mxnet.module.PythonLossModule¶
-
class
mxnet.module.
PythonLossModule
(name='pyloss', data_names=('data', ), label_names=('softmax_label', ), logger=<module 'logging' from '/var/lib/jenkins/miniconda3/envs/mxnet-docs/lib/python3.7/logging/__init__.py'>, grad_func=None)[source]¶ A convenient module class that implements many of the module APIs as empty functions.
- Parameters
name (str) – Names of the module. The outputs will be named [name + ‘_output’].
data_names (list of str) – Defaults to
['data']
. Names of the data expected by this module. Should be a list of only one name.label_names (list of str) – Default
['softmax_label']
. Names of the labels expected by the module. Should be a list of only one name.grad_func (function) – Optional. If not
None
, should be a function that takes scores and labels, both of type NDArray, and return the gradients with respect to the scores according to this loss function. The return value could be a numpy array or an NDArray.
-
__init__
(name='pyloss', data_names=('data', ), label_names=('softmax_label', ), logger=<module 'logging' from '/var/lib/jenkins/miniconda3/envs/mxnet-docs/lib/python3.7/logging/__init__.py'>, grad_func=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([name, data_names, label_names, …])Initialize self.
backward
([out_grads])Backward computation.
bind
(data_shapes[, label_shapes, …])Binds the symbols to construct executors.
fit
(train_data[, eval_data, eval_metric, …])Trains the module parameters.
forward
(data_batch[, is_train])Forward computation.
forward_backward
(data_batch)A convenient function that calls both
forward
andbackward
.get_input_grads
([merge_multi_context])Gets the gradients to the inputs, computed in the previous backward computation.
get_outputs
([merge_multi_context])Gets outputs of the previous forward computation.
get_params
()Gets parameters, those are potentially copies of the the actual parameters used to do computation on the device.
get_states
([merge_multi_context])Gets states from all devices
init_optimizer
([kvstore, optimizer, …])Installs and initializes optimizers.
init_params
([initializer, arg_params, …])Initializes the parameters and auxiliary states.
install_monitor
(mon)Installs monitor on all executors.
iter_predict
(eval_data[, num_batch, reset, …])Iterates over predictions.
load_params
(fname)Loads model parameters from file.
predict
(eval_data[, num_batch, …])Runs prediction and collects the outputs.
prepare
(data_batch[, sparse_row_id_fn])Prepares the module for processing a data batch.
save_params
(fname)Saves model parameters to file.
score
(eval_data, eval_metric[, num_batch, …])Runs prediction on
eval_data
and evaluates the performance according to the giveneval_metric
.set_params
(arg_params, aux_params[, …])Assigns parameter and aux state values.
set_states
([states, value])Sets value for states.
update
()Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch.
update_metric
(eval_metric, labels[, pre_sliced])Evaluates and accumulates evaluation metric on outputs of the last forward computation.
Attributes
data_names
A list of names for data required by this module.
data_shapes
A list of (name, shape) pairs specifying the data inputs to this module.
label_shapes
A list of (name, shape) pairs specifying the label inputs to this module.
output_names
A list of names for the outputs of this module.
output_shapes
A list of (name, shape) pairs specifying the outputs of this module.
symbol
Gets the symbol associated with this module.