def test_activation_functions(self):
# Test single activation functions (no other custom computations in layer).
space = FloatBox(shape=(3, ), add_batch_rank=True)
# ReLU.
relu_layer = NNLayer(activation="relu")
test = ComponentTest(component=relu_layer,
input_spaces=dict(inputs=space))
input_ = space.sample(size=5)
expected = relu(input_)
test.test(("apply", input_), expected_outputs=expected)
# Again manually in case util numpy-relu is broken.
input_ = np.array([[1.0, 2.0, -5.0], [-10.0, -100.1, 4.5]])
expected = np.array([[1.0, 2.0, 0.0], [0.0, 0.0, 4.5]])
test.test(("apply", input_), expected_outputs=expected)
# Sigmoid.
sigmoid_layer = NNLayer(activation="sigmoid")
test = ComponentTest(component=sigmoid_layer,
input_spaces=dict(inputs=space))
input_ = space.sample(size=10)
expected = sigmoid(input_)
test.test(("apply", input_), expected_outputs=expected)
def test_dummy_nn_layer(self):
# Tests simple pass through (no activation, no layer (graph_fn) computation).
space = FloatBox(shape=(3,), add_batch_rank=True)
# - fixed 1.0 weights, no biases
dummy_layer = NNLayer(activation=None)
test = ComponentTest(component=dummy_layer, input_spaces=dict(inputs=space))
input_ = space.sample(size=5)
test.test(("apply", input_), expected_outputs=input_)