3873029035
* added softmax activation layer * update docstring * reformatted * updates after review * updating missed method name update * update return statement * added logsumexp class and tests * update doc string
55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
"""
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segment_sum
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top_k
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"""
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from keras_core import backend
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from keras_core.backend import any_symbolic_tensors
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from keras_core.operations.operation import Operation
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class SegmentSum(Operation):
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def call(self, data, segment_ids, num_segments=None, sorted=False):
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return backend.math.segment_sum(data, segment_ids, num_segments, sorted)
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def segment_sum(data, segment_ids, num_segments=None, sorted=False):
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if any_symbolic_tensors((data,)):
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return SegmentSum().symbolic_call(
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data, segment_ids, num_segments, sorted
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)
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return backend.math.segment_sum(data, segment_ids, num_segments, sorted)
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class TopK(Operation):
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def call(self, x, k, sorted=True):
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return backend.math.top_k(x, k, sorted)
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def top_k(x, k, sorted=True):
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if any_symbolic_tensors((x,)):
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return TopK().symbolic_call(x, k, sorted)
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return backend.math.top_k(x, k, sorted)
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class InTopK(Operation):
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def call(self, targets, predictions, k):
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return backend.math.in_top_k(targets, predictions, k)
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def in_top_k(targets, predictions, k):
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if any_symbolic_tensors((targets, predictions)):
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return InTopK().symbolic_call(targets, predictions, k)
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return backend.math.in_top_k(targets, predictions, k)
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def logsumexp(x, axis=None, keepdims=False):
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if any_symbolic_tensors((x,)):
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return Logsumexp().symbolic_call(x, axis=axis, keepdims=keepdims)
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return backend.math.logsumexp(x, axis=axis, keepdims=keepdims)
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class Logsumexp(Operation):
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def call(self, x, axis=None, keepdims=False):
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return backend.math.logsumexp(x, axis=axis, keepdims=keepdims)
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