Emgu.TF Namespace

Buffer holds a pointer to a block of data and its associated length. Typically, the data consists of a serialized protocol buffer, but other data may also be held in a buffer.

A tensorflow function

Tensorflow Graph

ImportGraphDefOptions holds options that can be passed to Graph.ImportGraphDef

Represents a specific input of an operation.

The Library that can be used to load new Tensorflow modules.

Tensorflow operation

Operation being built. The underlying graph must outlive this

Represents a specific output of an operation.

In-process TensorFlow server functionality, for use in distributed training. A Server instance encapsulates a set of devices and a Session target that can participate in distributed training. A server belongs to a cluster (specified by a ClusterSpec), and corresponds to a particular task in a named job. The server can communicate with any other server in the same cluster.

API for driving Graph execution

The device for a session.

Tensorflow session options

The status

A status checker, during the disposal, it will throw exception if the status is not ok.

Tensor holds a multi-dimensional array of elements of a single data type. For all types other than STRING, the data buffer stores elements in row major order. E.g. if data is treated as a vector of TF_DataType: element 0: index (0, ..., 0) element 1: index (0, ..., 1) ... The format for TF_STRING tensors is: start_offset: array[uint64] data: byte[...] The string length (as a varint), followed by the contents of the string is encoded at data[start_offset[i]]]. StringEncode and StringDecode facilitate this encoding.

Entry points to the native Tensorflow library.

TString type

Describes the value of an attribute on an operation.

Describes the type of the value of an attribute on an operation.

The tensor flow datatype.

The status code

TString type