brainpy.layers.Conv2d#

class brainpy.layers.Conv2d(in_channels, out_channels, kernel_size, stride=None, strides=None, padding='SAME', lhs_dilation=1, rhs_dilation=1, groups=1, w_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=-2, out_axis=-1, distribution=truncated_normal, rng=[2813569993 2134935709]), b_initializer=ZeroInit, mask=None, mode=None, name=None)[source]#

Two-dimensional convolution.

Parameters

  • in_channels (int) – The number of input channels.

  • out_channels (int) – The number of output channels.

  • kernel_size (int, sequence of int) – The 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.

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

  • padding (str, int, sequence of int, sequence of tuple) – 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.

  • lhs_dilation (int, sequence of 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.

  • rhs_dilation (int, sequence of 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 (int) – If specified, divides the input features into groups. default 1.

  • w_initializer (Callable, ArrayType, Initializer) – The initializer for the convolutional kernel.

  • b_initializer (Callable, ArrayType, Initializer) – The initializer for the bias.

  • mask (ArrayType, Optional) – The optional mask of the weights.

  • mode (Mode) – The computation mode of the current object. Default it is training.

  • name (str, Optional) – The name of the object.

__init__(in_channels, out_channels, kernel_size, stride=None, strides=None, padding='SAME', lhs_dilation=1, rhs_dilation=1, groups=1, w_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=-2, out_axis=-1, distribution=truncated_normal, rng=[2813569993 2134935709]), b_initializer=ZeroInit, mask=None, mode=None, name=None)[source]#

Methods

__init__(in_channels, out_channels, kernel_size)

clear_input()

cpu()

Move all variable into the CPU device.

cuda()

Move all variables into the GPU device.

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

load_state_dict(state_dict[, warn])

Copy parameters and buffers from state_dict into this module and its descendants.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

offline_fit(target, fit_record)

offline_init()

online_fit(target, fit_record)

online_init()

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes[, node_cls])

register_implicit_vars(*variables, ...)

reset([batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state([batch_size])

Reset function which reset the states in the model.

save_states(filename[, variables])

Save the model states.

state_dict()

Returns a dictionary containing a whole state of the module.

to(device)

Moves all variables into the given device.

tpu()

Move all variables into the TPU device.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

tree_flatten()

Flattens the object as a PyTree.

tree_unflatten(aux, dynamic_values)

New in version 2.3.1.

unique_name([name, type_])

Get the unique name for this object.

update(*args)

The function to specify the updating rule.

update_local_delays([nodes])

Update local delay variables.

vars([method, level, include_self, ...])

Collect all variables in this node and the children nodes.

Attributes

global_delay_data

Global delay data, which stores the delay variables and corresponding delay targets.

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.