Upload Kabil_(EfficientNetB5_model).ipynb

Kabil_(EfficientNetB5_model).ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# EfficientNet B5\n",
+        "## Let's Begin...."
+      ],
+      "metadata": {
+        "id": "DGOlpli75Z_c"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "OylMWw9Sh3b3"
+      },
+      "outputs": [],
+      "source": [
+        "# Import Neccessary Lib...\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from matplotlib import pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "\n",
+        "import os\n",
+        "import random\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import confusion_matrix, classification_report\n",
+        "import cv2\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+        "from tensorflow.keras.applications import VGG19\n",
+        "from tensorflow.keras.optimizers import Adam, Adamax\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout, BatchNormalization\n",
+        "from tensorflow.keras import regularizers\n",
+        "from tensorflow.keras.regularizers import l1, l2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Directory paths\n",
+        "train_dir = 'drive/MyDrive/LungCancer-IITM/Data/train'\n",
+        "test_dir = 'drive/MyDrive/LungCancer-IITM/Data/test'\n",
+        "valid_dir = 'drive/MyDrive/LungCancer-IITM/Data/valid'"
+      ],
+      "metadata": {
+        "id": "4DHOnXmTh8a_"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PTGmzmm_iEqc",
+        "outputId": "6ecc5f01-8a51-4ef8-e672-ef60d5668eab"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train DataFrame:\n",
+            "                                          Image_Path  \\\n",
+            "0  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "1  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "2  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "3  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "4  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "\n",
+            "                                        Label  \n",
+            "0  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n",
+            "1  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n",
+            "2  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n",
+            "3  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n",
+            "4  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Function to create a DataFrame from image files in a folder\n",
+        "def create_dataframe(folder_path):\n",
+        "    # Initialize an empty dictionary to store image paths and labels\n",
+        "    data = {'Image_Path': [], 'Label': []}\n",
+        "\n",
+        "    # List all subdirectories (labels) in the given folder\n",
+        "    labels = os.listdir(folder_path)\n",
+        "\n",
+        "    # Loop through each label\n",
+        "    for label in labels:\n",
+        "        # Construct the full path to the label folder\n",
+        "        label_path = os.path.join(folder_path, label)\n",
+        "\n",
+        "        # Check if the path is a directory\n",
+        "        if os.path.isdir(label_path):\n",
+        "            # List all image files in the label folder\n",
+        "            images = os.listdir(label_path)\n",
+        "\n",
+        "            # Loop through each image\n",
+        "            for image in images:\n",
+        "                # Construct the full path to the image\n",
+        "                image_path = os.path.join(label_path, image)\n",
+        "\n",
+        "                # Append image path and label to the dictionary\n",
+        "                data['Image_Path'].append(image_path)\n",
+        "                data['Label'].append(label)\n",
+        "\n",
+        "    # Create a DataFrame from the dictionary\n",
+        "    df = pd.DataFrame(data)\n",
+        "    return df\n",
+        "\n",
+        "# Provide the path to your 'data' folder\n",
+        "data_folder = 'drive/MyDrive/LungCancer-IITM/Data'\n",
+        "\n",
+        "# Create DataFrames for train, test, and valid using the create_dataframe function\n",
+        "train_df = create_dataframe(os.path.join(data_folder, 'train'))\n",
+        "test_df = create_dataframe(os.path.join(data_folder, 'test'))\n",
+        "valid_df = create_dataframe(os.path.join(data_folder, 'valid'))\n",
+        "\n",
+        "# Print the created DataFrames for inspection\n",
+        "print(\"Train DataFrame:\")\n",
+        "print(train_df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "67d41380-a800-446f-e017-98e22cb99872",
+        "id": "U-4wnr0O8dvF"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train DataFrame:\n",
+            "                                          Image_Path  \\\n",
+            "0  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "1  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "2  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "3  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "4  drive/MyDrive/LungCancer-IITM/Data/train/adeno...   \n",
+            "\n",
+            "                                        Label  \n",
+            "0  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n",
+            "1  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n",
+            "2  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n",
+            "3  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n",
+            "4  adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\\nTest DataFrame:\")\n",
+        "print(test_df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c6iuI9JXiLQd",
+        "outputId": "e360f402-86ec-4aba-d390-be7e5ded6110"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Test DataFrame:\n",
+            "                                          Image_Path                 Label\n",
+            "0  drive/MyDrive/LungCancer-IITM/Data/test/large....  large.cell.carcinoma\n",
+            "1  drive/MyDrive/LungCancer-IITM/Data/test/large....  large.cell.carcinoma\n",
+            "2  drive/MyDrive/LungCancer-IITM/Data/test/large....  large.cell.carcinoma\n",
+            "3  drive/MyDrive/LungCancer-IITM/Data/test/large....  large.cell.carcinoma\n",
+            "4  drive/MyDrive/LungCancer-IITM/Data/test/large....  large.cell.carcinoma\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\\nValid DataFrame:\")\n",
+        "print(valid_df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0TX-BeALiOEZ",
+        "outputId": "68839461-8585-426f-c4e7-dce922db48bb"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Valid DataFrame:\n",
+            "                                          Image_Path  \\\n",
+            "0  drive/MyDrive/LungCancer-IITM/Data/valid/large...   \n",
+            "1  drive/MyDrive/LungCancer-IITM/Data/valid/large...   \n",
+            "2  drive/MyDrive/LungCancer-IITM/Data/valid/large...   \n",
+            "3  drive/MyDrive/LungCancer-IITM/Data/valid/large...   \n",
+            "4  drive/MyDrive/LungCancer-IITM/Data/valid/large...   \n",
+            "\n",
+            "                                           Label  \n",
+            "0  large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa  \n",
+            "1  large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa  \n",
+            "2  large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa  \n",
+            "3  large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa  \n",
+            "4  large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the number of unique classes (labels) in the 'Label' column of the training DataFrame\n",
+        "num_classes = len(train_df['Label'].unique())\n",
+        "\n",
+        "# Print the number of classes in the dataset\n",
+        "print(f\"We have {num_classes} classes\")\n",
+        "\n",
+        "# Print the total number of images in the training DataFrame (total rows)\n",
+        "print(f\"We have {train_df.shape[0]} images\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dKwwZ0aXiS8Y",
+        "outputId": "17a1d131-9684-4e97-eb98-d836de207eb6"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "We have 4 classes\n",
+            "We have 613 images\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the number of unique classes (labels) in the 'Label' column of the test DataFrame\n",
+        "num_classes = len(test_df['Label'].unique())\n",
+        "\n",
+        "# Print the number of classes in the dataset\n",
+        "print(f\"We have {num_classes} classes\")\n",
+        "\n",
+        "# Print the total number of images in the test DataFrame (total rows)\n",
+        "print(f\"We have {test_df.shape[0]} images\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "C8DIiGIwijaq",
+        "outputId": "6e7a4904-4c58-4dcc-84d6-4bd02b00df6a"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "We have 4 classes\n",
+            "We have 315 images\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the number of unique classes (labels) in the 'Label' column of the valid DataFrame\n",
+        "num_classes = len(valid_df['Label'].unique())\n",
+        "\n",
+        "# Print the number of classes in the dataset\n",
+        "print(f\"We have {num_classes} classes\")\n",
+        "\n",
+        "# Print the total number of images in the valid DataFrame (total rows)\n",
+        "print(f\"We have {valid_df.shape[0]} images\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OhbkmbZqinKY",
+        "outputId": "4ca3a84a-3ef7-41cd-dca5-024b6afe66fd"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "We have 4 classes\n",
+            "We have 72 images\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the size of the input images\n",
+        "img_size = (224, 224)\n",
+        "\n",
+        "# Specify the number of color channels in the images (3 for RGB)\n",
+        "channels = 3\n",
+        "\n",
+        "# Specify the color representation ('rgb' for red, green, blue)\n",
+        "color = 'rgb'\n",
+        "\n",
+        "# Define the shape of the input images based on size, channels, and color representation\n",
+        "img_shape = (img_size[0], img_size[1], channels)\n",
+        "\n",
+        "# Specify the batch size for training\n",
+        "batch_size = 32\n",
+        "\n",
+        "# Get the length of the test DataFrame\n",
+        "ts_length = len(test_df)\n",
+        "\n",
+        "# Determine an optimal test batch size that evenly divides the length of the test DataFrame\n",
+        "test_batch_size = max(sorted([ts_length // n for n in range(1, ts_length + 1) if ts_length % n == 0 and ts_length / n <= 80]))\n",
+        "\n",
+        "# Calculate the number of steps needed to cover the entire test dataset\n",
+        "test_steps = ts_length // test_batch_size\n",
+        "\n",
+        "# Define a function 'scalar' that takes an image as input (placeholder, no implementation provided)\n",
+        "def scalar(img):\n",
+        "    return img\n"
+      ],
+      "metadata": {
+        "id": "7H00Xv0riwXL"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tr_gen = ImageDataGenerator(preprocessing_function= scalar,\n",
+        "                            horizontal_flip= True)\n",
+        "\n",
+        "# Create an ImageDataGenerator for training with specified preprocessing and augmentation settings\n",
+        "tr_gen = ImageDataGenerator(preprocessing_function=scalar, horizontal_flip=True)\n",
+        "\n",
+        "# Create an ImageDataGenerator for testing with specified preprocessing settings\n",
+        "ts_gen = ImageDataGenerator(preprocessing_function=scalar)\n",
+        "\n",
+        "# Generate a flow from DataFrame for training data\n",
+        "train_gen = tr_gen.flow_from_dataframe(\n",
+        "    train_df,\n",
+        "    x_col='Image_Path',\n",
+        "    y_col='Label',\n",
+        "    target_size=img_size,\n",
+        "    class_mode='categorical',\n",
+        "    color_mode=color,\n",
+        "    shuffle=True,\n",
+        "    batch_size=batch_size\n",
+        ")\n",
+        "\n",
+        "# Generate a flow from DataFrame for validation data\n",
+        "valid_gen = ts_gen.flow_from_dataframe(\n",
+        "    valid_df,\n",
+        "    x_col='Image_Path',\n",
+        "    y_col='Label',\n",
+        "    target_size=img_size,\n",
+        "    class_mode='categorical',\n",
+        "    color_mode=color,\n",
+        "    shuffle=True,\n",
+        "    batch_size=batch_size\n",
+        ")\n",
+        "\n",
+        "# Generate a flow from DataFrame for test data\n",
+        "test_gen = ts_gen.flow_from_dataframe(\n",
+        "    test_df,\n",
+        "    x_col='Image_Path',\n",
+        "    y_col='Label',\n",
+        "    target_size=img_size,\n",
+        "    class_mode='categorical',\n",
+        "    color_mode=color,\n",
+        "    shuffle=False,\n",
+        "    batch_size=test_batch_size\n",
+        ")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QqSOiLrxjjOD",
+        "outputId": "e562f193-cc5c-439f-c7b9-18bad8e76fe2"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 613 validated image filenames belonging to 4 classes.\n",
+            "Found 72 validated image filenames belonging to 4 classes.\n",
+            "Found 315 validated image filenames belonging to 4 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Using the EfficientNetB5 pre-trained model as a base model (without the fully connected layers)\n",
+        "base_model = tf.keras.applications.efficientnet.EfficientNetB5(\n",
+        "    include_top=False,     # Exclude the fully connected layers\n",
+        "    weights=\"imagenet\",    # Load pre-trained ImageNet weights\n",
+        "    input_shape=img_shape,  # Specify the input shape for the model\n",
+        "    pooling='max'           # Use global max pooling as the final pooling layer\n",
+        ")\n",
+        "\n",
+        "# Constructing the complete model using Sequential API\n",
+        "model = Sequential([\n",
+        "    base_model,  # EfficientNetB5 as the base model\n",
+        "    BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001),  # Batch normalization layer\n",
+        "    Dense(256,\n",
+        "          kernel_regularizer=regularizers.l2(l=0.016),\n",
+        "          activity_regularizer=regularizers.l1(0.006),\n",
+        "          bias_regularizer=regularizers.l1(0.006),\n",
+        "          activation='relu'),  # Dense layer with regularization and ReLU activation\n",
+        "    Dropout(rate=0.45, seed=123),  # Dropout layer for regularization\n",
+        "    Dense(4, activation='softmax')  # Output layer with softmax activation for multi-class classification\n",
+        "])\n",
+        "\n",
+        "# Compile the model with specified optimizer, loss function, and evaluation metric\n",
+        "model.compile(\n",
+        "    optimizer=Adamax(learning_rate=0.001),\n",
+        "    loss='categorical_crossentropy',\n",
+        "    metrics=['accuracy']\n",
+        ")\n",
+        "\n",
+        "# Display a summary of the model architecture\n",
+        "model.summary()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "h2iZBYVFkm0n",
+        "outputId": "76e92170-c977-4a26-d134-b261838ef813"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " efficientnetb5 (Functional  (None, 2048)              28513527  \n",
+            " )                                                               \n",
+            "                                                                 \n",
+            " batch_normalization (Batch  (None, 2048)              8192      \n",
+            " Normalization)                                                  \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 256)               524544    \n",
+            "                                                                 \n",
+            " dropout (Dropout)           (None, 256)               0         \n",
+            "                                                                 \n",
+            " dense_1 (Dense)             (None, 4)                 1028      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 29047291 (110.81 MB)\n",
+            "Trainable params: 28870452 (110.13 MB)\n",
+            "Non-trainable params: 176839 (690.78 KB)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Retrieve the configuration of the optimizer used in the EfficientNetB5 base model\n",
+        "model.optimizer.get_config()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FhylF03qk8dp",
+        "outputId": "b9b8515d-048c-4a1f-829a-78906413760b"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'name': 'Adamax',\n",
+              " 'weight_decay': None,\n",
+              " 'clipnorm': None,\n",
+              " 'global_clipnorm': None,\n",
+              " 'clipvalue': None,\n",
+              " 'use_ema': False,\n",
+              " 'ema_momentum': 0.99,\n",
+              " 'ema_overwrite_frequency': None,\n",
+              " 'jit_compile': True,\n",
+              " 'is_legacy_optimizer': False,\n",
+              " 'learning_rate': 0.001,\n",
+              " 'beta_1': 0.9,\n",
+              " 'beta_2': 0.999,\n",
+              " 'epsilon': 1e-07}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define early stopping to halt training if the validation loss doesn't improve for 'patience' consecutive epochs\n",
+        "early_stop = EarlyStopping(monitor='val_loss',\n",
+        "                           patience=5,\n",
+        "                           verbose=1)\n",
+        "# Define model checkpoint to save the best weights during training based on validation loss\n",
+        "checkpoint = ModelCheckpoint('model_weights_efficient_B5_2.h5',\n",
+        "                             monitor='val_loss',\n",
+        "                             save_best_only=True,\n",
+        "                             save_weights_only=True,\n",
+        "                             mode='min',\n",
+        "                             verbose=1)\n",
+        "\n",
+        "# Train the EfficientNetB5 base model on the training data with validation using the generator\n",
+        "# - x: Training generator\n",
+        "# - steps_per_epoch: Number of batches to process in each epoch\n",
+        "# - epochs: Number of training epochs\n",
+        "# - callbacks: List of callbacks to apply during training (early stopping and model checkpoint)\n",
+        "# - validation_data: Validation generator for evaluating the model's performance on a separate dataset\n",
+        "\n",
+        "history = model.fit(x= train_gen,\n",
+        "                    steps_per_epoch = 20,\n",
+        "                    epochs= 100,\n",
+        "                    callbacks=[early_stop, checkpoint],\n",
+        "                    validation_data = valid_gen)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ymbza2MYlB2j",
+        "outputId": "d8eea6ac-dc3e-4e0c-8525-cdfa875f115f"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 8.9467 - accuracy: 0.6525\n",
+            "Epoch 1: val_loss improved from inf to 13.82872, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 330s 12s/step - loss: 8.9467 - accuracy: 0.6525 - val_loss: 13.8287 - val_accuracy: 0.4861\n",
+            "Epoch 2/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 7.9310 - accuracy: 0.8222\n",
+            "Epoch 2: val_loss improved from 13.82872 to 9.65489, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 935ms/step - loss: 7.9310 - accuracy: 0.8222 - val_loss: 9.6549 - val_accuracy: 0.5000\n",
+            "Epoch 3/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 7.1907 - accuracy: 0.9086\n",
+            "Epoch 3: val_loss improved from 9.65489 to 8.90058, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 947ms/step - loss: 7.1907 - accuracy: 0.9086 - val_loss: 8.9006 - val_accuracy: 0.5833\n",
+            "Epoch 4/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 6.6951 - accuracy: 0.9478\n",
+            "Epoch 4: val_loss improved from 8.90058 to 7.97767, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 932ms/step - loss: 6.6951 - accuracy: 0.9478 - val_loss: 7.9777 - val_accuracy: 0.5833\n",
+            "Epoch 5/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 6.2736 - accuracy: 0.9755\n",
+            "Epoch 5: val_loss improved from 7.97767 to 7.08031, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 932ms/step - loss: 6.2736 - accuracy: 0.9755 - val_loss: 7.0803 - val_accuracy: 0.6528\n",
+            "Epoch 6/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 5.9248 - accuracy: 0.9641\n",
+            "Epoch 6: val_loss improved from 7.08031 to 6.62661, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 951ms/step - loss: 5.9248 - accuracy: 0.9641 - val_loss: 6.6266 - val_accuracy: 0.7500\n",
+            "Epoch 7/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 5.6432 - accuracy: 0.9739\n",
+            "Epoch 7: val_loss improved from 6.62661 to 6.26470, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 932ms/step - loss: 5.6432 - accuracy: 0.9739 - val_loss: 6.2647 - val_accuracy: 0.6667\n",
+            "Epoch 8/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 5.4284 - accuracy: 0.9739\n",
+            "Epoch 8: val_loss improved from 6.26470 to 5.88624, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 975ms/step - loss: 5.4284 - accuracy: 0.9739 - val_loss: 5.8862 - val_accuracy: 0.7361\n",
+            "Epoch 9/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 5.1599 - accuracy: 0.9821\n",
+            "Epoch 9: val_loss improved from 5.88624 to 5.53767, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 933ms/step - loss: 5.1599 - accuracy: 0.9821 - val_loss: 5.5377 - val_accuracy: 0.8472\n",
+            "Epoch 10/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 4.9567 - accuracy: 0.9788\n",
+            "Epoch 10: val_loss improved from 5.53767 to 5.29575, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 934ms/step - loss: 4.9567 - accuracy: 0.9788 - val_loss: 5.2957 - val_accuracy: 0.8750\n",
+            "Epoch 11/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 4.7625 - accuracy: 0.9804\n",
+            "Epoch 11: val_loss improved from 5.29575 to 5.10167, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 948ms/step - loss: 4.7625 - accuracy: 0.9804 - val_loss: 5.1017 - val_accuracy: 0.8750\n",
+            "Epoch 12/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 4.6141 - accuracy: 0.9788\n",
+            "Epoch 12: val_loss improved from 5.10167 to 4.96450, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 957ms/step - loss: 4.6141 - accuracy: 0.9788 - val_loss: 4.9645 - val_accuracy: 0.8750\n",
+            "Epoch 13/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 4.4517 - accuracy: 0.9902\n",
+            "Epoch 13: val_loss improved from 4.96450 to 4.89537, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 938ms/step - loss: 4.4517 - accuracy: 0.9902 - val_loss: 4.8954 - val_accuracy: 0.8750\n",
+            "Epoch 14/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 4.3521 - accuracy: 0.9788\n",
+            "Epoch 14: val_loss improved from 4.89537 to 4.61144, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 941ms/step - loss: 4.3521 - accuracy: 0.9788 - val_loss: 4.6114 - val_accuracy: 0.8611\n",
+            "Epoch 15/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 4.1907 - accuracy: 0.9837\n",
+            "Epoch 15: val_loss improved from 4.61144 to 4.47061, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 980ms/step - loss: 4.1907 - accuracy: 0.9837 - val_loss: 4.4706 - val_accuracy: 0.8611\n",
+            "Epoch 16/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 4.0591 - accuracy: 0.9821\n",
+            "Epoch 16: val_loss improved from 4.47061 to 4.35734, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 930ms/step - loss: 4.0591 - accuracy: 0.9821 - val_loss: 4.3573 - val_accuracy: 0.8750\n",
+            "Epoch 17/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.9479 - accuracy: 0.9837\n",
+            "Epoch 17: val_loss improved from 4.35734 to 4.19360, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 940ms/step - loss: 3.9479 - accuracy: 0.9837 - val_loss: 4.1936 - val_accuracy: 0.8750\n",
+            "Epoch 18/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.8014 - accuracy: 0.9951\n",
+            "Epoch 18: val_loss improved from 4.19360 to 4.07113, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 977ms/step - loss: 3.8014 - accuracy: 0.9951 - val_loss: 4.0711 - val_accuracy: 0.8750\n",
+            "Epoch 19/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.7042 - accuracy: 0.9918\n",
+            "Epoch 19: val_loss improved from 4.07113 to 4.02841, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 940ms/step - loss: 3.7042 - accuracy: 0.9918 - val_loss: 4.0284 - val_accuracy: 0.8472\n",
+            "Epoch 20/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.6051 - accuracy: 0.9918\n",
+            "Epoch 20: val_loss improved from 4.02841 to 3.87404, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 943ms/step - loss: 3.6051 - accuracy: 0.9918 - val_loss: 3.8740 - val_accuracy: 0.9028\n",
+            "Epoch 21/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.5299 - accuracy: 0.9902\n",
+            "Epoch 21: val_loss improved from 3.87404 to 3.76933, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 947ms/step - loss: 3.5299 - accuracy: 0.9902 - val_loss: 3.7693 - val_accuracy: 0.9028\n",
+            "Epoch 22/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.4325 - accuracy: 0.9902\n",
+            "Epoch 22: val_loss improved from 3.76933 to 3.64684, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 964ms/step - loss: 3.4325 - accuracy: 0.9902 - val_loss: 3.6468 - val_accuracy: 0.8889\n",
+            "Epoch 23/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.3194 - accuracy: 0.9967\n",
+            "Epoch 23: val_loss improved from 3.64684 to 3.55495, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 934ms/step - loss: 3.3194 - accuracy: 0.9967 - val_loss: 3.5549 - val_accuracy: 0.8889\n",
+            "Epoch 24/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.2151 - accuracy: 0.9935\n",
+            "Epoch 24: val_loss improved from 3.55495 to 3.47809, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 1s/step - loss: 3.2151 - accuracy: 0.9935 - val_loss: 3.4781 - val_accuracy: 0.8889\n",
+            "Epoch 25/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.1480 - accuracy: 0.9869\n",
+            "Epoch 25: val_loss improved from 3.47809 to 3.46385, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 937ms/step - loss: 3.1480 - accuracy: 0.9869 - val_loss: 3.4639 - val_accuracy: 0.8889\n",
+            "Epoch 26/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 3.0889 - accuracy: 0.9837\n",
+            "Epoch 26: val_loss improved from 3.46385 to 3.30259, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 935ms/step - loss: 3.0889 - accuracy: 0.9837 - val_loss: 3.3026 - val_accuracy: 0.8889\n",
+            "Epoch 27/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.9959 - accuracy: 0.9902\n",
+            "Epoch 27: val_loss improved from 3.30259 to 3.23432, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 977ms/step - loss: 2.9959 - accuracy: 0.9902 - val_loss: 3.2343 - val_accuracy: 0.9167\n",
+            "Epoch 28/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.8889 - accuracy: 0.9967\n",
+            "Epoch 28: val_loss improved from 3.23432 to 3.13419, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 952ms/step - loss: 2.8889 - accuracy: 0.9967 - val_loss: 3.1342 - val_accuracy: 0.9028\n",
+            "Epoch 29/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.8285 - accuracy: 0.9918\n",
+            "Epoch 29: val_loss improved from 3.13419 to 3.05611, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 969ms/step - loss: 2.8285 - accuracy: 0.9918 - val_loss: 3.0561 - val_accuracy: 0.9167\n",
+            "Epoch 30/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.7386 - accuracy: 0.9967\n",
+            "Epoch 30: val_loss improved from 3.05611 to 2.98006, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 930ms/step - loss: 2.7386 - accuracy: 0.9967 - val_loss: 2.9801 - val_accuracy: 0.9167\n",
+            "Epoch 31/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.6883 - accuracy: 0.9935\n",
+            "Epoch 31: val_loss improved from 2.98006 to 2.91081, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 942ms/step - loss: 2.6883 - accuracy: 0.9935 - val_loss: 2.9108 - val_accuracy: 0.9167\n",
+            "Epoch 32/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.6405 - accuracy: 0.9788\n",
+            "Epoch 32: val_loss did not improve from 2.91081\n",
+            "20/20 [==============================] - 18s 901ms/step - loss: 2.6405 - accuracy: 0.9788 - val_loss: 2.9625 - val_accuracy: 0.8611\n",
+            "Epoch 33/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.5627 - accuracy: 0.9886\n",
+            "Epoch 33: val_loss improved from 2.91081 to 2.88892, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 938ms/step - loss: 2.5627 - accuracy: 0.9886 - val_loss: 2.8889 - val_accuracy: 0.9028\n",
+            "Epoch 34/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.5646 - accuracy: 0.9804\n",
+            "Epoch 34: val_loss did not improve from 2.88892\n",
+            "20/20 [==============================] - 18s 901ms/step - loss: 2.5646 - accuracy: 0.9804 - val_loss: 2.9084 - val_accuracy: 0.8611\n",
+            "Epoch 35/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.4740 - accuracy: 0.9935\n",
+            "Epoch 35: val_loss improved from 2.88892 to 2.79603, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 955ms/step - loss: 2.4740 - accuracy: 0.9935 - val_loss: 2.7960 - val_accuracy: 0.9028\n",
+            "Epoch 36/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.4113 - accuracy: 0.9853\n",
+            "Epoch 36: val_loss improved from 2.79603 to 2.72169, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 965ms/step - loss: 2.4113 - accuracy: 0.9853 - val_loss: 2.7217 - val_accuracy: 0.8333\n",
+            "Epoch 37/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.3420 - accuracy: 0.9869\n",
+            "Epoch 37: val_loss improved from 2.72169 to 2.62496, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 932ms/step - loss: 2.3420 - accuracy: 0.9869 - val_loss: 2.6250 - val_accuracy: 0.8611\n",
+            "Epoch 38/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.2655 - accuracy: 0.9951\n",
+            "Epoch 38: val_loss improved from 2.62496 to 2.49132, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 980ms/step - loss: 2.2655 - accuracy: 0.9951 - val_loss: 2.4913 - val_accuracy: 0.9167\n",
+            "Epoch 39/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.2046 - accuracy: 0.9967\n",
+            "Epoch 39: val_loss improved from 2.49132 to 2.45171, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 935ms/step - loss: 2.2046 - accuracy: 0.9967 - val_loss: 2.4517 - val_accuracy: 0.9028\n",
+            "Epoch 40/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.1569 - accuracy: 0.9935\n",
+            "Epoch 40: val_loss improved from 2.45171 to 2.36931, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 934ms/step - loss: 2.1569 - accuracy: 0.9935 - val_loss: 2.3693 - val_accuracy: 0.9306\n",
+            "Epoch 41/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.0928 - accuracy: 0.9967\n",
+            "Epoch 41: val_loss improved from 2.36931 to 2.30855, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 979ms/step - loss: 2.0928 - accuracy: 0.9967 - val_loss: 2.3086 - val_accuracy: 0.9306\n",
+            "Epoch 42/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 2.0393 - accuracy: 0.9967\n",
+            "Epoch 42: val_loss improved from 2.30855 to 2.24363, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 937ms/step - loss: 2.0393 - accuracy: 0.9967 - val_loss: 2.2436 - val_accuracy: 0.9306\n",
+            "Epoch 43/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.9881 - accuracy: 0.9984\n",
+            "Epoch 43: val_loss improved from 2.24363 to 2.19355, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 979ms/step - loss: 1.9881 - accuracy: 0.9984 - val_loss: 2.1935 - val_accuracy: 0.9167\n",
+            "Epoch 44/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.9369 - accuracy: 1.0000\n",
+            "Epoch 44: val_loss improved from 2.19355 to 2.13765, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 963ms/step - loss: 1.9369 - accuracy: 1.0000 - val_loss: 2.1376 - val_accuracy: 0.9306\n",
+            "Epoch 45/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.8963 - accuracy: 0.9967\n",
+            "Epoch 45: val_loss improved from 2.13765 to 2.11182, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 934ms/step - loss: 1.8963 - accuracy: 0.9967 - val_loss: 2.1118 - val_accuracy: 0.9306\n",
+            "Epoch 46/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.8555 - accuracy: 0.9967\n",
+            "Epoch 46: val_loss improved from 2.11182 to 2.08817, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 939ms/step - loss: 1.8555 - accuracy: 0.9967 - val_loss: 2.0882 - val_accuracy: 0.9306\n",
+            "Epoch 47/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.8443 - accuracy: 0.9869\n",
+            "Epoch 47: val_loss improved from 2.08817 to 2.08034, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 978ms/step - loss: 1.8443 - accuracy: 0.9869 - val_loss: 2.0803 - val_accuracy: 0.9306\n",
+            "Epoch 48/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.7713 - accuracy: 0.9984\n",
+            "Epoch 48: val_loss improved from 2.08034 to 1.98731, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 936ms/step - loss: 1.7713 - accuracy: 0.9984 - val_loss: 1.9873 - val_accuracy: 0.9306\n",
+            "Epoch 49/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.7160 - accuracy: 0.9984\n",
+            "Epoch 49: val_loss improved from 1.98731 to 1.93409, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 935ms/step - loss: 1.7160 - accuracy: 0.9984 - val_loss: 1.9341 - val_accuracy: 0.9306\n",
+            "Epoch 50/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.6692 - accuracy: 0.9967\n",
+            "Epoch 50: val_loss improved from 1.93409 to 1.88645, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 953ms/step - loss: 1.6692 - accuracy: 0.9967 - val_loss: 1.8864 - val_accuracy: 0.9306\n",
+            "Epoch 51/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.6335 - accuracy: 0.9967\n",
+            "Epoch 51: val_loss improved from 1.88645 to 1.87095, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 937ms/step - loss: 1.6335 - accuracy: 0.9967 - val_loss: 1.8709 - val_accuracy: 0.9306\n",
+            "Epoch 52/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.6030 - accuracy: 0.9935\n",
+            "Epoch 52: val_loss improved from 1.87095 to 1.81230, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 934ms/step - loss: 1.6030 - accuracy: 0.9935 - val_loss: 1.8123 - val_accuracy: 0.9306\n",
+            "Epoch 53/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.5754 - accuracy: 0.9951\n",
+            "Epoch 53: val_loss improved from 1.81230 to 1.80875, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 943ms/step - loss: 1.5754 - accuracy: 0.9951 - val_loss: 1.8088 - val_accuracy: 0.9306\n",
+            "Epoch 54/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.5490 - accuracy: 0.9902\n",
+            "Epoch 54: val_loss improved from 1.80875 to 1.77187, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 973ms/step - loss: 1.5490 - accuracy: 0.9902 - val_loss: 1.7719 - val_accuracy: 0.9167\n",
+            "Epoch 55/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.4940 - accuracy: 0.9967\n",
+            "Epoch 55: val_loss improved from 1.77187 to 1.72648, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 943ms/step - loss: 1.4940 - accuracy: 0.9967 - val_loss: 1.7265 - val_accuracy: 0.9167\n",
+            "Epoch 56/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.4628 - accuracy: 0.9951\n",
+            "Epoch 56: val_loss improved from 1.72648 to 1.66311, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 932ms/step - loss: 1.4628 - accuracy: 0.9951 - val_loss: 1.6631 - val_accuracy: 0.9306\n",
+            "Epoch 57/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.4283 - accuracy: 0.9951\n",
+            "Epoch 57: val_loss improved from 1.66311 to 1.58719, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 956ms/step - loss: 1.4283 - accuracy: 0.9951 - val_loss: 1.5872 - val_accuracy: 0.9306\n",
+            "Epoch 58/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.4072 - accuracy: 0.9967\n",
+            "Epoch 58: val_loss improved from 1.58719 to 1.56380, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 932ms/step - loss: 1.4072 - accuracy: 0.9967 - val_loss: 1.5638 - val_accuracy: 0.9306\n",
+            "Epoch 59/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.3953 - accuracy: 0.9902\n",
+            "Epoch 59: val_loss did not improve from 1.56380\n",
+            "20/20 [==============================] - 18s 935ms/step - loss: 1.3953 - accuracy: 0.9902 - val_loss: 1.5837 - val_accuracy: 0.9306\n",
+            "Epoch 60/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.3637 - accuracy: 0.9902\n",
+            "Epoch 60: val_loss improved from 1.56380 to 1.55265, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 941ms/step - loss: 1.3637 - accuracy: 0.9902 - val_loss: 1.5526 - val_accuracy: 0.9444\n",
+            "Epoch 61/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.3116 - accuracy: 0.9918\n",
+            "Epoch 61: val_loss improved from 1.55265 to 1.48927, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 955ms/step - loss: 1.3116 - accuracy: 0.9918 - val_loss: 1.4893 - val_accuracy: 0.9444\n",
+            "Epoch 62/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.2852 - accuracy: 0.9951\n",
+            "Epoch 62: val_loss improved from 1.48927 to 1.46638, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 947ms/step - loss: 1.2852 - accuracy: 0.9951 - val_loss: 1.4664 - val_accuracy: 0.9306\n",
+            "Epoch 63/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.2581 - accuracy: 0.9935\n",
+            "Epoch 63: val_loss improved from 1.46638 to 1.45661, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 934ms/step - loss: 1.2581 - accuracy: 0.9935 - val_loss: 1.4566 - val_accuracy: 0.9306\n",
+            "Epoch 64/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.2234 - accuracy: 0.9967\n",
+            "Epoch 64: val_loss improved from 1.45661 to 1.42951, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 932ms/step - loss: 1.2234 - accuracy: 0.9967 - val_loss: 1.4295 - val_accuracy: 0.9306\n",
+            "Epoch 65/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.1978 - accuracy: 0.9967\n",
+            "Epoch 65: val_loss improved from 1.42951 to 1.40270, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 972ms/step - loss: 1.1978 - accuracy: 0.9967 - val_loss: 1.4027 - val_accuracy: 0.9306\n",
+            "Epoch 66/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.1966 - accuracy: 0.9935\n",
+            "Epoch 66: val_loss did not improve from 1.40270\n",
+            "20/20 [==============================] - 18s 896ms/step - loss: 1.1966 - accuracy: 0.9935 - val_loss: 1.4201 - val_accuracy: 0.9167\n",
+            "Epoch 67/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.1682 - accuracy: 0.9984\n",
+            "Epoch 67: val_loss did not improve from 1.40270\n",
+            "20/20 [==============================] - 19s 931ms/step - loss: 1.1682 - accuracy: 0.9984 - val_loss: 1.4158 - val_accuracy: 0.9306\n",
+            "Epoch 68/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.1414 - accuracy: 0.9918\n",
+            "Epoch 68: val_loss improved from 1.40270 to 1.36896, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 939ms/step - loss: 1.1414 - accuracy: 0.9918 - val_loss: 1.3690 - val_accuracy: 0.9306\n",
+            "Epoch 69/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.1103 - accuracy: 0.9984\n",
+            "Epoch 69: val_loss improved from 1.36896 to 1.32771, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 20s 977ms/step - loss: 1.1103 - accuracy: 0.9984 - val_loss: 1.3277 - val_accuracy: 0.9306\n",
+            "Epoch 70/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.1277 - accuracy: 0.9886\n",
+            "Epoch 70: val_loss did not improve from 1.32771\n",
+            "20/20 [==============================] - 18s 897ms/step - loss: 1.1277 - accuracy: 0.9886 - val_loss: 1.3546 - val_accuracy: 0.9167\n",
+            "Epoch 71/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.0964 - accuracy: 0.9902\n",
+            "Epoch 71: val_loss improved from 1.32771 to 1.27567, saving model to model_weights_efficient_B5_2.h5\n",
+            "20/20 [==============================] - 19s 938ms/step - loss: 1.0964 - accuracy: 0.9902 - val_loss: 1.2757 - val_accuracy: 0.9306\n",
+            "Epoch 72/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.0688 - accuracy: 0.9886\n",
+            "Epoch 72: val_loss did not improve from 1.27567\n",
+            "20/20 [==============================] - 18s 907ms/step - loss: 1.0688 - accuracy: 0.9886 - val_loss: 1.3252 - val_accuracy: 0.9028\n",
+            "Epoch 73/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.0428 - accuracy: 0.9984\n",
+            "Epoch 73: val_loss did not improve from 1.27567\n",
+            "20/20 [==============================] - 18s 901ms/step - loss: 1.0428 - accuracy: 0.9984 - val_loss: 1.3001 - val_accuracy: 0.9028\n",
+            "Epoch 74/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.0202 - accuracy: 0.9951\n",
+            "Epoch 74: val_loss did not improve from 1.27567\n",
+            "20/20 [==============================] - 18s 895ms/step - loss: 1.0202 - accuracy: 0.9951 - val_loss: 1.4571 - val_accuracy: 0.8472\n",
+            "Epoch 75/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.0117 - accuracy: 0.9902\n",
+            "Epoch 75: val_loss did not improve from 1.27567\n",
+            "20/20 [==============================] - 19s 930ms/step - loss: 1.0117 - accuracy: 0.9902 - val_loss: 1.2980 - val_accuracy: 0.8889\n",
+            "Epoch 76/100\n",
+            "20/20 [==============================] - ETA: 0s - loss: 1.0119 - accuracy: 0.9918\n",
+            "Epoch 76: val_loss did not improve from 1.27567\n",
+            "20/20 [==============================] - 19s 968ms/step - loss: 1.0119 - accuracy: 0.9918 - val_loss: 1.2769 - val_accuracy: 0.9028\n",
+            "Epoch 76: early stopping\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the total number of samples in the test dataset\n",
+        "ts_length = len(test_df)\n",
+        "# Determine the optimal test batch size within a reasonable range (1 to 80)\n",
+        "test_batch_size = max(sorted([ts_length // n for n in range(1, ts_length + 1) if ts_length%n == 0 and ts_length/n <= 80]))\n",
+        "# Calculate the number of steps to cover the entire test dataset using the determined test batch size\n",
+        "test_steps = ts_length // test_batch_size\n",
+        "\n",
+        "# Evaluate the EfficientNetB5base model on the training dataset and print the results\n",
+        "train_score = model.evaluate(train_gen, steps= test_steps, verbose= 1)\n",
+        "# Evaluate the EfficientNetB5 base model on the validation dataset and print the results\n",
+        "valid_score = model.evaluate(valid_gen, steps= test_steps, verbose= 1)\n",
+        "# Evaluate the EfficientNetB5 base model on the test dataset and print the results\n",
+        "test_score = model.evaluate(test_gen, steps= test_steps, verbose= 1)\n",
+        "\n",
+        "# Print the evaluation results for the training dataset\n",
+        "print(\"Train Loss: \", train_score[0])\n",
+        "print(\"Train Accuracy: \", train_score[1])\n",
+        "print('-' * 20)\n",
+        "\n",
+        "# Print the evaluation results for the validation dataset\n",
+        "print(\"Validation Loss: \", valid_score[0])\n",
+        "print(\"Validation Accuracy: \", valid_score[1])\n",
+        "print('-' * 20)\n",
+        "\n",
+        "# Print the evaluation results for the test dataset\n",
+        "print(\"Test Loss: \", test_score[0])\n",
+        "print(\"Test Loss: \", test_score[0])\n",
+        "print(\"Test Accuracy: \", test_score[1])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mf1mrDrLpGXF",
+        "outputId": "85ada66f-d5a3-4cd5-b1ad-b6ab756c89bf"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5/5 [==============================] - 2s 276ms/step - loss: 0.9960 - accuracy: 1.0000\n",
+            "3/5 [=================>............] - ETA: 0s - loss: 1.2769 - accuracy: 0.9028"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 5 batches). You may need to use the repeat() function when building your dataset.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5/5 [==============================] - 1s 213ms/step - loss: 1.2769 - accuracy: 0.9028\n",
+            "5/5 [==============================] - 150s 37s/step - loss: 1.2655 - accuracy: 0.9111\n",
+            "Train Loss:  0.9959659576416016\n",
+            "Train Accuracy:  1.0\n",
+            "--------------------\n",
+            "Validation Loss:  1.2768819332122803\n",
+            "Validation Accuracy:  0.9027777910232544\n",
+            "--------------------\n",
+            "Test Loss:  1.2654653787612915\n",
+            "Test Accuracy:  0.9111111164093018\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# EfficientNet B5\n",
+        "## (The Above model is EfficientNetB5 which shows best accuracy compare to other models)\n",
+        "## Train Accuracy: 100%\n",
+        "## Validation Accuracy: 90.2%\n",
+        "## Test Accuracy: 91.11%"
+      ],
+      "metadata": {
+        "id": "3aYDXYnm71Wd"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# VGG19\n",
+        "## Train Accuracy: 100%\n",
+        "## Validation Accuracy: 80.56%\n",
+        "## Test Accuracy: 79.05%"
+      ],
+      "metadata": {
+        "id": "av1hgCOj-VLh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# VGG16\n",
+        "## Train Accuracy: 100%\n",
+        "## Validation Accuracy:  79.16%\n",
+        "## Test Accuracy:  76.19%"
+      ],
+      "metadata": {
+        "id": "shJGEpmM-iSU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import shutil\n",
+        "\n",
+        "# Source path\n",
+        "source_path = \"content/model_weights_efficient_B5_2.h5\"\n",
+        "\n",
+        "# Destination path (Data folder)\n",
+        "destination_path = \"drive/MyDrive/LungCancer-IITM/Data/model_weights_efficient_B5_2.h5\"\n",
+        "\n",
+        "# Move the file\n",
+        "shutil.move(source_path, destination_path)\n",
+        "\n",
+        "print(f\"File moved from {source_path} to {destination_path}\")"
+      ],
+      "metadata": {
+        "id": "nF-O7RYjEFCi"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# EfficientNetB5 model link:-\n",
+        "google_drive_link = \"https://drive.google.com/file/d/1ppJ_h5jE3tr2-n0x1TBzx8CEfCdAg9TD/view?usp=drive_link\""
+      ],
+      "metadata": {
+        "id": "qXQfOXJGEo9R"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Thank You..."
+      ],
+      "metadata": {
+        "id": "F1XWcOHaE8gc"
+      }
+    }
+  ]
+}