def test_invalid_act_function(repeat):
""" Test the errors raised by initialising an instance of the NeuralNetwork
class when there is an invalid value for the act_funcs argument """
set_random_seed_from_args("test_invalid_act_function", repeat)
with pytest.raises(TypeError):
# act_funcs argument should be a list of activation function objects
n = NeuralNetwork(act_funcs=models.activations.gaussian)
n = NeuralNetwork(act_funcs=[models.activations._Gaussian])
x, _ = get_random_inputs(n.input_dim)
with pytest.raises(TypeError):
# Network is initialised with the Gaussian class, not an instance
n.forward_prop(x)
n = NeuralNetwork(act_funcs=[None])
x, _ = get_random_inputs(n.input_dim)
with pytest.raises(TypeError):
# Activation function None is not callable
n.forward_prop(x)
n = NeuralNetwork(act_funcs=[abs])
x, N_D = get_random_inputs(n.input_dim)
t = get_random_targets(n.output_dim, N_D)
# Activation function abs is callable, so forward_prop is okay
n.forward_prop(x)
with pytest.raises(AttributeError):
# Activation function abs has no dydx method, so backprop fails
n.back_prop(x, t)
def test_invalid_error_function(repeat):
""" Test the errors raised by initialising an instance of the NeuralNetwork
class when there is an invalid value for the error_func argument """
set_random_seed_from_args("test_invalid_error_function", repeat)
n = NeuralNetwork(error_func=models.errors._SumOfSquares)
x, N_D = get_random_inputs(n.input_dim)
t = get_random_targets(n.output_dim, N_D)
n.forward_prop(x)
with pytest.raises(TypeError):
# Network is initialised with the SumOfSquares class, not an instance
n.mean_total_error(t)
n = NeuralNetwork(error_func=sum)
x, N_D = get_random_inputs(n.input_dim)
t = get_random_targets(n.output_dim, N_D)
n.forward_prop(x)
# Error function sum is callable, so mean_error is okay
n.mean_total_error(t)
with pytest.raises(AttributeError):
# Error function sum has no dydx method, so backprop fails
n.back_prop(x, t)