An example script to create a hyper model

hyper_model.py

def FCN_model(config, len_classes=5):
    
    input = tf.keras.layers.Input(shape=(None, None, 3))

    # Adding data augmentation layers
    x = augment_images(input, config)

    # You can create a fixed number of convolutional blocks or 
    # You can also use a loop if number of layers is also a hyperparameter
    x = tf.keras.layers.Conv2D(filters=config['conv_block1_filters'], kernel_size=3, strides=1)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    x = tf.keras.layers.Conv2D(filters=config['conv_block2_filters'], kernel_size=3, strides=1)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    if config['fc_layer_type'] == 'dense':
        if config['pool_type'] == 'max':
            x = tf.keras.layers.GlobalMaxPooling2D()(x)
        else:
            x = tf.keras.layers.GlobalAveragePooling2D()(x)

        # Fully connected layer 1
        x = tf.keras.layers.Dense(units=config['fc1_units'])(x)
        x = tf.keras.layers.Dropout(config['dropout_rate'])(x)
        x = tf.keras.layers.BatchNormalization()(x)
        x = tf.keras.layers.Activation('relu')(x)

        # Fully connected layer 2
        x = tf.keras.layers.Dense(units=len_classes)(x)
        x = tf.keras.layers.Dropout(config['dropout_rate'])(x)
        x = tf.keras.layers.BatchNormalization()(x)
        predictions = tf.keras.layers.Activation('softmax')(x)

    else:
        # Some other FC layer configuration
        .
        .
        .

    model = tf.keras.Model(inputs=input, outputs=predictions)
    return model