from keras.applications.mobilenet_v2 import MobileNetV2
MobileNetV2(input_shape=None, alpha=1.0, depth_multiplier=1, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)
MobileNetV2 model, with weights pre-trained on ImageNet.
Note that this model only supports the data format
'channels_last' (height, width, channels).
The default input size for this model is 224x224.
- input_shape: optional shape tuple, to be specified if you would like to use a model with an input img 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.
(160, 160, 3) would be one valid value.
- alpha: controls the width of the network. This is known as the width multiplier in the MobileNetV2 paper.
alpha < 1.0, proportionally decreases the number of filters in each layer.
alpha > 1.0, proportionally increases the number of filters in each layer.
alpha = 1, default number of filters from the paper are used at each layer.
- depth_multiplier: depth multiplier for depthwise convolution (also called the resolution multiplier)
- 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.
- pooling: Optional pooling mode for feature extraction when
None means that the output of the model will be the 4D tensor output of the last convolutional layer.
'avg' means that global average pooling will be applied to the output of the last convolutional layer, 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.
A Keras model instance.
ValueError: in case of invalid argument for
weights, or invalid input shape or invalid depth_multiplier, alpha, rows when weights='imagenet'
(@ keras.io) https://keras.io/applications/#mobilenetv2