04_t__FER_cnn-vgg-0.ipynb

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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "#for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    " #   for filename in filenames:\n",
    "  #      print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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     "text": [
      "Collecting kaleido\n",
      "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
      "     |████████████████████████████████| 79.9 MB 220 kB/s            \n",
      "\u001b[?25hInstalling collected packages: kaleido\n",
      "Successfully installed kaleido-0.2.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install kaleido"
   ]
  },
  {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import plotly.graph_objects as go\n",
    "import glob\n",
    "import os"
   ]
  },
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   "source": [
    "def create_dir (direct):\n",
    "  if not os.path.exists(direct):\n",
    "    os.mkdir(direct)"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "ROOT_DIR = \"/kaggle/input/ferset\" #MT Directory\n",
    "DATASET_02 = ROOT_DIR + \"/02_FER\"\n",
    "DATASET_02_TRAIN = DATASET_02 + \"/train\"\n",
    "DATASET_02_TEST = DATASET_02+ \"/test\""
   ]
  },
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     "text": [
      "L\n",
      "48 48\n"
     ]
    }
   ],
   "source": [
    "import PIL\n",
    "\n",
    "image = PIL.Image.open(DATASET_02_TRAIN + \"/surprise/Training_99984132.jpg\")\n",
    "#image to open\n",
    "\n",
    "width, height = image.size\n",
    "\n",
    "print(image.mode)\n",
    "print(width, height)"
   ]
  },
  {
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "Found 7178 images belonging to 7 classes.\n",
      "Found 28709 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator()\n",
    "image_size = (48, 48)\n",
    "colormode = 'rgb'\n",
    "test_set = datagen.flow_from_directory(DATASET_02_TEST, target_size=image_size, color_mode =colormode, batch_size=32, shuffle=False)\n",
    "train_set = datagen.flow_from_directory(DATASET_02_TRAIN, target_size=image_size, color_mode =colormode, batch_size=32)"
   ]
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   "outputs": [
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     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 46, 46, 64)        1792      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 23, 23, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 21, 21, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 10, 10, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 12800)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               3277056   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 7)                 1799      \n",
      "=================================================================\n",
      "Total params: 3,354,503\n",
      "Trainable params: 3,354,503\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-08 03:47:01.739597: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "def create_model_V1():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Conv2D(64, (3, 3), activation='relu', input_shape=(48, 48, 3)))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    #model.add(layers.Dropout(0.4))\n",
    "    #model.add(layers.Conv2D(32, (3, 3), activation='relu'))\n",
    "    #model.add(layers.MaxPooling2D((2, 2)))\n",
    "    #model.add(layers.Dropout(0.4))\n",
    "    #model.add(layers.Conv2D(16, (3, 3), activation='relu'))\n",
    "    #model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dense(256, activation='relu'))\n",
    "    model.add(layers.Dense(7, activation='softmax'))\n",
    "    \n",
    "    return model\n",
    "\n",
    "\n",
    "model_1 = create_model_V1()\n",
    "model_1.summary()"
   ]
  },
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   "source": [
    "def get_data_df(dir):\n",
    "  # modified code from: https://www.kaggle.com/namgalielei/simple-load-images-and-count-number-of-each-class\n",
    "\n",
    "  train_df = pd.DataFrame()\n",
    "\n",
    "  trainset = glob.glob(dir)\n",
    "  #print(trainset)\n",
    "  train_df['filename'] = [img.split(\"/\")[-2]+\"/\"+img.split(\"/\")[-1] for img in trainset]\n",
    "  train_df['label'] = [img.split(\"/\")[-2] for img in trainset]\n",
    "\n",
    "  return train_df"
   ]
  },
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surprise/Training_66056468.jpg</td>\n",
       "      <td>surprise</td>\n",
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       "      <th>1</th>\n",
       "      <td>surprise/Training_26522394.jpg</td>\n",
       "      <td>surprise</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>surprise/Training_63239185.jpg</td>\n",
       "      <td>surprise</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>surprise/Training_67725241.jpg</td>\n",
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       "      <th>28705</th>\n",
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       "<p>28709 rows × 2 columns</p>\n",
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       "                             filename     label\n",
       "0      surprise/Training_66056468.jpg  surprise\n",
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       "2      surprise/Training_63239185.jpg  surprise\n",
       "3      surprise/Training_67725241.jpg  surprise\n",
       "4      surprise/Training_71308969.jpg  surprise\n",
       "...                               ...       ...\n",
       "28704     happy/Training_18866880.jpg     happy\n",
       "28705     happy/Training_16513509.jpg     happy\n",
       "28706     happy/Training_10834907.jpg     happy\n",
       "28707     happy/Training_30459679.jpg     happy\n",
       "28708     happy/Training_95673040.jpg     happy\n",
       "\n",
       "[28709 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_train = get_data_df(DATASET_02_TRAIN+\"/*/*.jpg\")\n",
    "test_train"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "surprise\n",
      "fear\n",
      "angry\n",
      "neutral\n",
      "sad\n",
      "disgust\n",
      "happy\n",
      "35887\n",
      "[4002, 5121, 4953, 6198, 6077, 547, 8989]\n",
      "{0: 1.2810380524023703, 1: 1.001115853488437, 2: 1.035072539009547, 3: 0.8271562255105334, 4: 0.8436258492207152, 5: 9.372420997649517, 6: 0.5703319930708962}\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "value_list = []\n",
    "for item in os.listdir(DATASET_02_TRAIN):\n",
    "    y = len(os.listdir(DATASET_02_TRAIN+'/'+item))+len(os.listdir(DATASET_02_TEST+'/'+item))\n",
    "    value_list.append(y)\n",
    "    total += y\n",
    "    print(item)\n",
    "print(total)\n",
    "print(value_list)\n",
    "weights = {}\n",
    "for n in range(0,len(value_list)):\n",
    "    w = total/(len(value_list)*value_list[n])\n",
    "    weights[n] = w\n",
    "    \n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   "outputs": [],
   "source": [
    "create_dir('/kaggle/working/Saved_CNN_Models')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "183c4f6c",
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   "source": [
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "\n",
    "train_data = get_data_df(DATASET_02_TRAIN+\"/*/*.jpg\")\n",
    "Y = train_data[['label']]\n",
    "\n",
    "#Y = final_train[['label']]\n",
    "#n = len(final_train)   \n",
    "\n",
    "skf = StratifiedKFold(n_splits = 5, random_state = 7, shuffle = True) \n",
    "\n",
    "#Un needed preprocessing aside from rescale\n",
    "#idg = ImageDataGenerator(rescale=1./255)\n",
    "create_dir('/kaggle/working/Saved_CNN_Models')\n",
    "#model name\n",
    "def get_model_name(k):\n",
    "    return 'model_'+str(k)+'.h5'\n",
    "def model_test(fold_count, epoch, model_method, image_size, mode):\n",
    "    skf = StratifiedKFold(n_splits = fold_count, random_state = 7, shuffle = True)\n",
    "    train_data = get_data_df(DATASET_02_TRAIN+\"/*/*.jpg\")\n",
    "      #Incorperate preprocessing in the method \n",
    "      #####################################################################\n",
    "      #aug_train = get_data_df(DATASET_01_ROOT+\"/Aug_train/*/*.jpg\")\n",
    "      #aug_train['filename'] = aug_train['filename'].str[7:]\n",
    "      #final_train = test_train.append(aug_train, sort=False, ignore_index=True)\n",
    "\n",
    "\n",
    "      #######################################################################\n",
    "      #idg = ImageDataGenerator(rescale=1./255)\n",
    "    idg = ImageDataGenerator()\n",
    "    Y = train_data[['label']]\n",
    "    n = len(train_data)    \n",
    "    VALIDATION_ACCURACY = []\n",
    "    VALIDATION_LOSS = []\n",
    "\n",
    "      #create a directory\n",
    "    save_dir = '/kaggle/working/Saved_CNN_Models/'+model_method.__name__+'_'+str(epoch)+'epochs/'\n",
    "    create_dir(save_dir)\n",
    "    \n",
    "    img_dir = DATASET_02_TRAIN\n",
    "\n",
    "    fold_var = 1\n",
    "\n",
    "      #n is number of samples, doesn't care about x values for split, just y\n",
    "    for train_index, val_index in skf.split(np.zeros(n),Y):\n",
    "\n",
    "        #Index value for fold\n",
    "        training_data = train_data.iloc[train_index]\n",
    "        validation_data = train_data.iloc[val_index]\n",
    "        #generator for train\n",
    "        #print(\"hi\")\n",
    "        #print(validation_data.head())\n",
    "        #Modify the training size, have an input adjustability \n",
    "        train_data_generator = idg.flow_from_dataframe(training_data, directory = img_dir,\n",
    "                                                       target_size=image_size, batch_size=32, color_mode =mode,\n",
    "                                                       x_col = \"filename\", y_col = \"label\",shuffle = True)\n",
    "        #print(\"hi\")\n",
    "        #generator for validation\n",
    "        valid_data_generator  = idg.flow_from_dataframe(validation_data, directory = img_dir,\n",
    "                                                        target_size=image_size, color_mode =mode,batch_size=32,\n",
    "                                                        x_col = \"filename\", y_col = \"label\",shuffle = True)\n",
    "        #print(\"hi\")\n",
    "        #create a method for this\n",
    "        # CREATE NEW MODEL\n",
    "        model = model_method()\n",
    "        # COMPILE NEW MODEL\n",
    "        model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "                optimizer= tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
    "                metrics=['accuracy'])\n",
    "\n",
    "        # CREATE CALLBACKS\n",
    "        #Unsure about checpoint\n",
    "        checkpoint = tf.keras.callbacks.ModelCheckpoint(save_dir+model_method.__name__+\"_\"+get_model_name(fold_var), \n",
    "                    monitor='val_accuracy', verbose=1, \n",
    "                    save_best_only=True, mode='max')\n",
    "\n",
    "        callbacks_list = [checkpoint]\n",
    "        # There can be other callbacks, but just showing one because it involves the model name\n",
    "        # This saves the best model\n",
    "        # FIT THE MODEL\n",
    "        #Include the weights for classes\n",
    "        history = model.fit(train_data_generator,\n",
    "                epochs=epoch,\n",
    "                callbacks=callbacks_list,\n",
    "                validation_data=valid_data_generator,\n",
    "                class_weight = weights,\n",
    "                verbose = 2)\n",
    "\n",
    "        #PLOT HISTORY\n",
    "        #Plots for whichever\n",
    "        #\t\t:\n",
    "        #plt.plot(history.history['binary_accuracy'], label='accuracy')\n",
    "        #plt.plot(history.history['val_binary_accuracy'], label = 'val_accuracy')\n",
    "        #plt.xlabel('Epoch')\n",
    "        #plt.ylabel('Accuracy')\n",
    "        #plt.ylim([0.3, 1])\n",
    "        #plt.legend(loc='lower right')\n",
    "        #plt.show()\n",
    "        #frame = {'Accuracy': history.history['binary_accuracy'], 'Val_Accuracy': history.history['val_binary_accuracy']}\n",
    "        #fig = px.line(frame)\n",
    "        #fig.show()\n",
    "        fig = go.Figure()\n",
    "        fig.add_trace(go.Scatter(y= history.history['accuracy'],\n",
    "                          mode='lines+markers',\n",
    "                          name='Training Accuracy'))\n",
    "        fig.add_trace(go.Scatter(y= history.history['val_accuracy'],\n",
    "                          mode='lines+markers',\n",
    "                          name='Validation Accuracy'))\n",
    "        fig.update_yaxes(range=[0, 1])\n",
    "        fig.update_layout(\n",
    "            title=\"Fold \"+ str(fold_var)+ \"'s performance\",\n",
    "            title_x=0.5,\n",
    "            xaxis_title=\"Epochs\",\n",
    "            yaxis_title=\"Accuracy\",\n",
    "            autosize=False,\n",
    "            width=800,\n",
    "            height=400,\n",
    "            margin=dict(l=20, r=20, t=40, b=20),)\n",
    "        fig.show()\n",
    "        graph_dir = '/kaggle/working/img'\n",
    "        create_dir(graph_dir)\n",
    "        graph_dir = graph_dir+'/'+model_method.__name__\n",
    "        create_dir(graph_dir)\n",
    "        fig.write_image(graph_dir+'/fold'+str(fold_var)+'.png') \n",
    "\n",
    "        #\t\t:\n",
    "        # LOAD BEST MODEL to evaluate the performance of the model\n",
    "        model.load_weights(save_dir+model_method.__name__+\"_\" + \"model_\"+str(fold_var)+\".h5\")\n",
    "\n",
    "        results = model.evaluate(test_set)\n",
    "        results = dict(zip(model.metrics_names,results))\n",
    "\n",
    "        VALIDATION_ACCURACY.append(results['accuracy'])\n",
    "        VALIDATION_LOSS.append(results['loss'])\n",
    "\n",
    "        tf.keras.backend.clear_session()\n",
    "\n",
    "        fold_var += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8020f012",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 1, create_model_V1, (48,48), 'rgb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a330dd92",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94773248/94765736 [==============================] - 1s 0us/step\n",
      "94781440/94765736 [==============================] - 1s 0us/step\n",
      "175\n"
     ]
    }
   ],
   "source": [
    "def transfer_learning_model_resnet_50():\n",
    "\n",
    "  IMG_SHAPE = (48,48,3,)\n",
    " \n",
    "  inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
    "  #inputs = tf.image.grayscale_to_rgb(y)\n",
    " \n",
    "  x = tf.keras.applications.resnet50.preprocess_input(inputs)\n",
    "  base_model = tf.keras.applications.resnet50.ResNet50(input_shape=(48,48,3,),\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "  print(len(base_model.layers))\n",
    "  base_model.trainable = True\n",
    "\n",
    "  fine_tune_at = 140\n",
    "  for layer in base_model.layers[:fine_tune_at]:\n",
    "    layer.trainable = False\n",
    "\n",
    "  x = base_model(x)\n",
    "  global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
    "  x = global_average_layer(x)\n",
    "  x = tf.keras.layers.Dropout(0.4)(x)\n",
    "  prediction_layer = tf.keras.layers.Dense(7, activation = \"softmax\")\n",
    "  outputs = prediction_layer(x)\n",
    "  model = tf.keras.Model(inputs, outputs)\n",
    "  #print(len(model.trainable_variables))\n",
    "  return model\n",
    "\n",
    "transfer_model_resnet_50 = transfer_learning_model_resnet_50()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1829fa55",
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    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 48, 48, 3)]       0         \n",
      "_________________________________________________________________\n",
      "tf.__operators__.getitem (Sl (None, 48, 48, 3)         0         \n",
      "_________________________________________________________________\n",
      "tf.nn.bias_add (TFOpLambda)  (None, 48, 48, 3)         0         \n",
      "_________________________________________________________________\n",
      "resnet50 (Functional)        (None, 2, 2, 2048)        23587712  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 7)                 14343     \n",
      "=================================================================\n",
      "Total params: 23,602,055\n",
      "Trainable params: 14,992,391\n",
      "Non-trainable params: 8,609,664\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "transfer_model_resnet_50.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7808d23d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-08T03:47:05.777779Z",
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     "exception": false,
     "start_time": "2022-05-08T03:47:05.709155",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, transfer_learning_model_resnet_50, (48,48,), 'rgb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c202ac67",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 0s 0us/step\n",
      "58900480/58889256 [==============================] - 0s 0us/step\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "def transfer_learning_model_vgg_16():\n",
    "\n",
    "  IMG_SHAPE = (48,48,3,)\n",
    " \n",
    "  inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
    "  #inputs = tf.image.grayscale_to_rgb(y)\n",
    " \n",
    "  x = tf.keras.applications.vgg16.preprocess_input(inputs)\n",
    "  base_model = tf.keras.applications.vgg16.VGG16(input_shape=(48,48,3,),\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "  print(len(base_model.layers))\n",
    "  base_model.trainable = False\n",
    "\n",
    "  fine_tune_at = 14\n",
    "  for layer in base_model.layers[:fine_tune_at]:\n",
    "    layer.trainable = False\n",
    "\n",
    "  x = base_model(x)\n",
    "  global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
    "  x = global_average_layer(x)\n",
    "  x = tf.keras.layers.Dropout(0.4)(x)\n",
    "  prediction_layer = tf.keras.layers.Dense(7, activation = \"softmax\")\n",
    "  outputs = prediction_layer(x)\n",
    "  model = tf.keras.Model(inputs, outputs)\n",
    "  #print(len(model.trainable_variables))\n",
    "  return model\n",
    "\n",
    "transfer_model_vgg_16 = transfer_learning_model_vgg_16()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         [(None, 48, 48, 3)]       0         \n",
      "_________________________________________________________________\n",
      "tf.__operators__.getitem_1 ( (None, 48, 48, 3)         0         \n",
      "_________________________________________________________________\n",
      "tf.nn.bias_add_1 (TFOpLambda (None, 48, 48, 3)         0         \n",
      "_________________________________________________________________\n",
      "vgg16 (Functional)           (None, 1, 1, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 7)                 3591      \n",
      "=================================================================\n",
      "Total params: 14,718,279\n",
      "Trainable params: 3,591\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "transfer_model_vgg_16.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b88c98d2",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-04-10T05:20:10.075747Z"
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    "papermill": {
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     "exception": false,
     "start_time": "2022-05-08T03:47:06.897628",
     "status": "running"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-08 03:47:21.519286: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "718/718 - 311s - loss: 40.5200 - accuracy: 0.1314 - val_loss: 19.2547 - val_accuracy: 0.1639\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.16388, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_1.h5\n",
      "Epoch 2/30\n",
      "718/718 - 303s - loss: 22.0924 - accuracy: 0.1728 - val_loss: 13.4896 - val_accuracy: 0.1832\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.16388 to 0.18321, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_1.h5\n",
      "Epoch 3/30\n",
      "718/718 - 307s - loss: 16.2240 - accuracy: 0.1909 - val_loss: 10.5418 - val_accuracy: 0.1966\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.18321 to 0.19662, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_1.h5\n",
      "Epoch 4/30\n",
      "718/718 - 304s - loss: 12.5810 - accuracy: 0.2059 - val_loss: 8.5080 - val_accuracy: 0.2045\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.19662 to 0.20446, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_1.h5\n",
      "Epoch 5/30\n",
      "718/718 - 305s - loss: 10.0372 - accuracy: 0.2172 - val_loss: 7.0316 - val_accuracy: 0.2092\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.20446 to 0.20916, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_1.h5\n",
      "Epoch 6/30\n",
      "718/718 - 303s - loss: 8.2412 - accuracy: 0.2208 - val_loss: 5.9328 - val_accuracy: 0.2118\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.20916 to 0.21177, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_1.h5\n",
      "Epoch 7/30\n",
      "718/718 - 304s - loss: 6.9345 - accuracy: 0.2250 - val_loss: 4.9364 - val_accuracy: 0.2233\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.21177 to 0.22327, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_1.h5\n",
      "Epoch 8/30\n",
      "718/718 - 304s - loss: 5.9270 - accuracy: 0.2255 - val_loss: 4.4233 - val_accuracy: 0.2177\n",
      "\n",
      "Epoch 00008: val_accuracy did not improve from 0.22327\n",
      "Epoch 9/30\n",
      "718/718 - 304s - loss: 5.1451 - accuracy: 0.2218 - val_loss: 3.9937 - val_accuracy: 0.2116\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.22327\n",
      "Epoch 10/30\n",
      "718/718 - 305s - loss: 4.5069 - accuracy: 0.2218 - val_loss: 3.5686 - val_accuracy: 0.2090\n",
      "\n",
      "Epoch 00010: val_accuracy did not improve from 0.22327\n",
      "Epoch 11/30\n",
      "718/718 - 304s - loss: 4.1288 - accuracy: 0.2210 - val_loss: 3.3530 - val_accuracy: 0.2001\n",
      "\n",
      "Epoch 00011: val_accuracy did not improve from 0.22327\n",
      "Epoch 12/30\n",
      "718/718 - 303s - loss: 3.7918 - accuracy: 0.2143 - val_loss: 3.0623 - val_accuracy: 0.2036\n",
      "\n",
      "Epoch 00012: val_accuracy did not improve from 0.22327\n",
      "Epoch 13/30\n",
      "718/718 - 302s - loss: 3.5420 - accuracy: 0.2135 - val_loss: 2.9639 - val_accuracy: 0.1982\n",
      "\n",
      "Epoch 00013: val_accuracy did not improve from 0.22327\n",
      "Epoch 14/30\n",
      "718/718 - 303s - loss: 3.3374 - accuracy: 0.2161 - val_loss: 2.7741 - val_accuracy: 0.1949\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.22327\n",
      "Epoch 15/30\n",
      "718/718 - 304s - loss: 3.1488 - accuracy: 0.2126 - val_loss: 2.7143 - val_accuracy: 0.1992\n",
      "\n",
      "Epoch 00015: val_accuracy did not improve from 0.22327\n",
      "Epoch 16/30\n",
      "718/718 - 302s - loss: 3.0426 - accuracy: 0.2153 - val_loss: 2.7058 - val_accuracy: 0.1872\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.22327\n",
      "Epoch 17/30\n",
      "718/718 - 302s - loss: 2.9804 - accuracy: 0.2093 - val_loss: 2.5099 - val_accuracy: 0.1869\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.22327\n",
      "Epoch 18/30\n",
      "718/718 - 300s - loss: 2.8916 - accuracy: 0.2101 - val_loss: 2.4374 - val_accuracy: 0.1968\n",
      "\n",
      "Epoch 00018: val_accuracy did not improve from 0.22327\n",
      "Epoch 19/30\n",
      "718/718 - 303s - loss: 2.8025 - accuracy: 0.2103 - val_loss: 2.4212 - val_accuracy: 0.1970\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.22327\n",
      "Epoch 20/30\n",
      "718/718 - 301s - loss: 2.7566 - accuracy: 0.2122 - val_loss: 2.3874 - val_accuracy: 0.1951\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.22327\n",
      "Epoch 21/30\n",
      "718/718 - 304s - loss: 2.7287 - accuracy: 0.2095 - val_loss: 2.2654 - val_accuracy: 0.1935\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.22327\n",
      "Epoch 22/30\n",
      "718/718 - 303s - loss: 2.7146 - accuracy: 0.2081 - val_loss: 2.3222 - val_accuracy: 0.1923\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.22327\n",
      "Epoch 23/30\n",
      "718/718 - 303s - loss: 2.6591 - accuracy: 0.2127 - val_loss: 2.2486 - val_accuracy: 0.2029\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.22327\n",
      "Epoch 24/30\n",
      "718/718 - 303s - loss: 2.6520 - accuracy: 0.2130 - val_loss: 2.1397 - val_accuracy: 0.2038\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.22327\n",
      "Epoch 25/30\n",
      "718/718 - 303s - loss: 2.6439 - accuracy: 0.2096 - val_loss: 2.3024 - val_accuracy: 0.1897\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.22327\n",
      "Epoch 26/30\n",
      "718/718 - 305s - loss: 2.6136 - accuracy: 0.2064 - val_loss: 2.1068 - val_accuracy: 0.2041\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.22327\n",
      "Epoch 27/30\n",
      "718/718 - 302s - loss: 2.6069 - accuracy: 0.2094 - val_loss: 2.1961 - val_accuracy: 0.1898\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.22327\n",
      "Epoch 28/30\n",
      "718/718 - 301s - loss: 2.5999 - accuracy: 0.2081 - val_loss: 2.2269 - val_accuracy: 0.1992\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.22327\n",
      "Epoch 29/30\n",
      "718/718 - 302s - loss: 2.5841 - accuracy: 0.2090 - val_loss: 2.2601 - val_accuracy: 0.1898\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.22327\n",
      "Epoch 30/30\n",
      "718/718 - 307s - loss: 2.5786 - accuracy: 0.2096 - val_loss: 2.1881 - val_accuracy: 0.1942\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.22327\n"
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      "225/225 [==============================] - 80s 356ms/step - loss: 4.9307 - accuracy: 0.2320\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "19\n",
      "Epoch 1/30\n",
      "718/718 - 307s - loss: 37.2025 - accuracy: 0.1739 - val_loss: 20.5532 - val_accuracy: 0.1677\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.16771, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_2.h5\n",
      "Epoch 2/30\n",
      "718/718 - 307s - loss: 23.9813 - accuracy: 0.1810 - val_loss: 14.9412 - val_accuracy: 0.1823\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.16771 to 0.18234, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_2.h5\n",
      "Epoch 3/30\n",
      "718/718 - 314s - loss: 17.6269 - accuracy: 0.1956 - val_loss: 10.9934 - val_accuracy: 0.1966\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.18234 to 0.19662, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_2.h5\n",
      "Epoch 4/30\n",
      "718/718 - 309s - loss: 13.2806 - accuracy: 0.2086 - val_loss: 8.8863 - val_accuracy: 0.1996\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.19662 to 0.19958, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_2.h5\n",
      "Epoch 5/30\n",
      "718/718 - 307s - loss: 10.2736 - accuracy: 0.2198 - val_loss: 6.5130 - val_accuracy: 0.2252\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.19958 to 0.22518, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_2.h5\n",
      "Epoch 6/30\n",
      "718/718 - 306s - loss: 8.2089 - accuracy: 0.2245 - val_loss: 5.5797 - val_accuracy: 0.2252\n",
      "\n",
      "Epoch 00006: val_accuracy did not improve from 0.22518\n",
      "Epoch 7/30\n",
      "718/718 - 307s - loss: 6.7733 - accuracy: 0.2259 - val_loss: 4.9547 - val_accuracy: 0.2062\n",
      "\n",
      "Epoch 00007: val_accuracy did not improve from 0.22518\n",
      "Epoch 8/30\n",
      "718/718 - 308s - loss: 5.7653 - accuracy: 0.2201 - val_loss: 4.1298 - val_accuracy: 0.2187\n",
      "\n",
      "Epoch 00008: val_accuracy did not improve from 0.22518\n",
      "Epoch 9/30\n",
      "718/718 - 308s - loss: 5.0060 - accuracy: 0.2233 - val_loss: 3.8869 - val_accuracy: 0.2017\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.22518\n",
      "Epoch 10/30\n",
      "718/718 - 308s - loss: 4.4216 - accuracy: 0.2169 - val_loss: 3.5614 - val_accuracy: 0.2046\n",
      "\n",
      "Epoch 00010: val_accuracy did not improve from 0.22518\n",
      "Epoch 11/30\n",
      "718/718 - 308s - loss: 4.0369 - accuracy: 0.2181 - val_loss: 3.1595 - val_accuracy: 0.2018\n",
      "\n",
      "Epoch 00011: val_accuracy did not improve from 0.22518\n",
      "Epoch 12/30\n",
      "718/718 - 307s - loss: 3.7271 - accuracy: 0.2176 - val_loss: 2.9164 - val_accuracy: 0.2057\n",
      "\n",
      "Epoch 00012: val_accuracy did not improve from 0.22518\n",
      "Epoch 13/30\n",
      "718/718 - 309s - loss: 3.4603 - accuracy: 0.2150 - val_loss: 2.7912 - val_accuracy: 0.1959\n",
      "\n",
      "Epoch 00013: val_accuracy did not improve from 0.22518\n",
      "Epoch 14/30\n",
      "718/718 - 308s - loss: 3.2791 - accuracy: 0.2138 - val_loss: 2.5843 - val_accuracy: 0.2015\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.22518\n",
      "Epoch 15/30\n",
      "718/718 - 308s - loss: 3.1246 - accuracy: 0.2118 - val_loss: 2.4044 - val_accuracy: 0.2081\n",
      "\n",
      "Epoch 00015: val_accuracy did not improve from 0.22518\n",
      "Epoch 16/30\n",
      "718/718 - 310s - loss: 2.9866 - accuracy: 0.2139 - val_loss: 2.4062 - val_accuracy: 0.2020\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.22518\n",
      "Epoch 17/30\n",
      "718/718 - 309s - loss: 2.9497 - accuracy: 0.2142 - val_loss: 2.3731 - val_accuracy: 0.2032\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.22518\n",
      "Epoch 18/30\n",
      "718/718 - 310s - loss: 2.8400 - accuracy: 0.2122 - val_loss: 2.3877 - val_accuracy: 0.1930\n",
      "\n",
      "Epoch 00018: val_accuracy did not improve from 0.22518\n",
      "Epoch 19/30\n",
      "718/718 - 311s - loss: 2.8032 - accuracy: 0.2086 - val_loss: 2.3152 - val_accuracy: 0.1919\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.22518\n",
      "Epoch 20/30\n",
      "718/718 - 309s - loss: 2.7449 - accuracy: 0.2072 - val_loss: 2.2738 - val_accuracy: 0.1964\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.22518\n",
      "Epoch 21/30\n",
      "718/718 - 308s - loss: 2.7018 - accuracy: 0.2134 - val_loss: 2.3344 - val_accuracy: 0.1884\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.22518\n",
      "Epoch 22/30\n",
      "718/718 - 310s - loss: 2.6644 - accuracy: 0.2119 - val_loss: 2.2819 - val_accuracy: 0.1961\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.22518\n",
      "Epoch 23/30\n",
      "718/718 - 308s - loss: 2.6577 - accuracy: 0.2106 - val_loss: 2.1861 - val_accuracy: 0.1992\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.22518\n",
      "Epoch 24/30\n",
      "718/718 - 308s - loss: 2.6437 - accuracy: 0.2103 - val_loss: 2.1440 - val_accuracy: 0.2029\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.22518\n",
      "Epoch 25/30\n",
      "718/718 - 309s - loss: 2.6202 - accuracy: 0.2113 - val_loss: 2.1501 - val_accuracy: 0.1970\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.22518\n",
      "Epoch 26/30\n",
      "718/718 - 308s - loss: 2.6130 - accuracy: 0.2095 - val_loss: 2.1865 - val_accuracy: 0.1898\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.22518\n",
      "Epoch 27/30\n",
      "718/718 - 307s - loss: 2.5951 - accuracy: 0.2093 - val_loss: 2.3351 - val_accuracy: 0.1926\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.22518\n",
      "Epoch 28/30\n",
      "718/718 - 309s - loss: 2.5791 - accuracy: 0.2107 - val_loss: 2.3714 - val_accuracy: 0.1890\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.22518\n",
      "Epoch 29/30\n",
      "718/718 - 309s - loss: 2.5638 - accuracy: 0.2115 - val_loss: 2.1115 - val_accuracy: 0.1937\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.22518\n",
      "Epoch 30/30\n",
      "718/718 - 310s - loss: 2.5723 - accuracy: 0.2069 - val_loss: 2.2456 - val_accuracy: 0.1907\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.22518\n"
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      "225/225 [==============================] - 78s 347ms/step - loss: 6.6018 - accuracy: 0.2239\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "19\n",
      "Epoch 1/30\n",
      "718/718 - 311s - loss: 28.0338 - accuracy: 0.1663 - val_loss: 16.4251 - val_accuracy: 0.1684\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.16841, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_3.h5\n",
      "Epoch 2/30\n",
      "718/718 - 311s - loss: 19.9272 - accuracy: 0.1856 - val_loss: 12.5461 - val_accuracy: 0.1799\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.16841 to 0.17990, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_3.h5\n",
      "Epoch 3/30\n",
      "718/718 - 313s - loss: 14.8268 - accuracy: 0.2005 - val_loss: 9.9797 - val_accuracy: 0.1881\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.17990 to 0.18809, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_3.h5\n",
      "Epoch 4/30\n",
      "718/718 - 310s - loss: 11.4595 - accuracy: 0.2090 - val_loss: 7.9412 - val_accuracy: 0.2010\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.18809 to 0.20098, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_3.h5\n",
      "Epoch 5/30\n",
      "718/718 - 315s - loss: 9.2891 - accuracy: 0.2201 - val_loss: 6.5064 - val_accuracy: 0.2072\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.20098 to 0.20724, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_3.h5\n",
      "Epoch 6/30\n",
      "718/718 - 310s - loss: 7.6114 - accuracy: 0.2218 - val_loss: 5.3822 - val_accuracy: 0.2198\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.20724 to 0.21978, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_3.h5\n",
      "Epoch 7/30\n",
      "718/718 - 311s - loss: 6.3789 - accuracy: 0.2237 - val_loss: 4.7087 - val_accuracy: 0.2106\n",
      "\n",
      "Epoch 00007: val_accuracy did not improve from 0.21978\n",
      "Epoch 8/30\n",
      "718/718 - 313s - loss: 5.5345 - accuracy: 0.2191 - val_loss: 4.1982 - val_accuracy: 0.2071\n",
      "\n",
      "Epoch 00008: val_accuracy did not improve from 0.21978\n",
      "Epoch 9/30\n",
      "718/718 - 312s - loss: 4.8454 - accuracy: 0.2199 - val_loss: 3.6577 - val_accuracy: 0.2142\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.21978\n",
      "Epoch 10/30\n",
      "718/718 - 313s - loss: 4.3161 - accuracy: 0.2204 - val_loss: 3.5534 - val_accuracy: 0.1991\n",
      "\n",
      "Epoch 00010: val_accuracy did not improve from 0.21978\n",
      "Epoch 11/30\n",
      "718/718 - 311s - loss: 3.9364 - accuracy: 0.2235 - val_loss: 3.1201 - val_accuracy: 0.2059\n",
      "\n",
      "Epoch 00011: val_accuracy did not improve from 0.21978\n",
      "Epoch 12/30\n",
      "718/718 - 310s - loss: 3.6443 - accuracy: 0.2145 - val_loss: 2.8213 - val_accuracy: 0.2107\n",
      "\n",
      "Epoch 00012: val_accuracy did not improve from 0.21978\n",
      "Epoch 13/30\n",
      "718/718 - 309s - loss: 3.4460 - accuracy: 0.2125 - val_loss: 2.9126 - val_accuracy: 0.1947\n",
      "\n",
      "Epoch 00013: val_accuracy did not improve from 0.21978\n",
      "Epoch 14/30\n",
      "718/718 - 310s - loss: 3.2461 - accuracy: 0.2108 - val_loss: 2.5687 - val_accuracy: 0.2060\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.21978\n",
      "Epoch 15/30\n",
      "718/718 - 309s - loss: 3.1103 - accuracy: 0.2081 - val_loss: 2.4736 - val_accuracy: 0.2060\n",
      "\n",
      "Epoch 00015: val_accuracy did not improve from 0.21978\n",
      "Epoch 16/30\n",
      "718/718 - 310s - loss: 2.9981 - accuracy: 0.2116 - val_loss: 2.4651 - val_accuracy: 0.2057\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.21978\n",
      "Epoch 17/30\n",
      "718/718 - 310s - loss: 2.8718 - accuracy: 0.2113 - val_loss: 2.4621 - val_accuracy: 0.1938\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.21978\n",
      "Epoch 18/30\n",
      "718/718 - 309s - loss: 2.8174 - accuracy: 0.2130 - val_loss: 2.2387 - val_accuracy: 0.2088\n",
      "\n",
      "Epoch 00018: val_accuracy did not improve from 0.21978\n",
      "Epoch 19/30\n",
      "718/718 - 309s - loss: 2.7758 - accuracy: 0.2132 - val_loss: 2.2495 - val_accuracy: 0.2036\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.21978\n",
      "Epoch 20/30\n",
      "718/718 - 308s - loss: 2.7203 - accuracy: 0.2105 - val_loss: 2.3609 - val_accuracy: 0.1947\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.21978\n",
      "Epoch 21/30\n",
      "718/718 - 307s - loss: 2.6788 - accuracy: 0.2090 - val_loss: 2.4382 - val_accuracy: 0.1926\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.21978\n",
      "Epoch 22/30\n",
      "718/718 - 309s - loss: 2.6626 - accuracy: 0.2120 - val_loss: 2.2765 - val_accuracy: 0.1999\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.21978\n",
      "Epoch 23/30\n",
      "718/718 - 307s - loss: 2.6504 - accuracy: 0.2119 - val_loss: 2.2149 - val_accuracy: 0.2083\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.21978\n",
      "Epoch 24/30\n",
      "718/718 - 308s - loss: 2.6132 - accuracy: 0.2111 - val_loss: 2.2580 - val_accuracy: 0.1952\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.21978\n",
      "Epoch 25/30\n",
      "718/718 - 310s - loss: 2.6047 - accuracy: 0.2126 - val_loss: 2.1629 - val_accuracy: 0.2017\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.21978\n",
      "Epoch 26/30\n",
      "718/718 - 311s - loss: 2.6030 - accuracy: 0.2110 - val_loss: 2.1056 - val_accuracy: 0.2069\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.21978\n",
      "Epoch 27/30\n",
      "718/718 - 311s - loss: 2.5849 - accuracy: 0.2112 - val_loss: 2.2724 - val_accuracy: 0.1992\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.21978\n",
      "Epoch 28/30\n",
      "718/718 - 313s - loss: 2.5778 - accuracy: 0.2094 - val_loss: 2.2110 - val_accuracy: 0.1999\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.21978\n",
      "Epoch 29/30\n",
      "718/718 - 312s - loss: 2.5686 - accuracy: 0.2085 - val_loss: 2.1435 - val_accuracy: 0.2076\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.21978\n",
      "Epoch 30/30\n",
      "718/718 - 312s - loss: 2.5710 - accuracy: 0.2087 - val_loss: 2.1186 - val_accuracy: 0.2064\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.21978\n"
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      "225/225 [==============================] - 79s 353ms/step - loss: 5.5462 - accuracy: 0.2186\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "19\n",
      "Epoch 1/30\n",
      "718/718 - 314s - loss: 34.4255 - accuracy: 0.1503 - val_loss: 18.8265 - val_accuracy: 0.1693\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.16928, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_4.h5\n",
      "Epoch 2/30\n",
      "718/718 - 313s - loss: 20.8758 - accuracy: 0.1773 - val_loss: 12.8494 - val_accuracy: 0.1809\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.16928 to 0.18095, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_4.h5\n",
      "Epoch 3/30\n",
      "718/718 - 312s - loss: 15.1414 - accuracy: 0.1968 - val_loss: 9.6978 - val_accuracy: 0.2001\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.18095 to 0.20010, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_4.h5\n",
      "Epoch 4/30\n",
      "718/718 - 312s - loss: 12.0885 - accuracy: 0.2068 - val_loss: 8.0661 - val_accuracy: 0.2095\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.20010 to 0.20951, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_4.h5\n",
      "Epoch 5/30\n",
      "718/718 - 315s - loss: 9.5227 - accuracy: 0.2172 - val_loss: 6.4813 - val_accuracy: 0.2201\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.20951 to 0.22013, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_4.h5\n",
      "Epoch 6/30\n",
      "718/718 - 315s - loss: 7.7680 - accuracy: 0.2247 - val_loss: 5.7260 - val_accuracy: 0.2107\n",
      "\n",
      "Epoch 00006: val_accuracy did not improve from 0.22013\n",
      "Epoch 7/30\n",
      "718/718 - 314s - loss: 6.4893 - accuracy: 0.2252 - val_loss: 4.7710 - val_accuracy: 0.2113\n",
      "\n",
      "Epoch 00007: val_accuracy did not improve from 0.22013\n",
      "Epoch 8/30\n",
      "718/718 - 314s - loss: 5.6125 - accuracy: 0.2242 - val_loss: 4.3907 - val_accuracy: 0.2022\n",
      "\n",
      "Epoch 00008: val_accuracy did not improve from 0.22013\n",
      "Epoch 9/30\n",
      "718/718 - 314s - loss: 4.8386 - accuracy: 0.2222 - val_loss: 3.7169 - val_accuracy: 0.2059\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.22013\n",
      "Epoch 10/30\n",
      "718/718 - 314s - loss: 4.3748 - accuracy: 0.2207 - val_loss: 3.4592 - val_accuracy: 0.2024\n",
      "\n",
      "Epoch 00010: val_accuracy did not improve from 0.22013\n",
      "Epoch 11/30\n",
      "718/718 - 315s - loss: 3.9704 - accuracy: 0.2173 - val_loss: 3.0990 - val_accuracy: 0.2146\n",
      "\n",
      "Epoch 00011: val_accuracy did not improve from 0.22013\n",
      "Epoch 12/30\n",
      "718/718 - 314s - loss: 3.6890 - accuracy: 0.2147 - val_loss: 2.9554 - val_accuracy: 0.2071\n",
      "\n",
      "Epoch 00012: val_accuracy did not improve from 0.22013\n",
      "Epoch 13/30\n",
      "718/718 - 312s - loss: 3.4388 - accuracy: 0.2145 - val_loss: 2.7712 - val_accuracy: 0.1954\n",
      "\n",
      "Epoch 00013: val_accuracy did not improve from 0.22013\n",
      "Epoch 14/30\n",
      "718/718 - 314s - loss: 3.2440 - accuracy: 0.2100 - val_loss: 2.7329 - val_accuracy: 0.1964\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.22013\n",
      "Epoch 15/30\n",
      "718/718 - 313s - loss: 3.0853 - accuracy: 0.2120 - val_loss: 2.4658 - val_accuracy: 0.2025\n",
      "\n",
      "Epoch 00015: val_accuracy did not improve from 0.22013\n",
      "Epoch 16/30\n",
      "718/718 - 314s - loss: 2.9980 - accuracy: 0.2114 - val_loss: 2.4058 - val_accuracy: 0.2036\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.22013\n",
      "Epoch 17/30\n",
      "718/718 - 315s - loss: 2.8988 - accuracy: 0.2144 - val_loss: 2.4310 - val_accuracy: 0.2006\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.22013\n",
      "Epoch 18/30\n",
      "718/718 - 313s - loss: 2.8405 - accuracy: 0.2131 - val_loss: 2.3696 - val_accuracy: 0.1916\n",
      "\n",
      "Epoch 00018: val_accuracy did not improve from 0.22013\n",
      "Epoch 19/30\n",
      "718/718 - 313s - loss: 2.7823 - accuracy: 0.2114 - val_loss: 2.4675 - val_accuracy: 0.1898\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.22013\n",
      "Epoch 20/30\n",
      "718/718 - 313s - loss: 2.7463 - accuracy: 0.2097 - val_loss: 2.2930 - val_accuracy: 0.2018\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.22013\n",
      "Epoch 21/30\n",
      "718/718 - 312s - loss: 2.7049 - accuracy: 0.2139 - val_loss: 2.3703 - val_accuracy: 0.1926\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.22013\n",
      "Epoch 22/30\n",
      "718/718 - 314s - loss: 2.6745 - accuracy: 0.2110 - val_loss: 2.2553 - val_accuracy: 0.1930\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.22013\n",
      "Epoch 23/30\n",
      "718/718 - 313s - loss: 2.6509 - accuracy: 0.2102 - val_loss: 2.3232 - val_accuracy: 0.1917\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.22013\n",
      "Epoch 24/30\n",
      "718/718 - 313s - loss: 2.6268 - accuracy: 0.2099 - val_loss: 2.1004 - val_accuracy: 0.2067\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.22013\n",
      "Epoch 25/30\n",
      "718/718 - 314s - loss: 2.6085 - accuracy: 0.2113 - val_loss: 2.0980 - val_accuracy: 0.2034\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.22013\n",
      "Epoch 26/30\n",
      "718/718 - 313s - loss: 2.6186 - accuracy: 0.2087 - val_loss: 2.2369 - val_accuracy: 0.1890\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.22013\n",
      "Epoch 27/30\n",
      "718/718 - 313s - loss: 2.5874 - accuracy: 0.2074 - val_loss: 2.1456 - val_accuracy: 0.2032\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.22013\n",
      "Epoch 28/30\n",
      "718/718 - 314s - loss: 2.5866 - accuracy: 0.2098 - val_loss: 2.2525 - val_accuracy: 0.1998\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.22013\n",
      "Epoch 29/30\n",
      "718/718 - 312s - loss: 2.5685 - accuracy: 0.2130 - val_loss: 2.2301 - val_accuracy: 0.1909\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.22013\n",
      "Epoch 30/30\n",
      "718/718 - 313s - loss: 2.5691 - accuracy: 0.2090 - val_loss: 2.2276 - val_accuracy: 0.1945\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.22013\n"
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      "225/225 [==============================] - 80s 353ms/step - loss: 6.5621 - accuracy: 0.2226\n",
      "Found 22968 validated image filenames belonging to 7 classes.\n",
      "Found 5741 validated image filenames belonging to 7 classes.\n",
      "19\n",
      "Epoch 1/30\n",
      "718/718 - 314s - loss: 35.7245 - accuracy: 0.1590 - val_loss: 18.4770 - val_accuracy: 0.1751\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.17506, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_5.h5\n",
      "Epoch 2/30\n",
      "718/718 - 315s - loss: 22.0709 - accuracy: 0.1789 - val_loss: 13.3037 - val_accuracy: 0.1895\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.17506 to 0.18951, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_5.h5\n",
      "Epoch 3/30\n",
      "718/718 - 310s - loss: 16.3888 - accuracy: 0.1894 - val_loss: 10.4729 - val_accuracy: 0.2015\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.18951 to 0.20153, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_5.h5\n",
      "Epoch 4/30\n",
      "718/718 - 312s - loss: 12.7629 - accuracy: 0.2048 - val_loss: 8.4620 - val_accuracy: 0.2141\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.20153 to 0.21407, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_5.h5\n",
      "Epoch 5/30\n",
      "718/718 - 309s - loss: 10.0530 - accuracy: 0.2147 - val_loss: 6.7769 - val_accuracy: 0.2165\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.21407 to 0.21651, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_5.h5\n",
      "Epoch 6/30\n",
      "718/718 - 309s - loss: 8.2285 - accuracy: 0.2207 - val_loss: 5.6201 - val_accuracy: 0.2294\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.21651 to 0.22940, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_vgg_16_30epochs/transfer_learning_model_vgg_16_model_5.h5\n",
      "Epoch 7/30\n",
      "718/718 - 310s - loss: 6.8311 - accuracy: 0.2213 - val_loss: 4.9074 - val_accuracy: 0.2090\n",
      "\n",
      "Epoch 00007: val_accuracy did not improve from 0.22940\n",
      "Epoch 8/30\n",
      "718/718 - 313s - loss: 5.8067 - accuracy: 0.2172 - val_loss: 4.2087 - val_accuracy: 0.2120\n",
      "\n",
      "Epoch 00008: val_accuracy did not improve from 0.22940\n",
      "Epoch 9/30\n",
      "718/718 - 315s - loss: 4.9903 - accuracy: 0.2158 - val_loss: 3.7001 - val_accuracy: 0.2115\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.22940\n",
      "Epoch 10/30\n",
      "718/718 - 314s - loss: 4.4254 - accuracy: 0.2168 - val_loss: 3.3866 - val_accuracy: 0.2066\n",
      "\n",
      "Epoch 00010: val_accuracy did not improve from 0.22940\n",
      "Epoch 11/30\n",
      "718/718 - 314s - loss: 3.9954 - accuracy: 0.2136 - val_loss: 3.0664 - val_accuracy: 0.2082\n",
      "\n",
      "Epoch 00011: val_accuracy did not improve from 0.22940\n",
      "Epoch 12/30\n",
      "718/718 - 313s - loss: 3.7564 - accuracy: 0.2133 - val_loss: 2.9806 - val_accuracy: 0.2001\n",
      "\n",
      "Epoch 00012: val_accuracy did not improve from 0.22940\n",
      "Epoch 13/30\n",
      "718/718 - 313s - loss: 3.4468 - accuracy: 0.2150 - val_loss: 2.8655 - val_accuracy: 0.2015\n",
      "\n",
      "Epoch 00013: val_accuracy did not improve from 0.22940\n",
      "Epoch 14/30\n",
      "718/718 - 314s - loss: 3.2617 - accuracy: 0.2096 - val_loss: 2.6766 - val_accuracy: 0.2026\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.22940\n",
      "Epoch 15/30\n",
      "718/718 - 313s - loss: 3.1274 - accuracy: 0.2090 - val_loss: 2.7459 - val_accuracy: 0.1980\n",
      "\n",
      "Epoch 00015: val_accuracy did not improve from 0.22940\n",
      "Epoch 16/30\n",
      "718/718 - 313s - loss: 3.0093 - accuracy: 0.2099 - val_loss: 2.4629 - val_accuracy: 0.2022\n"
     ]
    }
   ],
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    "model_test(5, 30, transfer_learning_model_vgg_16, (48,48,), 'rgb')"
   ]
  }
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