module.py

class Module(object):
    """ Base class for neural network's layers
    """
    def forward(self, X):
        """ Apply the layer function to the input data
        Parameters
        ----------
        X : array-like, shape = [n_samples, depth_in, height_in, width_in]
        Returns
        -------
        transformed data : array-like, shape = [n_samples, depth_out, height_out, width_out]
        """
        raise NotImplementedError()

    def __call__(self, X):
        return self.forward(X)

    def backward(self, output_grad):
        """ Compute the gradient of the loss with respect to its parameters and to its input
        Parameters
        ----------
        output_grad : array-like, shape = [n_samples, depth_out, height_out, width_out]
                      gradient returned by the above layer.
        Returns
        -------
        gradient : array-like, shape = [n_samples, depth_in, height_in, width_in]
                   gradient to be forwarded to bottom layers
        """
        raise NotImplementedError()

    def step(self, optimizer):
        """ Do an optimization step in the direction given by the optimizer
        Parameters
        ----------
        optimizer : instance of Optimizer
        """
        self._bias = optimizer(id(self), 'bias', self._bias, self._grad_bias)
        self._weight = optimizer(id(self), 'weight', self._weight, self._grad_weight)