Instantiates the Inception v3 architecture.
include_top=True, weights='imagenet', input_tensor=None,
input_shape=None, pooling=None, classes=1000,
Used in the notebooks
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.
Boolean, whether to include the fully-connected
layer at the top, as the last layer of the network. Default to
None (random initialization),
imagenet (pre-training on ImageNet),
or the path to the weights file to be loaded. Default to
Optional Keras tensor (i.e. output of
to use as image input for the model.
input_tensor is useful for sharing
inputs between multiple different networks. Default to None.
Optional shape tuple, only to be specified
include_top is False (otherwise the input shape
has to be
(299, 299, 3) (with
channels_last data format)
(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.
(150, 150, 3) would be one valid value.
input_shape will be ignored if the
input_tensor is provided.
Optional pooling mode for feature extraction
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.
optional number of classes to classify images
into, only to be specified if
include_top is True, and
weights argument is specified. Default to 1000.
str or callable. The activation function to use
on the "top" layer. Ignored unless
classifier_activation=None to return the logits of the "top" layer.
When loading pretrained weights,
classifier_activation can only