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_bernoulli_action_adapter(self):
# Last NN layer.
previous_nn_layer_space = FloatBox(shape=(16, ), add_batch_rank=True)
adapter_outputs_space = FloatBox(shape=(2, ), add_batch_rank=True)
# Action Space.
action_space = BoolBox(shape=(2, ))
action_adapter = BernoulliDistributionAdapter(
action_space=action_space, activation="relu")
test = ComponentTest(component=action_adapter,
input_spaces=dict(
inputs=previous_nn_layer_space,
adapter_outputs=adapter_outputs_space,
),
action_space=action_space)
action_adapter_params = test.read_variable_values(
action_adapter.variable_registry)
# Batch of n samples.
inputs = previous_nn_layer_space.sample(32)
expected_logits = relu(
np.matmul(
inputs, action_adapter_params[
"action-adapter/action-network/action-layer/dense/kernel"])
)
test.test(("call", inputs),
expected_outputs=expected_logits,
decimals=5)
expected_probs = sigmoid(expected_logits)
expected_log_probs = np.log(expected_probs)
test.test(("get_parameters", inputs),
expected_outputs=dict(adapter_outputs=expected_logits,
parameters=expected_probs,
probabilities=expected_probs,
log_probs=expected_log_probs),
decimals=5)