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
NULL (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded. Defaults to
Optional Keras tensor
(i.e. output of
to use as image input for the model.
optional shape list, only to be specified
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.
(150, 150, 3) would be one valid value.
Optional pooling mode for feature extraction
FALSE. Defaults to
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
Optional number of classes to classify images into, only to be
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.
'softmax'. When loading pretrained weights,
classifier_activation can only be
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
A Keras model instance.
Do note that the input image format for this model is different than for the VGG16 and ResNet models (299x299 instead of 224x224).
inception_resnet_v2_preprocess_input() function should be used for image
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(https://arxiv.org/abs/1512.00567)