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NTONGA_Babongs_FRANCOIS_TENSORFLOW_SN.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-02-21 19:54:44.189059: 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 os\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.applications import ResNet50\n",
+    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
+    "from tensorflow.keras.models import Model\n",
+    "from tensorflow.keras.optimizers import Adam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Définition des chemins des images et des labels\n",
+    "data_dir = \"kidney_disease_classification/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone\"\n",
+    "img_size = (224, 224)\n",
+    "batch_size = 32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 9959 images belonging to 4 classes.\n",
+      "Found 2487 images belonging to 4 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prétraitement des images avec ImageDataGenerator\n",
+    "datagen = ImageDataGenerator(\n",
+    "    rescale=1./255,\n",
+    "    validation_split=0.2\n",
+    ")\n",
+    "\n",
+    "train_generator = datagen.flow_from_directory(\n",
+    "    data_dir,\n",
+    "    target_size=img_size,\n",
+    "    batch_size=batch_size,\n",
+    "    class_mode='categorical',\n",
+    "    subset='training'\n",
+    ")\n",
+    "\n",
+    "val_generator = datagen.flow_from_directory(\n",
+    "    data_dir,\n",
+    "    target_size=img_size,\n",
+    "    batch_size=batch_size,\n",
+    "    class_mode='categorical',\n",
+    "    subset='validation'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Chargement du modèle ResNet50 en transfer learning\n",
+    "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
+    "base_model.trainable = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ajout des couches de classification\n",
+    "x = GlobalAveragePooling2D()(base_model.output)\n",
+    "x = Dense(128, activation='relu')(x)\n",
+    "x = Dense(4, activation='softmax')(x)\n",
+    "\n",
+    "model = Model(inputs=base_model.input, outputs=x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compilation du modèle\n",
+    "model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+      "  self._warn_if_super_not_called()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1701s\u001b[0m 5s/step - accuracy: 0.5352 - loss: 1.1548 - val_accuracy: 0.5665 - val_loss: 1.1305\n",
+      "Epoch 2/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1719s\u001b[0m 6s/step - accuracy: 0.7341 - loss: 0.7263 - val_accuracy: 0.5794 - val_loss: 1.0655\n",
+      "Epoch 3/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1640s\u001b[0m 5s/step - accuracy: 0.7898 - loss: 0.5801 - val_accuracy: 0.5726 - val_loss: 1.0368\n",
+      "Epoch 4/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1649s\u001b[0m 5s/step - accuracy: 0.8287 - loss: 0.4765 - val_accuracy: 0.6064 - val_loss: 1.0409\n",
+      "Epoch 5/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1581s\u001b[0m 5s/step - accuracy: 0.8593 - loss: 0.4083 - val_accuracy: 0.6164 - val_loss: 1.1470\n",
+      "Epoch 6/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1518s\u001b[0m 5s/step - accuracy: 0.8684 - loss: 0.3721 - val_accuracy: 0.6228 - val_loss: 1.1132\n",
+      "Epoch 7/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1501s\u001b[0m 5s/step - accuracy: 0.8858 - loss: 0.3224 - val_accuracy: 0.6409 - val_loss: 1.1094\n",
+      "Epoch 8/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1378s\u001b[0m 4s/step - accuracy: 0.8951 - loss: 0.3011 - val_accuracy: 0.6530 - val_loss: 1.0900\n",
+      "Epoch 9/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1372s\u001b[0m 4s/step - accuracy: 0.9095 - loss: 0.2659 - val_accuracy: 0.5682 - val_loss: 1.1625\n",
+      "Epoch 10/10\n",
+      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1511s\u001b[0m 5s/step - accuracy: 0.9062 - loss: 0.2695 - val_accuracy: 0.6421 - val_loss: 1.1116\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Entraînement du modèle\n",
+    "epochs = 10\n",
+    "history = model.fit(train_generator, validation_data=val_generator, epochs=epochs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "# Sauvegarde du modèle\n",
+    "model.save(\"babong_kidney_classification_model.h5\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

babong_kidney_classification_model_Tensorflow.h5

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resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

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