self.inputs[:, np.newaxis]
assert self.weights.shape == gradient.shape
self.weights -= gradient*learning_rate
def calculate_delta(self):
"""
Calculate the delta values associated with neurons in this hidden layer.
equivalent to g'(a(j))*sum(delta(k)w(kj))
"""
next_deltas = self.next_layer.get_deltas()
next_weights = self.next_layer.get_weights()
next_errors = np.sum(np.tile(next_deltas, (len(next_weights), 1))*\
next_weights, axis=1)
return self.output_wrt_inputs()*next_errors
if __name__ == "__main__":
from output_layer import OutputLayer
from functions import *
hl = HiddenLayer(2, 2, sigmoid_vectorized, sigmoid_derv_vectorized, 0)
ol = OutputLayer(2, 2, sigmoid_vectorized, sigmoid_derv_vectorized, error_derv, 0)
hl.set_next_layer(ol)
inputs = np.array([-0.7, 0.7])
targets = np.array([0.0, 0.5])
for i in range(10000):
print(hl.bias)
hl.forward_pass(inputs)
ol.forward_pass(hl.outputs)
ol.update_weight(targets, 0.5)
hl.update_weight(0.4)
print(ol.outputs)
