"output": [1]
}, {
"input": [0, 0],
"output": [0]
}]
for input_output in inputs_outputs:
print(
f"input = {input_output['input']} , output = {net.GetOutputs(input_output['input'])}"
)
print()
last_loss = 1
timer = time.time()
for _ in range(8000):
loss = 0
for input_output in inputs_outputs:
net.CalculateSlopeValues(input_output["input"], input_output["output"])
loss += net.GetLoss(input_output["input"], input_output["output"])
loss_change = loss - last_loss
net.MutateSlopeValues(coefficient=loss * 2)
print(loss)
last_loss = loss
print(time.time() - timer)
print()
for input_output in inputs_outputs:
print(
f"input = {input_output['input']} , output = {net.GetOutputs(input_output['input'])}"
)
print()
# hidden_weights = np.array([[0.8, 0.4], [0.1, 0.05]])
net.hidden_nodes[0].input_links[0].factor = 0.8
net.hidden_nodes[0].input_links[1].factor = 0.1
net.hidden_nodes[1].input_links[0].factor = 0.4
net.hidden_nodes[1].input_links[1].factor = 0.05
# output_bias = np.array([[0.7]])
net.output_nodes[0].bias = 0.7
# output_weights = np.array([[0.35], [0.21]])
net.output_nodes[0].input_links[0].factor = 0.35
net.output_nodes[0].input_links[1].factor = 0.21
inputs = [[0, 0], [0, 1], [1, 0], [1, 1]]
expected_output = [[0], [1], [1], [0]]
for index in range(4):
output = net.GetOutputs(inputs[index])
print(output)
net.CalculateSlopeValues(inputs[index], expected_output[index])
for node in net.node_dictionary.values():
print(node, end="")
if isinstance(node, InputLinkedNode):
print(f"bias_change = {node.cost_bias_der}")
print("----links----")
for link in node.input_links:
link: NodeLink
print(f"factor change = {link.cost_factor_der}")
print()
