def test_simple_nn(self):
# Space must contain batch dimension (otherwise, NNlayer will complain).
space = FloatBox(shape=(3, ), add_batch_rank=True)
# Create a simple neural net from json.
neural_net = NeuralNetwork.from_spec(
config_from_path(
"configs/test_simple_nn.json")) # type: NeuralNetwork
# Do not seed, we calculate expectations manually.
test = ComponentTest(component=neural_net,
input_spaces=dict(nn_input=space))
# Batch of size=3.
input_ = np.array([[0.1, 0.2, 0.3], [1.0, 2.0, 3.0],
[10.0, 20.0, 30.0]])
# Calculate output manually.
var_dict = neural_net.get_variables("hidden-layer/dense/kernel",
"hidden-layer/dense/bias",
global_scope=False)
w1_value = test.read_variable_values(
var_dict["hidden-layer/dense/kernel"])
b1_value = test.read_variable_values(
var_dict["hidden-layer/dense/bias"])
expected = dense_layer(input_, w1_value, b1_value)
test.test(("apply", input_),
expected_outputs=dict(output=expected),
decimals=5)
test.terminate()
def test_add_layer_to_simple_nn(self):
# Space must contain batch dimension (otherwise, NNlayer will complain).
space = FloatBox(shape=(3, ), add_batch_rank=True)
# Create a simple neural net from json.
neural_net = NeuralNetwork.from_spec(
config_from_path(
"configs/test_simple_nn.json")) # type: NeuralNetwork
# Add another layer to it.
neural_net.add_layer(DenseLayer(units=10, scope="last-layer"))
# Do not seed, we calculate expectations manually.
test = ComponentTest(component=neural_net,
input_spaces=dict(nn_input=space))
# Batch of size=3.
input_ = space.sample(3)
# Calculate output manually.
var_dict = test.read_variable_values(neural_net.variable_registry)
expected = dense_layer(
dense_layer(input_,
var_dict["test-network/hidden-layer/dense/kernel"],
var_dict["test-network/hidden-layer/dense/bias"]),
var_dict["test-network/last-layer/dense/kernel"],
var_dict["test-network/last-layer/dense/bias"])
test.test(("apply", input_),
expected_outputs=dict(output=expected),
decimals=5)
test.terminate()
def test_lstm_nn(self):
# Space must contain batch dimension (otherwise, NNlayer will complain).
#units = 3
batch_size = 2
time_steps = 4
input_nodes = 2
input_space = FloatBox(shape=(input_nodes, ),
add_batch_rank=True,
add_time_rank=True)
#internal_states_space = Tuple(FloatBox(shape=(units,)), FloatBox(shape=(units,)), add_batch_rank=True)
neural_net = NeuralNetwork.from_spec(
config_from_path("configs/test_dense_to_lstm_nn.json"))
# Do not seed, we calculate expectations manually.
test = ComponentTest(component=neural_net,
input_spaces=dict(inputs=input_space))
# Batch of size=2, time-steps=3.
input_ = input_space.sample((batch_size, time_steps))
# Calculate output manually.
w0_value = test.read_variable_values(
neural_net.
variable_registry["test-lstm-network/dense-layer/dense/kernel"])
b0_value = test.read_variable_values(
neural_net.
variable_registry["test-lstm-network/dense-layer/dense/bias"])
lstm_w_value = test.read_variable_values(
neural_net.
variable_registry["test-lstm-network/lstm-layer/lstm-cell/kernel"])
lstm_b_value = test.read_variable_values(
neural_net.
variable_registry["test-lstm-network/lstm-layer/lstm-cell/bias"])
d0_out = dense_layer(input_, w0_value, b0_value)
lstm_out, last_internal_states = lstm_layer(d0_out,
lstm_w_value,
lstm_b_value,
time_major=False)
expected = [lstm_out, last_internal_states]
test.test(("call", input_),
expected_outputs=tuple(expected),
decimals=5)
test.terminate()