def test_glorot_uniform_initialization():
np.random.seed(42)
nodes = [
InputNode("a", shape=(3, 4, 5)),
DenseNode("b"),
ReLUNode("e"),
]
sequential = SequentialNode("c", nodes)
hp_node = HyperparameterNode("d",
sequential,
num_units=1000,
inits=[GlorotUniformInit()])
network = hp_node.network()
fc_node = network["b"]
W_value = fc_node.get_variable("W").value
b_value = fc_node.get_variable("b").value
np.testing.assert_allclose(0,
W_value.mean(),
atol=1e-2)
np.testing.assert_allclose(np.sqrt(2.0 / (20 + 1000)),
W_value.std(),
atol=1e-2)
np.testing.assert_allclose(np.zeros(1000),
b_value,
rtol=1e-5,
atol=1e-8)
def test_hyperparameter_node():
input_node = InputNode("a", shape=(3, 4, 5))
hp_node = HyperparameterNode("b", input_node, foo=3, bar=2)
network = hp_node.network()
assert network["b"].find_hyperparameter(["foo"]) == 3
assert network["a"].find_hyperparameter(["foo"]) == 3
assert network["a"].find_hyperparameter(["choo", "foo"]) == 3
assert network["a"].find_hyperparameter(["choo"], 32) == 32
def test_fully_connected_and_relu_node():
np.random.seed(42)
nodes = [InputNode("a", shape=(3, 4, 5)), DenseNode("b"), ReLUNode("e")]
sequential = SequentialNode("c", nodes)
hp_node = HyperparameterNode("d", sequential, num_units=14, inits=[treeano.inits.ConstantInit(1)])
network = hp_node.network()
fn = network.function(["a"], ["d"])
x = np.random.randn(3, 4, 5).astype(fX)
res = np.dot(x.reshape(3, 20), np.ones((20, 14))) + np.ones(14)
np.testing.assert_allclose(fn(x)[0], np.clip(res, 0, np.inf), rtol=1e-5, atol=1e-8)
