@@ -564,7 +564,7 @@ class Model(Container):
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"""
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def compile (self , optimizer , loss , metrics = None , loss_weights = None ,
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- sample_weight_mode = None , weight_metrics = False , ** kwargs ):
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+ sample_weight_mode = None , weigh_metrics = False , ** kwargs ):
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"""Configures the model for training.
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# Arguments
@@ -597,7 +597,7 @@ def compile(self, optimizer, loss, metrics=None, loss_weights=None,
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If the model has multiple outputs, you can use a different
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`sample_weight_mode` on each output by passing a
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dictionary or a list of modes.
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- weight_metrics : bool whether or not to apply `sample_weight` or
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+ weigh_metrics : bool whether or not to apply `sample_weight` or
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`class_weight` to the supplied metrics during training and testing
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**kwargs: when using the Theano/CNTK backends, these arguments
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are passed into K.function. When using the TensorFlow backend,
@@ -835,7 +835,7 @@ def append_metric(layer_num, metric_name, metric_tensor):
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continue
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y_true = self .targets [i ]
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y_pred = self .outputs [i ]
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- weights = sample_weights [i ] if weight_metrics else None
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+ weights = sample_weights [i ] if weigh_metrics else None
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output_metrics = nested_metrics [i ]
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for metric in output_metrics :
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if metric == 'accuracy' or metric == 'acc' :
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