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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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mindspore.nn.WithEvalCell

class mindspore.nn.WithEvalCell(network, loss_fn, add_cast_fp32=False)

Wraps the forward network with the loss function.

It returns loss, forward output and label to calculate the metrics.

Parameters

• network (Cell) – The forward network.

• loss_fn (Cell) – The loss function.

• add_cast_fp32 (bool) – Whether to adjust the data type to float32. Default: False .

Inputs:

• data (Tensor) - Tensor of shape $(N,\dots )$.

• label (Tensor) - Tensor of shape $(N,\dots )$.

Outputs:

Tuple(Tensor), containing a scalar loss Tensor, a network output Tensor of shape $(N,\dots )$ and a label Tensor of shape $(N,\dots )$.

Raises

TypeError – If add_cast_fp32 is not a bool.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore.nn as nn
>>> # Define a forward network without loss function, taking LeNet5 as an example.
>>> # Refer to https://gitee.com/mindspore/docs/blob/master/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
>>> eval_net = nn.WithEvalCell(net, loss_fn)