package tensorflow.tfprof

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• repeated OpLogEntry log_entries = 1

Used in: OpLog

• optional string name = 1

  op name.

• optional int64 float_ops = 2

  float_ops is filled by tfprof Python API when called. It requires the op has RegisterStatistics defined. Currently, Conv2D, MatMul, etc, are implemented.

• repeated string types = 3

  User can define extra op type information for an op. This allows the user to select a group of ops precisely using op_type as a key.

• optional string name = 1

  op name.

• optional TFProfTensorProto tensor_value = 15

  tensor value restored from checkpoint.

• optional int64 exec_micros = 2

  op execution time.

• optional int64 requested_bytes = 3

  Total requested bytes by the op.

• optional int64 parameters = 4

  Number of parameters if available.

• optional int64 float_ops = 13

  Number of float operations.

• optional int64 inputs = 5

  Number of inputs to the op.

• optional string device = 10

  Device the op is assigned to.

• optional int64 total_exec_micros = 6

  The following are the aggregated stats from all accounted descendants and the op itself. The actual descendants depend on the data structure used (scope, graph).

• optional int64 total_requested_bytes = 7

• optional int64 total_parameters = 8

• optional int64 total_float_ops = 14

• optional int64 total_inputs = 9

• repeated TensorShapeProto shapes = 11

  shape information, if available.

• repeated TFProfNode children = 12

  Descendants of the graph. The actual descendants depend on the data structure used (scope, graph).

Used in: TFProfNode

• optional DataType dtype = 1

• repeated double value_double = 2

  Flatten tensor in row-major. Only one of the following array is set.

• repeated int64 value_int64 = 3

• repeated string value_str = 4