def test_neural_network_error():
input_node = InputNode(0)
relu = ReluNode(input_node)
network = NeuralNetwork(relu, [input_node])
inputs = [-2]
label = 1
assert_that(network.evaluate(inputs)).is_equal_to(0)
assert_that(network.compute_error(inputs, label)).is_equal_to(1)
def test_pretty_print():
const = ConstantNode()
input_node = InputNode(0)
sigmoid = SigmoidNode(const)
sigmoid.evaluate([])
relu = ReluNode(input_node)
relu.evaluate([2])
assert_that(sigmoid.pretty_print()).is_equal_to(
"Sigmoid output=0.73\n Constant(1)\n")
assert_that(relu.pretty_print()).is_equal_to(
"Relu output=2.00\n InputNode(0) output = 2.00\n")
network = single_linear_relu_network(3, [-20, 3, 2, 1])
network.evaluate([1, 2, 3])
network.compute_error([1, 2, 3], 1)
assert_that(network.pretty_print()).is_equal_to("""Relu output=0.00
Linear weights=-20.00,3.00,2.00,1.00 gradient=0.00,0.00,0.00,0.00 output=-10.00
Constant(1)
InputNode(0) output = 1.00
InputNode(1) output = 2.00
InputNode(2) output = 3.00
""")
def test_relu_evaluate_negative():
input_node = InputNode(0)
relu = ReluNode(input_node)
assert_that(relu.evaluate([-2])).is_equal_to(0)
def test_input_output():
node = InputNode(0)
assert_that(node.compute_output([3])).is_equal_to(3)
assert_that(node.compute_output([-4])).is_equal_to(-4)
def test_relu_local_parameter_gradient_empty():
input_node = InputNode(0)
relu = ReluNode(input_node)
relu.evaluate([3])
assert_that(len(relu.local_parameter_gradient)).is_equal_to(0)
def test_relu_local_gradient_negative():
input_node = InputNode(0)
relu = ReluNode(input_node)
relu.evaluate([-3])
assert_that(relu.local_gradient_for_argument(input_node)).is_equal_to(0)