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[keras/applications/efficientnet.py] Standardise docstring usage of "Defaults to"
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keras/applications/efficientnet.py

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@@ -192,7 +192,7 @@
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Args:
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include_top: Whether to include the fully-connected
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layer at the top of the network. Defaults to True.
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layer at the top of the network. Defaults to `True`.
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weights: One of `None` (random initialization),
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'imagenet' (pre-training on ImageNet),
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or the path to the weights file to be loaded. Defaults to 'imagenet'.
@@ -203,7 +203,7 @@
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if `include_top` is False.
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It should have exactly 3 inputs channels.
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pooling: Optional pooling mode for feature extraction
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when `include_top` is `False`. Defaults to None.
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when `include_top` is `False`. Defaults to `None`.
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- `None` means that the output of the model will be
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the 4D tensor output of the
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last convolutional layer.
@@ -215,8 +215,8 @@
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be applied.
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classes: Optional number of classes to classify images
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into, only to be specified if `include_top` is True, and
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if no `weights` argument is specified. Defaults to 1000 (number of
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ImageNet classes).
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if no `weights` argument is specified. 1000 is how many
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ImageNet classes there are. Defaults to `1000`.
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classifier_activation: A `str` or callable. The activation function to use
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on the "top" layer. Ignored unless `include_top=True`. Set
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`classifier_activation=None` to return the logits of the "top" layer.
@@ -852,10 +852,10 @@ def preprocess_input(x, data_format=None):
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Args:
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x: A floating point `numpy.array` or a `tf.Tensor`.
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data_format: Optional data format of the image tensor/array. Defaults to
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None, in which case the global setting
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`tf.keras.backend.image_data_format()` is used (unless you changed it,
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it defaults to "channels_last").{mode}
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data_format: Optional data format of the image tensor/array. `None` means
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the global setting `tf.keras.backend.image_data_format()` is used
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(unless you changed it, it uses "channels_last").{mode}.
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Defaults to `None`.
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Returns:
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Unchanged `numpy.array` or `tf.Tensor`.

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