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.

Note: This function diverges from default Numpy behavior for float and string types when None is present in a Python list or scalar. Rather than silently converting None values, an error will be thrown.