brainpy.nn.nodes.ANN.GeneralConv

class brainpy.nn.nodes.ANN.GeneralConv(out_channels, kernel_size, strides=None, padding='SAME', input_dilation=None, kernel_dilation=None, groups=1, w_init=XavierNormal(scale=1.0, mode=fan_avg, in_axis=- 2, out_axis=- 1, distribution=truncated_normal, seed=None), b_init=ZeroInit, **kwargs)

Applies a convolution to the inputs.

Parameters

• out_channels – integer number of output channels.

• kernel_size – sequence[int] shape of the convolutional kernel. For 1D convolution, the kernel size can be passed as an integer. For all other cases, it must be a sequence of integers.

• strides – sequence[int] an integer or a sequence of n integers, representing the inter-window strides (default: 1).

• padding – str, sequence[int] either the string ‘SAME’, the string ‘VALID’, the string ‘CIRCULAR’ (periodic boundary conditions), or a sequence of n (low, high) integer pairs that give the padding to apply before and after each spatial dimension. A single int is interpeted as applying the same padding in all dims and passign a single int in a sequence causes the same padding to be used on both sides.

• input_dilation – integer, sequence[int] an integer or a sequence of n integers, giving the dilation factor to apply in each spatial dimension of inputs (default: 1). Convolution with input dilation d is equivalent to transposed convolution with stride d.

• kernel_dilation – integer, sequence[int] an integer or a sequence of n integers, giving the dilation factor to apply in each spatial dimension of the convolution kernel (default: 1). Convolution with kernel dilation is also known as ‘atrous convolution’.

• groups – integer, default 1. If specified divides the input features into groups.

• kernel_init – brainpy.init.Initializer initializer for the convolutional kernel.

• bias_init – brainpy.init.Initializer initializer for the bias.

__init__(out_channels, kernel_size, strides=None, padding='SAME', input_dilation=None, kernel_dilation=None, groups=1, w_init=XavierNormal(scale=1.0, mode=fan_avg, in_axis=- 2, out_axis=- 1, distribution=truncated_normal, seed=None), b_init=ZeroInit, **kwargs)

Methods

__init__(out_channels, kernel_size[, ...])
copy([name, shallow])	Returns a copy of the Node.
feedback(ff_output, **shared_kwargs)	The feedback computation function of a node.
forward(ff[, fb])	The feedforward computation function of a node.
init_fb_conn()	Initialize the feedback connections.
init_fb_output([num_batch])	Set the initial node feedback state.
init_ff_conn()	Initialize the feedforward connections.
init_state([num_batch])	Set the initial node state.
initialize([num_batch])	Initialize the node.
load_states(filename[, verbose])	Load the model states.
nodes([method, level, include_self])	Collect all children nodes.
offline_fit(targets, ffs[, fbs])	Offline training interface.
online_fit(target, ff[, fb])	Online training fitting interface.
online_init()	Online training initialization interface.
register_implicit_nodes(nodes)
register_implicit_vars(variables)
save_states(filename[, variables])	Save the model states.
set_fb_output(state)	Safely set the feedback state of the node.
set_feedback_shapes(fb_shapes)
set_feedforward_shapes(feedforward_shapes)
set_output_shape(shape)
set_state(state)	Safely set the state of the node.
train_vars([method, level, include_self])	The shortcut for retrieving all trainable variables.
unique_name([name, type_])	Get the unique name for this object.
vars([method, level, include_self])	Collect all variables in this node and the children nodes.

Attributes

data_pass	Offline fitting method.
fb_output	rtype Optional[TypeVar(Tensor, JaxArray, ndarray)]
feedback_shapes	Output data size.
feedforward_shapes	Input data size.
is_feedback_input_supported
is_feedback_supported
is_initialized	rtype bool
name
output_shape	Output data size.
state	Node current internal state.
trainable	Returns if the Node can be trained.