parameters: (values, dtype=None, shape=None, verify_shape=False)
return_statement: return tensor_proto
docstring: Create a TensorProto.
  
    Args:
      values:         Values to put in the TensorProto.
      dtype:          Optional tensor_pb2 DataType value.
      shape:          List of integers representing the dimensions of tensor.
      verify_shape:   Boolean that enables verification of a shape of values.
  
    Returns:
      A TensorProto. Depending on the type, it may contain data in the
      "tensor_content" attribute, which is not directly useful to Python programs.
      To access the values you should convert the proto back to a numpy ndarray
      with tensor_util.MakeNdarray(proto).
  
    Raises:
      TypeError:  if unsupported types are provided.
      ValueError: if arguments have inappropriate values or if verify_shape is
       True and shape of values is not equals to a shape from the argument.
  
    make_tensor_proto accepts "values" of a python scalar, a python list, a
    numpy ndarray, or a numpy scalar.
  
    If "values" is a python scalar or a python list, make_tensor_proto
    first convert it to numpy ndarray. If dtype is None, the
    conversion tries its best to infer the right numpy data
    type. Otherwise, the resulting numpy array has a compatible data
    type with the given dtype.
  
    In either case above, the numpy ndarray (either the caller provided
    or the auto converted) must have the compatible type with dtype.
  
    make_tensor_proto then converts the numpy array to a tensor proto.
  
    If "shape" is None, the resulting tensor proto represents the numpy
    array precisely.
  
    Otherwise, "shape" specifies the tensor's shape and the numpy array
    can not have more elements than what "shape" specifies.
docstring_summary: Create a TensorProto.
docstring_tokens: Create a TensorProto .
function: def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False):
    """Create a TensorProto.
  
    Args:
      values:         Values to put in the TensorProto.
      dtype:          Optional tensor_pb2 DataType value.
      shape:          List of integers representing the dimensions of tensor.
      verify_shape:   Boolean that enables verification of a shape of values.
  
    Returns:
      A TensorProto. Depending on the type, it may contain data in the
      "tensor_content" attribute, which is not directly useful to Python programs.
      To access the values you should convert the proto back to a numpy ndarray
      with tensor_util.MakeNdarray(proto).
  
    Raises:
      TypeError:  if unsupported types are provided.
      ValueError: if arguments have inappropriate values or if verify_shape is
       True and shape of values is not equals to a shape from the argument.
  
    make_tensor_proto accepts "values" of a python scalar, a python list, a
    numpy ndarray, or a numpy scalar.
  
    If "values" is a python scalar or a python list, make_tensor_proto
    first convert it to numpy ndarray. If dtype is None, the
    conversion tries its best to infer the right numpy data
    type. Otherwise, the resulting numpy array has a compatible data
    type with the given dtype.
  
    In either case above, the numpy ndarray (either the caller provided
    or the auto converted) must have the compatible type with dtype.
  
    make_tensor_proto then converts the numpy array to a tensor proto.
  
    If "shape" is None, the resulting tensor proto represents the numpy
    array precisely.
  
    Otherwise, "shape" specifies the tensor's shape and the numpy array
    can not have more elements than what "shape" specifies.
  
    """
    if dtype:
      dtype = dtypes.as_dtype(dtype)
  
    is_quantized = (dtype in [dtypes.qint8, dtypes.quint8, dtypes.qint16,
                              dtypes.quint16, dtypes.qint32])
  
    # We first convert value to a numpy array or scalar.
    if isinstance(values, (np.ndarray, np.generic)):
      if dtype:
        nparray = values.astype(dtype.as_numpy_dtype)
      else:
        nparray = values
    elif callable(getattr(values, "__array__", None)):
      # If a class has the __array__ method, then it is possible to convert
      # to numpy array.
      nparray = np.asarray(values, dtype=dtype)
    else:
      if values is None:
        raise ValueError("None values not supported.")
      # if dtype is provided, forces numpy array to be the type
      # provided if possible.
      if dtype and dtype.is_numpy_compatible:
        np_dt = dtype.as_numpy_dtype
      else:
        np_dt = None
      # If shape is None, numpy.prod returns None when dtype is not set, but raises
      # exception when dtype is set to np.int64
      if shape is not None and np.prod(shape, dtype=np.int64) == 0:
        nparray = np.empty(shape, dtype=np_dt)
      else:
        _AssertCompatible(values, dtype)
        nparray = np.array(values, dtype=np_dt)
        # check to them.
        # We need to pass in quantized values as tuples, so don't apply the shape
        if (list(nparray.shape) != _GetDenseDimensions(values) and
            not is_quantized):
          raise ValueError("""Argument must be a dense tensor: %s"""
                           """ - got shape %s, but wanted %s.""" % (
                               values, list(nparray.shape),
                               _GetDenseDimensions(values)))
  
      # python/numpy default float type is float64. We prefer float32 instead.
      if (nparray.dtype == np.float64) and dtype is None:
        nparray = nparray.astype(np.float32)
      # python/numpy default int type is int64. We prefer int32 instead.
      elif (nparray.dtype == np.int64) and dtype is None:
        downcasted_array = nparray.astype(np.int32)
        # Do not down cast if it leads to precision loss.
        if np.array_equal(downcasted_array, nparray):
          nparray = downcasted_array
  
    # if dtype is provided, it must be compatible with what numpy
    # conversion says.
    numpy_dtype = dtypes.as_dtype(nparray.dtype)
    if numpy_dtype is None:
      raise TypeError("Unrecognized data type: %s" % nparray.dtype)
  
    # If dtype was specified and is a quantized type, we convert
    # numpy_dtype back into the quantized version.
    if is_quantized:
      numpy_dtype = dtype
  
    if dtype is not None and (not hasattr(dtype, "base_dtype") or
                              dtype.base_dtype != numpy_dtype.base_dtype):
      raise TypeError("Incompatible types: %s vs. %s" % (dtype, nparray.dtype))
  
    # If shape is not given, get the shape from the numpy array.
    if shape is None:
      shape = nparray.shape
      is_same_size = True
      shape_size = nparray.size
    else:
      shape = [int(dim) for dim in shape]
      shape_size = np.prod(shape, dtype=np.int64)
      is_same_size = shape_size == nparray.size
  
      if verify_shape:
        if not nparray.shape == tuple(shape):
          raise TypeError("Expected Tensor's shape: %s, got %s." %
                          (tuple(shape), nparray.shape))
  
      if nparray.size > shape_size:
        raise ValueError(
            "Too many elements provided. Needed at most %d, but received %d" %
            (shape_size, nparray.size))
  
    tensor_proto = tensor_pb2.TensorProto(
        dtype=numpy_dtype.as_datatype_enum,
        tensor_shape=tensor_shape.as_shape(shape).as_proto())
  
    if is_same_size and numpy_dtype in _TENSOR_CONTENT_TYPES and shape_size > 1:
      if nparray.size * nparray.itemsize >= (1 << 31):
        raise ValueError(
            "Cannot create a tensor proto whose content is larger than 2GB.")
      tensor_proto.tensor_content = nparray.tostring()
      return tensor_proto
  
    # If we were not given values as a numpy array, compute the proto_values
    # from the given values directly, to avoid numpy trimming nulls from the
    # strings. Since values could be a list of strings, or a multi-dimensional
    # list of lists that might or might not correspond to the given shape,
    # we flatten it conservatively.
    if numpy_dtype == dtypes.string and not isinstance(values, np.ndarray):
      proto_values = _FlattenToStrings(values)
  
      # At this point, values may be a list of objects that we could not
      # identify a common type for (hence it was inferred as
      # np.object/dtypes.string).  If we are unable to convert it to a
      # string, we raise a more helpful error message.
      #
      # Ideally, we'd be able to convert the elements of the list to a
      # common type, but this type inference requires some thinking and
      # so we defer it for now.
      try:
        str_values = [compat.as_bytes(x) for x in proto_values]
      except TypeError:
        raise TypeError("Failed to convert object of type %s to Tensor. "
                        "Contents: %s. Consider casting elements to a "
                        "supported type." % (type(values), values))
      tensor_proto.string_val.extend(str_values)
      return tensor_proto
  
    # TensorFlow expects C order (a.k.a., eigen row major).
    proto_values = nparray.ravel()
  
    append_fn = GetNumpyAppendFn(proto_values.dtype)
    if append_fn is None:
      raise TypeError("Element type not supported in TensorProto: %s" %
                      numpy_dtype.name)
    append_fn(tensor_proto, proto_values)
  
    return tensor_proto
function_tokens: def make_tensor_proto ( values , dtype = None , shape = None , verify_shape = False ) : if dtype : dtype = dtypes . as_dtype ( dtype ) is_quantized = ( dtype in [ dtypes . qint8 , dtypes . quint8 , dtypes . qint16 , dtypes . quint16 , dtypes . qint32 ] ) # We first convert value to a numpy array or scalar. if isinstance ( values , ( np . ndarray , np . generic ) ) : if dtype : nparray = values . astype ( dtype . as_numpy_dtype ) else : nparray = values elif callable ( getattr ( values , "__array__" , None ) ) : # If a class has the __array__ method, then it is possible to convert # to numpy array. nparray = np . asarray ( values , dtype = dtype ) else : if values is None : raise ValueError ( "None values not supported." ) # if dtype is provided, forces numpy array to be the type # provided if possible. if dtype and dtype . is_numpy_compatible : np_dt = dtype . as_numpy_dtype else : np_dt = None # If shape is None, numpy.prod returns None when dtype is not set, but raises # exception when dtype is set to np.int64 if shape is not None and np . prod ( shape , dtype = np . int64 ) == 0 : nparray = np . empty ( shape , dtype = np_dt ) else : _AssertCompatible ( values , dtype ) nparray = np . array ( values , dtype = np_dt ) # check to them. # We need to pass in quantized values as tuples, so don't apply the shape if ( list ( nparray . shape ) != _GetDenseDimensions ( values ) and not is_quantized ) : raise ValueError ( """Argument must be a dense tensor: %s""" """ - got shape %s, but wanted %s.""" % ( values , list ( nparray . shape ) , _GetDenseDimensions ( values ) ) ) # python/numpy default float type is float64. We prefer float32 instead. if ( nparray . dtype == np . float64 ) and dtype is None : nparray = nparray . astype ( np . float32 ) # python/numpy default int type is int64. We prefer int32 instead. elif ( nparray . dtype == np . int64 ) and dtype is None : downcasted_array = nparray . astype ( np . int32 ) # Do not down cast if it leads to precision loss. if np . array_equal ( downcasted_array , nparray ) : nparray = downcasted_array # if dtype is provided, it must be compatible with what numpy # conversion says. numpy_dtype = dtypes . as_dtype ( nparray . dtype ) if numpy_dtype is None : raise TypeError ( "Unrecognized data type: %s" % nparray . dtype ) # If dtype was specified and is a quantized type, we convert # numpy_dtype back into the quantized version. if is_quantized : numpy_dtype = dtype if dtype is not None and ( not hasattr ( dtype , "base_dtype" ) or dtype . base_dtype != numpy_dtype . base_dtype ) : raise TypeError ( "Incompatible types: %s vs. %s" % ( dtype , nparray . dtype ) ) # If shape is not given, get the shape from the numpy array. if shape is None : shape = nparray . shape is_same_size = True shape_size = nparray . size else : shape = [ int ( dim ) for dim in shape ] shape_size = np . prod ( shape , dtype = np . int64 ) is_same_size = shape_size == nparray . size if verify_shape : if not nparray . shape == tuple ( shape ) : raise TypeError ( "Expected Tensor's shape: %s, got %s." % ( tuple ( shape ) , nparray . shape ) ) if nparray . size > shape_size : raise ValueError ( "Too many elements provided. Needed at most %d, but received %d" % ( shape_size , nparray . size ) ) tensor_proto = tensor_pb2 . TensorProto ( dtype = numpy_dtype . as_datatype_enum , tensor_shape = tensor_shape . as_shape ( shape ) . as_proto ( ) ) if is_same_size and numpy_dtype in _TENSOR_CONTENT_TYPES and shape_size > 1 : if nparray . size * nparray . itemsize >= ( 1 << 31 ) : raise ValueError ( "Cannot create a tensor proto whose content is larger than 2GB." ) tensor_proto . tensor_content = nparray . tostring ( ) return tensor_proto # If we were not given values as a numpy array, compute the proto_values # from the given values directly, to avoid numpy trimming nulls from the # strings. Since values could be a list of strings, or a multi-dimensional # list of lists that might or might not correspond to the given shape, # we flatten it conservatively. if numpy_dtype == dtypes . string and not isinstance ( values , np . ndarray ) : proto_values = _FlattenToStrings ( values ) # At this point, values may be a list of objects that we could not # identify a common type for (hence it was inferred as # np.object/dtypes.string).  If we are unable to convert it to a # string, we raise a more helpful error message. # # Ideally, we'd be able to convert the elements of the list to a # common type, but this type inference requires some thinking and # so we defer it for now. try : str_values = [ compat . as_bytes ( x ) for x in proto_values ] except TypeError : raise TypeError ( "Failed to convert object of type %s to Tensor. " "Contents: %s. Consider casting elements to a " "supported type." % ( type ( values ) , values ) ) tensor_proto . string_val . extend ( str_values ) return tensor_proto # TensorFlow expects C order (a.k.a., eigen row major). proto_values = nparray . ravel ( ) append_fn = GetNumpyAppendFn ( proto_values . dtype ) if append_fn is None : raise TypeError ( "Element type not supported in TensorProto: %s" % numpy_dtype . name ) append_fn ( tensor_proto , proto_values ) return tensor_proto