tf.convert_to_tensor(
value,
dtype=None,
name=None,
preferred_dtype=None
)
Defined in tensorflow/python/framework/ops.py.
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:
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.
Args:
value: An object whose type has a registeredTensorconversion function.dtype: Optional element type for the returned tensor. If missing, the type is inferred from the type ofvalue.name: Optional name to use if a newTensoris 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 topreferred_dtypeis not possible, this argument has no effect.
Returns:
An Tensor based on value.
Raises:
TypeError: If no conversion function is registered forvaluetodtype.RuntimeError: If a registered conversion function returns an invalid value.ValueError: If thevalueis a tensor not of givendtypein graph mode.