def _test_back_propagate():
mynet = Network(4, 2, 3)
mynet.create_connections()
mynet.feed_forward([1, 1, 1, 1])
print(mynet.output_ouputs)
mynet.matrix_who = np.matrix([[0.11, 0.12, 0.13], [0.21, 0.22, 0.23]])
mynet.output_ouputs = np.matrix([[0.1, 0.2, 0.3]])
target = [0.9, 0.8, 0.7]
mynet.back_propagate(target)
def _test_result_from_taring():
mynet = Network(3, 2, 3)
mynet.create_connections()
mynet.feed_forward([1, 1, 1])
print(mynet.output_ouputs)
target = [1, 0, 0]
mynet.back_propagate(target)
mynet.feed_forward([1, 1, 0])
print(mynet.output_ouputs)
def _test_ouput_deltas():
mynet = Network(4, 2, 3)
mynet.create_connections()
mynet.feed_forward([1, 1, 1, 1])
print(mynet.output_ouputs)
mynet.matrix_who = np.matrix([[0.11, 0.12, 0.13], [0.21, 0.22, 0.23]])
mynet.output_ouputs = np.matrix([[0.1, 0.2, 0.3]])
target = [0.9, 0.8, 0.7]
ods = mynet.get_output_deltas(target)
np.testing.assert_array_almost_equal(
np.matrix([(0.9 - 0.1) * mynet.gradient(0.1),
(0.8 - 0.2) * mynet.gradient(0.2),
(0.7 - 0.3) * mynet.gradient(0.3)]), ods)
def _test_train_urls():
mynet = Network(3, 4, 3)
mynet.create_connections()
#mynet.feed_forward([1,1,1])
#print(mynet.output_ouputs)
print(mynet.matrix_wih)
print(mynet.matrix_who)
for i in range(1000):
#print('='*20)
mynet.train([1, 0, 1], [1, 0, 0])
#print(mynet.matrix_wih)
#print(mynet.matrix_who)
# print('='*30)
# for i in range(30):
mynet.train([0, 1, 1], [0, 1, 0])
# print('='*30)
# for i in range(30):
mynet.train([1, 0, 0], [0, 0, 1])
print('Results:')
mynet.feed_forward([1, 0, 1])
print('{} expected {}'.format(mynet.output_ouputs, [1, 0, 0]))
mynet.feed_forward([0, 1, 1])
print('{} expected {}'.format(mynet.output_ouputs, [0, 1, 0]))
mynet.feed_forward([1, 0, 1])
print('{} expected {}'.format(mynet.output_ouputs, [1, 0, 0]))
mynet.feed_forward([0, 0, 1])
print('{} expected {}'.format(mynet.output_ouputs, [1, 0, 0]))
def _test_train_31():
mynet = Network(3, 3, 1)
mynet.create_connections()
print(mynet.matrix_wih)
print(mynet.matrix_who)
for i in range(10000):
mynet.train([0, 0, 1], [0], learning_rate=1)
# print(mynet.matrix_wih)
# print(mynet.matrix_who)
mynet.train([1, 1, 1], [1], learning_rate=1)
mynet.train([1, 0, 1], [1], learning_rate=1)
mynet.train([0, 1, 1], [0], learning_rate=1)
mynet.feed_forward([1, 0, 0])
print('[1,0,0]= %s' % mynet.output_ouputs)
mynet.feed_forward([1, 0, 0])
print('[0,0,0]= %s' % mynet.output_ouputs)
def _test_hidden_deltas():
mynet = Network(4, 2, 3)
mynet.create_connections()
mynet.feed_forward([1, 1, 1, 1])
print(mynet.output_ouputs)
mynet.matrix_who = np.matrix([[0.11, 0.12, 0.13], [0.21, 0.22, 0.23]])
mynet.output_ouputs = np.matrix([[0.1, 0.2, 0.3]])
target = [0.9, 0.8, 0.7]
ods = mynet.get_output_deltas(target)
mynet.matrix_who = np.matrix([[1, 2, 3], [4, 5, 6]])
mynet.hidden_outputs = np.matrix([[0.5, 0.6]])
ods = np.matrix([[0.05, 0.06, 0.07]])
hds = mynet.get_hidden_deltas(ods)
np.testing.assert_array_almost_equal(
np.matrix([mynet.gradient(0.5) * 0.38,
mynet.gradient(0.6) * 0.92]), hds)
def _test_train_xor_1o():
mynet = Network(2, 3, 1)
mynet.create_connections()
# print(mynet.matrix_wih)
# print(mynet.matrix_who)
for i in range(1000):
# 1 xor 1 = 0 [prob 0, prob1]
mynet.train([1, 1], [0], learning_rate=1)
# print(mynet.matrix_wih)
# print(mynet.matrix_who)https://stevenmiller888.github.io/mind-how-to-build-a-neural-network/
mynet.train([1, 0], [1], learning_rate=1)
mynet.train([0, 0], [0], learning_rate=1)
# mynet.train([0, 1], [1], learning_rate=1)
mynet.feed_forward([1, 1])
print('[1,1]= %s' % mynet.output_ouputs)
mynet.feed_forward([0, 0])
print('[0,0]= %s' % mynet.output_ouputs)
mynet.feed_forward([1, 0])
print('[1,0]= %s' % mynet.output_ouputs)
mynet.feed_forward([0, 1])
print('[0,1]= %s' % mynet.output_ouputs)
mynet.feed_forward([1, 1])
print('[1,1]= %s' % mynet.output_ouputs)
def _test_forward():
mynet = Network(2, 1, 3)
mynet.create_connections()
mynet.feed_forward([1, 1])
print(mynet.output_ouputs)