{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/agilecpu154/Documents/Task-8 Skin Cancer Detection/env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Path to dataset files: /home/agilecpu154/.cache/kagglehub/datasets/kmader/skin-cancer-mnist-ham10000/versions/2\n" ] } ], "source": [ "import kagglehub\n", "\n", "# Download latest version\n", "path = kagglehub.dataset_download(\"kmader/skin-cancer-mnist-ham10000\")\n", "\n", "print(\"Path to dataset files:\", path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-01-31 11:54:34.550935: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", "2025-01-31 11:54:34.601784: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", "2025-01-31 11:54:34.631089: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1738304674.660236 59431 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1738304674.670043 59431 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2025-01-31 11:54:34.705842: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "from imblearn.over_sampling import RandomOverSampler\n", "from sklearn.model_selection import train_test_split\n", "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Conv2D, Flatten, Dense, MaxPool2D, Input\n", "from tensorflow.keras.callbacks import ModelCheckpoint" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1.) Importing Tabular Data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " lesion_id image_id dx dx_type age sex localization\n", "0 HAM_0000118 ISIC_0027419 bkl histo 80.0 male scalp\n", "1 HAM_0000118 ISIC_0025030 bkl histo 80.0 male scalp\n", "2 HAM_0002730 ISIC_0026769 bkl histo 80.0 male scalp\n", "3 HAM_0002730 ISIC_0025661 bkl histo 80.0 male scalp\n", "4 HAM_0001466 ISIC_0031633 bkl histo 75.0 male ear\n" ] } ], "source": [ "tabular_data = pd.read_csv(r'/home/agilecpu154/.cache/kagglehub/datasets/kmader/skin-cancer-mnist-ham10000/versions/2/HAM10000_metadata.csv')\n", "\n", "print(tabular_data.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importing Data Images with Pixel Values and Labels" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('/home/agilecpu154/.cache/kagglehub/datasets/kmader/skin-cancer-mnist-ham10000/versions/2/hmnist_28_28_RGB.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Splitting Data into Features(x) and Labels(y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "x = data.drop('label', axis=1)\n", "y = data['label']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OverSampling to Overcome Class Imbalance" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "oversample = RandomOverSampler(random_state=42)\n", "x_resampled, y_resampled = oversample.fit_resample(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reshaping x to match image dimensions(28,28,3)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of x after oversampling and reshaping: (46935, 28, 28, 3)\n" ] } ], "source": [ "x_resampled = np.array(x_resampled).reshape(-1, 28, 28, 3)\n", "\n", "print('Shape of x after oversampling and reshaping:', x_resampled.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Standardizing Data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "mean = np.mean(x_resampled)\n", "std = np.std(x_resampled)\n", "x_resampled = (x_resampled - mean) / std" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Splitting Data into Train and Test DataSets" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set size: (37548, 28, 28, 3), Testing set size: (9387, 28, 28, 3)\n" ] } ], "source": [ "X_train, X_test, Y_train, Y_test = train_test_split(\n", " x_resampled, y_resampled, test_size=0.2, random_state=1\n", ")\n", "\n", "print(f'Training set size: {X_train.shape}, Testing set size: {X_test.shape}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Building the CNN Model" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-01-31 11:54:53.175525: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:152] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" ] } ], "source": [ "model = Sequential([\n", " Input(shape=(28, 28, 3)), # Adjusted input shape to match data\n", " Conv2D(16, kernel_size=(3, 3), activation='relu', padding='same'),\n", " Conv2D(32, kernel_size=(3, 3), activation='relu'),\n", " MaxPool2D(pool_size=(2, 2)),\n", " Conv2D(32, kernel_size=(3, 3), activation='relu', padding='same'),\n", " Conv2D(64, kernel_size=(3, 3), activation='relu'),\n", " MaxPool2D(pool_size=(2, 2), padding='same'),\n", " Flatten(),\n", " Dense(64, activation='relu'),\n", " Dense(32, activation='relu'),\n", " Dense(7, activation='softmax')\n", "])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (Conv2D)                 │ (None, 28, 28, 16)     │           448 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (Conv2D)               │ (None, 26, 26, 32)     │         4,640 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (MaxPooling2D)    │ (None, 13, 13, 32)     │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (Conv2D)               │ (None, 13, 13, 32)     │         9,248 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (Conv2D)               │ (None, 11, 11, 64)     │        18,496 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (MaxPooling2D)  │ (None, 6, 6, 64)       │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (Flatten)               │ (None, 2304)           │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (Dense)                   │ (None, 64)             │       147,520 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (Dense)                 │ (None, 7)              │           231 │\n",
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accuracy: 0.4152 - loss: 1.4796\n", "Epoch 1: val_accuracy improved from -inf to 0.66405, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 251ms/step - accuracy: 0.4157 - loss: 1.4784 - val_accuracy: 0.6640 - val_loss: 0.8702\n", "Epoch 2/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.7215 - loss: 0.7513\n", "Epoch 2: val_accuracy improved from 0.66405 to 0.80786, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 248ms/step - accuracy: 0.7216 - loss: 0.7509 - val_accuracy: 0.8079 - val_loss: 0.5179\n", "Epoch 3/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - accuracy: 0.8334 - loss: 0.4516\n", "Epoch 3: val_accuracy improved from 0.80786 to 0.87390, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 245ms/step - accuracy: 0.8335 - loss: 0.4514 - val_accuracy: 0.8739 - val_loss: 0.3529\n", "Epoch 4/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - accuracy: 0.8858 - loss: 0.3141\n", "Epoch 4: val_accuracy improved from 0.87390 to 0.89028, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 246ms/step - accuracy: 0.8859 - loss: 0.3140 - val_accuracy: 0.8903 - val_loss: 0.3060\n", "Epoch 5/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 274ms/step - accuracy: 0.9219 - loss: 0.2212\n", "Epoch 5: val_accuracy improved from 0.89028 to 0.91771, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 282ms/step - accuracy: 0.9219 - loss: 0.2212 - val_accuracy: 0.9177 - val_loss: 0.2388\n", "Epoch 6/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 232ms/step - accuracy: 0.9372 - loss: 0.1705\n", "Epoch 6: val_accuracy improved from 0.91771 to 0.93609, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 240ms/step - accuracy: 0.9372 - loss: 0.1705 - val_accuracy: 0.9361 - val_loss: 0.2019\n", "Epoch 7/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - accuracy: 0.9511 - loss: 0.1382\n", "Epoch 7: val_accuracy did not improve from 0.93609\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 231ms/step - accuracy: 0.9511 - loss: 0.1382 - val_accuracy: 0.9254 - val_loss: 0.2217\n", "Epoch 8/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 225ms/step - accuracy: 0.9618 - loss: 0.1094\n", "Epoch 8: val_accuracy did not improve from 0.93609\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 232ms/step - accuracy: 0.9618 - loss: 0.1094 - val_accuracy: 0.9301 - val_loss: 0.2329\n", "Epoch 9/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - accuracy: 0.9613 - loss: 0.1121\n", "Epoch 9: val_accuracy improved from 0.93609 to 0.95166, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 231ms/step - accuracy: 0.9613 - loss: 0.1121 - val_accuracy: 0.9517 - val_loss: 0.1555\n", "Epoch 10/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 228ms/step - accuracy: 0.9778 - loss: 0.0632\n", "Epoch 10: val_accuracy did not improve from 0.95166\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 234ms/step - accuracy: 0.9778 - loss: 0.0632 - val_accuracy: 0.9445 - val_loss: 0.2164\n", "Epoch 11/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 245ms/step - accuracy: 0.9753 - loss: 0.0678\n", "Epoch 11: val_accuracy improved from 0.95166 to 0.95393, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 254ms/step - accuracy: 0.9753 - loss: 0.0678 - val_accuracy: 0.9539 - val_loss: 0.1431\n", "Epoch 12/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - accuracy: 0.9730 - loss: 0.0767\n", "Epoch 12: val_accuracy improved from 0.95393 to 0.96099, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 238ms/step - accuracy: 0.9730 - loss: 0.0766 - val_accuracy: 0.9610 - val_loss: 0.1470\n", "Epoch 13/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 227ms/step - accuracy: 0.9833 - loss: 0.0493\n", "Epoch 13: val_accuracy did not improve from 0.96099\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 235ms/step - accuracy: 0.9832 - loss: 0.0494 - val_accuracy: 0.9591 - val_loss: 0.1506\n", "Epoch 14/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - accuracy: 0.9835 - loss: 0.0516\n", "Epoch 14: val_accuracy improved from 0.96099 to 0.96471, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 237ms/step - accuracy: 0.9835 - loss: 0.0516 - val_accuracy: 0.9647 - val_loss: 0.1172\n", "Epoch 15/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 227ms/step - accuracy: 0.9898 - loss: 0.0331\n", "Epoch 15: val_accuracy improved from 0.96471 to 0.96951, saving model to best_model.keras\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 235ms/step - accuracy: 0.9898 - loss: 0.0331 - val_accuracy: 0.9695 - val_loss: 0.1228\n", "Epoch 16/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - accuracy: 0.9874 - loss: 0.0386\n", "Epoch 16: val_accuracy did not improve from 0.96951\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 239ms/step - accuracy: 0.9874 - loss: 0.0386 - val_accuracy: 0.9692 - val_loss: 0.1255\n", "Epoch 17/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 228ms/step - accuracy: 0.9862 - loss: 0.0423\n", "Epoch 17: val_accuracy did not improve from 0.96951\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 235ms/step - accuracy: 0.9862 - loss: 0.0423 - val_accuracy: 0.9594 - val_loss: 0.1685\n", "Epoch 18/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 225ms/step - accuracy: 0.9931 - loss: 0.0209\n", "Epoch 18: val_accuracy did not improve from 0.96951\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 233ms/step - accuracy: 0.9931 - loss: 0.0209 - val_accuracy: 0.9643 - val_loss: 0.1693\n", "Epoch 19/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 225ms/step - accuracy: 0.9893 - loss: 0.0331\n", "Epoch 19: val_accuracy did not improve from 0.96951\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 233ms/step - accuracy: 0.9893 - loss: 0.0331 - val_accuracy: 0.9658 - val_loss: 0.1554\n", "Epoch 20/20\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - accuracy: 0.9925 - loss: 0.0237\n", "Epoch 20: val_accuracy did not improve from 0.96951\n", "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 231ms/step - accuracy: 0.9925 - loss: 0.0237 - val_accuracy: 0.9643 - val_loss: 0.1802\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n" ] }, { "ename": "PermissionError", "evalue": "[Errno 13] Permission denied: '/final'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[15], line 9\u001b[0m\n\u001b[1;32m 1\u001b[0m history \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit(\n\u001b[1;32m 2\u001b[0m X_train, Y_train,\n\u001b[1;32m 3\u001b[0m validation_split\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6\u001b[0m callbacks\u001b[38;5;241m=\u001b[39m[callback]\n\u001b[1;32m 7\u001b[0m )\n\u001b[0;32m----> 9\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/final/final_model.h5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaved \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfinal_model.h5\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Documents/Task-8 Skin Cancer Detection/env/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m 120\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n", "File \u001b[0;32m/usr/lib/python3.10/os.py:225\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 225\u001b[0m mkdir(name, mode)\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m 227\u001b[0m \u001b[38;5;66;03m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exist_ok \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39misdir(name):\n", "\u001b[0;31mPermissionError\u001b[0m: [Errno 13] Permission denied: '/final'" ] } ], "source": [ "history = model.fit(\n", " X_train, Y_train,\n", " validation_split=0.2,\n", " batch_size=128,\n", " epochs=20,\n", " callbacks=[callback]\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved 'final_model.h5'\n" ] } ], "source": [ "model.save('final_model.h5')\n", "print(\"Saved 'final_model.h5'\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "np.save('mean.npy', mean)\n", "np.save('std.npy', std)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Validate loss/acc" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def plot_training(hist):\n", " tr_acc = hist.history['accuracy']\n", " tr_loss = hist.history['loss']\n", " val_acc = hist.history['val_accuracy']\n", " val_loss = hist.history['val_loss']\n", " index_loss = np.argmin(val_loss)\n", " val_lowest = val_loss[index_loss]\n", " index_acc = np.argmax(val_acc)\n", " acc_highest = val_acc[index_acc]\n", "\n", " plt.figure(figsize= (20, 8))\n", " plt.style.use('fivethirtyeight')\n", " Epochs = [i+1 for i in range(len(tr_acc))]\n", " loss_label = f'best epoch= {str(index_loss + 1)}'\n", " acc_label = f'best epoch= {str(index_acc + 1)}'\n", " \n", " plt.subplot(1, 2, 1)\n", " plt.plot(Epochs, tr_loss, 'r', label= 'Training loss')\n", " plt.plot(Epochs, val_loss, 'g', label= 'Validation loss')\n", " plt.scatter(index_loss + 1, val_lowest, s= 150, c= 'blue', label= loss_label)\n", " plt.title('Training and Validation Loss')\n", " plt.xlabel('Epochs')\n", " plt.ylabel('Loss')\n", " plt.legend()\n", " \n", " plt.subplot(1, 2, 2)\n", " plt.plot(Epochs, tr_acc, 'r', label= 'Training Accuracy')\n", " plt.plot(Epochs, val_acc, 'g', label= 'Validation Accuracy')\n", " plt.scatter(index_acc + 1 , acc_highest, s= 150, c= 'blue', label= acc_label)\n", " plt.title('Training and Validation Accuracy')\n", " plt.xlabel('Epochs')\n", " plt.ylabel('Accuracy')\n", " plt.legend()\n", " \n", " plt.tight_layout\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_training(history)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-01-31 12:16:01.979402: W external/local_xla/xla/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 353251584 exceeds 10% of free system memory.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1174/1174\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 13ms/step - accuracy: 0.9915 - loss: 0.0269\n", "\u001b[1m294/294\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9617 - loss: 0.1890\n", "Train Loss: 0.056288037449121475\n", "Train Accuracy: 0.9864174127578735\n", "--------------------\n", "Test Loss: 0.18527135252952576\n", "Test Accuracy: 0.9640992879867554\n" ] } ], "source": [ "train_score = model.evaluate(X_train, Y_train, verbose= 1)\n", "test_score = model.evaluate(X_test, Y_test, verbose= 1)\n", "\n", "print(\"Train Loss: \", train_score[0])\n", "print(\"Train Accuracy: \", train_score[1])\n", "print('-' * 20)\n", "print(\"Test Loss: \", test_score[0])\n", "print(\"Test Accuracy: \", test_score[1])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m294/294\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 15ms/step\n" ] } ], "source": [ "y_true = np.array(Y_test)\n", "y_pred = model.predict(X_test)\n", "\n", "y_pred = np.argmax(y_pred, axis=1)\n", "\n", "# Kiểm tra xem y_true có phải là một mảng một chiều hay không\n", "if y_true.ndim == 1:\n", " # Nếu y_true là một mảng một chiều, không cần sử dụng np.argmax\n", " pass\n", "else:\n", " y_true = np.argmax(y_true, axis=1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4, 6, 2, 1, 5, 0, 3]\n" ] } ], "source": [ "classes = {4: ('nv', ' melanocytic nevi'),\n", " 6: ('mel', 'melanoma'),\n", " 2 :('bkl', 'benign keratosis-like lesions'), \n", " 1:('bcc' , ' basal cell carcinoma'),\n", " 5: ('vasc', ' pyogenic granulomas and hemorrhage'),\n", " 0: ('akiec', 'Actinic keratoses and intraepithelial carcinomae'),\n", " 3: ('df', 'dermatofibroma')}\n", "classes_labels = []\n", "for key in classes.keys():\n", " classes_labels.append(key)\n", "\n", "print(classes_labels)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix, classification_report\n", "import itertools\n", "# Confusion matrix\n", "cm = cm = confusion_matrix(y_true, y_pred, labels=classes_labels)\n", "\n", "plt.figure(figsize= (10, 10))\n", "plt.imshow(cm, interpolation= 'nearest', cmap= plt.cm.Blues)\n", "plt.title('Confusion Matrix')\n", "plt.colorbar()\n", "\n", "tick_marks = np.arange(len(classes))\n", "plt.xticks(tick_marks, classes, rotation= 45)\n", "plt.yticks(tick_marks, classes)\n", "\n", "\n", "thresh = cm.max() / 2.\n", "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, cm[i, j], horizontalalignment= 'center', color= 'white' if cm[i, j] > thresh else 'black')\n", "\n", "plt.tight_layout()\n", "plt.ylabel('True Label')\n", "plt.xlabel('Predicted Label')\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting app.py\n" ] } ], "source": [ "%%writefile app.py\n", "import streamlit as st\n", "import tensorflow as tf\n", "import numpy as np\n", "from PIL import Image\n", "import io\n", "\n", "# Set seed for reproducibility\n", "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "# Define the correct class labels mapping\n", "class_labels = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc', 'mel']\n", "\n", "# Load the trained model\n", "@st.cache_resource\n", "def load_model():\n", " return tf.keras.models.load_model('final_model.h5')\n", "\n", "model = load_model()\n", "\n", "# Load mean and std from training (you'll need to provide these)\n", "@st.cache_resource\n", "def load_mean_std():\n", " mean = np.load('mean.npy')\n", " std = np.load('std.npy')\n", " return mean, std\n", "\n", "mean, std = load_mean_std()\n", "\n", "\n", "\n", "# Define the image preprocessing function\n", "def preprocess_image(image):\n", " image = image.resize((28, 28))\n", " image = np.asarray(image)\n", " image = (image - mean) / std\n", " image = np.expand_dims(image, axis=0)\n", " return image\n", "\n", "# Streamlit app\n", "st.title('Skin Lesion Predictor')\n", "\n", "uploaded_file = st.file_uploader(\"Choose an image...\", type=[\"jpg\", \"jpeg\", \"png\"])\n", "\n", "if uploaded_file is not None:\n", " image = Image.open(io.BytesIO(uploaded_file.read())).convert('RGB')\n", " st.image(image, caption='Uploaded Image', use_contanier_width=True)\n", " \n", " # Preprocess the image\n", " processed_image = preprocess_image(image)\n", " \n", " # Make prediction\n", " predictions = model.predict(processed_image)\n", " \n", " # Get predicted class index and name\n", " predicted_class_idx = np.argmax(predictions, axis=1)[0]\n", " predicted_class = class_labels[predicted_class_idx]\n", " confidence = predictions[0][predicted_class_idx]\n", " \n", " # Display results\n", " st.write(f\"Predicted Class: {predicted_class}\")\n", " st.write(f\"Confidence: {confidence:.2f}\")\n", " \n", " # Display bar chart of all predictions\n", " st.bar_chart(dict(zip(class_labels, predictions[0])))\n", "\n", "st.write(\"Note: This is a demo application and should not be used for medical diagnosis. Always consult with a healthcare professional.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "env", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }