Delete train_model.py

train_model.py

@@ -1,57 +0,0 @@
-import tensorflow as tf
-import numpy as np
-from datasets import load_dataset
-from sklearn.preprocessing import LabelBinarizer
-from tensorflow.keras.applications import MobileNetV2
-from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
-from tensorflow.keras.models import Model
-from tensorflow.keras.optimizers import Adam
-from tqdm import tqdm
-
-# Load dataset dari Hugging Face
-from datasets import load_dataset
-
-ds = load_dataset("tanganke/stanford_cars")
-NUM_CLASSES = 196
-IMG_SIZE = 224
-BATCH_SIZE = 32
-EPOCHS = 5
-
-# Preprocessing fungsi
-def preprocess(example):
-    image = example["image"].resize((IMG_SIZE, IMG_SIZE))
-    image = np.array(image) / 255.0
-    return image, example["label"]
-
-# Apply preprocessing
-X_train, y_train = zip(*[preprocess(x) for x in tqdm(ds["train"])])
-X_test, y_test = zip(*[preprocess(x) for x in tqdm(ds["test"])])
-
-X_train = np.array(X_train)
-X_test = np.array(X_test)
-
-# One-hot encoding label
-lb = LabelBinarizer()
-y_train = lb.fit_transform(y_train)
-y_test = lb.transform(y_test)
-
-# Load base model MobileNetV2
-base_model = MobileNetV2(include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3), weights='imagenet')
-x = GlobalAveragePooling2D()(base_model.output)
-x = Dense(256, activation='relu')(x)
-preds = Dense(NUM_CLASSES, activation='softmax')(x)
-
-model = Model(inputs=base_model.input, outputs=preds)
-
-# Freeze base layers
-for layer in base_model.layers:
-    layer.trainable = False
-
-model.compile(optimizer=Adam(1e-4), loss='categorical_crossentropy', metrics=['accuracy'])
-
-# Training
-model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=EPOCHS, batch_size=BATCH_SIZE)
-
-# Simpan model
-model.save("car_model.h5")
-print("✅ Model saved to car_model.h5")