Source code for epynn.dense.backward

# EpyNN/epynn/dense/
# Related third party imports
import numpy as np

# Local application/library specific imports
from epynn.commons.maths import hadamard

[docs]def initialize_backward(layer, dX): """Backward cache initialization. :param layer: An instance of dense layer. :type layer: :class:`epynn.dense.models.Dense` :param dX: Output of backward propagation from next layer. :type dX: :class:`numpy.ndarray` :return: Input of backward propagation for current layer. :rtype: :class:`numpy.ndarray` """ dA = layer.bc['dA'] = dX return dA
[docs]def dense_backward(layer, dX): """Backward propagate error gradients to previous layer. """ # (1) Initialize cache dA = initialize_backward(layer, dX) # (2) Gradient of the loss with respect to Z dZ = layer.bc['dZ'] = hadamard( dA, layer.activate(layer.fc['Z'], deriv=True) ) # dL/dZ # (3) Gradient of the loss with respect to X dX = layer.bc['dX'] =, layer.p['W'].T) # dL/dX return dX # To previous layer