Difference between revisions of "Data Flow Graph (DFG)"
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| − | [http://www.youtube.com/results?search_query=Data+Flow+ Graph+ | + | [http://www.youtube.com/results?search_query=Data+Flow+Graph+DFG+deep+machine+learning+ML YouTube search...] |
| − | [http://www.google.com/search?q=Data+Flow+ Graph+ | + | [http://www.google.com/search?q=Data+Flow+Graph+DFG+deep+machine+learning+ML+artificial+intelligence ...Google search] |
A data-flow graph (DFG) is a graph which represents a data dependencies between a number of operations. Any algorithm consists of a number of ordered operations. | A data-flow graph (DFG) is a graph which represents a data dependencies between a number of operations. Any algorithm consists of a number of ordered operations. | ||
Revision as of 21:32, 22 February 2019
YouTube search... ...Google search
A data-flow graph (DFG) is a graph which represents a data dependencies between a number of operations. Any algorithm consists of a number of ordered operations.
- Node: In TensorFlow, each node represents the instantion of an operation. Each operation has >= inputs and >= 0 outputs.
- Edges: In TensorFlow, there are two types of edge:
- Normal Edges: They are carriers of data structures (tensors), where an output of one operation (from one node) becomes the input for another operation.
- Special Edges: These edges are not data carriers between the output of a node (operator) and the input of another node. A special edge indicates a control dependency between two nodes. Let's suppose we have two nodes A and B and a special edges connecting A to B; it means that B will start its operation only when the operation in A ends. Special edges are used in Data Flow Graph to set the happens-before relationship between operations on the tensors.
