update_wb()

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

kero.multib.NeuralNetwork.py

class NetworkUpdater:
  def update_wb(self, input_set, Y0_set, weights, biases, AF,	
		mse_mode="compute_only",	
		verbose=False):
    return weights_next, biases_next, mse_list

Arguments/Return

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.

Default=”compute_only”

verbose Bool False or integer

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

Default=False

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