Class MobileNetV1

MobileNet 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.

Inheritance
System.Object
Keras
Base
BaseModel
Model
AppModelBase
MobileNetV1
Implements
System.IDisposable
Inherited Members
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.ToString()
Namespace: Keras.Applications.MobileNet
Assembly: Keras.dll
Syntax
public class MobileNetV1 : AppModelBase, IDisposable

Constructors

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MobileNetV1()

Initializes a new instance of the MobileNetV1 class.

Declaration
public MobileNetV1()
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MobileNetV1(Shape, Single, Int32, Single, Boolean, String, NDarray, String, Int32)

Initializes a new instance of the MobileNetV1 class.

Declaration
public MobileNetV1(Shape input_shape = null, float alpha = 1F, int depth_multiplier = 1, float dropout = 0.001F, bool include_top = true, string weights = "imagenet", NDarray input_tensor = null, string pooling = "None", int classes = 1000)
Parameters
Type Name Description
Shape 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.
System.Single alpha

controls the width of the network. If alpha < 1.0, proportionally decreases the number of filters in each layer. If alpha > 1.0, proportionally increases the number of filters in each layer.
System.Int32 depth_multiplier

depth multiplier for depthwise convolution (also called the resolution multiplier)
System.Single dropout

The dropout rate.
System.Boolean include_top

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) for NASNetMobile or (331, 331, 3) (with 'channels_last' data format) or (3, 331, 331) (with 'channels_first' data format) for NASNetLarge. 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.
System.String weights

one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
Numpy.NDarray input_tensor

optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
System.String pooling

optional pooling mode for feature extraction when include_top is False. 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.
System.Int32 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.

Implements

System.IDisposable