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Instantiates the Inception v3 architecture.
tf.keras.applications.inception_v3.InceptionV3(
include_top=True, weights='imagenet', input_tensor=None,
input_shape=None, pooling=None, classes=1000,
classifier_activation='softmax'
)
Reference:
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
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.
Args | |
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include_top
|
Boolean, whether to include the fully-connected
layer at the top, as the last layer of the network. Default to True .
|
weights
|
One of None (random initialization),
imagenet (pre-training on ImageNet),
or the path to the weights file to be loaded. Default to imagenet .
|
input_tensor
|
Optional Keras tensor (i.e. output of layers.Input() )
to use as image input for the model. input_tensor is useful for sharing
inputs between multiple different networks. Default to None.
|
input_shape
|
Optional shape tuple, only to be specified
if include_top is False (otherwise the input shape
has to be (299, 299, 3) (with channels_last data format)
or (3, 299, 299) (with channels_first data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 75.
E.g. (150, 150, 3) would be one valid value.
input_shape will be ignored if the input_tensor is provided.
|
pooling
|
Optional pooling mode for feature extraction
when include_top is False .
|
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. Default 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.
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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