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Instantiates the MobileNetV2 architecture.
See Migration guide for more details.
tf.keras.applications.MobileNetV2( input_shape=None, alpha=1.0, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs )
Optionally loads weights pre-trained on ImageNet.
| || Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels (224, 224, 3). You can also omit this option if you would like to infer input_shape from an input_tensor. If you choose to include both input_tensor and input_shape then input_shape will be used if they match, if the shapes do not match then we will throw an error. E.g. |
| || Float between 0 and 1. controls the width of the network. This is known as the width multiplier in the MobileNetV2 paper, but the name is kept for consistency with |
| || Boolean, whether to include the fully-connected layer at the top of the network. Defaults to |
| || String, one of |
| || Optional Keras tensor (i.e. output of |
| || String, optional pooling mode for feature extraction when |
| || Integer, optional number of classes to classify images into, only to be specified if |
| || A |
| ||For backwards compatibility only.|
| A |
| || in case of invalid argument for |
| || if |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.