Inception V3 model, with weights pre-trained on ImageNet.


Inception V3 model, with weights pre-trained on ImageNet.


  include_top = TRUE, 
  weights = "imagenet", 
  input_tensor = NULL, 
  input_shape = NULL, 
  pooling = NULL, 
  classes = 1000, 
  classifier_activation = "softmax", 


Arguments Description
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].


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_v3_preprocess_input() function should be used for image preprocessing.




A Keras model instance.