Source code for mindspore.train.callback._history

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

import numpy as np

from mindspore.common.tensor import Tensor
from mindspore.train.callback._callback import Callback


[docs]class History(Callback): """ Records the network outputs and metrics information into a `History` object. The network outputs information will be the loss value if not custimizing the train network or eval network; if the custimized network returns a `Tensor` or `numpy.ndarray`, the mean value of network output will be recorded, if the custimized network returns a `tuple` or `list`, the first element of network outputs will be recorded. Note: Normally used in `mindspore.train.Model.train` or `mindspore.train.Model.fit`. Examples: >>> import numpy as np >>> import mindspore.dataset as ds >>> from mindspore import nn >>> from mindspore.train import Model, History >>> 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) >>> history_cb = History() >>> model = Model(network=net, optimizer=opt, loss_fn=crit, metrics={"recall"}) >>> model.train(2, train_dataset, callbacks=[history_cb]) >>> print(history_cb.epoch) >>> print(history_cb.history) {'epoch': [1, 2]} {'net_output': [1.607877, 1.6033841]} """ def __init__(self): super(History, self).__init__() self.history = {} self.epoch = None
[docs] def begin(self, run_context): """ Initialize the `epoch` property at the begin of training. Args: run_context (RunContext): Context of the `mindspore.train.Model.{train | eval}`. For more details, please refer to :class:`mindspore.train.RunContext`. """ self.epoch = {"epoch": []}
[docs] def epoch_end(self, run_context): """ Records the first element of network outputs and metrics information at the end of epoch. Args: run_context (RunContext): Context of the `mindspore.train.Model.{train | eval}`. For more details, please refer to :class:`mindspore.train.RunContext`. """ cb_params = run_context.original_args() epoch = cb_params.get("cur_epoch_num", 1) self.epoch.get("epoch").append(epoch) net_output = cb_params.net_outputs if isinstance(net_output, (tuple, list)): if isinstance(net_output[0], Tensor) and isinstance(net_output[0].asnumpy(), np.ndarray): net_output = net_output[0] if isinstance(net_output, Tensor) and isinstance(net_output.asnumpy(), np.ndarray): net_output = np.mean(net_output.asnumpy()) metrics = cb_params.get("metrics") cur_history = {"net_output": net_output} if metrics: cur_history.update(metrics) for k, v in cur_history.items(): self.history.setdefault(k, []).append(v)