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