tf.keras.applications.InceptionV3

Instantiates the Inception v3 architecture.

View aliases

Main aliases

tf.keras.applications.inception_v3.InceptionV3

Compat aliases for migration

tf.compat.v1.keras.applications.InceptionV3, tf.compat.v1.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'
)

Reference:

Rethinking the Inception Architecture for Computer Vision (CVPR 2016)

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in the tf.keras.backend.image_data_format().

Note: each Keras Application expects a specific kind of input preprocessing. For InceptionV3, call tf.keras.applications.inception_v3.preprocess_input on your inputs before passing them to the model.

Arguments
`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`. `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. 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.

Returns
A `keras.Model` instance.

Raises
`ValueError`	in case of invalid argument for `weights`, or invalid input shape.
`ValueError`	if `classifier_activation` is not `softmax` or `None` when using a pretrained top layer.

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/applications/InceptionV3