add binary cross entropy with logit loss #26468
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Thanks for your contribution! |
wawltor
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Aug 20, 2020
python/paddle/nn/functional/loss.py
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| label = paddle.to_tensor([1.0, 0.0, 1.0], dtype="float32") | ||
| output = paddle.nn.functional.binary_cross_entropy_with_logits(input, label) | ||
| print(output.numpy()) # [0.45618808] | ||
| paddle.enable_static() |
python/paddle/nn/layer/loss.py
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| N is batch_size, `*` means number of additional dimensions. The ``logit`` | ||
| is usually the output of Linear layer. Available dtype is float32, float64. | ||
| label (Tensor): The target labels tensor. 2-D tensor with the same shape as | ||
| ``input``. The target labels which values should be numbers between 0 and 1. |
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add binary cross entropy with logit loss