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KerasAPIReference.txt

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weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs
)
Instantiates the Inception-ResNet v2 architecture.
Reference
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (AAAI 2017)
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
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.
Note: each Keras Application expects a specific kind of input preprocessing. For InceptionResNetV2, call tf.keras.applications.inception_resnet_v2.preprocess_input on your inputs before passing them to the model. inception_resnet_v2.preprocess_input will scale input pixels between -1 and 1.
Arguments
include_top: whether to include the fully-connected layer at the top of the network.
weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (299, 299, 3) (with 'channels_last' data format) or (3, 299, 299) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. (150, 150, 3) would be one valid value.
pooling: Optional pooling mode for feature extraction when include_top is False.
None means that the output of the model will be the 4D tensor output of the last convolutional block.
'avg' means that global average pooling will be applied to the output of the last convolutional block, 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.
classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".
**kwargs: For backwards compatibility only.
Returns
A keras.Model instance.
Xception
Xception function
tf.keras.applications.Xception(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the Xception architecture.
Reference
Xception: Deep Learning with Depthwise Separable Convolutions (CVPR 2017)
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.
Note: each Keras Application expects a specific kind of input preprocessing. For Xception, call tf.keras.applications.xception.preprocess_input on your inputs before passing them to the model. xception.preprocess_input will scale input pixels between -1 and 1.
Arguments
include_top: whether to include the fully-connected layer at the top of the network.
weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
input_shape: optional shape tuple, 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.
None means that the output of the model will be the 4D tensor output of the last convolutional block.
avg means that global average pooling will be applied to the output of the last convolutional block, 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.
classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".
Returns
A keras.Model instance.DenseNet
DenseNet121 function
tf.keras.applications.DenseNet121(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
)
Instantiates the Densenet121 architecture.
Reference
Densely Connected Convolutional Networks (CVPR 2017)
Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json.
Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call tf.keras.applications.densenet.preprocess_input on your inputs before passing them to the model.
Arguments
include_top: whether to include the fully-connected layer at the top of the network.
weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.