def test_simple_nn_using_layers(self):
# Space must contain batch dimension (otherwise, NNlayer will complain).
space = FloatBox(shape=(4, ), add_batch_rank=True)
# Create a simple neural net from json.
nn_layers = config_from_path("configs/test_simple_nn.json")
neural_net = NeuralNetwork(*nn_layers["layers"])
# Do not seed, we calculate expectations manually.
test = ComponentTest(component=neural_net,
input_spaces=dict(inputs=space))
# Batch of size=3.
input_ = space.sample(4)
# 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(("call", input_), expected_outputs=expected, decimals=5)
test.terminate()
def test_keras_style_simple_nn(self):
# Input Space of the network.
input_space = FloatBox(shape=(3,), add_batch_rank=True)
# Create a DenseLayer with a fixed `call` method input space for the arg `inputs`.
output1 = DenseLayer(units=5, activation="linear", scope="a")(input_space)
# Create a DenseLayer whose `inputs` arg is the resulting DataOpRec of output1's `call` output.
output2 = DenseLayer(units=7, activation="relu", scope="b")(output1)
# This will trace back automatically through the given output DataOpRec(s) and add all components
# on the way to the input-space to this network.
neural_net = NeuralNetwork(outputs=output2)
test = ComponentTest(component=neural_net, input_spaces=dict(inputs=input_space))
# Batch of size=n.
input_ = input_space.sample(5)
# Calculate output manually.
var_dict = neural_net.get_variables("a/dense/kernel", "a/dense/bias", "b/dense/kernel", "b/dense/bias", global_scope=False)
w1_value = test.read_variable_values(var_dict["a/dense/kernel"])
b1_value = test.read_variable_values(var_dict["a/dense/bias"])
w2_value = test.read_variable_values(var_dict["b/dense/kernel"])
b2_value = test.read_variable_values(var_dict["b/dense/bias"])
expected = relu(dense_layer(dense_layer(input_, w1_value, b1_value), w2_value, b2_value))
test.test(("call", input_), expected_outputs=expected, decimals=5)
test.terminate()