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
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.
An object whose type has a registered Tensor conversion function.
Optional element type for the returned tensor. If missing, the type
is inferred from the type of value.
Optional name to use if a new Tensor is created.
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.
same meaning as preferred_dtype, and overrides it.
A Tensor based on value.
If no conversion function is registered for value to dtype.
If a registered conversion function returns an invalid value.
If the value is a tensor not of given dtype in graph mode.