tensorflow::
    
   ops::
    
   Fingerprint
  
  
   #include <array_ops.h>
  
  Generates fingerprint values.
Summary
   Generates fingerprint values of
   
    data
   
   .
  
   
    Fingerprint
   
   op considers the first dimension of
   
    data
   
   as the batch dimension, and
   
    output[i]
   
   contains the fingerprint value generated from contents in
   
    data[i, ...]
   
   for all
   
    i
   
   .
  
   
    Fingerprint
   
   op writes fingerprint values as byte arrays. For example, the default method
   
    farmhash64
   
   generates a 64-bit fingerprint value at a time. This 8-byte value is written out as an
   
    uint8
   
   array of size 8, in little-endian order.
  
   For example, suppose that
   
    data
   
   has data type
   
    DT_INT32
   
   and shape (2, 3, 4), and that the fingerprint method is
   
    farmhash64
   
   . In this case, the output shape is (2, 8), where 2 is the batch dimension size of
   
    data
   
   , and 8 is the size of each fingerprint value in bytes.
   
    output[0, :]
   
   is generated from 12 integers in
   
    data[0, :, :]
   
   and similarly
   
    output[1, :]
   
   is generated from other 12 integers in
   
    data[1, :, :]
   
   .
  
Note that this op fingerprints the raw underlying buffer, and it does not fingerprint Tensor 's metadata such as data type and/or shape. For example, the fingerprint values are invariant under reshapes and bitcasts as long as the batch dimension remain the same:
Fingerprint(data) == Fingerprint(Reshape(data, ...)) Fingerprint(data) == Fingerprint(Bitcast(data, ...))
   For string data, one should expect
   
    Fingerprint(data) != Fingerprint(ReduceJoin(data))
   
   in general.
  
Args:
- scope: A Scope object
 - data: Must have rank 1 or higher.
 - 
     method:
     
      Fingerprint
     
     method used by this op. Currently available method is
     
farmhash::fingerprint64. 
Returns:
- 
     
Output: A two-dimensionalTensorof typetf.uint8. The first dimension equals todata's first dimension, and the second dimension size depends on the fingerprint algorithm. 
     Constructors and Destructors | 
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       Fingerprint
      
      (const ::
      
       tensorflow::Scope
      
      & scope, ::
      
       tensorflow::Input
      
      data, ::
      
       tensorflow::Input
      
      method)
     
      | 
   
     Public attributes | 
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|---|---|
     
      
       fingerprint
      
     
     | 
    |
     
      
       operation
      
     
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     Public functions | 
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       node
      
      () const
     
     | 
    
     
       ::tensorflow::Node *
      
      | 
   
     
      
       operator::tensorflow::Input
      
      () const
     
     | 
    
     
      
      | 
   
     
      
       operator::tensorflow::Output
      
      () const
     
     | 
    
     
      
      | 
   
Public attributes
Public functions
Fingerprint
Fingerprint( const ::tensorflow::Scope & scope, ::tensorflow::Input data, ::tensorflow::Input method )
node
::tensorflow::Node * node() const
operator::tensorflow::Input
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const