forked from mindspore-Ecosystem/mindspore
!42104 move Model and Callbacks from mindspore to mindspore.train
Merge pull request !42104 from 吕昱峰（Nate.River）/code_docs_master
This commit is contained in:
Gitee
commit
3016e92c1c
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@ -63,32 +63,6 @@ mindspore
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mindspore.get_algo_parameters
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mindspore.reset_algo_parameters
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模型
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-----
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.. mscnautosummary::
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:toctree: mindspore
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mindspore.Model
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回调函数
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---------
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.. mscnautosummary::
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:toctree: mindspore
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mindspore.Callback
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mindspore.CheckpointConfig
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mindspore.EarlyStopping
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mindspore.History
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mindspore.LambdaCallback
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mindspore.LearningRateScheduler
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mindspore.LossMonitor
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mindspore.ModelCheckpoint
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mindspore.ReduceLROnPlateau
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mindspore.RunContext
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mindspore.TimeMonitor
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数据处理工具
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-------------------
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@ -0,0 +1,28 @@
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mindspore.train
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===============
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模型
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-----
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.. mscnautosummary::
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:toctree: mindspore
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mindspore.train.Model
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回调函数
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---------
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.. mscnautosummary::
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:toctree: mindspore
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mindspore.train.Callback
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mindspore.train.CheckpointConfig
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mindspore.train.EarlyStopping
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mindspore.train.History
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mindspore.train.LambdaCallback
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mindspore.train.LearningRateScheduler
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mindspore.train.LossMonitor
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mindspore.train.ModelCheckpoint
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mindspore.train.ReduceLROnPlateau
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mindspore.train.RunContext
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mindspore.train.TimeMonitor
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@ -1,7 +1,7 @@
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mindspore.Callback
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===================
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mindspore.train.Callback
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========================
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.. py:class:: mindspore.Callback
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.. py:class:: mindspore.train.Callback
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用于构建Callback函数的基类。Callback函数是一个上下文管理器，在运行模型时被调用。
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可以使用此机制进行一些自定义操作。
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@ -1,7 +1,7 @@
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mindspore.CheckpointConfig
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===========================
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mindspore.train.CheckpointConfig
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================================
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.. py:class:: mindspore.CheckpointConfig(save_checkpoint_steps=1, save_checkpoint_seconds=0, keep_checkpoint_max=5, keep_checkpoint_per_n_minutes=0, integrated_save=True, async_save=False, saved_network=None, append_info=None, enc_key=None, enc_mode='AES-GCM', exception_save=False)
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.. py:class:: mindspore.train.CheckpointConfig(save_checkpoint_steps=1, save_checkpoint_seconds=0, keep_checkpoint_max=5, keep_checkpoint_per_n_minutes=0, integrated_save=True, async_save=False, saved_network=None, append_info=None, enc_key=None, enc_mode='AES-GCM', exception_save=False)
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保存checkpoint时的配置策略。
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@ -1,7 +1,7 @@
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mindspore.EarlyStopping
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================================
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mindspore.train.EarlyStopping
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=============================
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.. py:class:: mindspore.EarlyStopping(monitor='eval_loss', min_delta=0, patience=0, verbose=False, mode='auto', baseline=None, restore_best_weights=False)
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.. py:class:: mindspore.train.EarlyStopping(monitor='eval_loss', min_delta=0, patience=0, verbose=False, mode='auto', baseline=None, restore_best_weights=False)
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当监控的指标停止改进时停止训练。
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@ -1,7 +1,7 @@
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mindspore.History
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===========================
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mindspore.train.History
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=======================
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.. py:class:: mindspore.History
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.. py:class:: mindspore.train.History
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将网络输出和评估指标的相关信息记录到 `History` 对象中。
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@ -1,7 +1,7 @@
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mindspore.LambdaCallback
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===========================
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mindspore.train.LambdaCallback
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==============================
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.. py:class:: mindspore.LambdaCallback(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)
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.. py:class:: mindspore.train.LambdaCallback(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)
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用于自定义简单的callback。
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@ -1,7 +1,7 @@
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mindspore.LearningRateScheduler
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================================
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mindspore.train.LearningRateScheduler
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=====================================
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.. py:class:: mindspore.LearningRateScheduler(learning_rate_function)
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.. py:class:: mindspore.train.LearningRateScheduler(learning_rate_function)
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用于在训练期间更改学习率。
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@ -1,7 +1,7 @@
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mindspore.LossMonitor
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================================
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mindspore.train.LossMonitor
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===========================
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.. py:class:: mindspore.LossMonitor(per_print_times=1)
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.. py:class:: mindspore.train.LossMonitor(per_print_times=1)
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训练场景下，监控训练的loss；边训练边推理场景下，监控训练的loss和推理的metrics。
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@ -1,7 +1,7 @@
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mindspore.Model
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================
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mindspore.train.Model
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======================
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.. py:class:: mindspore.Model(network, loss_fn=None, optimizer=None, metrics=None, eval_network=None, eval_indexes=None, amp_level="O0", boost_level="O0", **kwargs)
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.. py:class:: mindspore.train.Model(network, loss_fn=None, optimizer=None, metrics=None, eval_network=None, eval_indexes=None, amp_level="O0", boost_level="O0", **kwargs)
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模型训练或推理的高阶接口。 `Model` 会根据用户传入的参数封装可训练或推理的实例。
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@ -1,7 +1,7 @@
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mindspore.ModelCheckpoint
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================================
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mindspore.train.ModelCheckpoint
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===============================
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.. py:class:: mindspore.ModelCheckpoint(prefix='CKP', directory=None, config=None)
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.. py:class:: mindspore.train.ModelCheckpoint(prefix='CKP', directory=None, config=None)
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checkpoint的回调函数。
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@ -1,7 +1,7 @@
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mindspore.ReduceLROnPlateau
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================================
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mindspore.train.ReduceLROnPlateau
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=================================
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.. py:class:: mindspore.ReduceLROnPlateau(monitor='eval_loss', factor=0.1, patience=10, verbose=False, mode='auto', min_delta=1e-4, cooldown=0, min_lr=0)
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.. py:class:: mindspore.train.ReduceLROnPlateau(monitor='eval_loss', factor=0.1, patience=10, verbose=False, mode='auto', min_delta=1e-4, cooldown=0, min_lr=0)
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当 `monitor` 停止改进时降低学习率。
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@ -1,7 +1,7 @@
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mindspore.RunContext
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================================
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mindspore.train.RunContext
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==========================
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.. py:class:: mindspore.RunContext(original_args)
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.. py:class:: mindspore.train.RunContext(original_args)
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保存和管理模型的相关信息。
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@ -1,7 +1,7 @@
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mindspore.TimeMonitor
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================================
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mindspore.train.TimeMonitor
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===========================
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.. py:class:: mindspore.TimeMonitor(data_size=None)
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.. py:class:: mindspore.train.TimeMonitor(data_size=None)
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监控训练或推理的时间。
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@ -174,36 +174,6 @@ Context
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mindspore.get_algo_parameters
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mindspore.reset_algo_parameters
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Model
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-----
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.. autosummary::
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:toctree: mindspore
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:nosignatures:
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:template: classtemplate.rst
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mindspore.Model
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Callback
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--------
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.. autosummary::
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:toctree: mindspore
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:nosignatures:
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:template: classtemplate.rst
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mindspore.Callback
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mindspore.CheckpointConfig
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mindspore.EarlyStopping
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mindspore.History
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mindspore.LambdaCallback
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mindspore.LearningRateScheduler
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mindspore.LossMonitor
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mindspore.ModelCheckpoint
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mindspore.ReduceLROnPlateau
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mindspore.RunContext
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mindspore.TimeMonitor
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Dataset Helper
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---------------
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@ -0,0 +1,32 @@
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mindspore.train
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===============
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Model
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-----
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.. autosummary::
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:toctree: mindspore
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:nosignatures:
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:template: classtemplate.rst
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mindspore.Model
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Callback
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--------
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.. autosummary::
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:toctree: mindspore
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:nosignatures:
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:template: classtemplate.rst
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mindspore.Callback
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mindspore.CheckpointConfig
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mindspore.EarlyStopping
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mindspore.History
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mindspore.LambdaCallback
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mindspore.LearningRateScheduler
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mindspore.LossMonitor
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mindspore.ModelCheckpoint
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mindspore.ReduceLROnPlateau
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mindspore.RunContext
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mindspore.TimeMonitor
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@ -97,10 +97,10 @@ class Callback:
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Examples:
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>>> import numpy as np
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>>> import mindspore as ms
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>>> from mindspore import nn
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>>> from mindspore import dataset as ds
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>>> class Print_info(ms.Callback):
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>>> from mindspore.train import Model, Callback
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>>> class Print_info(Callback):
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... def step_end(self, run_context):
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... cb_params = run_context.original_args()
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... print("step_num: ", cb_params.cur_step_num)
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@ -111,7 +111,7 @@ class Callback:
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>>> net = nn.Dense(10, 5)
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>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
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>>> model = ms.Model(net, loss_fn=loss, optimizer=optim)
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>>> model = Model(net, loss_fn=loss, optimizer=optim)
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>>> model.train(1, dataset, callbacks=print_cb)
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step_num: 2
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"""
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@ -105,9 +105,9 @@ class CheckpointConfig:
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ValueError: If input parameter is not the correct type.
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Examples:
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>>> import mindspore as ms
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>>> from mindspore import nn
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>>> from mindspore.common.initializer import Normal
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>>> from mindspore.train import Model, CheckpointConfig, ModelCheckpoint
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>>>
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>>> class LeNet5(nn.Cell):
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... def __init__(self, num_class=10, num_channel=1):
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@ -133,11 +133,11 @@ class CheckpointConfig:
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>>> net = LeNet5()
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>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
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>>> model = ms.Model(net, loss_fn=loss, optimizer=optim)
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>>> model = Model(net, loss_fn=loss, optimizer=optim)
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>>> data_path = './MNIST_Data'
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>>> dataset = create_dataset(data_path)
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>>> config = ms.CheckpointConfig(saved_network=net)
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>>> ckpoint_cb = ms.ModelCheckpoint(prefix='LeNet5', directory='./checkpoint', config=config)
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>>> config = CheckpointConfig(saved_network=net)
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>>> ckpoint_cb = ModelCheckpoint(prefix='LeNet5', directory='./checkpoint', config=config)
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>>> model.train(10, dataset, callbacks=ckpoint_cb)
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"""
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@ -82,8 +82,8 @@ class EarlyStopping(Callback):
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ValueError: The monitor value is not a scalar.
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Examples:
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>>> from mindspore.train.callback import EarlyStopping
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>>> from mindspore import Model, nn
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>>> from mindspore import nn
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>>> from mindspore.train import Model, EarlyStopping
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>>> net = LeNet5()
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>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
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@ -35,16 +35,16 @@ class History(Callback):
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Examples:
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>>> import numpy as np
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>>> import mindspore as ms
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>>> import mindspore.dataset as ds
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>>> from mindspore import nn
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>>> from mindspore.train import Model, History
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>>> data = {"x": np.float32(np.random.rand(64, 10)), "y": np.random.randint(0, 5, (64,))}
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>>> train_dataset = ds.NumpySlicesDataset(data=data).batch(32)
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>>> net = nn.Dense(10, 5)
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>>> crit = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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>>> opt = nn.Momentum(net.trainable_params(), 0.01, 0.9)
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>>> history_cb = ms.History()
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>>> model = ms.Model(network=net, optimizer=opt, loss_fn=crit, metrics={"recall"})
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>>> history_cb = History()
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>>> model = Model(network=net, optimizer=opt, loss_fn=crit, metrics={"recall"})
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>>> model.train(2, train_dataset, callbacks=[history_cb])
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>>> print(history_cb.epoch)
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>>> print(history_cb.history)
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@ -45,17 +45,17 @@ class LambdaCallback(Callback):
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Examples:
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>>> import numpy as np
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>>> import mindspore as ms
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>>> import mindspore.dataset as ds
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>>> from mindspore import nn
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>>> from mindspore.train import Model, LambdaCallback
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>>> data = {"x": np.float32(np.random.rand(64, 10)), "y": np.random.randint(0, 5, (64,))}
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>>> train_dataset = ds.NumpySlicesDataset(data=data).batch(32)
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>>> net = nn.Dense(10, 5)
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>>> crit = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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>>> opt = nn.Momentum(net.trainable_params(), 0.01, 0.9)
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>>> lambda_callback = ms.LambdaCallback(on_train_epoch_end=
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>>> lambda_callback = LambdaCallback(on_train_epoch_end=
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... lambda run_context: print("loss: ", run_context.original_args().net_outputs))
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>>> model = ms.Model(network=net, optimizer=opt, loss_fn=crit, metrics={"recall"})
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>>> model = Model(network=net, optimizer=opt, loss_fn=crit, metrics={"recall"})
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>>> model.train(2, train_dataset, callbacks=[lambda_callback])
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loss: 1.6127687
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loss: 1.6106578
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|
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@ -181,6 +181,7 @@ class SummaryLandscape:
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>>> import mindspore as ms
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>>> import mindspore.nn as nn
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>>> from mindspore.nn import Loss, Accuracy
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>>> from mindspore.train import Model, SummaryCollector, SummaryLandscape
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>>>
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>>> if __name__ == '__main__':
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... # If the device_target is Ascend, set the device_target to "Ascend"
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@ -192,10 +193,10 @@ class SummaryLandscape:
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... network = LeNet5(10)
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... net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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... net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
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... model = ms.Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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... model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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... # Simple usage for collect landscape information:
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... interval_1 = [1, 2, 3, 4, 5]
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... summary_collector = ms.SummaryCollector(summary_dir='./summary/lenet_interval_1',
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... summary_collector = SummaryCollector(summary_dir='./summary/lenet_interval_1',
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... collect_specified_data={'collect_landscape':{"landscape_size": 4,
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... "unit": "step",
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... "create_landscape":{"train":True,
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@ -215,7 +216,7 @@ class SummaryLandscape:
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... ds_eval = create_dataset(mnist_dataset_dir, 32)
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... return model, network, ds_eval, metrics
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...
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... summary_landscape = ms.SummaryLandscape('./summary/lenet_interval_1')
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... summary_landscape = SummaryLandscape('./summary/lenet_interval_1')
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... # parameters of collect_landscape can be modified or unchanged
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... summary_landscape.gen_landscapes_with_multi_process(callback_fn,
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... collect_landscape={"landscape_size": 4,
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|
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@ -38,13 +38,13 @@ class LossMonitor(Callback):
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ValueError: If per_print_times is not an integer or less than zero.
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Examples:
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>>> import mindspore as ms
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>>> from mindspore import nn
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>>> from mindspore.train import Model, LossMonitor
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>>>
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>>> net = LeNet5()
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>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
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>>> model = ms.Model(net, loss_fn=loss, optimizer=optim)
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>>> model = Model(net, loss_fn=loss, optimizer=optim)
|
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>>> data_path = './MNIST_Data'
|
||||
>>> dataset = create_dataset(data_path)
|
||||
>>> loss_monitor = LossMonitor()
|
||||
|
|
|
|||
|
|
@ -34,10 +34,8 @@ class LearningRateScheduler(Callback):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> import mindspore as ms
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore import LearningRateScheduler
|
||||
>>> import mindspore.nn as nn
|
||||
>>> from mindspore.train import Model, LearningRateScheduler
|
||||
>>> from mindspore import dataset as ds
|
||||
...
|
||||
>>> def learning_rate_function(lr, cur_step_num):
|
||||
|
|
@ -50,7 +48,7 @@ class LearningRateScheduler(Callback):
|
|||
>>> net = nn.Dense(10, 5)
|
||||
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
||||
>>> optim = nn.Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum)
|
||||
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim)
|
||||
>>> model = Model(net, loss_fn=loss, optimizer=optim)
|
||||
...
|
||||
>>> data = {"x": np.float32(np.random.rand(64, 10)), "y": np.random.randint(0, 5, (64,))}
|
||||
>>> dataset = ds.NumpySlicesDataset(data=data).batch(32)
|
||||
|
|
|
|||
|
|
@ -81,8 +81,8 @@ class ReduceLROnPlateau(Callback):
|
|||
ValueError: The learning rate is not a Parameter.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.train.callback import ReduceLROnPlateau
|
||||
>>> from mindspore import Model, nn
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore.train import Model, ReduceLROnPlateau
|
||||
>>> net = LeNet5()
|
||||
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
||||
>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
|
||||
|
|
|
|||
|
|
@ -177,6 +177,7 @@ class SummaryCollector(Callback):
|
|||
Examples:
|
||||
>>> import mindspore as ms
|
||||
>>> import mindspore.nn as nn
|
||||
>>> from mindspore.train import Model, SummaryCollector
|
||||
>>> from mindspore.nn import Accuracy
|
||||
>>>
|
||||
>>> if __name__ == '__main__':
|
||||
|
|
@ -189,15 +190,15 @@ class SummaryCollector(Callback):
|
|||
... network = LeNet5(10)
|
||||
... net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
||||
... net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
|
||||
... model = ms.Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}, amp_level="O2")
|
||||
... model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}, amp_level="O2")
|
||||
...
|
||||
... # Simple usage:
|
||||
... summary_collector = ms.SummaryCollector(summary_dir='./summary_dir')
|
||||
... summary_collector = SummaryCollector(summary_dir='./summary_dir')
|
||||
... model.train(1, ds_train, callbacks=[summary_collector], dataset_sink_mode=False)
|
||||
...
|
||||
... # Do not collect metric and collect the first layer parameter, others are collected by default
|
||||
... specified={'collect_metric': False, 'histogram_regular': '^conv1.*'}
|
||||
... summary_collector = ms.SummaryCollector(summary_dir='./summary_dir', collect_specified_data=specified)
|
||||
... summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_specified_data=specified)
|
||||
... model.train(1, ds_train, callbacks=[summary_collector], dataset_sink_mode=False)
|
||||
"""
|
||||
|
||||
|
|
|
|||
|
|
@ -34,13 +34,13 @@ class TimeMonitor(Callback):
|
|||
ValueError: If data_size is not positive int.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore as ms
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore.train import Model, TimeMonitor
|
||||
>>>
|
||||
>>> net = LeNet5()
|
||||
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
||||
>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
|
||||
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim)
|
||||
>>> model = Model(net, loss_fn=loss, optimizer=optim)
|
||||
>>> data_path = './MNIST_Data'
|
||||
>>> dataset = create_dataset(data_path)
|
||||
>>> time_monitor = TimeMonitor()
|
||||
|
|
|
|||
|
|
@ -162,8 +162,8 @@ class Model:
|
|||
the Graph mode + Ascend platform, and for better acceleration, refer to the documentation to configure
|
||||
boost_config_dict.
|
||||
Examples:
|
||||
>>> import mindspore as ms
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore.train import Model
|
||||
>>>
|
||||
>>> class Net(nn.Cell):
|
||||
... def __init__(self, num_class=10, num_channel=1):
|
||||
|
|
@ -189,7 +189,7 @@ class Model:
|
|||
>>> net = Net()
|
||||
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
|
||||
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
||||
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim, metrics=None)
|
||||
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
|
||||
>>> # For details about how to build the dataset, please refer to the variable `dataset_train` in tutorial
|
||||
>>> # document on the official website:
|
||||
>>> # https://www.mindspore.cn/tutorials/zh-CN/master/beginner/quick_start.html
|
||||
|
|
@ -989,8 +989,8 @@ class Model:
|
|||
Default: 0.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore as ms
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore.train import Model
|
||||
>>>
|
||||
>>> # For details about how to build the dataset, please refer to the tutorial
|
||||
>>> # document on the official website.
|
||||
|
|
@ -999,7 +999,7 @@ class Model:
|
|||
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
|
||||
>>> loss_scale_manager = ms.FixedLossScaleManager()
|
||||
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
||||
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim, metrics=None,
|
||||
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None,
|
||||
... loss_scale_manager=loss_scale_manager)
|
||||
>>> model.train(2, dataset)
|
||||
"""
|
||||
|
|
@ -1126,8 +1126,8 @@ class Model:
|
|||
Default: 0.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore as ms
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore.train import Model
|
||||
>>>
|
||||
>>> # For details about how to build the dataset, please refer to the tutorial
|
||||
>>> # document on the official website.
|
||||
|
|
@ -1136,7 +1136,7 @@ class Model:
|
|||
>>> net = Net()
|
||||
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
|
||||
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
||||
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim, metrics={"accuracy"})
|
||||
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics={"accuracy"})
|
||||
>>> model.fit(2, train_dataset, valid_dataset)
|
||||
"""
|
||||
|
||||
|
|
@ -1211,17 +1211,18 @@ class Model:
|
|||
epoch (int): Control the training epochs. Default: 1.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore as ms
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore.train import Model
|
||||
>>> from mindspore.amp import FixedLossScaleManager
|
||||
>>>
|
||||
>>> # For details about how to build the dataset, please refer to the tutorial
|
||||
>>> # document on the official website.
|
||||
>>> dataset = create_custom_dataset()
|
||||
>>> net = Net()
|
||||
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
|
||||
>>> loss_scale_manager = ms.FixedLossScaleManager()
|
||||
>>> loss_scale_manager = FixedLossScaleManager()
|
||||
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
||||
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim, metrics=None,
|
||||
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None,
|
||||
... loss_scale_manager=loss_scale_manager)
|
||||
>>> model.build(dataset, epoch=2)
|
||||
>>> model.train(2, dataset)
|
||||
|
|
@ -1380,15 +1381,15 @@ class Model:
|
|||
the model in the test mode.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore as ms
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore.train import Model
|
||||
>>>
|
||||
>>> # For details about how to build the dataset, please refer to the tutorial
|
||||
>>> # document on the official website.
|
||||
>>> dataset = create_custom_dataset()
|
||||
>>> net = Net()
|
||||
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
|
||||
>>> model = ms.Model(net, loss_fn=loss, optimizer=None, metrics={'acc'})
|
||||
>>> model = Model(net, loss_fn=loss, optimizer=None, metrics={'acc'})
|
||||
>>> acc = model.eval(dataset, dataset_sink_mode=False)
|
||||
"""
|
||||
dataset_sink_mode = Validator.check_bool(dataset_sink_mode)
|
||||
|
|
@ -1451,11 +1452,12 @@ class Model:
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> import mindspore as ms
|
||||
>>> import mindspore
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Model
|
||||
>>>
|
||||
>>> input_data = Tensor(np.random.randint(0, 255, [1, 1, 32, 32]), ms.float32)
|
||||
>>> model = ms.Model(Net())
|
||||
>>> input_data = Tensor(np.random.randint(0, 255, [1, 1, 32, 32]), mindspore.float32)
|
||||
>>> model = Model(Net())
|
||||
>>> result = model.predict(input_data)
|
||||
"""
|
||||
self._check_network_mode(self._predict_network, False)
|
||||
|
|
@ -1537,6 +1539,7 @@ class Model:
|
|||
>>> import numpy as np
|
||||
>>> import mindspore as ms
|
||||
>>> from mindspore import Tensor, nn
|
||||
>>> from mindspore.train import Model
|
||||
>>> from mindspore.communication import init
|
||||
>>>
|
||||
>>> ms.set_context(mode=ms.GRAPH_MODE)
|
||||
|
|
@ -1550,7 +1553,7 @@ class Model:
|
|||
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
|
||||
>>> loss_scale_manager = ms.FixedLossScaleManager()
|
||||
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
||||
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim, metrics=None,
|
||||
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None,
|
||||
... loss_scale_manager=loss_scale_manager)
|
||||
>>> layout_dict = model.infer_train_layout(dataset)
|
||||
"""
|
||||
|
|
@ -1595,13 +1598,14 @@ class Model:
|
|||
>>> import numpy as np
|
||||
>>> import mindspore as ms
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Model
|
||||
>>> from mindspore.communication import init
|
||||
>>>
|
||||
>>> ms.set_context(mode=ms.GRAPH_MODE)
|
||||
>>> init()
|
||||
>>> ms.set_auto_parallel_context(full_batch=True, parallel_mode=ms.ParallelMode.SEMI_AUTO_PARALLEL)
|
||||
>>> input_data = Tensor(np.random.randint(0, 255, [1, 1, 32, 32]), ms.float32)
|
||||
>>> model = ms.Model(Net())
|
||||
>>> model = Model(Net())
|
||||
>>> predict_map = model.infer_predict_layout(input_data)
|
||||
"""
|
||||
if context.get_context("mode") != context.GRAPH_MODE:
|
||||
|
|
|
|||
Loading…
Reference in New Issue