tf.keras.applications.InceptionV3

Instantiates the Inception v3 architecture.

View aliases

Main aliases

tf.keras.applications.inception_v3.InceptionV3

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

Used in the notebooks

Used in the tutorials
DeepDream

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.

Args
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".

Returns
A model instance.