model.py

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

def get_model(width=128, height=128, depth=64):
    inputs = keras.Input((width, height, depth, 1))
  
    x = layers.Conv3D(filters=64, kernel_size=3, activation="relu")(inputs)
    x = layers.MaxPool3D(pool_size=2)(x)
    x = layers.BatchNormalization()(x)

    x = layers.Conv3D(filters=64, kernel_size=3, activation="relu")(x)
    x = layers.MaxPool3D(pool_size=2)(x)
    x = layers.BatchNormalization()(x)

    x = layers.Conv3D(filters=128, kernel_size=3, activation="relu")(x)
    x = layers.MaxPool3D(pool_size=2)(x)
    x = layers.BatchNormalization()(x)

    x = layers.Conv3D(filters=256, kernel_size=3, activation="relu")(x)
    x = layers.MaxPool3D(pool_size=2)(x)
    x = layers.BatchNormalization()(x)

    x = layers.GlobalAveragePooling3D()(x)
    x = layers.Dense(units=512, activation="relu")(x)
    x = layers.Dropout(0.3)(x)

    outputs = layers.Dense(units=1, activation="sigmoid")(x)

    # Define the model
    model = keras.Model(inputs, outputs, name="3DCNN")
    return model

if __name__ == '__main__':
    model = get_model()
    model.load_weights("3d-img-class.h5")
    model.save("3d-img-class-model.h5")