import keras from src.inception_model import InceptionModelBase from src.flatten import flatten_tail def my_test_model(activation, window_history_size, channels, dropout_rate, add_minor_branch=False): inception_model = InceptionModelBase() conv_settings_dict1 = { 'tower_1': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (3, 1), 'activation': activation}, 'tower_2': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (5, 1), 'activation': activation}, } pool_settings_dict1 = {'pool_kernel': (3, 1), 'tower_filter': 8 * 2, 'activation': activation} X_input = keras.layers.Input(shape=(window_history_size + 1, 1, channels)) X_in = inception_model.inception_block(X_input, conv_settings_dict1, pool_settings_dict1) if add_minor_branch: out = [flatten_tail(X_in, 'Minor_1', activation=activation)] else: out = [] X_in = keras.layers.Dropout(dropout_rate)(X_in) out.append(flatten_tail(X_in, 'Main', activation=activation)) return keras.Model(inputs=X_input, outputs=out)