Instantiates the ResNet152 architecture.
tf.keras.applications.resnet.ResNet152( include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs )
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 ResNet, calltf.keras.applications.resnet.preprocess_input
on your inputs before passing them to the model.resnet.preprocess_input
will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.
Args | |
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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. When loading pretrained weights, classifier_activation can only be None or "softmax" . |
Returns | |
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A Keras model instance. |
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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/applications/resnet/ResNet152