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Perform neural network training using the specified methods in the specified settings.


class NetworkUpdater:
  def update_wb(self, input_set, Y0_set, weights, biases, AF,	
    return weights_next, biases_next, mse_list


input_set list of numpy matrix [x]. Each x a column vector m x 1, m the number of neurons in input layer
Y0_set list of numpy matrix [Y0]. Each Y0 nx1, where n is the no of neurons in layer l=L. The true/observed values in the output layer corresponding to the input set. In another words, for each k=1,…,N, Y0_set[k] = f(x[k]) where f is the true function that our neural network is modelling and N the number of data points.
weights the collection of weights in the neural network.

weights is a list [w_l], where w_l is the collection of weights between the (l-1)-th and l-th layer for l=2,3,…,L where l=1 is the input layer, l=2 the first hidden layer and l=L is the output layer. w_l is a matrix (list of list) so that w_l[i][j] is the weight between neuron j at layer l-1 and neuron i at layer l

biases the collection of biases in the neural network.

biases is a list [b_l], where b_l is the collection of biases in the l-th layer for l=2,3,…,L

AF AF (activationFunction). Assume it is initiated.
mse_mode String.

If mse_mode=”compute_only”, then mse_list will be returned, containing the cost function MSE (mean squared value) at each epoch of training.

If mse_mode= “compute_and_print”, the MSE value at each epoch will be printed.

If mse_mode=None, mse_list is None i.e. MSE value is not computed.


verbose Bool False or integer

The larger the integer, the more information is printed. Set them to suitable integers for debugging.


return weights_next Same as weights, but has undergone 1 gradient descent iteration.
return biases_next Same as biases, but has undergone 1 gradient descent iteration.
return mse_list List of float [mse]. See mse_mode.

Example Usage 1

See Deep Learning and Neural Network with kero PART 2.

kero version: 0.6.2