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[人工智能]tensorflow学习笔记

Graph

message NodeDef {
  // The name given to this operator. Used for naming inputs,
  // logging, visualization, etc.  Unique within a single GraphDef.
  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
  string name = 1;

  // The operation name.  There may be custom parameters in attrs.
  // Op names starting with an underscore are reserved for internal use.
  string op = 2;  // OpDef的名称

  // Each input is "node:src_output" with "node" being a string name and
  // "src_output" indicating which output tensor to use from "node". If
  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
  // may optionally be followed by control inputs that have the format
  // "^node".
  repeated string input = 3;  // 输入信息

  // A (possibly partial) specification for the device on which this
  // node should be placed.
  // The expected syntax for this string is as follows:
  //
  // DEVICE_SPEC ::= PARTIAL_SPEC
  //
  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
  // CONSTRAINT ::= ("job:" JOB_NAME)
  //              | ("replica:" [1-9][0-9]*)
  //              | ("task:" [1-9][0-9]*)
  //              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
  //
  // Valid values for this string include:
  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
  // * "/job:worker/device:GPU:3"                   (partial specification)
  // * ""                                    (no specification)
  //
  // If the constraints do not resolve to a single device (or if this
  // field is empty or not present), the runtime will attempt to
  // choose a device automatically.
  string device = 4;

  // Operation-specific graph-construction-time configuration.
  // Note that this should include all attrs defined in the
  // corresponding OpDef, including those with a value matching
  // the default -- this allows the default to change and makes
  // NodeDefs easier to interpret on their own.  However, if
  // an attr with a default is not specified in this list, the
  // default will be used.
  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
  // one of the names from the corresponding OpDef's attr field).
  // The values must have a type matching the corresponding OpDef
  // attr's type field.
  // TODO(josh11b): Add some examples here showing best practices.
  map<string, AttrValue> attr = 5;
};
message OpDef {
  // Op names starting with an underscore are reserved for internal use.
  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
  string name = 1;

  // For describing inputs and outputs.
  message ArgDef {
    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
    string name = 1;

    // Human readable description.
    string description = 2;

    // Describes the type of one or more tensors that are accepted/produced
    // by this input/output arg.  The only legal combinations are:
    // * For a single tensor: either the "type" field is set or the
    //   "type_attr" field is set to the name of an attr with type "type".
    // * For a sequence of tensors with the same type: the "number_attr"
    //   field will be set to the name of an attr with type "int", and
    //   either the "type" or "type_attr" field will be set as for
    //   single tensors.
    // * For a sequence of tensors, the "type_list_attr" field will be set
    //   to the name of an attr with type "list(type)".
    DataType type = 3;
    string type_attr = 4;    // if specified, attr must have type "type"
    string number_attr = 5;  // if specified, attr must have type "int"
    // If specified, attr must have type "list(type)", and none of
    // type, type_attr, and number_attr may be specified.
    string type_list_attr = 6;

    // For inputs: if true, the inputs are required to be refs.
    //   By default, inputs can be either refs or non-refs.
    // For outputs: if true, outputs are refs, otherwise they are not.
    bool is_ref = 16;
  };

  // Description of the input(s).
  repeated ArgDef input_arg = 2;

  // Description of the output(s).
  repeated ArgDef output_arg = 3;

  // Description of the graph-construction-time configuration of this
  // Op.  That is to say, this describes the attr fields that will
  // be specified in the NodeDef.
  message AttrDef {
    // A descriptive name for the argument.  May be used, e.g. by the
    // Python client, as a keyword argument name, and so should match
    // the regexp "[a-z][a-z0-9_]+".
    string name = 1;

    // One of the type names from attr_value.proto ("string", "list(string)",
    // "int", etc.).
    string type = 2;

    // A reasonable default for this attribute if the user does not supply
    // a value.  If not specified, the user must supply a value.
    AttrValue default_value = 3;

    // Human-readable description.
    string description = 4;

    // TODO(josh11b): bool is_optional?

    // --- Constraints ---
    // These constraints are only in effect if specified.  Default is no
    // constraints.

    // For type == "int", this is a minimum value.  For "list(___)"
    // types, this is the minimum length.
    bool has_minimum = 5;
    int64 minimum = 6;

    // The set of allowed values.  Has type that is the "list" version
    // of the "type" field above (uses the "list" field of AttrValue).
    // If type == "type" or "list(type)" above, then the "type" field
    // of "allowed_values.list" has the set of allowed DataTypes.
    // If type == "string" or "list(string)", then the "s" field of
    // "allowed_values.list" has the set of allowed strings.
    AttrValue allowed_values = 7;
  }
  repeated AttrDef attr = 4;

  // Optional deprecation based on GraphDef versions.
  OpDeprecation deprecation = 8;

  // One-line human-readable description of what the Op does.
  string summary = 5;

  // Additional, longer human-readable description of what the Op does.
  string description = 6;

  // -------------------------------------------------------------------------
  // Which optimizations this operation can participate in.

  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
  bool is_commutative = 18;

  // If is_aggregate is true, then this operation accepts N >= 2
  // inputs and produces 1 output all of the same type.  Should be
  // associative and commutative, and produce output with the same
  // shape as the input.  The optimizer may replace an aggregate op
  // taking input from multiple devices with a tree of aggregate ops
  // that aggregate locally within each device (and possibly within
  // groups of nearby devices) before communicating.
  // TODO(josh11b): Implement that optimization.
  bool is_aggregate = 16;  // for things like add

  // Other optimizations go here, like
  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.

  // -------------------------------------------------------------------------
  // Optimization constraints.

  // By default Ops may be moved between devices.  Stateful ops should
  // either not be moved, or should only be moved if that state can also
  // be moved (e.g. via some sort of save / restore).
  // Stateful ops are guaranteed to never be optimized away by Common
  // Subexpression Elimination (CSE).
  bool is_stateful = 17;  // for things like variables, queue

  // -------------------------------------------------------------------------
  // Non-standard options.

  // By default, all inputs to an Op must be initialized Tensors.  Ops
  // that may initialize tensors for the first time should set this
  // field to true, to allow the Op to take an uninitialized Tensor as
  // input.
  bool allows_uninitialized_input = 19;  // for Assign, etc.
};
Operation (python端) : 
1. 包含_node_def ,_op_def 和 _graph
2. 保存_inputs和构建_outputs(Tensor(self, i, output_type))
3.  inputs(Tensor)._add_consumer(self)
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