InceptionV3

• 원본 링크 : https://keras.io/api/applications/inceptionv3/
• 최종 확인 : 2024-11-25

source

InceptionV3 function

keras.applications.InceptionV3(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    name="inception_v3",
)

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 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.
  • 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 is True, and if no weights argument is specified. 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. When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: The name of the model (string).

Returns

A model instance.