Source code for mindspore.train.callback._loss_monitor

# Copyright 2020 Huawei Technologies Co., Ltd
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"""LossMonitor Callback class."""

import numpy as np
from mindspore.common.tensor import Tensor

from ._callback import Callback


[docs]class LossMonitor(Callback): """ Monitor the loss in training. If the loss is NAN or INF, it will terminate training. Note: If per_print_times is 0, do not print loss. Args: per_print_times (int): Print the loss every seconds. Default: 1. Raises: ValueError: If per_print_times is not an integer or less than zero. """ def __init__(self, per_print_times=1): super(LossMonitor, self).__init__() if not isinstance(per_print_times, int) or per_print_times < 0: raise ValueError("The argument 'per_print_times' must be int and >= 0, " "but got {}".format(per_print_times)) self._per_print_times = per_print_times
[docs] def step_end(self, run_context): """ Print training loss at the end of step. Args: run_context (RunContext): Context of the train running. """ cb_params = run_context.original_args() loss = cb_params.net_outputs if isinstance(loss, (tuple, list)): if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray): loss = loss[0] if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray): loss = np.mean(loss.asnumpy()) cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1 if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)): raise ValueError("epoch: {} step: {}. Invalid loss, terminating training.".format( cb_params.cur_epoch_num, cur_step_in_epoch)) if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0: print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num, cur_step_in_epoch, loss), flush=True)