mindspore.train.callback._loss_monitor 源代码

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"""LossMonitor Callback class."""
from __future__ import absolute_import

import numpy as np

from mindspore import _checkparam as Validator
from mindspore.train.callback._callback import Callback, _handle_loss
from mindspore._c_expression import _collect_host_info


[文档]class LossMonitor(Callback): """ Monitor the loss in train or monitor the loss and eval metrics in fit. 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): How many steps to print once loss. During sink mode, it will print loss in the nearest step. Default: ``1`` . Raises: ValueError: If per_print_times is not an integer or less than zero. Examples: >>> from mindspore import nn >>> from mindspore.train import Model, LossMonitor >>> >>> # Define the network structure of LeNet5. Refer to >>> # https://gitee.com/mindspore/docs/blob/r2.2/docs/mindspore/code/lenet.py >>> net = LeNet5() >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') >>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9) >>> model = Model(net, loss_fn=loss, optimizer=optim) >>> # Create the dataset taking MNIST as an example. Refer to >>> # https://gitee.com/mindspore/docs/blob/r2.2/docs/mindspore/code/mnist.py >>> dataset = create_dataset() >>> loss_monitor = LossMonitor() >>> model.train(10, dataset, callbacks=loss_monitor) """ def __init__(self, per_print_times=1): super(LossMonitor, self).__init__() Validator.check_non_negative_int(per_print_times) self._per_print_times = per_print_times self._last_print_time = 0
[文档] def step_end(self, run_context): """ Print training loss at the end of step. Args: run_context (RunContext): Include some information of the model. For more details, please refer to :class:`mindspore.train.RunContext`. """ cb_params = run_context.original_args() _collect_host_info("Callback", "LossMonitor", "step_end", level=1) cur_epoch_num = cb_params.get("cur_epoch_num", 1) loss = _handle_loss(cb_params.net_outputs) 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("In epoch: {} step: {}, loss is NAN or INF, training process cannot continue, " "terminating training.".format(cur_epoch_num, cur_step_in_epoch)) # In disaster recovery scenario, the cb_params.cur_step_num may be rollback to previous step # and be less than self._last_print_time, so self._last_print_time need to be updated. if self._per_print_times != 0 and (cb_params.cur_step_num <= self._last_print_time): while cb_params.cur_step_num <= self._last_print_time: self._last_print_time -=\ max(self._per_print_times, cb_params.batch_num if cb_params.dataset_sink_mode else 1) if self._per_print_times != 0 and (cb_params.cur_step_num - self._last_print_time) >= self._per_print_times: self._last_print_time = cb_params.cur_step_num print("epoch: %s step: %s, loss is %s" % (cur_epoch_num, cur_step_in_epoch, loss), flush=True)
[文档] def on_train_epoch_end(self, run_context): """ When LossMonitor used in `model.fit`, print eval metrics at the end of epoch if current epoch should do evaluation. Args: run_context (RunContext): Include some information of the model. For more details, please refer to :class:`mindspore.train.RunContext`. """ cb_params = run_context.original_args() _collect_host_info("Callback", "LossMonitor", "train_epoch_end", level=1) metrics = cb_params.get("metrics") if metrics: print("Eval result: epoch %d, metrics: %s" % (cb_params.cur_epoch_num, metrics))