Class ResNet50

ResNet 50

Inheritance

System.Object

Keras

Base

BaseModel

Model

AppModelBase

ResNet50

Implements

System.IDisposable

Inherited Members

AppModelBase.DecodePredictions(NDarray, Int32)

AppModelBase.PreprocessInput(NDarray, String)

BaseModel.Compile(StringOrInstance, String, String[], Single[], String, String[], NDarray[])

BaseModel.Fit(NDarray, NDarray, Nullable<Int32>, Int32, Int32, Callback[], Single, NDarray[], Boolean, Dictionary<Int32, Single>, NDarray, Int32, Nullable<Int32>, Nullable<Int32>)

BaseModel.Evaluate(NDarray, NDarray, Nullable<Int32>, Int32, NDarray, Nullable<Int32>, Callback[])

BaseModel.Predict(NDarray, Nullable<Int32>, Int32, Nullable<Int32>, Callback[])

BaseModel.TrainOnBatch(NDarray, NDarray, NDarray, Dictionary<Int32, Single>)

BaseModel.TestOnBatch(NDarray, NDarray, NDarray)

BaseModel.PredictOnBatch(NDarray)

BaseModel.ToJson()

BaseModel.SaveWeight(String)

BaseModel.Save(String)

BaseModel.LoadWeight(String)

BaseModel.LoadModel(String)

BaseModel.ModelFromJson(String)

BaseModel.ModelFromYaml(String)

BaseModel.SaveOnnx(String)

BaseModel.Summary(Nullable<Int32>, Single[])

BaseModel.SaveTensorflowJSFormat(String, Boolean)

Base.Parameters

Base.None

Base.Init()

Base.ToPython()

Base.InvokeStaticMethod(Object, String, Dictionary<String, Object>)

Base.InvokeMethod(String, Dictionary<String, Object>)

Base.Item[String]

Keras.Instance

Keras.keras

Keras.keras2onnx

Keras.tfjs

Keras.Dispose()

Keras.ToTuple(Array)

Keras.ToList(Array)

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.ResNet

Assembly: Keras.dll

Syntax

public class ResNet50 : AppModelBase, IDisposable

Constructors

ResNet50(Boolean, String, NDarray, Shape, String, Int32)

Initializes a new instance of the ResNet50 class.

Declaration

public ResNet50(bool include_top = true, string weights = "imagenet", NDarray input_tensor = null, Shape input_shape = null, string pooling = "None", int classes = 1000)

Parameters

Type	Name	Description
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.
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.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