tf.experimental.numpy: NumPy API on TensorFlow.
This module provides a subset of NumPy API, built on top of TensorFlow operations. APIs are based on and have been tested with NumPy 1.16 version.
The set of supported APIs may be expanded over time. Also future releases may change the baseline version of NumPy API being supported. A list of some systematic differences with NumPy is listed later in the "Differences with NumPy" section.
Please also see TensorFlow NumPy Guide.
In the code snippets below, we will assume that
tnp and NumPy is imported as
print(tnp.ones([2,1]) + np.ones([1, 2]))
The module provides an
ndarray class which wraps an immutable
Additional functions are provided which accept array-like objects. Here
array-like objects include
ndarrays as defined by this module, as well as
tf.Tensor, in addition to types accepted by NumPy.
A subset of NumPy dtypes are supported. Type promotion follows NumPy semantics.
print(tnp.ones([1, 2], dtype=tnp.int16) + tnp.ones([2, 1], dtype=tnp.uint8))
ndarray class implements the
__array__ interface. This should allow
these objects to be passed into contexts that expect a NumPy or array-like
object (e.g. matplotlib).
np.sum(tnp.ones([1, 2]) + np.ones([2, 1]))