396 - val_AD_Recall: 0.1758 - val_CN_Recall: 0.5946 - val_MCI_Recall: 0.9874\n",
      "Epoch 40/100\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.3379 - Accuracy: 0.8648 - AD_Precision: 0.8576 - CN_Precision: 0.8683 - MCI_Precision: 0.8850 - AD_Recall: 0.7620 - CN_Recall: 0.8194 - MCI_Recall: 0.8985 - val_loss: 0.5386 - val_Accuracy: 0.8096 - val_AD_Precision: 0.8116 - val_CN_Precision: 0.8846 - val_MCI_Precision: 0.7934 - val_AD_Recall: 0.6154 - val_CN_Recall: 0.7770 - val_MCI_Recall: 0.8996\n",
      "Epoch 41/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.3210 - Accuracy: 0.8764 - AD_Precision: 0.8729 - CN_Precision: 0.8663 - MCI_Precision: 0.8962 - AD_Recall: 0.7651 - CN_Recall: 0.8427 - MCI_Recall: 0.9089 - val_loss: 1.1461 - val_Accuracy: 0.6987 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6114 - val_MCI_Precision: 0.7832 - val_AD_Recall: 0.1429 - val_CN_Recall: 0.9459 - val_MCI_Recall: 0.7406\n",
      "Epoch 42/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3529 - Accuracy: 0.8701 - AD_Precision: 0.8442 - CN_Precision: 0.8691 - MCI_Precision: 0.8948 - AD_Recall: 0.7831 - CN_Recall: 0.8252 - MCI_Recall: 0.9042 - val_loss: 0.6089 - val_Accuracy: 0.7699 - val_AD_Precision: 0.5448 - val_CN_Precision: 0.8897 - val_MCI_Precision: 0.8895 - val_AD_Recall: 0.8022 - val_CN_Recall: 0.8176 - val_MCI_Recall: 0.7071\n",
      "Epoch 43/100\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.3346 - Accuracy: 0.8711 - AD_Precision: 0.8462 - CN_Precision: 0.8778 - MCI_Precision: 0.8907 - AD_Recall: 0.7620 - CN_Recall: 0.8369 - MCI_Recall: 0.9042 - val_loss: 0.5281 - val_Accuracy: 0.8117 - val_AD_Precision: 0.7143 - val_CN_Precision: 0.8050 - val_MCI_Precision: 0.8688 - val_AD_Recall: 0.6593 - val_CN_Recall: 0.8649 - val_MCI_Recall: 0.8033\n",
      "Epoch 44/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3020 - Accuracy: 0.8864 - AD_Precision: 0.8754 - CN_Precision: 0.8846 - MCI_Precision: 0.9018 - AD_Recall: 0.8042 - CN_Recall: 0.8485 - MCI_Recall: 0.9146 - val_loss: 0.5378 - val_Accuracy: 0.8180 - val_AD_Precision: 0.8500 - val_CN_Precision: 0.8523 - val_MCI_Precision: 0.8196 - val_AD_Recall: 0.5604 - val_CN_Recall: 0.8581 - val_MCI_Recall: 0.8745\n",
      "Epoch 45/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3214 - Accuracy: 0.8716 - AD_Precision: 0.8388 - CN_Precision: 0.8784 - MCI_Precision: 0.8948 - AD_Recall: 0.7681 - CN_Recall: 0.8272 - MCI_Recall: 0.9118 - val_loss: 0.7189 - val_Accuracy: 0.7678 - val_AD_Precision: 0.9400 - val_CN_Precision: 0.6651 - val_MCI_Precision: 0.8744 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.9662 - val_MCI_Recall: 0.7280\n",
      "Epoch 46/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.3212 - Accuracy: 0.8790 - AD_Precision: 0.8689 - CN_Precision: 0.8699 - MCI_Precision: 0.8995 - AD_Recall: 0.7982 - CN_Recall: 0.8311 - MCI_Recall: 0.9089 - val_loss: 1.1459 - val_Accuracy: 0.6444 - val_AD_Precision: 0.7903 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.6082 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.1351 - val_MCI_Recall: 0.9874\n",
      "Epoch 47/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2939 - Accuracy: 0.8811 - AD_Precision: 0.8576 - CN_Precision: 0.8680 - MCI_Precision: 0.9057 - AD_Recall: 0.7982 - CN_Recall: 0.8427 - MCI_Recall: 0.9108 - val_loss: 0.6332 - val_Accuracy: 0.7971 - val_AD_Precision: 0.8800 - val_CN_Precision: 0.9279 - val_MCI_Precision: 0.7525 - val_AD_Recall: 0.4835 - val_CN_Recall: 0.6959 - val_MCI_Recall: 0.9540\n",
      "Epoch 48/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2797 - Accuracy: 0.8890 - AD_Precision: 0.8800 - CN_Precision: 0.8853 - MCI_Precision: 0.9059 - AD_Recall: 0.7952 - CN_Recall: 0.8544 - MCI_Recall: 0.9222 - val_loss: 0.6805 - val_Accuracy: 0.7741 - val_AD_Precision: 0.9130 - val_CN_Precision: 0.9099 - val_MCI_Precision: 0.7220 - val_AD_Recall: 0.4615 - val_CN_Recall: 0.6824 - val_MCI_Recall: 0.9456\n",
      "Epoch 49/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2890 - Accuracy: 0.8869 - AD_Precision: 0.8738 - CN_Precision: 0.8783 - MCI_Precision: 0.9045 - AD_Recall: 0.8343 - CN_Recall: 0.8408 - MCI_Recall: 0.9165 - val_loss: 0.7300 - val_Accuracy: 0.8054 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7988 - val_MCI_Precision: 0.7870 - val_AD_Recall: 0.3736 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.9121\n",
      "Epoch 50/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2996 - Accuracy: 0.8806 - AD_Precision: 0.8738 - CN_Precision: 0.8742 - MCI_Precision: 0.8986 - AD_Recall: 0.8133 - CN_Recall: 0.8369 - MCI_Recall: 0.9080 - val_loss: 0.5358 - val_Accuracy: 0.8180 - val_AD_Precision: 0.7500 - val_CN_Precision: 0.8750 - val_MCI_Precision: 0.8203 - val_AD_Recall: 0.5934 - val_CN_Recall: 0.8041 - val_MCI_Recall: 0.8787\n",
      "Epoch 51/100\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.2917 - Accuracy: 0.8848 - AD_Precision: 0.8758 - CN_Precision: 0.8846 - MCI_Precision: 0.9072 - AD_Recall: 0.7861 - CN_Recall: 0.8485 - MCI_Recall: 0.9184 - val_loss: 0.6513 - val_Accuracy: 0.7866 - val_AD_Precision: 0.9333 - val_CN_Precision: 0.7128 - val_MCI_Precision: 0.8377 - val_AD_Recall: 0.4615 - val_CN_Recall: 0.9392 - val_MCI_Recall: 0.7992\n",
      "Epoch 52/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2819 - Accuracy: 0.8932 - AD_Precision: 0.8871 - CN_Precision: 0.8882 - MCI_Precision: 0.9096 - AD_Recall: 0.8283 - CN_Recall: 0.8641 - MCI_Recall: 0.9165 - val_loss: 0.7335 - val_Accuracy: 0.7950 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7674 - val_MCI_Precision: 0.7925 - val_AD_Recall: 0.3846 - val_CN_Recall: 0.8919 - val_MCI_Recall: 0.8787\n",
      "Epoch 53/100\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.2978 - Accuracy: 0.8822 - AD_Precision: 0.8613 - CN_Precision: 0.8837 - MCI_Precision: 0.9007 - AD_Recall: 0.8042 - CN_Recall: 0.8408 - MCI_Recall: 0.9127 - val_loss: 0.8194 - val_Accuracy: 0.7699 - val_AD_Precision: 0.9583 - val_CN_Precision: 0.8440 - val_MCI_Precision: 0.7240 - val_AD_Recall: 0.2527 - val_CN_Recall: 0.8041 - val_MCI_Recall: 0.9331\n",
      "Epoch 54/100\n",
      "476/476 [==============================] - 28s 60ms/step - loss: 0.2838 - Accuracy: 0.8932 - AD_Precision: 0.8914 - CN_Precision: 0.8940 - MCI_Precision: 0.9020 - AD_Recall: 0.8163 - CN_Recall: 0.8680 - MCI_Recall: 0.9165 - val_loss: 1.2827 - val_Accuracy: 0.6464 - val_AD_Precision: 0.8750 - val_CN_Precision: 1.0000 - val_MCI_Precision: 0.5934 - val_AD_Recall: 0.4615 - val_CN_Recall: 0.2095 - val_MCI_Recall: 0.9833\n",
      "Epoch 55/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2690 - Accuracy: 0.9053 - AD_Precision: 0.8840 - CN_Precision: 0.8926 - MCI_Precision: 0.9202 - AD_Recall: 0.8494 - CN_Recall: 0.8874 - MCI_Recall: 0.9194 - val_loss: 0.7806 - val_Accuracy: 0.7322 - val_AD_Precision: 0.4785 - val_CN_Precision: 0.9135 - val_MCI_Precision: 0.8473 - val_AD_Recall: 0.8571 - val_CN_Recall: 0.6419 - val_MCI_Recall: 0.7197\n",
      "Epoch 56/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.3131 - Accuracy: 0.8738 - AD_Precision: 0.8498 - CN_Precision: 0.8753 - MCI_Precision: 0.8962 - AD_Recall: 0.8012 - CN_Recall: 0.8175 - MCI_Recall: 0.9089 - val_loss: 0.7221 - val_Accuracy: 0.7908 - val_AD_Precision: 0.9737 - val_CN_Precision: 0.7446 - val_MCI_Precision: 0.8145 - val_AD_Recall: 0.4066 - val_CN_Recall: 0.9257 - val_MCI_Recall: 0.8452\n",
      "Epoch 57/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2251 - Accuracy: 0.9169 - AD_Precision: 0.9054 - CN_Precision: 0.9150 - MCI_Precision: 0.9328 - AD_Recall: 0.8645 - CN_Recall: 0.8990 - MCI_Recall: 0.9355 - val_loss: 0.5710 - val_Accuracy: 0.8243 - val_AD_Precision: 0.8667 - val_CN_Precision: 0.8377 - val_MCI_Precision: 0.8224 - val_AD_Recall: 0.5714 - val_CN_Recall: 0.8716 - val_MCI_Recall: 0.8912\n",
      "Epoch 58/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2651 - Accuracy: 0.9048 - AD_Precision: 0.8868 - CN_Precision: 0.9006 - MCI_Precision: 0.9209 - AD_Recall: 0.8494 - CN_Recall: 0.8796 - MCI_Recall: 0.9279 - val_loss: 0.5466 - val_Accuracy: 0.8264 - val_AD_Precision: 0.7632 - val_CN_Precision: 0.8768 - val_MCI_Precision: 0.8171 - val_AD_Recall: 0.6374 - val_CN_Recall: 0.8176 - val_MCI_Recall: 0.8787\n",
      "Epoch 59/100\n",
      "476/476 [==============================] - 28s 59ms/step - loss: 0.2774 - Accuracy: 0.8969 - AD_Precision: 0.8650 - CN_Precision: 0.9113 - MCI_Precision: 0.9086 - AD_Recall: 0.8102 - CN_Recall: 0.8777 - MCI_Recall: 0.9241 - val_loss: 0.5742 - val_Accuracy: 0.8180 - val_AD_Precision: 0.8909 - val_CN_Precision: 0.8288 - val_MCI_Precision: 0.8015 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.8176 - val_MCI_Recall: 0.9121\n",
      "Epoch 60/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2618 - Accuracy: 0.8979 - AD_Precision: 0.8697 - CN_Precision: 0.9076 - MCI_Precision: 0.9192 - AD_Recall: 0.8042 - CN_Recall: 0.8777 - MCI_Recall: 0.9288 - val_loss: 0.5437 - val_Accuracy: 0.8264 - val_AD_Precision: 0.8413 - val_CN_Precision: 0.8786 - val_MCI_Precision: 0.8051 - val_AD_Recall: 0.5824 - val_CN_Recall: 0.8311 - val_MCI_Recall: 0.9163\n",
      "Epoch 61/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.2455 - Accuracy: 0.9111 - AD_Precision: 0.8951 - CN_Precision: 0.9078 - MCI_Precision: 0.9243 - AD_Recall: 0.8735 - CN_Recall: 0.8796 - MCI_Recall: 0.9269 - val_loss: 0.6662 - val_Accuracy: 0.8054 - val_AD_Precision: 0.8704 - val_CN_Precision: 0.9391 - val_MCI_Precision: 0.7475 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.7297 - val_MCI_Recall: 0.9540\n",
      "Epoch 62/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2674 - Accuracy: 0.8937 - AD_Precision: 0.8715 - CN_Precision: 0.9004 - MCI_Precision: 0.9053 - AD_Recall: 0.8373 - CN_Recall: 0.8777 - MCI_Recall: 0.9156 - val_loss: 0.9074 - val_Accuracy: 0.7720 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9304 - val_MCI_Precision: 0.6976 - val_AD_Recall: 0.3077 - val_CN_Recall: 0.7230 - val_MCI_Recall: 0.9749\n",
      "Epoch 63/100\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.2978 - Accuracy: 0.8901 - AD_Precision: 0.8635 - CN_Precision: 0.8821 - MCI_Precision: 0.9130 - AD_Recall: 0.8193 - CN_Recall: 0.8718 - MCI_Recall: 0.9156 - val_loss: 0.6830 - val_Accuracy: 0.7824 - val_AD_Precision: 0.7941 - val_CN_Precision: 0.9770 - val_MCI_Precision: 0.7302 - val_AD_Recall: 0.5934 - val_CN_Recall: 0.5743 - val_MCI_Recall: 0.9623\n",
      "Epoch 64/100\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.2433 - Accuracy: 0.9048 - AD_Precision: 0.9161 - CN_Precision: 0.8874 - MCI_Precision: 0.9165 - AD_Recall: 0.8554 - CN_Recall: 0.8718 - MCI_Recall: 0.9269 - val_loss: 0.8478 - val_Accuracy: 0.7720 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.6915 - val_MCI_Precision: 0.8069 - val_AD_Recall: 0.3956 - val_CN_Recall: 0.9392 - val_MCI_Recall: 0.7866\n",
      "Epoch 65/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2663 - Accuracy: 0.8948 - AD_Precision: 0.8766 - CN_Precision: 0.8931 - MCI_Precision: 0.9055 - AD_Recall: 0.8133 - CN_Recall: 0.8602 - MCI_Recall: 0.9269 - val_loss: 0.6119 - val_Accuracy: 0.8096 - val_AD_Precision: 0.7097 - val_CN_Precision: 0.7684 - val_MCI_Precision: 0.9227 - val_AD_Recall: 0.7253 - val_CN_Recall: 0.9189 - val_MCI_Recall: 0.7490\n",
      "Epoch 66/100\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.2509 - Accuracy: 0.9064 - AD_Precision: 0.8846 - CN_Precision: 0.9035 - MCI_Precision: 0.9197 - AD_Recall: 0.8313 - CN_Recall: 0.8913 - MCI_Recall: 0.9241 - val_loss: 0.6568 - val_Accuracy: 0.7992 - val_AD_Precision: 0.9111 - val_CN_Precision: 0.7727 - val_MCI_Precision: 0.8193 - val_AD_Recall: 0.4505 - val_CN_Recall: 0.9189 - val_MCI_Recall: 0.8536\n",
      "Epoch 67/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2493 - Accuracy: 0.9027 - AD_Precision: 0.8816 - CN_Precision: 0.8915 - MCI_Precision: 0.9239 - AD_Recall: 0.8072 - CN_Recall: 0.8777 - MCI_Recall: 0.9336 - val_loss: 0.5433 - val_Accuracy: 0.8326 - val_AD_Precision: 0.7922 - val_CN_Precision: 0.8849 - val_MCI_Precision: 0.8320 - val_AD_Recall: 0.6703 - val_CN_Recall: 0.8311 - val_MCI_Recall: 0.8912\n",
      "Epoch 68/100\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.2374 - Accuracy: 0.9116 - AD_Precision: 0.9085 - CN_Precision: 0.8978 - MCI_Precision: 0.9256 - AD_Recall: 0.8675 - CN_Recall: 0.8874 - MCI_Recall: 0.9326 - val_loss: 0.7081 - val_Accuracy: 0.8013 - val_AD_Precision: 0.8413 - val_CN_Precision: 0.9510 - val_MCI_Precision: 0.7492 - val_AD_Recall: 0.5824 - val_CN_Recall: 0.6554 - val_MCI_Recall: 0.9623\n",
      "Epoch 69/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2325 - Accuracy: 0.9127 - AD_Precision: 0.9191 - CN_Precision: 0.9035 - MCI_Precision: 0.9239 - AD_Recall: 0.8554 - CN_Recall: 0.8913 - MCI_Recall: 0.9326 - val_loss: 0.6282 - val_Accuracy: 0.8096 - val_AD_Precision: 0.7792 - val_CN_Precision: 0.9524 - val_MCI_Precision: 0.7713 - val_AD_Recall: 0.6593 - val_CN_Recall: 0.6757 - val_MCI_Recall: 0.9456\n",
      "Epoch 70/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.2230 - Accuracy: 0.9143 - AD_Precision: 0.9052 - CN_Precision: 0.9020 - MCI_Precision: 0.9277 - AD_Recall: 0.8916 - CN_Recall: 0.8757 - MCI_Recall: 0.9374 - val_loss: 0.7703 - val_Accuracy: 0.8013 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.7988 - val_MCI_Precision: 0.7870 - val_AD_Recall: 0.3516 - val_CN_Recall: 0.8851 - val_MCI_Recall: 0.9121\n",
      "Epoch 71/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2148 - Accuracy: 0.9200 - AD_Precision: 0.9146 - CN_Precision: 0.9205 - MCI_Precision: 0.9311 - AD_Recall: 0.8705 - CN_Recall: 0.8990 - MCI_Recall: 0.9355 - val_loss: 0.6421 - val_Accuracy: 0.8075 - val_AD_Precision: 0.8194 - val_CN_Precision: 0.7446 - val_MCI_Precision: 0.8779 - val_AD_Recall: 0.6484 - val_CN_Recall: 0.9257 - val_MCI_Recall: 0.7824\n",
      "Epoch 72/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.2647 - Accuracy: 0.8985 - AD_Precision: 0.8825 - CN_Precision: 0.8876 - MCI_Precision: 0.9195 - AD_Recall: 0.8373 - CN_Recall: 0.8738 - MCI_Recall: 0.9213 - val_loss: 0.8573 - val_Accuracy: 0.7741 - val_AD_Precision: 0.9268 - val_CN_Precision: 0.9238 - val_MCI_Precision: 0.7134 - val_AD_Recall: 0.4176 - val_CN_Recall: 0.6554 - val_MCI_Recall: 0.9791\n",
      "Epoch 73/100\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.2020 - Accuracy: 0.9258 - AD_Precision: 0.9006 - CN_Precision: 0.9279 - MCI_Precision: 0.9371 - AD_Recall: 0.8735 - CN_Recall: 0.8990 - MCI_Recall: 0.9469 - val_loss: 0.6033 - val_Accuracy: 0.8347 - val_AD_Precision: 0.8929 - val_CN_Precision: 0.8681 - val_MCI_Precision: 0.8109 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.8446 - val_MCI_Recall: 0.9331\n",
      "Epoch 74/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2304 - Accuracy: 0.9095 - AD_Precision: 0.8931 - CN_Precision: 0.8965 - MCI_Precision: 0.9272 - AD_Recall: 0.8554 - CN_Recall: 0.8913 - MCI_Recall: 0.9298 - val_loss: 0.8561 - val_Accuracy: 0.7594 - val_AD_Precision: 0.8947 - val_CN_Precision: 0.9390 - val_MCI_Precision: 0.6976 - val_AD_Recall: 0.5604 - val_CN_Recall: 0.5203 - val_MCI_Recall: 0.9749\n",
      "Epoch 75/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2620 - Accuracy: 0.9016 - AD_Precision: 0.8797 - CN_Precision: 0.8962 - MCI_Precision: 0.9218 - AD_Recall: 0.8373 - CN_Recall: 0.8718 - MCI_Recall: 0.9288 - val_loss: 0.8360 - val_Accuracy: 0.7615 - val_AD_Precision: 0.9388 - val_CN_Precision: 0.9647 - val_MCI_Precision: 0.6880 - val_AD_Recall: 0.5055 - val_CN_Recall: 0.5541 - val_MCI_Recall: 0.9874\n",
      "Epoch 76/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.2512 - Accuracy: 0.9053 - AD_Precision: 0.8997 - CN_Precision: 0.8907 - MCI_Precision: 0.9183 - AD_Recall: 0.8645 - CN_Recall: 0.8699 - MCI_Recall: 0.9279 - val_loss: 0.5382 - val_Accuracy: 0.8305 - val_AD_Precision: 0.8333 - val_CN_Precision: 0.7964 - val_MCI_Precision: 0.8619 - val_AD_Recall: 0.6044 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.8619\n",
      "Epoch 77/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2586 - Accuracy: 0.8974 - AD_Precision: 0.8773 - CN_Precision: 0.8880 - MCI_Precision: 0.9211 - AD_Recall: 0.8614 - CN_Recall: 0.8621 - MCI_Recall: 0.9194 - val_loss: 0.5319 - val_Accuracy: 0.8243 - val_AD_Precision: 0.7000 - val_CN_Precision: 0.9055 - val_MCI_Precision: 0.8519 - val_AD_Recall: 0.7692 - val_CN_Recall: 0.7770 - val_MCI_Recall: 0.8661\n",
      "Epoch 78/100\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.2402 - Accuracy: 0.9164 - AD_Precision: 0.9022 - CN_Precision: 0.9037 - MCI_Precision: 0.9273 - AD_Recall: 0.8614 - CN_Recall: 0.8932 - MCI_Recall: 0.9317 - val_loss: 0.8343 - val_Accuracy: 0.7950 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9194 - val_MCI_Precision: 0.7250 - val_AD_Recall: 0.3626 - val_CN_Recall: 0.7703 - val_MCI_Recall: 0.9707\n",
      "Epoch 79/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2347 - Accuracy: 0.9006 - AD_Precision: 0.8635 - CN_Precision: 0.9111 - MCI_Precision: 0.9152 - AD_Recall: 0.8193 - CN_Recall: 0.8757 - MCI_Recall: 0.9317 - val_loss: 0.6119 - val_Accuracy: 0.8285 - val_AD_Precision: 0.9583 - val_CN_Precision: 0.8786 - val_MCI_Precision: 0.7915 - val_AD_Recall: 0.5055 - val_CN_Recall: 0.8311 - val_MCI_Recall: 0.9372\n",
      "Epoch 80/100\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.2059 - Accuracy: 0.9169 - AD_Precision: 0.9161 - CN_Precision: 0.8936 - MCI_Precision: 0.9326 - AD_Recall: 0.8886 - CN_Recall: 0.8971 - MCI_Recall: 0.9317 - val_loss: 0.5685 - val_Accuracy: 0.8368 - val_AD_Precision: 0.8852 - val_CN_Precision: 0.8160 - val_MCI_Precision: 0.8413 - val_AD_Recall: 0.5934 - val_CN_Recall: 0.8986 - val_MCI_Recall: 0.8870\n",
      "Epoch 81/100\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.2382 - Accuracy: 0.9111 - AD_Precision: 0.8861 - CN_Precision: 0.9122 - MCI_Precision: 0.9282 - AD_Recall: 0.8434 - CN_Recall: 0.8874 - MCI_Recall: 0.9326 - val_loss: 0.6010 - val_Accuracy: 0.8326 - val_AD_Precision: 0.9615 - val_CN_Precision: 0.8947 - val_MCI_Precision: 0.7860 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.8041 - val_MCI_Recall: 0.9372\n",
      "Epoch 82/100\n",
      "476/476 [==============================] - 31s 65ms/step - loss: 0.2409 - Accuracy: 0.9127 - AD_Precision: 0.8875 - CN_Precision: 0.9124 - MCI_Precision: 0.9317 - AD_Recall: 0.8554 - CN_Recall: 0.8893 - MCI_Recall: 0.9317 - val_loss: 0.5389 - val_Accuracy: 0.8410 - val_AD_Precision: 0.9138 - val_CN_Precision: 0.8750 - val_MCI_Precision: 0.8178 - val_AD_Recall: 0.5824 - val_CN_Recall: 0.8514 - val_MCI_Recall: 0.9205\n",
      "Epoch 83/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.1996 - Accuracy: 0.9264 - AD_Precision: 0.9063 - CN_Precision: 0.9206 - MCI_Precision: 0.9376 - AD_Recall: 0.9036 - CN_Recall: 0.9010 - MCI_Recall: 0.9412 - val_loss: 1.0656 - val_Accuracy: 0.7720 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8411 - val_MCI_Precision: 0.7329 - val_AD_Recall: 0.1868 - val_CN_Recall: 0.8581 - val_MCI_Recall: 0.9414\n",
      "Epoch 84/100\n",
      "476/476 [==============================] - 29s 60ms/step - loss: 0.2061 - Accuracy: 0.9243 - AD_Precision: 0.8991 - CN_Precision: 0.9270 - MCI_Precision: 0.9377 - AD_Recall: 0.8584 - CN_Recall: 0.9126 - MCI_Recall: 0.9421 - val_loss: 0.6022 - val_Accuracy: 0.8305 - val_AD_Precision: 0.9400 - val_CN_Precision: 0.8302 - val_MCI_Precision: 0.8251 - val_AD_Recall: 0.5165 - val_CN_Recall: 0.8919 - val_MCI_Recall: 0.9079\n",
      "Epoch 85/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2120 - Accuracy: 0.9174 - AD_Precision: 0.9136 - CN_Precision: 0.8917 - MCI_Precision: 0.9407 - AD_Recall: 0.8916 - CN_Recall: 0.8951 - MCI_Recall: 0.9326 - val_loss: 0.5478 - val_Accuracy: 0.8368 - val_AD_Precision: 0.7895 - val_CN_Precision: 0.9297 - val_MCI_Precision: 0.8088 - val_AD_Recall: 0.6593 - val_CN_Recall: 0.8041 - val_MCI_Recall: 0.9205\n",
      "Epoch 86/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.1970 - Accuracy: 0.9232 - AD_Precision: 0.9022 - CN_Precision: 0.9286 - MCI_Precision: 0.9325 - AD_Recall: 0.8614 - CN_Recall: 0.9087 - MCI_Recall: 0.9440 - val_loss: 0.5530 - val_Accuracy: 0.8389 - val_AD_Precision: 0.7216 - val_CN_Precision: 0.9225 - val_MCI_Precision: 0.8474 - val_AD_Recall: 0.7692 - val_CN_Recall: 0.8041 - val_MCI_Recall: 0.8828\n",
      "Epoch 87/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2299 - Accuracy: 0.9169 - AD_Precision: 0.9122 - CN_Precision: 0.9087 - MCI_Precision: 0.9286 - AD_Recall: 0.8765 - CN_Recall: 0.8893 - MCI_Recall: 0.9374 - val_loss: 0.5177 - val_Accuracy: 0.8410 - val_AD_Precision: 0.7805 - val_CN_Precision: 0.8841 - val_MCI_Precision: 0.8452 - val_AD_Recall: 0.7033 - val_CN_Recall: 0.8243 - val_MCI_Recall: 0.8912\n",
      "Epoch 88/100\n",
      "476/476 [==============================] - 30s 64ms/step - loss: 0.1953 - Accuracy: 0.9221 - AD_Precision: 0.9133 - CN_Precision: 0.9273 - MCI_Precision: 0.9306 - AD_Recall: 0.8886 - CN_Recall: 0.8913 - MCI_Recall: 0.9421 - val_loss: 0.5577 - val_Accuracy: 0.8431 - val_AD_Precision: 0.8553 - val_CN_Precision: 0.7976 - val_MCI_Precision: 0.8750 - val_AD_Recall: 0.7143 - val_CN_Recall: 0.9054 - val_MCI_Recall: 0.8494\n",
      "Epoch 89/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2439 - Accuracy: 0.9027 - AD_Precision: 0.8956 - CN_Precision: 0.8924 - MCI_Precision: 0.9169 - AD_Recall: 0.8524 - CN_Recall: 0.8854 - MCI_Recall: 0.9213 - val_loss: 0.6749 - val_Accuracy: 0.8285 - val_AD_Precision: 0.9608 - val_CN_Precision: 0.9500 - val_MCI_Precision: 0.7682 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.7703 - val_MCI_Recall: 0.9707\n",
      "Epoch 90/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.1975 - Accuracy: 0.9211 - AD_Precision: 0.9159 - CN_Precision: 0.9050 - MCI_Precision: 0.9387 - AD_Recall: 0.8855 - CN_Recall: 0.8874 - MCI_Recall: 0.9440 - val_loss: 0.7987 - val_Accuracy: 0.7782 - val_AD_Precision: 0.8000 - val_CN_Precision: 0.9639 - val_MCI_Precision: 0.7355 - val_AD_Recall: 0.6593 - val_CN_Recall: 0.5405 - val_MCI_Recall: 0.9540\n",
      "Epoch 91/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2011 - Accuracy: 0.9269 - AD_Precision: 0.9146 - CN_Precision: 0.9200 - MCI_Precision: 0.9397 - AD_Recall: 0.9036 - CN_Recall: 0.8932 - MCI_Recall: 0.9459 - val_loss: 0.7394 - val_Accuracy: 0.8054 - val_AD_Precision: 0.9737 - val_CN_Precision: 0.8714 - val_MCI_Precision: 0.7671 - val_AD_Recall: 0.4066 - val_CN_Recall: 0.8243 - val_MCI_Recall: 0.9372\n",
      "Epoch 92/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.1957 - Accuracy: 0.9269 - AD_Precision: 0.9169 - CN_Precision: 0.9170 - MCI_Precision: 0.9397 - AD_Recall: 0.8976 - CN_Recall: 0.9010 - MCI_Recall: 0.9469 - val_loss: 0.7180 - val_Accuracy: 0.8096 - val_AD_Precision: 0.9286 - val_CN_Precision: 0.7292 - val_MCI_Precision: 0.8571 - val_AD_Recall: 0.5714 - val_CN_Recall: 0.9459 - val_MCI_Recall: 0.8033\n",
      "Epoch 93/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2338 - Accuracy: 0.9169 - AD_Precision: 0.8934 - CN_Precision: 0.9091 - MCI_Precision: 0.9365 - AD_Recall: 0.8584 - CN_Recall: 0.8932 - MCI_Recall: 0.9374 - val_loss: 1.0464 - val_Accuracy: 0.7824 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.9120 - val_MCI_Precision: 0.7130 - val_AD_Recall: 0.2967 - val_CN_Recall: 0.7703 - val_MCI_Recall: 0.9665\n",
      "Epoch 94/100\n",
      "476/476 [==============================] - 30s 62ms/step - loss: 0.1831 - Accuracy: 0.9327 - AD_Precision: 0.8951 - CN_Precision: 0.9442 - MCI_Precision: 0.9426 - AD_Recall: 0.8735 - CN_Recall: 0.9204 - MCI_Recall: 0.9497 - val_loss: 0.5514 - val_Accuracy: 0.8326 - val_AD_Precision: 0.8209 - val_CN_Precision: 0.8269 - val_MCI_Precision: 0.8413 - val_AD_Recall: 0.6044 - val_CN_Recall: 0.8716 - val_MCI_Recall: 0.8870\n",
      "Epoch 95/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.2339 - Accuracy: 0.9190 - AD_Precision: 0.8938 - CN_Precision: 0.9222 - MCI_Precision: 0.9308 - AD_Recall: 0.8614 - CN_Recall: 0.8971 - MCI_Recall: 0.9440 - val_loss: 0.5714 - val_Accuracy: 0.8285 - val_AD_Precision: 0.9074 - val_CN_Precision: 0.8487 - val_MCI_Precision: 0.8074 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.8716 - val_MCI_Recall: 0.9121\n",
      "Epoch 96/100\n",
      "476/476 [==============================] - 31s 64ms/step - loss: 0.1987 - Accuracy: 0.9274 - AD_Precision: 0.9105 - CN_Precision: 0.9211 - MCI_Precision: 0.9422 - AD_Recall: 0.8886 - CN_Recall: 0.9068 - MCI_Recall: 0.9431 - val_loss: 0.5787 - val_Accuracy: 0.8285 - val_AD_Precision: 0.8772 - val_CN_Precision: 0.8354 - val_MCI_Precision: 0.8242 - val_AD_Recall: 0.5495 - val_CN_Recall: 0.8919 - val_MCI_Recall: 0.8828\n",
      "Epoch 97/100\n",
      "476/476 [==============================] - 29s 62ms/step - loss: 0.2020 - Accuracy: 0.9190 - AD_Precision: 0.9169 - CN_Precision: 0.9142 - MCI_Precision: 0.9293 - AD_Recall: 0.8976 - CN_Recall: 0.8893 - MCI_Recall: 0.9355 - val_loss: 0.6052 - val_Accuracy: 0.8347 - val_AD_Precision: 0.8909 - val_CN_Precision: 0.8849 - val_MCI_Precision: 0.8051 - val_AD_Recall: 0.5385 - val_CN_Recall: 0.8311 - val_MCI_Recall: 0.9331\n",
      "Epoch 98/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.1707 - Accuracy: 0.9385 - AD_Precision: 0.9226 - CN_Precision: 0.9350 - MCI_Precision: 0.9498 - AD_Recall: 0.8976 - CN_Recall: 0.9223 - MCI_Recall: 0.9507 - val_loss: 1.1659 - val_Accuracy: 0.7657 - val_AD_Precision: 1.0000 - val_CN_Precision: 0.8098 - val_MCI_Precision: 0.7327 - val_AD_Recall: 0.1319 - val_CN_Recall: 0.8919 - val_MCI_Recall: 0.9289\n",
      "Epoch 99/100\n",
      "476/476 [==============================] - 29s 61ms/step - loss: 0.1884 - Accuracy: 0.9306 - AD_Precision: 0.9156 - CN_Precision: 0.9269 - MCI_Precision: 0.9407 - AD_Recall: 0.8825 - CN_Recall: 0.9107 - MCI_Recall: 0.9478 - val_loss: 0.5771 - val_Accuracy: 0.8389 - val_AD_Precision: 0.8308 - val_CN_Precision: 0.8767 - val_MCI_Precision: 0.8289 - val_AD_Recall: 0.5934 - val_CN_Recall: 0.8649 - val_MCI_Recall: 0.9121\n",
      "Epoch 100/100\n",
      "476/476 [==============================] - 30s 63ms/step - loss: 0.2142 - Accuracy: 0.9169 - AD_Precision: 0.8773 - CN_Precision: 0.9241 - MCI_Precision: 0.9288 - AD_Recall: 0.8614 - CN_Recall: 0.9223 - MCI_Recall: 0.9279 - val_loss: 0.7342 - val_Accuracy: 0.7531 - val_AD_Precision: 0.5130 - val_CN_Precision: 0.9153 - val_MCI_Precision: 0.8607 - val_AD_Recall: 0.8681 - val_CN_Recall: 0.7297 - val_MCI_Recall: 0.7238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/apps/tensorflow/2.7.0/lib/python3.9/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  layer_config = serialize_layer_fn(layer)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          AD       0.86      0.71      0.78        91\n",
      "          CN       0.79      0.91      0.85       148\n",
      "         MCI       0.88      0.85      0.86       239\n",
      "\n",
      "    accuracy                           0.84       478\n",
      "   macro avg       0.84      0.82      0.83       478\n",
      "weighted avg       0.85      0.84      0.84       478\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "savepath = 'Finetuning/efficientnet/efficientnet_100_0.00001/'\n",
    "lr=0.00001\n",
    "if not os.path.isdir(savepath):\n",
    "      os.makedirs(savepath)\n",
    "csvloger_callback = tf.keras.callbacks.CSVLogger(f'{savepath}history.csv', separator=',', append=False)\n",
    "\n",
    "modelcheckpoint_callback = tf.keras.callbacks.ModelCheckpoint(savepath+'efficientnet_row_10E-5_weights-epoch:{epoch:03d}-val_Accuracy:{val_Accuracy:.4f}.h5',monitor='val_Accuracy'\n",
    "    ,verbose=0,save_best_only=True,save_weights_only=True,mode='max',save_freq='epoch' , initial_value_threshold=0.85)\n",
    "\n",
    "OPT = tf.keras.optimizers.Adam(learning_rate=lr)\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "history04 = model04.fit(training_image_datagen, epochs=EPOCH, validation_data=testing_image_datagen,callbacks=[csvloger_callback,modelcheckpoint_callback])\n",
    "model04 = efficientnet_row(OPT, LOOS,METRICS,image_Shape)\n",
    "model04.load_weights(glob.glob(f\"{savepath}efficientnet_row_10E-5_weights-epoch:*-val_Accuracy:{max(history04.history['val_Accuracy']):.4f}.h5\")[0])\n",
    "model04.save(f\"{savepath}/efficientnet_100_0.00001.h5\")\n",
    "p2 = model04.predict(testing_image_datagen)\n",
    "predicted_lab = p2.argmax(axis=-1)\n",
    "true_lab = testing_image_datagen.classes\n",
    "plot_confusion_matrix(true_lab, predicted_lab, testing_image_datagen.class_indices.keys(),savepath)\n",
    "report = classification_report(true_lab, predicted_lab, target_names=testing_image_datagen.class_indices.keys())\n",
    "print(report)\n",
    "dump_text(report,f'{savepath}classification_report.txt')\n",
    "np.save(\"efficientnet_100_0.00001.npy\",p2 )   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1856e076-b348-4b78-a781-6cc570ed3fed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Tensorflow-2.7.0",
   "language": "python",
   "name": "tensorflow-2.7.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}