Class TensorBoard

TensorBoard basic visualizations. TensorBoard is a visualization tool provided with TensorFlow. This callback writes a log for TensorBoard, which allows you to visualize dynamic graphs of your training and test metrics, as well as activation histograms for the different layers in your model. If you have installed TensorFlow with pip, you should be able to launch TensorBoard from the command line: tensorboard --logdir=/ full_path_to_your_logs When using a backend other than TensorFlow, TensorBoard will still work(if you have TensorFlow installed), but the only feature available will be the display of the losses and metrics plots.

Inheritance

System.Object

Keras

Base

Callback

TensorBoard

Implements

System.IDisposable

Inherited Members

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.Callbacks

Assembly: Keras.dll

Syntax

public class TensorBoard : Callback, IDisposable

Constructors

TensorBoard(String, Int32, Int32, Boolean, Boolean, Boolean, Int32, String[], Dictionary<String, String>, NDarray, String)

Initializes a new instance of the LearningRateScheduler class.

Declaration

public TensorBoard(string log_dir = "./logs", int histogram_freq = 0, int batch_size = 32, bool write_graph = true, bool write_grads = false, bool write_images = false, int embeddings_freq = 0, string[] embeddings_layer_names = null, Dictionary<string, string> embeddings_metadata = null, NDarray embeddings_data = null, string update_freq = "epoch")

Parameters

Type	Name	Description
System.String	log_dir	the path of the directory where to save the log files to be parsed by TensorBoard.
System.Int32	histogram_freq	frequency (in epochs) at which to compute activation and weight histograms for the layers of the model. If set to 0, histograms won't be computed. Validation data (or split) must be specified for histogram visualizations.
System.Int32	batch_size	size of batch of inputs to feed to the network for histograms computation.
System.Boolean	write_graph	whether to visualize the graph in TensorBoard. The log file can become quite large when write_graph is set to True.
System.Boolean	write_grads	whether to visualize gradient histograms in TensorBoard. histogram_freq must be greater than 0.
System.Boolean	write_images	whether to write model weights to visualize as image in TensorBoard.
System.Int32	embeddings_freq	frequency (in epochs) at which selected embedding layers will be saved. If set to 0, embeddings won't be computed. Data to be visualized in TensorBoard's Embedding tab must be passed as embeddings_data.
System.String[]	embeddings_layer_names	a list of names of layers to keep eye on. If None or empty list all the embedding layer will be watched.
System.Collections.Generic.Dictionary<System.String, System.String>	embeddings_metadata	a dictionary which maps layer name to a file name in which metadata for this embedding layer is saved. See the details about metadata files format. In case if the same metadata file is used for all embedding layers, string can be passed.
Numpy.NDarray	embeddings_data	data to be embedded at layers specified in embeddings_layer_names. Numpy array (if the model has a single input) or list of Numpy arrays (if the model has multiple inputs). Learn more about embeddings.
System.String	update_freq

Implements

System.IDisposable