Function convert_to_tensor

tensorflow/python/framework/ops.py:1128–1184

Converts the given `value` to a `Tensor`. This function converts Python objects of various types to `Tensor` objects. It accepts `Tensor` objects, numpy arrays, Python lists, and Python scalars. For example: ```python import numpy as np def my_func(arg): arg = tf.convert_to_tensor

(value,
                      dtype=None,
                      name=None,
                      preferred_dtype=None,
                      dtype_hint=None)

@tf_export(v1=["convert_to_tensor"])
def convert_to_tensor(value,
                      dtype=None,
                      name=None,
                      preferred_dtype=None,
                      dtype_hint=None):
  """Converts the given `value` to a `Tensor`.

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

  ```python
  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.

  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.

  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`.
    name: Optional name to use if a new `Tensor` is created.
    preferred_dtype: 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 preferred_dtype can be used as a soft
      preference.  If the conversion to `preferred_dtype` is not possible, this
      argument has no effect.
    dtype_hint: same meaning as preferred_dtype, and overrides it.

  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.
  """
  preferred_dtype = deprecation.deprecated_argument_lookup(
      "dtype_hint", dtype_hint, "preferred_dtype", preferred_dtype)
  return convert_to_tensor_v2(value, dtype, preferred_dtype, name)