Don't grab learning_phase
to pass to functional model construction when in v2 & the global learning phase has not been set, because it isn't needed.
PiperOrigin-RevId: 313463994 Change-Id: Ice070617d0a82451aaa9f1ea164404263a213c8e
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@ -873,11 +873,6 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
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# Priority 3a: `learning_phase()` has been set.
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# Priority 3a: `learning_phase()` has been set.
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elif backend.global_learning_phase_is_set():
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elif backend.global_learning_phase_is_set():
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training_value = backend.learning_phase()
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training_value = backend.learning_phase()
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# Priority 3b: Pass the `learning_phase()` if in the Keras FuncGraph.
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elif build_graph:
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with backend.get_graph().as_default():
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if base_layer_utils.is_in_keras_graph():
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training_value = backend.learning_phase()
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if self._expects_training_arg and training_value is not None:
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if self._expects_training_arg and training_value is not None:
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# Force the training_value to be bool type which matches to the contract
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# Force the training_value to be bool type which matches to the contract
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@ -1491,7 +1486,9 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
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self._metrics.append(metric_obj)
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self._metrics.append(metric_obj)
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else:
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else:
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from tensorflow.python.keras import metrics as metrics_mod # pylint:disable=g-import-not-at-top
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from tensorflow.python.keras import metrics as metrics_mod # pylint:disable=g-import-not-at-top
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metric_obj = metrics_mod.Mean(name=name, dtype=value.dtype)
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# Build the metric object with the value's dtype if it defines one
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metric_obj = metrics_mod.Mean(
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name=name, dtype=getattr(value, 'dtype', None))
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self._metrics.append(metric_obj)
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self._metrics.append(metric_obj)
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if should_update_state:
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if should_update_state:
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