TensorFlow 1 version View source on GitHub

Converts the given value to a Tensor.

    value, dtype=None, dtype_hint=None, name=None

Used in the notebooks

Used in the guide Used in the tutorials

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

import numpy as np

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

# The following calls are equivalent.
value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))
value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])
value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.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.


  • 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.


A Tensor based on value.


  • 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.