brainpy.BPFF
brainpy.BPFF#
- class brainpy.BPFF(target, loss_fun, optimizer=None, loss_has_aux=False, logger=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>, seed=None, shuffle_data=None, **kwargs)[source]#
The trainer implementing back propagation algorithm for feedforward neural networks.
For more parameters, users should refer to
DSRunner
.- __init__(target, loss_fun, optimizer=None, loss_has_aux=False, logger=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>, seed=None, shuffle_data=None, **kwargs)#
Methods
__init__
(target, loss_fun[, optimizer, ...])cpu
()Move all variable into the CPU device.
cuda
()Move all variables into the GPU device.
fit
(train_data[, test_data, num_epoch, ...])Fit the target model according to the given training data.
get_hist_metric
([phase, metric, which])Get history losses.
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.
predict
(inputs[, reset_state, shared_args, ...])Predict a series of input data with the given target model.
register_implicit_nodes
(*nodes[, node_cls])register_implicit_vars
(*variables, ...)reset_state
()Reset state of the
DSRunner
.run
(*args, **kwargs)Same as
predict()
.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.
vars
([method, level, include_self, ...])Collect all variables in this node and the children nodes.
Attributes
name
Name of the model.
test_losses
train_losses