Converts the given value to a Tensor.
tfp.experimental.distributions.marginal_fns.ps.convert_to_shape_tensor(
value, dtype=None, dtype_hint=None, name=None
) -> tensor_lib.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 npdef my_func(arg):arg = tf.convert_to_tensor(arg, dtype=tf.float32)return arg
# The following calls are equivalent.value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))print(value_1)tf.Tensor([[1. 2.][3. 4.]], shape=(2, 2), dtype=float32)value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])print(value_2)tf.Tensor([[1. 2.][3. 4.]], shape=(2, 2), dtype=float32)value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32))print(value_3)tf.Tensor([[1. 2.][3. 4.]], shape=(2, 2), dtype=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.
Returns | |
|---|---|
A Tensor based on value.
|