Inception-ResNet v2 model, with weights trained on ImageNet
application_inception_resnet_v2(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
classifier_activation = "softmax",
...
)inception_resnet_v2_preprocess_input(x)
Whether to include the fully-connected
layer at the top of the network. Defaults to TRUE.
One of NULL (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded. Defaults to 'imagenet'.
Optional Keras tensor
(i.e. output of layer_input())
to use as image input for the model.
optional shape list, only to be specified
if include_top is FALSE (otherwise the input shape
has to be (299, 299, 3).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 71.
E.g. (150, 150, 3) would be one valid value.
Optional pooling mode for feature extraction
when include_top is FALSE. Defaults to NULL.
NULL means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
'avg' means that global average pooling
will be applied to the output of the
last convolutional layer, 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 if no weights argument is
specified. Defaults to 1000 (number of ImageNet classes).
A string or callable. The activation function to
use on the "top" layer. Ignored unless include_top = TRUE. Set
classifier_activation = NULL to return the logits of the "top" layer.
Defaults to 'softmax'. When loading pretrained weights,
classifier_activation can only be NULL or "softmax".
For backwards and forwards compatibility
preprocess_input() takes an array or floating point tensor, 3D or
4D with 3 color channels, with values in the range [0, 255].
A Keras model instance.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(https://arxiv.org/abs/1512.00567)
Do note that the input image format for this model is different than for the VGG16 and ResNet models (299x299 instead of 224x224).
The inception_resnet_v2_preprocess_input() function should be used for image
preprocessing.