brainpy.nn.nodes.ANN.MaxPool

class brainpy.nn.nodes.ANN.MaxPool(window_shape, strides=None, padding='VALID')

Pools the input by taking the maximum over a window.

Parameters

• window_shape – tuple a shape tuple defining the window to reduce over.

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

• padding – str, sequence[int] either the string ‘SAME’, the string ‘VALID’, or a sequence of n (low, high) integer pairs that give the padding to apply before and after each spatial dimension (default: ‘VALID’).

Returns

The maximum for each window slice.

__init__(window_shape, strides=None, padding='VALID')

Pooling functions are implemented using the ReduceWindow XLA op.

Parameters

• init_v – scalar the initial value for the reduction

• reduce_fn – callable a reduce function of the form (T, T) -> T.

• window_shape – tuple a shape tuple defining the window to reduce over.

• strides – sequence[int] a sequence of n integers, representing the inter-window strides.

• padding –

  str, sequence[int]

  either the string ‘SAME’, the string ‘VALID’, or a sequence of n (low, high) integer pairs that give the padding to apply before and after each spatial dimension.

  Returns:

  The output of the reduction for each window slice.

Methods

__init__(window_shape[, strides, padding])	Pooling functions are implemented using the ReduceWindow XLA op.
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.