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Instantiates the ResNet152V2 architecture.
tf.keras.applications.ResNet152V2( include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax' )
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 | |
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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 (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_top is False .
|
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. |
Returns | |
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A keras.Model instance. |
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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/ResNet152V2