MobileNet

application.mobilenet.MobileNet

MobileNet(include_top=True, weights='hasc', input_shape=None, pooling=None, classes=6, classifier_activation='softmax',
              alpha=1.0, depth_multiplier=1, dropout=1e-3)

Reference: - MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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'.
• alpha : Controls the width of the network. This is known as the width multiplier in the MobileNet paper. Default to 1.0.
• depth_multipliter : Depth multiplier for depthwise convolution. This is called the resolution multiplier in the MobileNet paper. Default to 1.0.
• dropout : Dropout rate. Default to 0.001.

Returns

• tensorflow.keras.Model instance.