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@ -291,6 +291,15 @@ class SampledSoftmaxLoss(_Loss):
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Outputs:
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Outputs:
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Tensor, a tensor of shape (N) with the per-example sampled softmax losses.
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Tensor, a tensor of shape (N) with the per-example sampled softmax losses.
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Examples:
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>>> loss = nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, num_true=1)
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>>> weights = Tensor(np.random.randint(0, 9, [7, 10]), mindspore.float32)
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>>> biases = Tensor(np.random.randint(0, 9, [7]), mindspore.float32)
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>>> labels = Tensor([0, 1, 2])
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>>> inputs = Tensor(np.random.randint(0, 9, [3, 10]), mindspore.float32)
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>>> output = loss(weights, biases, labels, inputs)
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>>> print(output) # output is ranndom
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[ 4.0181947 46.050743 7.0009117]
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"""
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"""
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def __init__(self, num_sampled, num_classes, num_true=1,
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def __init__(self, num_sampled, num_classes, num_true=1,
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