def test_node_to_node_dense():
print('\ntest_node_to_node_dense():')
i2n = InputToNode(hidden_layer_size=5,
sparsity=1.,
activation='tanh',
input_scaling=1.,
bias_scaling=1.,
random_state=42)
n2n = NodeToNode(hidden_layer_size=5,
sparsity=1.,
activation='tanh',
spectral_radius=1.,
bias_scaling=1.,
random_state=42)
X = np.zeros(shape=(10, 3))
i2n.fit(X)
n2n.fit(i2n.transform(X))
i2n_hidden = i2n.transform(X)
print(n2n.transform(i2n_hidden))
print(n2n._recurrent_weights)
assert n2n._recurrent_weights.shape == (5, 5)
assert safe_sparse_dot(i2n.transform(X),
n2n._recurrent_weights).shape == (10, 5)
assert safe_sparse_dot(i2n.transform(X),
n2n._recurrent_weights).shape == (10, 5)
def test_input_to_node_sparse():
print('\ntest_input_to_node_sparse():')
i2n = InputToNode(hidden_layer_size=5,
sparsity=2 / 5,
activation='tanh',
input_scaling=1.,
bias_scaling=1.,
random_state=42)
X = np.zeros(shape=(10, 3))
i2n.fit(X)
print(i2n._input_weights.toarray())
assert i2n._input_weights.shape == (3, 5)
assert safe_sparse_dot(X, i2n._input_weights).shape == (10, 5)
def test_transform_bounded_relu():
print('\ntest_transform_bounded_relu():')
rs = np.random.RandomState(42)
i2n = InputToNode(hidden_layer_size=5,
sparsity=1.,
activation='bounded_relu',
input_scaling=1.,
bias_scaling=1.,
random_state=rs)
X = rs.uniform(low=-1., high=1., size=(10, 3))
i2n.fit(X)
y = i2n.transform(X)
print('tests bounded relu')
print(y)
assert y.shape == (10, 5)