def test_InceptionTime_metrics(self): """InceptionTime model should be compiled with the metrics that we give it""" metrics = ['accuracy', 'mae'] x_shape = (None, 20, 3) nr_classes = 2 X_train, y_train = generate_train_data(x_shape, nr_classes) model_type = InceptionTime(x_shape, nr_classes, metrics=metrics) model = model_type.create_model(16) model.fit(X_train, y_train) model_metrics = [m.name for m in model.metrics] for metric in metrics: assert metric in model_metrics
def test_ResNet_metrics(self): """ResNet model should be compiled with the metrics that we give it""" metrics = ['accuracy', 'mae'] x_shape = (None, 20, 3) nr_classes = 2 X_train, y_train = generate_train_data(x_shape, nr_classes) model_type = ResNet(x_shape, nr_classes, metrics=metrics) model = model_type.create_model(16, 20) model.fit(X_train, y_train, epochs=1) model_metrics = [m.name for m in model.metrics] for metric in metrics: assert metric in model_metrics
def test_cnn_metrics(self): """CNN model should be compiled with the metrics that we give it""" metrics = ['accuracy', 'mae'] x_shape = (None, 20, 3) nr_classes = 2 X_train, y_train = generate_train_data(x_shape, nr_classes) model_type = CNN(x_shape, nr_classes, metrics=metrics) model = model_type.create_model(**{ "filters": [32, 32], "fc_hidden_nodes": 100 }) model.fit(X_train, y_train, epochs=1) model_metrics = [m.name for m in model.metrics] for metric in metrics: assert metric in model_metrics