Fix learning_phase int check (#5749)
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@ -1518,7 +1518,7 @@ class Model(Container):
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check_batch_axis=False,
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batch_size=batch_size)
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# prepare inputs, delegate logic to _test_loop
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if self.uses_learning_phase and not isinstance(K.learning_phase, int):
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if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
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ins = x + y + sample_weights + [0.]
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else:
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ins = x + y + sample_weights
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@ -1562,7 +1562,7 @@ class Model(Container):
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'Batch size: ' + str(batch_size) + '.')
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# prepare inputs, delegate logic to _predict_loop
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if self.uses_learning_phase and not isinstance(K.learning_phase, int):
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if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
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ins = x + [0.]
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else:
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ins = x
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@ -1612,7 +1612,7 @@ class Model(Container):
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sample_weight=sample_weight,
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class_weight=class_weight,
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check_batch_axis=True)
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if self.uses_learning_phase and not isinstance(K.learning_phase, int):
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if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
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ins = x + y + sample_weights + [1.]
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else:
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ins = x + y + sample_weights
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@ -1654,7 +1654,7 @@ class Model(Container):
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x, y,
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sample_weight=sample_weight,
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check_batch_axis=True)
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if self.uses_learning_phase and not isinstance(K.learning_phase, int):
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if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
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ins = x + y + sample_weights + [0.]
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else:
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ins = x + y + sample_weights
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@ -1675,7 +1675,7 @@ class Model(Container):
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"""
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x = _standardize_input_data(x, self._feed_input_names,
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self._feed_input_shapes)
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if self.uses_learning_phase and not isinstance(K.learning_phase, int):
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if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
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ins = x + [0.]
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else:
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ins = x
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