InceptionV3
function
keras.applications.InceptionV3(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the Inception v3 architecture.
Reference
- Rethinking the Inception Architecture for Computer Vision (CVPR 2016)
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.
Note: each Keras Application expects a specific kind of input preprocessing. For InceptionV3
, call keras.applications.inception_v3.preprocess_input
on your inputs before passing them to the model. inception_v3.preprocess_input
will scale input pixels between -1 and 1.
Arguments
- include_top: Boolean, whether to include the fully-connected layer at the top, as the last layer 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_tensor
is useful for sharing inputs between multiple different networks. Defaults toNone
. - input_shape: Optional shape tuple, only to be specified if
include_top
is False (otherwise the input shape has to be(299, 299, 3)
(withchannels_last
data format) or(3, 299, 299)
(withchannels_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 theinput_tensor
is provided. - pooling: Optional pooling mode for feature extraction when
include_top
isFalse
.None
(default) means that the output of the model will be the 4D tensor output of the last convolutional block.avg
means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.max
means that global max pooling will be applied.
- classes: optional number of classes to classify images into, only to be specified if
include_top
isTrue
, and if noweights
argument is specified. Defaults to 1000. - classifier_activation: A
str
or callable. The activation function to use on the “top” layer. Ignored unlessinclude_top=True
. Setclassifier_activation=None
to return the logits of the “top” layer. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Leave a Reply