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tf.keras.applications.ResNet101V2
Instantiates the ResNet101V2 architecture.
tf.keras.applications.ResNet101V2(
    include_top=True, weights='imagenet', input_tensor=None,
    input_shape=None, pooling=None, classes=1000,
    classifier_activation='softmax'
)
Reference:
- Identity Mappings in Deep Residual Networks (CVPR 2016)
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 ResNetV2, call tf.keras.applications.resnet_v2.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. | 
| input_shape | optional shape tuple, only to be specified if include_topis False (otherwise the input shape has to be(224, 224, 3)(with'channels_last'data format) or(3, 224, 224)(with'channels_first'data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g.(200, 200, 3)would be one valid value. | 
| pooling | Optional pooling mode for feature extraction when include_topisFalse.
 | 
| classes | optional number of classes to classify images into, only to be specified if include_topis True, and if noweightsargument is specified. | 
| classifier_activation | A stror callable. The activation function to use on the "top" layer. Ignored unlessinclude_top=True. Setclassifier_activation=Noneto return the logits of the "top" layer. | 
| Returns | |
|---|---|
| A keras.Modelinstance. | 
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
 https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/applications/ResNet101V2