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NASNet

applications.nasnet.NASNetLarge

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applications.nasnet.NASNetLarge(
    include_top=True, weights='hasc', input_shape=None, pooling=None, 
    classes=6, classifier_activation='softmax'
)

Reference: - Learning Transferable Architectures for Scalable Image Recognition (CVPR 2018)

By default, it loads weights pre-trained on HASC. Check 'weights' for other options.

The default input size for this model is 768 (256 * 3).

Arguments

  • include_top : whether to include the fully-connected layer at the top of the network.
  • weights : one of 'None' (he_normal initialization), 'hasc' (pre-training on HASC), or the path to the weights file to be loaded.
  • input_shape : optional shape tuple, default (768, 1) (with channels_last data format).
  • pooling : optional pooling mode for feature extraction when include_top is False.
    • None means that the output of the model will be applied to the 3D tensor output of the last convolutional block.
    • avg means that global average pooling will be applied to the output of the last convolutioinal 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, default 6.
  • 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, default 'softmax'.

Returns

  • tensorflow.keras.Model instance.

applications.nasnet.NASNetMobile

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applications.nasnet.NASNetMobile(
    include_top=True, weights='hasc', input_shape=None, pooling=None, 
    classes=6, classifier_activation='softmax'
)

Reference: - Learning Transferable Architectures for Scalable Image Recognition (CVPR 2018)

By default, it loads weights pre-trained on HASC. Check 'weights' for other options.

The default input size for this model is 768 (256 * 3).

Arguments

  • include_top : whether to include the fully-connected layer at the top of the network.
  • weights : one of 'None' (he_normal initialization), 'hasc' (pre-training on HASC), or the path to the weights file to be loaded.
  • input_shape : optional shape tuple, default (768, 1) (with channels_last data format).
  • pooling : optional pooling mode for feature extraction when include_top is False.
    • None means that the output of the model will be applied to the 3D tensor output of the last convolutional block.
    • avg means that global average pooling will be applied to the output of the last convolutioinal 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, default 6.
  • 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, default 'softmax'.

Returns

  • tensorflow.keras.Model instance.