application_xception
Instantiates the Xception architecture
Description
Instantiates the Xception architecture
Usage
application_xception(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
classifier_activation = "softmax",
...
)
xception_preprocess_input(x) Arguments
| 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]. |
Details
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. The default input image size for this model is 299x299.
Section
Reference
Note
Each Keras Application typically expects a specific kind of input preprocessing. For Xception, call xception_preprocess_input() on your inputs before passing them to the model. xception_preprocess_input() will scale input pixels between -1 and 1.