brainpy.nn.nodes.ANN.AvgPool
brainpy.nn.nodes.ANN.AvgPool#
- class brainpy.nn.nodes.ANN.AvgPool(window_shape, strides=None, padding='VALID')[source]#
Pools the input by taking the average 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 average for each window slice.
- __init__(window_shape, strides=None, padding='VALID')[source]#
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
feedback_shapes
Output data size.
feedforward_shapes
Input data size.
is_feedback_input_supported
is_feedback_supported
is_initialized
- rtype
name
output_shape
Output data size.
state
Node current internal state.
trainable
Returns if the Node can be trained.