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PythonNN_demo003
def train(self, input_list, target_list):
inputs = np.array(input_list, ndmin=2).T
targets = np.array(target_list, ndmin=2).T
# signals into hidden layer
hidden_inputs = np.dot(self.wih, inputs)
# signals from hidden layer
hidden_outputs = self.activation_func(hidden_inputs)
# signals into output
final_inputs = np.dot(self.who, hidden_outputs)
# signals from output
outputs = self.activation_func(final_inputs)
# error between hidden and output
output_errors = targets - outputs
# error between input and hiddent
hidden_errors = np.dot(self.who.T, output_errors)
# updating weight between hidden and output layers
self.who += self.alpha * np.dot((output_errors * outputs * (1.0 - outputs)),
np.transpose(hidden_outputs))
# updating weight between input and hidden
self.wih += self.alpha * np.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),
np.transpose(inputs))
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