def test_default_output(regtest):
"""default output should not change"""
fv3fit.set_random_seed(0)
config = fv3fit.DenseNetworkConfig()
array = np.random.randn(5, 10)
out = config.build(array, n_features_out=3)
print_result(out, decimals=5, file=regtest)
def test_network_has_correct_number_of_hidden_layers(depth):
fv3fit.set_random_seed(0)
config = fv3fit.DenseNetworkConfig(depth=depth)
n_features_in, n_features_out = 5, 5
input = tf.keras.layers.Input(shape=(n_features_in, ))
dense_network = config.build(input, n_features_out=n_features_out)
# one layer of depth is the output
assert len(dense_network.hidden_outputs) == depth - 1
def test_output_type():
config = fv3fit.DenseNetworkConfig()
array = np.random.randn(5, 10)
dense_network = config.build(array, n_features_out=3)
assert isinstance(dense_network, fv3fit.DenseNetwork)
assert isinstance(dense_network.output, tf.Tensor)
assert isinstance(dense_network.hidden_outputs, Sequence)
assert all(
isinstance(item, tf.Tensor) for item in dense_network.hidden_outputs)
def test_network_has_gaussian_noise_layer():
fv3fit.set_random_seed(0)
config = fv3fit.DenseNetworkConfig(gaussian_noise=0.1)
n_features_in, n_features_out = 5, 5
input = tf.keras.layers.Input(shape=(n_features_in, ))
dense_network = config.build(input, n_features_out=n_features_out)
model = tf.keras.Model(inputs=input, outputs=dense_network.output)
assert any(
isinstance(layer, tf.keras.layers.GaussianNoise)
for layer in model.layers)
def test_output_is_correct_shape(n_samples, n_features, n_features_out):
config = fv3fit.DenseNetworkConfig()
array = np.random.randn(n_samples, n_features)
dense_network = config.build(array, n_features_out=n_features_out)
assert dense_network.output.shape == (n_samples, n_features_out)