tf.convert_to_tensor

Converts the given value to a Tensor.

tf.convert_to_tensor(
    value, dtype=None, dtype_hint=None, name=None
)

This function converts Python objects of various types to Tensor objects. It accepts Tensor objects, numpy arrays, Python lists, and Python scalars. For example:

def my_func(arg):
  arg = tf.convert_to_tensor(arg, dtype=tf.float32)
  return arg

# The following calls are equivalent.
value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))
print(value_1)
tf.Tensor(
  [[1. 2.]
   [3. 4.]], shape=(2, 2), dtype=float32)
value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])
print(value_2)
tf.Tensor(
  [[1. 2.]
   [3. 4.]], shape=(2, 2), dtype=float32)
value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32))
print(value_3)
tf.Tensor(
  [[1. 2.]
   [3. 4.]], shape=(2, 2), dtype=float32)

This function can be useful when composing a new operation in Python (such as my_func in the example above). All standard Python op constructors apply this function to each of their Tensor-valued inputs, which allows those ops to accept numpy arrays, Python lists, and scalars in addition to Tensor objects.

Args
`value`	An object whose type has a registered `Tensor` conversion function.
`dtype`	Optional element type for the returned tensor. If missing, the type is inferred from the type of `value`.
`dtype_hint`	Optional element type for the returned tensor, used when dtype is None. In some cases, a caller may not have a dtype in mind when converting to a tensor, so dtype_hint can be used as a soft preference. If the conversion to `dtype_hint` is not possible, this argument has no effect.
`name`	Optional name to use if a new `Tensor` is created.

Returns
A `Tensor` based on `value`.

Raises
`TypeError`	If no conversion function is registered for `value` to `dtype`.
`RuntimeError`	If a registered conversion function returns an invalid value.
`ValueError`	If the `value` is a tensor not of given `dtype` in graph mode.

tf.convert_to_tensor

Args

Returns

Raises