84 lines
3.2 KiB
ReStructuredText
84 lines
3.2 KiB
ReStructuredText
mindspore.experimental
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=======================
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The experimental modules.
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Experimental Optimizer
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-----------------------
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.. msplatformautosummary::
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:toctree: experimental/optim
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:nosignatures:
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:template: classtemplate.rst
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mindspore.experimental.optim.Optimizer
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mindspore.experimental.optim.Adadelta
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mindspore.experimental.optim.Adagrad
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mindspore.experimental.optim.Adam
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mindspore.experimental.optim.Adamax
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mindspore.experimental.optim.AdamW
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mindspore.experimental.optim.ASGD
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mindspore.experimental.optim.NAdam
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mindspore.experimental.optim.RAdam
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mindspore.experimental.optim.RMSprop
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mindspore.experimental.optim.Rprop
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mindspore.experimental.optim.SGD
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LRScheduler Class
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^^^^^^^^^^^^^^^^^^
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The dynamic learning rates in this module are all subclasses of LRScheduler, this module should be used with optimizers
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in mindspore.experimental.optim, pass the optimizer instance to a LRScheduler when used. During the training process, the
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LRScheduler subclass dynamically changes the learning rate by calling the `step` method.
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.. code-block::
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import mindspore
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from mindspore import nn
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from mindspore.experimental import optim
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# Define the network structure of LeNet5. Refer to
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# https://gitee.com/mindspore/docs/blob/master/docs/mindspore/code/lenet.py
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net = LeNet5()
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loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
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optimizer = optim.Adam(net.trainable_params(), lr=0.05)
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scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.1)
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def forward_fn(data, label):
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logits = net(data)
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loss = loss_fn(logits, label)
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return loss, logits
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grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
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def train_step(data, label):
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(loss, _), grads = grad_fn(data, label)
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optimizer(grads)
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return loss
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for epoch in range(6):
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# Create the dataset taking MNIST as an example. Refer to
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# https://gitee.com/mindspore/docs/blob/master/docs/mindspore/code/mnist.py
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for data, label in create_dataset(need_download=False):
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train_step(data, label)
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scheduler.step()
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.. msplatformautosummary::
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:toctree: experimental/optim
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:nosignatures:
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:template: classtemplate.rst
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mindspore.experimental.optim.lr_scheduler.LRScheduler
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mindspore.experimental.optim.lr_scheduler.ConstantLR
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mindspore.experimental.optim.lr_scheduler.CosineAnnealingLR
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mindspore.experimental.optim.lr_scheduler.CosineAnnealingWarmRestarts
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mindspore.experimental.optim.lr_scheduler.CyclicLR
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mindspore.experimental.optim.lr_scheduler.ExponentialLR
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mindspore.experimental.optim.lr_scheduler.LambdaLR
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mindspore.experimental.optim.lr_scheduler.LinearLR
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mindspore.experimental.optim.lr_scheduler.MultiplicativeLR
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mindspore.experimental.optim.lr_scheduler.MultiStepLR
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mindspore.experimental.optim.lr_scheduler.PolynomialLR
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mindspore.experimental.optim.lr_scheduler.ReduceLROnPlateau
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mindspore.experimental.optim.lr_scheduler.SequentialLR
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mindspore.experimental.optim.lr_scheduler.StepLR
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