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import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, regularizers
IMG_SIZE = (224, 224)
NUM_CLASSES = 25
# ---- this MUST match your training build_mobilenetv2_model_v2 ----
def build_mobilenetv2_model_v2():
inputs = keras.Input(shape=(*IMG_SIZE, 3), name="input_layer")
data_augmentation = keras.Sequential(
[
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.04), # ~±15°
layers.RandomZoom(0.1),
layers.RandomContrast(0.15),
layers.Lambda(
lambda x: tf.image.random_brightness(x, max_delta=0.15)
),
layers.Lambda(
lambda x: tf.image.random_saturation(x, 0.85, 1.15)
),
],
name="data_augmentation", # 👈 same name as training
)
x = data_augmentation(inputs)
x = layers.Lambda(
keras.applications.mobilenet_v2.preprocess_input,
name="mobilenetv2_preprocess",
)(x)
base_model = keras.applications.MobileNetV2(
include_top=False,
weights="imagenet",
input_shape=(*IMG_SIZE, 3),
)
x = base_model(x)
x = layers.GlobalAveragePooling2D(name="global_average_pooling2d")(x)
x = layers.BatchNormalization(name="head_batchnorm_1")(x)
x = layers.Dropout(0.4, name="head_dropout_1")(x)
x = layers.Dense(
256,
activation="relu",
kernel_regularizer=regularizers.l2(1e-4),
name="head_dense_1",
)(x)
x = layers.BatchNormalization(name="head_batchnorm_2")(x)
x = layers.Dropout(0.5, name="head_dropout_2")(x)
outputs = layers.Dense(
NUM_CLASSES, activation="softmax", name="predictions"
)(x)
model = keras.Model(
inputs=inputs,
outputs=outputs,
name="MobileNetV2_smartvision_v2",
)
return model
if __name__ == "__main__":
old_path = os.path.join("saved_models", "mobilenetv2_v2_stage2_best.h5")
new_path = os.path.join("saved_models", "mobilenetv2_v2_stage2_best.weights.h5")
print("Building MobileNetV2 architecture...")
model = build_mobilenetv2_model_v2()
print("Loading weights from full .h5 (by_name, skip_mismatch)...")
model.load_weights(old_path, by_name=True, skip_mismatch=True)
print("Saving clean weights-only file...")
model.save_weights(new_path)
print("✅ Done. Saved weights-only file to:", new_path)
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