brainpy.nn.nodes.ANN.Dropout

class brainpy.nn.nodes.ANN.Dropout(prob, seed=None, **kwargs)

A layer that stochastically ignores a subset of inputs each training step.

In training, to compensate for the fraction of input values dropped (rate), all surviving values are multiplied by 1 / (1 - rate).

The parameter shared_axes allows to specify a list of axes on which the mask will be shared: we will use size 1 on those axes for dropout mask and broadcast it. Sharing reduces randomness, but can save memory.

This layer is active only during training (mode=’train’). In other circumstances it is a no-op.

Parameters

• prob (float) – Probability to keep element of the tensor.

• seed (optional, int) – The random sampling seed.

• name (str, optional) – The name of the dynamic system.

References

1

Srivastava, Nitish, et al. “Dropout: a simple way to prevent neural networks from overfitting.” The journal of machine learning research 15.1 (2014): 1929-1958.

__init__(prob, seed=None, **kwargs)

Methods

__init__(prob[, seed])
copy([name, shallow])	Returns a copy of the Node.
feedback(ff_output, **shared_kwargs)	The feedback computation function of a node.
forward(ff, **shared_kwargs)	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.