Source code for mindspore.train.callback._lambda_callback

# Copyright 2021 Huawei Technologies Co., Ltd
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"""Lambda Callback class."""
from __future__ import absolute_import

from mindspore.train.callback._callback import Callback


[docs]class LambdaCallback(Callback): """ Callback for creating simple, custom callbacks. This callback is constructed with anonymous functions that will be called at the appropriate time (during `mindspore.train.Model.{train | eval | fit}`). Note that each stage of callbacks expects one positional arguments: `run_context`. .. warning:: This is an experimental API that is subject to change or deletion. Args: on_train_epoch_begin (Function): called at each train epoch begin. on_train_epoch_end (Function): called at each train epoch end. on_train_step_begin (Function): called at each train step begin. on_train_step_end (Function): called at each train step end. on_train_begin (Function): called at the beginning of model train. on_train_end (Function): called at the end of model train. on_eval_epoch_begin (Function): called at eval epoch begin. on_eval_epoch_end (Function): called at eval epoch end. on_eval_step_begin (Function): called at each eval step begin. on_eval_step_end (Function): called at each eval step end. on_eval_begin (Function): called at the beginning of model eval. on_eval_end (Function): called at the end of model eval. Examples: >>> import numpy as np >>> import mindspore.dataset as ds >>> from mindspore import nn >>> from mindspore.train import Model, LambdaCallback >>> data = {"x": np.float32(np.random.rand(64, 10)), "y": np.random.randint(0, 5, (64,))} >>> train_dataset = ds.NumpySlicesDataset(data=data).batch(32) >>> net = nn.Dense(10, 5) >>> crit = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') >>> opt = nn.Momentum(net.trainable_params(), 0.01, 0.9) >>> lambda_callback = LambdaCallback(on_train_epoch_end= ... lambda run_context: print("loss: ", run_context.original_args().net_outputs)) >>> model = Model(network=net, optimizer=opt, loss_fn=crit, metrics={"recall"}) >>> model.train(2, train_dataset, callbacks=[lambda_callback]) loss: 1.6127687 loss: 1.6106578 """ def __init__(self, on_train_epoch_begin=None, on_train_epoch_end=None, on_train_step_begin=None, on_train_step_end=None, on_train_begin=None, on_train_end=None, on_eval_epoch_begin=None, on_eval_epoch_end=None, on_eval_step_begin=None, on_eval_step_end=None, on_eval_begin=None, on_eval_end=None): super(LambdaCallback, self).__init__() self.on_train_epoch_begin = on_train_epoch_begin if on_train_epoch_begin else lambda run_context: None self.on_train_epoch_end = on_train_epoch_end if on_train_epoch_end else lambda run_context: None self.on_train_step_begin = on_train_step_begin if on_train_step_begin else lambda run_context: None self.on_train_step_end = on_train_step_end if on_train_step_end else lambda run_context: None self.on_train_begin = on_train_begin if on_train_begin else lambda run_context: None self.on_train_end = on_train_end if on_train_end else lambda run_context: None self.on_eval_epoch_begin = on_eval_epoch_begin if on_eval_epoch_begin else lambda run_context: None self.on_eval_epoch_end = on_eval_epoch_end if on_eval_epoch_end else lambda run_context: None self.on_eval_step_begin = on_eval_step_begin if on_eval_step_begin else lambda run_context: None self.on_eval_step_end = on_eval_step_end if on_eval_step_end else lambda run_context: None self.on_eval_begin = on_eval_begin if on_eval_begin else lambda run_context: None self.on_eval_end = on_eval_end if on_eval_end else lambda run_context: None