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 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
Optional name to use if a new Tensor is created.
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.