1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
|
#!/usr/bin/python
from pyfann import libfann
connection_rate = 1
learning_rate = 0.7
num_input = 2
num_neurons_hidden = 4
num_output = 1
desired_error = 0.0001
max_iterations = 100000
iterations_between_reports = 1000
ann = libfann.neural_net()
ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden, num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
ann.train_on_file("../../examples/xor.data", max_iterations, iterations_between_reports, desired_error)
ann.save("nets/xor_float.net")
|
