bgd.layers.conv2d module

class bgd.layers.conv2d.Convolutional2D(filter_shape, n_filters, strides=(1, 1), dilations=(1, 1), with_bias=True, copy=False, initializer=<bgd.initializers.GlorotUniformInitializer object>, bias_initializer=<bgd.initializers.ZeroInitializer object>, n_jobs=4)

Bases: bgd.layers.layer.Layer

_backward(error)

Wrapped method for applying a backward pass on input X.

_forward(X)

Wrapped method for applying a forward pass on input X.

get_parameters()

Retuns a tuple containing the parameters of the layer. If layer is non-learnable, None is returned instead. The tuple can have a size > 1 for convenience. For example, a convolutional neural layer has one array for the filters weights and one array for the biases.

update_parameters(delta_fragments)

Update parameters: base class has no learnable parameters. Subclasses that are learnable must override this method.

Parameters

delta_fragments (tuple) – Tuple of NumPy arrays. Each array is a parameter update vector and is used to update parameters using the following formula: params = params - delta_fragment. The tuple can have a size > 1 for convenience. For example, a convolutional neural layer has one array to update the filters weights and one array to update the biases: weights = weights - delta_fragments[0] biases = biases - delta_fragments[1]