def train(self, input_list, target_list):
"""
Trains the neural network using the given input_list and target_array.
Args:
input_list: List of numbers containing the input values.
target_list: List of numbers containing the target values.
Returns:
Returns the resulting neural network.
"""
inputs = Matrix.from_list(input_list)
hidden = Matrix.dot_product(self.weights_ih, inputs)
hidden.add_matrix(self.bias_h)
hidden.map(self.activation_function.func)
outputs = Matrix.dot_product(self.weights_ho, hidden)
outputs.add_matrix(self.bias_o)
outputs.map(self.activation_function.func)
targets = Matrix.from_list(target_list)
# Calculate the error
output_errors = targets.sub_matrix(outputs, False)
# Calculate gradient
gradients = outputs.map(self.activation_function.dfunc, False)
gradients.mul_matrix(output_errors)
gradients.mul_scalar(self.learning_rate)
# Calculate deltas
hidden_t = Matrix.transpose(hidden)
weights_ho_deltas = Matrix.dot_product(gradients, hidden_t)
# Adjust the weights and the biases by the calculated deltas
self.weights_ho.add_matrix(weights_ho_deltas)
self.bias_o.add_matrix(gradients)
# Calculate the hidden layer errors
weights_ho_t = Matrix.transpose(self.weights_ho)
hidden_errors = Matrix.dot_product(weights_ho_t, output_errors)
# Calculate hidden gradient
hidden_gradients = hidden.map(self.activation_function.dfunc, False)
hidden_gradients.mul_matrix(hidden_errors)
hidden_gradients.mul_scalar(self.learning_rate)
# Calculate input -> hidden deltas
inputs_t = Matrix.transpose(inputs)
weights_ih_deltas = Matrix.dot_product(hidden_gradients, inputs_t)
# Adjust the weights and the biases by the calculated deltas
self.weights_ih.add_matrix(weights_ih_deltas)
self.bias_h.add_matrix(hidden_gradients)
return self
def predict(self, input_list):
"""
Calculates the outputs using the given inputs.
Args:
input_list: List of numbers containing the input values.
Returns:
Returns a list of numbers containing the outputs.
"""
inputs = Matrix.from_list(input_list)
hidden = Matrix.dot_product(self.weights_ih, inputs)
hidden.add_matrix(self.bias_h)
hidden.map(self.activation_function.func)
outputs = Matrix.dot_product(self.weights_ho, hidden)
outputs.add_matrix(self.bias_o)
outputs.map(self.activation_function.func)
return outputs.to_list()
