application_inception_resnet_v2: Inception-ResNet v2 model, with weights trained on ImageNet

Description

Inception-ResNet v2 model, with weights trained on ImageNet

Usage

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)

Value

A Keras model instance.

Arguments

include_top

Whether to include the fully-connected layer at the top of the network. Defaults to TRUE.

weights

One of NULL (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 layer_input()) to use as image input for the model.

input_shape

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.

pooling

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.

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 (number of ImageNet classes).

classifier_activation

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

x

preprocess_input() takes an array or floating point tensor, 3D or 4D with 3 color channels, with values in the range [0, 255].

Reference

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(https://arxiv.org/abs/1512.00567)

Details

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.