4_VGG16_finetune.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "807149e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:17.635299Z",
     "start_time": "2024-03-10T20:05:15.443895Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ac4ea89",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:18.283479Z",
     "start_time": "2024-03-10T20:05:17.636306Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import random, numpy as np\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.utils import image_dataset_from_directory\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, Rescaling, RandomRotation, RandomFlip, RandomCrop, RandomTranslation, RandomZoom\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from tensorflow.keras.optimizers import Adam, SGD, RMSprop\n",
    "from keras.callbacks import *\n",
    "\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f646771d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:18.893266Z",
     "start_time": "2024-03-10T20:05:18.807224Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras, tensorflow as tf\n",
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d67ea4c",
   "metadata": {},
   "source": [
    "## Set random seed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389c2eeb",
   "metadata": {},
   "source": [
    "#### กำหนด random seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "22009e57",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:19.984823Z",
     "start_time": "2024-03-10T20:05:19.966823Z"
    }
   },
   "outputs": [],
   "source": [
    "import random, os, tensorflow as tf\n",
    "\n",
    "def set_seed(seed_value=12345):\n",
    "    random.seed(seed_value)\n",
    "    np.random.seed(seed_value)\n",
    "    tf.random.set_seed(seed_value)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed_value)\n",
    "\n",
    "#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # to Disable GPU training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6530db81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:20.217203Z",
     "start_time": "2024-03-10T20:05:20.207192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.10.1'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8fdb24f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:20.789255Z",
     "start_time": "2024-03-10T20:05:20.399099Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[name: \"/device:CPU:0\"\n",
       " device_type: \"CPU\"\n",
       " memory_limit: 268435456\n",
       " locality {\n",
       " }\n",
       " incarnation: 18161624660699618854\n",
       " xla_global_id: -1,\n",
       " name: \"/device:GPU:0\"\n",
       " device_type: \"GPU\"\n",
       " memory_limit: 1802502144\n",
       " locality {\n",
       "   bus_id: 1\n",
       "   links {\n",
       "   }\n",
       " }\n",
       " incarnation: 12883334165653490134\n",
       " physical_device_desc: \"device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\"\n",
       " xla_global_id: 416903419]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "device_lib.list_local_devices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91d166ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:20.805254Z",
     "start_time": "2024-03-10T20:05:20.791255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default GPU Device: /device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "print('Default GPU Device:', tf.test.gpu_device_name() or 'None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6d24ac4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:20.867637Z",
     "start_time": "2024-03-10T20:05:20.857637Z"
    }
   },
   "outputs": [],
   "source": [
    "data = './KanjiN5_dataset/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8f9d7b",
   "metadata": {},
   "source": [
    "## Model Finetuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7c907d0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:21.536449Z",
     "start_time": "2024-03-10T20:05:21.529453Z"
    }
   },
   "outputs": [],
   "source": [
    "num_classes = 81\n",
    "batch_size = 64\n",
    "image_size = 120"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2efd5089",
   "metadata": {},
   "source": [
    "#### ทำ image augmentation โดยใช้ ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56e21db0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:22.240564Z",
     "start_time": "2024-03-10T20:05:22.229564Z"
    }
   },
   "outputs": [],
   "source": [
    "data_augmentation = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "                                                height_shift_range=0.1,\n",
    "                                                width_shift_range=0.1,\n",
    "                                                zoom_range=0.1,\n",
    ") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f613274a",
   "metadata": {},
   "source": [
    "#### นำข้อมูลเข้าจาก Train, Validation, Test จาก datasets โดยใช้ flow_from_directory กำหนดขนาดภาพที่ขนาด 120 x 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "192c9fb9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:25.473081Z",
     "start_time": "2024-03-10T20:05:24.972726Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 9071 images belonging to 81 classes.\n",
      "Found 2592 images belonging to 81 classes.\n",
      "Found 1377 images belonging to 81 classes.\n"
     ]
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/60927259/keras-flow-from-directory-shuffle-not-working-properly\n",
    "train_ds = data_augmentation.flow_from_directory(\n",
    "    directory=os.path.join(data, \"Train\"),\n",
    "    class_mode='categorical',\n",
    "    batch_size=batch_size,\n",
    "    target_size=(image_size, image_size),\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "valid_ds = data_augmentation.flow_from_directory(\n",
    "directory=os.path.join(data, \"Valid\"),\n",
    "class_mode='categorical',\n",
    "batch_size=batch_size,\n",
    "target_size=(image_size, image_size),\n",
    "shuffle=False\n",
    ")\n",
    "\n",
    "test_ds = data_augmentation.flow_from_directory(\n",
    "    directory=os.path.join(data, \"Test\"),\n",
    "    class_mode='categorical',\n",
    "    batch_size=batch_size,\n",
    "    target_size=(image_size, image_size),\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e4c7e0e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:05:26.111893Z",
     "start_time": "2024-03-10T20:05:26.102545Z"
    }
   },
   "outputs": [],
   "source": [
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, GlobalAveragePooling2D\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b047835",
   "metadata": {},
   "source": [
    "#### import VGG16 model จาก keras.applications  เพื่อทำการ fine-tune model\n",
    "#### - กำหนด Dropout ที่ 0.5 และใช้ Output layer เป็น softmax activation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a25e4551",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:11:10.013997Z",
     "start_time": "2024-03-10T20:11:09.670458Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 120, 120, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 120, 120, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 120, 120, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 60, 60, 64)        0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 60, 60, 128)       73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 60, 60, 128)       147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 30, 30, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 30, 30, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 30, 30, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 30, 30, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 15, 15, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 15, 15, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 15, 15, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 15, 15, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 7, 7, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 7, 7, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 7, 7, 512)         2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " global_average_pooling2d_1   (None, 512)              0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1024)              525312    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 81)                83025     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 15,323,025\n",
      "Trainable params: 608,337\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                   input_shape=(image_size, image_size, 3))\n",
    "\n",
    "base_model.trainable = False\n",
    "\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "# let's add a fully-connected layer\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "# and a logistic layer -- let's say we have 200 classes\n",
    "predictions = Dense(num_classes, activation='softmax')(x)\n",
    "\n",
    "# this is the model we will train\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "# first: train only the top layers (which were randomly initialized)\n",
    "#  i.e. freeze all convolutional InceptionV3 layers\n",
    "# for layer in base_model.layers[:-2]:\n",
    "#     layer.trainable = False\n",
    "    \n",
    "# compile the model (should be done *after* setting layers to non-trainable)\n",
    "model.compile(optimizer=\"Adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7cade2b",
   "metadata": {},
   "source": [
    "#### ทำการ freeze model  แล้วเทรนโครงสร้าง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "585063b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:12:47.384121Z",
     "start_time": "2024-03-10T20:11:10.570586Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "142/142 [==============================] - 33s 227ms/step - loss: 2.7400 - accuracy: 0.4832 - val_loss: 0.7873 - val_accuracy: 0.7901\n",
      "Epoch 2/3\n",
      "142/142 [==============================] - 32s 227ms/step - loss: 0.8791 - accuracy: 0.7517 - val_loss: 0.5752 - val_accuracy: 0.8380\n",
      "Epoch 3/3\n",
      "142/142 [==============================] - 31s 220ms/step - loss: 0.6427 - accuracy: 0.8130 - val_loss: 0.5506 - val_accuracy: 0.8507\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x22bd8237670>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs = 3\n",
    "model.fit(train_ds, epochs=epochs, validation_data=valid_ds) \n",
    "#add augmentation flow from a directory path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1880d0dd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:13:19.933106Z",
     "start_time": "2024-03-10T20:13:19.928106Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 input_2\n",
      "1 block1_conv1\n",
      "2 block1_conv2\n",
      "3 block1_pool\n",
      "4 block2_conv1\n",
      "5 block2_conv2\n",
      "6 block2_pool\n",
      "7 block3_conv1\n",
      "8 block3_conv2\n",
      "9 block3_conv3\n",
      "10 block3_pool\n",
      "11 block4_conv1\n",
      "12 block4_conv2\n",
      "13 block4_conv3\n",
      "14 block4_pool\n",
      "15 block5_conv1\n",
      "16 block5_conv2\n",
      "17 block5_conv3\n",
      "18 block5_pool\n"
     ]
    }
   ],
   "source": [
    "# let's visualize layer names and layer indices to see how many layers\n",
    "# we should freeze:\n",
    "for i, layer in enumerate(base_model.layers):\n",
    "   print(i, layer.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bcb82f",
   "metadata": {},
   "source": [
    "#### ทำการ unfreezing model เพื่อเทรนกับข้อมูลที่เราต้องการจะทำการ fine tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a855d3bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:13:21.211607Z",
     "start_time": "2024-03-10T20:13:21.202600Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.engine.input_layer.InputLayer object at 0x0000022BA771EB60>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022BD8235AB0>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022BDE0EAD70>\n",
      "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x0000022BA771E1D0>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022C7EA628F0>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022D5383F6D0>\n",
      "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x0000022D5385CEB0>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022D5383FF10>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022D5385C880>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022D5385EBF0>\n",
      "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x0000022D5385FF10>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022D5385FB50>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022D5385E9B0>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000022D5385EB90>\n",
      "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x0000022D538688E0>\n"
     ]
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/64227483/what-is-the-right-way-to-gradually-unfreeze-layers-in-neural-network-while-learn\n",
    "# we chose to train the top 2 inception blocks, i.e. we will freeze\n",
    "# the first 249 layers and unfreeze the rest:\n",
    "base_model.trainable = True\n",
    "for layer in model.layers[:15]:\n",
    "    layer.trainable = False\n",
    "    print(layer)\n",
    "# for layer in model.layers[15:]:\n",
    "#    layer.trainable = True\n",
    "#    print(layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d35abd0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T19:34:56.415899Z",
     "start_time": "2024-03-10T19:34:55.595876Z"
    }
   },
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[WinError 183] Cannot create a file when that file already exists: 'new_labels_weight'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[17], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnew_labels_weight\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mFileExistsError\u001b[0m: [WinError 183] Cannot create a file when that file already exists: 'new_labels_weight'"
     ]
    }
   ],
   "source": [
    "os.mkdir('new_labels_weight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5893c566",
   "metadata": {},
   "source": [
    "#### กำหนด callbacks โดยใช้ EarlyStopping\n",
    "##### - monitering เพื่อที่จะ break จาก validation_accuracy\n",
    "##### -  เก็บ best model ที่ filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "72c16c7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:13:30.037241Z",
     "start_time": "2024-03-10T20:13:30.030237Z"
    }
   },
   "outputs": [],
   "source": [
    "filepath = \"./new_labels_weight/save-{epoch:02d}-{loss:.4f}.h5\"\n",
    "callback = [EarlyStopping(patience=3, monitor='val_accuracy'), \n",
    "        ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a787615a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:24:41.499564Z",
     "start_time": "2024-03-10T20:13:31.903191Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.4604 - accuracy: 0.8582\n",
      "Epoch 1: val_accuracy improved from -inf to 0.88349, saving model to ./new_labels_weight\\save-01-0.4604.h5\n",
      "142/142 [==============================] - 32s 223ms/step - loss: 0.4604 - accuracy: 0.8582 - val_loss: 0.3834 - val_accuracy: 0.8835\n",
      "Epoch 2/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.3832 - accuracy: 0.8815\n",
      "Epoch 2: val_accuracy improved from 0.88349 to 0.90046, saving model to ./new_labels_weight\\save-02-0.3832.h5\n",
      "142/142 [==============================] - 34s 236ms/step - loss: 0.3832 - accuracy: 0.8815 - val_loss: 0.3501 - val_accuracy: 0.9005\n",
      "Epoch 3/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.3223 - accuracy: 0.9042\n",
      "Epoch 3: val_accuracy improved from 0.90046 to 0.90394, saving model to ./new_labels_weight\\save-03-0.3223.h5\n",
      "142/142 [==============================] - 35s 247ms/step - loss: 0.3223 - accuracy: 0.9042 - val_loss: 0.3047 - val_accuracy: 0.9039\n",
      "Epoch 4/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.3045 - accuracy: 0.9059\n",
      "Epoch 4: val_accuracy improved from 0.90394 to 0.92747, saving model to ./new_labels_weight\\save-04-0.3045.h5\n",
      "142/142 [==============================] - 34s 242ms/step - loss: 0.3045 - accuracy: 0.9059 - val_loss: 0.2664 - val_accuracy: 0.9275\n",
      "Epoch 5/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.2728 - accuracy: 0.9141\n",
      "Epoch 5: val_accuracy did not improve from 0.92747\n",
      "142/142 [==============================] - 32s 227ms/step - loss: 0.2728 - accuracy: 0.9141 - val_loss: 0.2703 - val_accuracy: 0.9147\n",
      "Epoch 6/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.2627 - accuracy: 0.9180\n",
      "Epoch 6: val_accuracy improved from 0.92747 to 0.92785, saving model to ./new_labels_weight\\save-06-0.2627.h5\n",
      "142/142 [==============================] - 32s 226ms/step - loss: 0.2627 - accuracy: 0.9180 - val_loss: 0.2574 - val_accuracy: 0.9279\n",
      "Epoch 7/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.2418 - accuracy: 0.9231\n",
      "Epoch 7: val_accuracy improved from 0.92785 to 0.93596, saving model to ./new_labels_weight\\save-07-0.2418.h5\n",
      "142/142 [==============================] - 33s 234ms/step - loss: 0.2418 - accuracy: 0.9231 - val_loss: 0.2206 - val_accuracy: 0.9360\n",
      "Epoch 8/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.2227 - accuracy: 0.9323\n",
      "Epoch 8: val_accuracy did not improve from 0.93596\n",
      "142/142 [==============================] - 33s 233ms/step - loss: 0.2227 - accuracy: 0.9323 - val_loss: 0.2358 - val_accuracy: 0.9344\n",
      "Epoch 9/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.2149 - accuracy: 0.9345\n",
      "Epoch 9: val_accuracy improved from 0.93596 to 0.93634, saving model to ./new_labels_weight\\save-09-0.2149.h5\n",
      "142/142 [==============================] - 32s 224ms/step - loss: 0.2149 - accuracy: 0.9345 - val_loss: 0.2166 - val_accuracy: 0.9363\n",
      "Epoch 10/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.2044 - accuracy: 0.9365\n",
      "Epoch 10: val_accuracy did not improve from 0.93634\n",
      "142/142 [==============================] - 33s 236ms/step - loss: 0.2044 - accuracy: 0.9365 - val_loss: 0.2251 - val_accuracy: 0.9360\n",
      "Epoch 11/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.2017 - accuracy: 0.9371\n",
      "Epoch 11: val_accuracy improved from 0.93634 to 0.94136, saving model to ./new_labels_weight\\save-11-0.2017.h5\n",
      "142/142 [==============================] - 33s 233ms/step - loss: 0.2017 - accuracy: 0.9371 - val_loss: 0.2070 - val_accuracy: 0.9414\n",
      "Epoch 12/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.1873 - accuracy: 0.9428\n",
      "Epoch 12: val_accuracy improved from 0.94136 to 0.94599, saving model to ./new_labels_weight\\save-12-0.1873.h5\n",
      "142/142 [==============================] - 36s 251ms/step - loss: 0.1873 - accuracy: 0.9428 - val_loss: 0.1819 - val_accuracy: 0.9460\n",
      "Epoch 13/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.1689 - accuracy: 0.9474\n",
      "Epoch 13: val_accuracy did not improve from 0.94599\n",
      "142/142 [==============================] - 35s 249ms/step - loss: 0.1689 - accuracy: 0.9474 - val_loss: 0.1846 - val_accuracy: 0.9441\n",
      "Epoch 14/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.1691 - accuracy: 0.9445\n",
      "Epoch 14: val_accuracy improved from 0.94599 to 0.94907, saving model to ./new_labels_weight\\save-14-0.1691.h5\n",
      "142/142 [==============================] - 34s 242ms/step - loss: 0.1691 - accuracy: 0.9445 - val_loss: 0.1718 - val_accuracy: 0.9491\n",
      "Epoch 15/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.1641 - accuracy: 0.9502\n",
      "Epoch 15: val_accuracy did not improve from 0.94907\n",
      "142/142 [==============================] - 33s 234ms/step - loss: 0.1641 - accuracy: 0.9502 - val_loss: 0.1931 - val_accuracy: 0.9421\n",
      "Epoch 16/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.1578 - accuracy: 0.9491\n",
      "Epoch 16: val_accuracy did not improve from 0.94907\n",
      "142/142 [==============================] - 34s 243ms/step - loss: 0.1578 - accuracy: 0.9491 - val_loss: 0.1805 - val_accuracy: 0.9464\n",
      "Epoch 17/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.1501 - accuracy: 0.9528\n",
      "Epoch 17: val_accuracy improved from 0.94907 to 0.95332, saving model to ./new_labels_weight\\save-17-0.1501.h5\n",
      "142/142 [==============================] - 33s 231ms/step - loss: 0.1501 - accuracy: 0.9528 - val_loss: 0.1525 - val_accuracy: 0.9533\n",
      "Epoch 18/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.1398 - accuracy: 0.9563\n",
      "Epoch 18: val_accuracy did not improve from 0.95332\n",
      "142/142 [==============================] - 33s 232ms/step - loss: 0.1398 - accuracy: 0.9563 - val_loss: 0.1708 - val_accuracy: 0.9487\n",
      "Epoch 19/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.1471 - accuracy: 0.9530\n",
      "Epoch 19: val_accuracy improved from 0.95332 to 0.95756, saving model to ./new_labels_weight\\save-19-0.1471.h5\n",
      "142/142 [==============================] - 33s 231ms/step - loss: 0.1471 - accuracy: 0.9530 - val_loss: 0.1381 - val_accuracy: 0.9576\n",
      "Epoch 20/20\n",
      "142/142 [==============================] - ETA: 0s - loss: 0.1336 - accuracy: 0.9595\n",
      "Epoch 20: val_accuracy improved from 0.95756 to 0.96219, saving model to ./new_labels_weight\\save-20-0.1336.h5\n",
      "142/142 [==============================] - 33s 235ms/step - loss: 0.1336 - accuracy: 0.9595 - val_loss: 0.1327 - val_accuracy: 0.9622\n"
     ]
    }
   ],
   "source": [
    "set_seed(12345)\n",
    "#https://datascience.stackexchange.com/questions/73093/what-does-from-logits-true-do-in-sparsecategoricalcrossentropy-loss-function\n",
    "epochs = 20\n",
    "\n",
    "from keras.optimizers import SGD\n",
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-6), loss=keras.losses.CategoricalCrossentropy(from_logits=False), metrics=['accuracy'])\n",
    "# when using sigmoid on last Dense we need to set from_logits=False\n",
    "\n",
    "# alongside the top Dense layers\n",
    "history = model.fit(train_ds, epochs=epochs, validation_data=valid_ds, callbacks=callback)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "912ccf02",
   "metadata": {},
   "source": [
    "## Loss visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "46d8716d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:24:46.102574Z",
     "start_time": "2024-03-10T20:24:45.849973Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22d55b92e00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1120x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss accuracy\n",
    "fig = plt.figure(figsize=(14, 5), dpi=80)\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax.plot(history.history['loss'])\n",
    "ax.plot(history.history['val_loss'])\n",
    "ax.set_title('Loss')\n",
    "ax.set_ylabel('loss')\n",
    "ax.set_xlabel('epoch')\n",
    "ax.legend(['train', 'val'], loc='upper right')\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax.plot(history.history['accuracy'])\n",
    "\n",
    "ax.plot(history.history['val_accuracy'])\n",
    "ax.set_title('Accuracy')\n",
    "ax.set_ylabel('accuracy')\n",
    "ax.set_xlabel('epoch')\n",
    "ax.legend(['train', 'val'], loc='lower right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fab0f41c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:27:45.352337Z",
     "start_time": "2024-03-10T20:27:10.079549Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.056786, Accuracy: 0.982472\n",
      "Validation Loss: 0.163081, Accuracy: 0.952546\n",
      "Test Loss: 0.050809, Accuracy: 0.983297\n"
     ]
    }
   ],
   "source": [
    "print('Train Loss: {:.6f}, Accuracy: {:.6f}'.format(*model.evaluate(train_ds, verbose=0)))\n",
    "print('Validation Loss: {:.6f}, Accuracy: {:.6f}'.format(*model.evaluate(valid_ds, verbose=0)))\n",
    "print('Test Loss: {:.6f}, Accuracy: {:.6f}'.format(*model.evaluate(test_ds, verbose=0)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "561f4915",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:27:48.404038Z",
     "start_time": "2024-03-10T20:27:48.395037Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
       " array([80, 80, 80, 80, 80, 80, 80, 80, 80, 80]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ds.classes[:10], test_ds.classes[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dfe80276",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:27:52.740649Z",
     "start_time": "2024-03-10T20:27:48.891947Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 4s 163ms/step\n"
     ]
    }
   ],
   "source": [
    "# Generate predictions\n",
    "predictions = model.predict(test_ds)\n",
    "y_pred = np.argmax(predictions, axis=1)\n",
    "\n",
    "# Get true labels\n",
    "true_labels = test_ds.classes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5fee7f92",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:27:52.756648Z",
     "start_time": "2024-03-10T20:27:52.742647Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1377"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(true_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cba3f9b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:27:58.814095Z",
     "start_time": "2024-03-10T20:27:52.757648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm = confusion_matrix(true_labels, y_pred)\n",
    "# Define class labels (assuming your classes are named '0', '1', ..., '19')\n",
    "class_names = [i for i in range(80)]\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(18, 11))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=class_names, yticklabels=class_names)\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"True Labels\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3f993e23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:27:59.873187Z",
     "start_time": "2024-03-10T20:27:59.866191Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.metrics import classification_report\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e366fdfb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:28:00.388298Z",
     "start_time": "2024-03-10T20:28:00.369298Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           一       1.00      1.00      1.00        17\n",
      "           七       1.00      1.00      1.00        17\n",
      "           万       1.00      1.00      1.00        17\n",
      "           三       0.94      1.00      0.97        17\n",
      "           上       1.00      0.94      0.97        17\n",
      "           下       1.00      1.00      1.00        17\n",
      "           中       1.00      1.00      1.00        17\n",
      "           九       1.00      0.94      0.97        17\n",
      "           二       1.00      0.94      0.97        17\n",
      "           五       1.00      1.00      1.00        17\n",
      "           人       0.94      1.00      0.97        17\n",
      "           今       1.00      1.00      1.00        17\n",
      "           休       0.94      1.00      0.97        17\n",
      "           何       1.00      1.00      1.00        17\n",
      "           先       1.00      1.00      1.00        17\n",
      "           入       1.00      0.94      0.97        17\n",
      "           八       1.00      1.00      1.00        17\n",
      "           六       1.00      1.00      1.00        17\n",
      "           円       1.00      1.00      1.00        17\n",
      "           出       1.00      1.00      1.00        17\n",
      "           分       1.00      1.00      1.00        17\n",
      "           前       1.00      1.00      1.00        17\n",
      "           北       1.00      1.00      1.00        17\n",
      "           十       1.00      1.00      1.00        17\n",
      "           千       0.94      1.00      0.97        17\n",
      "           午       1.00      0.94      0.97        17\n",
      "           半       1.00      1.00      1.00        17\n",
      "           南       1.00      0.94      0.97        17\n",
      "           友       1.00      1.00      1.00        17\n",
      "           右       1.00      0.94      0.97        17\n",
      "           名       1.00      1.00      1.00        17\n",
      "           四       0.89      1.00      0.94        17\n",
      "           国       1.00      1.00      1.00        17\n",
      "           土       0.89      1.00      0.94        17\n",
      "           外       1.00      1.00      1.00        17\n",
      "           大       1.00      1.00      1.00        17\n",
      "           天       1.00      1.00      1.00        17\n",
      "           女       1.00      1.00      1.00        17\n",
      "           子       1.00      1.00      1.00        17\n",
      "           学       1.00      1.00      1.00        17\n",
      "           小       1.00      1.00      1.00        17\n",
      "           山       0.94      1.00      0.97        17\n",
      "           川       1.00      1.00      1.00        17\n",
      "           左       0.94      1.00      0.97        17\n",
      "           年       1.00      0.94      0.97        17\n",
      "           後       1.00      1.00      1.00        17\n",
      "           日       1.00      1.00      1.00        17\n",
      "           明       1.00      0.82      0.90        17\n",
      "           時       1.00      1.00      1.00        17\n",
      "           書       1.00      1.00      1.00        17\n",
      "           月       1.00      1.00      1.00        17\n",
      "           木       0.94      1.00      0.97        17\n",
      "           本       1.00      1.00      1.00        17\n",
      "           来       1.00      0.94      0.97        17\n",
      "           東       0.94      1.00      0.97        17\n",
      "           校       1.00      1.00      1.00        17\n",
      "           母       0.94      1.00      0.97        17\n",
      "           毎       1.00      1.00      1.00        17\n",
      "           気       1.00      1.00      1.00        17\n",
      "           水       1.00      0.88      0.94        17\n",
      "           火       1.00      1.00      1.00        17\n",
      "           父       1.00      1.00      1.00        17\n",
      "           生       0.94      0.94      0.94        17\n",
      "           男       1.00      1.00      1.00        17\n",
      "           白       1.00      1.00      1.00        17\n",
      "           百       1.00      1.00      1.00        17\n",
      "           聞       1.00      0.94      0.97        17\n",
      "           行       1.00      1.00      1.00        17\n",
      "           西       1.00      1.00      1.00        17\n",
      "           見       0.94      1.00      0.97        17\n",
      "           話       1.00      0.94      0.97        17\n",
      "           語       0.89      1.00      0.94        17\n",
      "           読       0.94      1.00      0.97        17\n",
      "           車       1.00      1.00      1.00        17\n",
      "           金       0.94      1.00      0.97        17\n",
      "           長       1.00      1.00      1.00        17\n",
      "           間       0.94      1.00      0.97        17\n",
      "           雨       0.94      0.94      0.94        17\n",
      "           電       1.00      0.94      0.97        17\n",
      "           食       1.00      0.94      0.97        17\n",
      "           高       1.00      0.94      0.97        17\n",
      "\n",
      "    accuracy                           0.98      1377\n",
      "   macro avg       0.99      0.98      0.98      1377\n",
      "weighted avg       0.99      0.98      0.98      1377\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate classification report\n",
    "report = classification_report(true_labels, y_pred, target_names=list(train_ds.class_indices.keys()))\n",
    "print(\"Classification Report:\")\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bd15cd6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load a model\n",
    "# from keras.models import load_model\n",
    "# from keras.preprocessing import image\n",
    "# import numpy as np\n",
    "\n",
    "# Load the saved model\n",
    "# model = load_model(\"./Indiannet_weight/save-14-0.1555.h5\")\n",
    "\n",
    "# class_name = ['burger', 'butter_naan', 'chai', 'chapati', 'chole_bhature', 'dal_makhani', 'dhokla', 'fried_rice', 'idli', 'jalebi', 'kaathi_rolls', 'kadai_paneer', 'kulfi', 'masala_dosa', 'momos', 'paani_puri', 'pakode', 'pav_bhaji', 'pizza', 'samosa']\n",
    "\n",
    "# # Load and preprocess the image\n",
    "# img_path = \"./Indiannet_weight/save-14-0.1555.h5\"\n",
    "# img = image.load_img(img_path, target_size=(256, 256))  # adjust target_size as needed\n",
    "# img_array = image.img_to_array(img)\n",
    "# img_array = np.expand_dims(img_array, axis=0)\n",
    "# img_array /= 255.  # Normalize the image data\n",
    "\n",
    "# # Make predictions\n",
    "# predictions = model.predict(img_array)\n",
    "# accuracy_percent = np.max(predictions) * 100\n",
    "\n",
    "# # Print the predicted class\n",
    "# predicted_class = np.argmax(predictions)\n",
    "# print(f\"Predicted class: {class_name[predicted_class]} [{predicted_class}]\")\n",
    "# print(f\"with accuracy {accuracy_percent:.2f} %\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f2b31065",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-10T20:28:46.328012Z",
     "start_time": "2024-03-10T20:28:46.107972Z"
    }
   },
   "outputs": [],
   "source": [
    "model.save('vgg16_unfreeze3.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d65b05c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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