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Merge pull request #17758 from SamuelMarks:keras.applications.efficie…
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…ntnet

PiperOrigin-RevId: 529748654
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tensorflower-gardener committed May 5, 2023
2 parents 8e1b76e + f8622ce commit b7e9134
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16 changes: 8 additions & 8 deletions keras/applications/efficientnet.py
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Args:
include_top: Whether to include the fully-connected
layer at the top of the network. Defaults to True.
layer at the top of the network. Defaults to `True`.
weights: One of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded. Defaults to 'imagenet'.
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if `include_top` is False.
It should have exactly 3 inputs channels.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`. Defaults to None.
when `include_top` is `False`. Defaults to `None`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
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be applied.
classes: Optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified. Defaults to 1000 (number of
ImageNet classes).
if no `weights` argument is specified. 1000 is how many
ImageNet classes there are. Defaults to `1000`.
classifier_activation: A `str` or callable. The activation function to use
on the "top" layer. Ignored unless `include_top=True`. Set
`classifier_activation=None` to return the logits of the "top" layer.
Expand Down Expand Up @@ -852,10 +852,10 @@ def preprocess_input(x, data_format=None):
Args:
x: A floating point `numpy.array` or a `tf.Tensor`.
data_format: Optional data format of the image tensor/array. Defaults to
None, in which case the global setting
`tf.keras.backend.image_data_format()` is used (unless you changed it,
it defaults to "channels_last").{mode}
data_format: Optional data format of the image tensor/array. `None` means
the global setting `tf.keras.backend.image_data_format()` is used
(unless you changed it, it uses "channels_last").
Defaults to `None`.
Returns:
Unchanged `numpy.array` or `tf.Tensor`.
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