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Instantiates the RegNetY008 architecture.
tf.keras.applications.regnet.RegNetY008(
model_name='regnety008',
include_top=True,
include_preprocessing=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation='softmax'
)
Reference | |
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For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
The naming of models is as follows: RegNet<block_type><flops>
where
block_type
is one of (X, Y)
and flops
signifies hundred million
floating point operations. For example RegNetY064 corresponds to RegNet with
Y block and 6.4 giga flops (64 hundred million flops).
Args | |
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include_top
|
Whether to include the fully-connected 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" .
|
input_tensor
|
Optional Keras tensor
(i.e. output of layers.Input() )
to use as image input for the model.
|
input_shape
|
Optional shape tuple, only to be specified
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.
|
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).
|
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.
Defaults to "softmax" .
When loading pretrained weights, classifier_activation can only
be None or "softmax" .
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Returns | |
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A keras.Model instance.
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