Teknik-Informatika-PSDKU-Ng.../backend/evaluate.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e44065e8-7336-43e5-9232-a7d4981b52de",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
"from tensorflow.keras.models import load_model\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.applications.mobilenet_v2 import preprocess_input"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6a118ef9-0eac-41b5-81f9-ef3bfac0e45d",
"metadata": {},
"outputs": [],
"source": [
"BASE_DIR = os.getcwd()\n",
"\n",
"MODEL1_PATH = os.path.join(BASE_DIR, \"model_1\", \"model1_best.h5\")\n",
"MODEL2_PATH = os.path.join(BASE_DIR, \"model_2\", \"model2_best.h5\")\n",
"\n",
"TEST_DIR_MODEL1 = os.path.join(BASE_DIR, \"dataset_model1\", \"test\")\n",
"TEST_DIR_MODEL2 = os.path.join(BASE_DIR, \"dataset_model2\", \"test\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3f1fe559-3f78-4126-8b16-9521318110e5",
"metadata": {},
"outputs": [],
"source": [
"def evaluate(model_path, test_dir, title):\n",
"\n",
" print(\"\\n\" + \"=\"*50)\n",
" print(f\"🔍 Evaluasi: {title}\")\n",
" print(\"=\"*50)\n",
"\n",
" model = load_model(model_path, compile=False)\n",
"\n",
" datagen = ImageDataGenerator(preprocessing_function=preprocess_input)\n",
"\n",
" test_gen = datagen.flow_from_directory(\n",
" test_dir,\n",
" target_size=(224, 224),\n",
" batch_size=32,\n",
" class_mode=\"categorical\",\n",
" shuffle=False\n",
" )\n",
"\n",
" class_names = list(test_gen.class_indices.keys())\n",
"\n",
" model.compile(\n",
" optimizer=\"adam\",\n",
" loss=\"categorical_crossentropy\",\n",
" metrics=[\"accuracy\"]\n",
" )\n",
"\n",
" loss, acc = model.evaluate(test_gen, verbose=1)\n",
"\n",
" print(f\"\\nLoss: {loss:.4f}\")\n",
" print(f\"Accuracy: {acc:.4f}\")\n",
"\n",
" preds = model.predict(test_gen, verbose=0)\n",
"\n",
" y_pred = np.argmax(preds, axis=1)\n",
" y_true = test_gen.classes\n",
"\n",
" # CONFUSION MATRIX\n",
" cm = confusion_matrix(y_true, y_pred)\n",
"\n",
" disp = ConfusionMatrixDisplay(cm, display_labels=class_names)\n",
" disp.plot(values_format=\"d\")\n",
" plt.title(title)\n",
" plt.show()\n",
"\n",
" # CLASSIFICATION REPORT\n",
" print(\"\\n=== CLASSIFICATION REPORT ===\")\n",
" print(classification_report(\n",
" y_true,\n",
" y_pred,\n",
" target_names=class_names,\n",
" digits=4\n",
" ))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0f553e33-221f-47cb-8901-536f87570347",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"==================================================\n",
"🔍 Evaluasi: Model 1 (Jagung vs Non)\n",
"==================================================\n",
"Found 1233 images belonging to 2 classes.\n",
"39/39 [==============================] - 47s 1s/step - loss: 0.0129 - accuracy: 0.9968\n",
"\n",
"Loss: 0.0129\n",
"Accuracy: 0.9968\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== CLASSIFICATION REPORT ===\n",
" precision recall f1-score support\n",
"\n",
" jagung 0.9990 0.9969 0.9980 983\n",
" non_jagung 0.9881 0.9960 0.9920 250\n",
"\n",
" accuracy 0.9968 1233\n",
" macro avg 0.9935 0.9965 0.9950 1233\n",
"weighted avg 0.9968 0.9968 0.9968 1233\n",
"\n",
"\n",
"==================================================\n",
"🔍 Evaluasi: Model 2 (Penyakit Jagung)\n",
"==================================================\n",
"Found 983 images belonging to 3 classes.\n",
"31/31 [==============================] - 36s 1s/step - loss: 0.0262 - accuracy: 0.9939\n",
"\n",
"Loss: 0.0262\n",
"Accuracy: 0.9939\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== CLASSIFICATION REPORT ===\n",
" precision recall f1-score support\n",
"\n",
" hawar_daun 0.9963 0.9818 0.9890 275\n",
" karat_daun 0.9972 1.0000 0.9986 359\n",
" sehat 0.9886 0.9971 0.9929 349\n",
"\n",
" accuracy 0.9939 983\n",
" macro avg 0.9941 0.9930 0.9935 983\n",
"weighted avg 0.9939 0.9939 0.9939 983\n",
"\n"
]
}
],
"source": [
"evaluate(MODEL1_PATH, TEST_DIR_MODEL1, \"Model 1 (Jagung vs Non)\")\n",
"evaluate(MODEL2_PATH, TEST_DIR_MODEL2, \"Model 2 (Penyakit Jagung)\")"
]
},
{
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"id": "c0ee47e9-3a28-4111-bad7-6c87d1d23045",
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"outputs": [],
"source": []
}
],
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"name": "python3"
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