# tensordot numpy

• ### torch.tensordot — PyTorch 1.9.0 documentation

2021-7-22 · torch.tensordot. torch.tensordot(a b dims=2 out=None) source Returns a contraction of a and b over multiple dimensions. tensordot implements a generalized matrix product. Parameters. a ( Tensor)Left tensor to contract. b ( Tensor)Right tensor to contract. dims ( int or Tuple List int List int or List List int containing two

• ### Let s code a Neural Network in plain NumPy by Piotr

2018-10-12 · For comparison I also prepared a model in a high-level framework — Keras. Both models have the same architecture and learning rate. Still this is a really uneven fight as the implementation that we have prepared is probably the simplest possible one. Ultimately both the NumPy and Keras model achieved similar accuracy of 95 on the test set.

• ### NumPyeinsum

2018-12-6 ·  einsumNumPy dotinnerBLASeinsum tensordot einsum https //github

• ### numpy.einsum — NumPy v1.21 Manual

2021-6-22 · Tensor contractions numpy.tensordot. Chained array operations in efficient calculation order numpy.einsum_path. The subscripts string is a comma-separated list of subscript labels where each label refers to a dimension of the corresponding operand.

• ### Numpy

2018-12-20 · numpy.tensordotNumPy v1.15 Manual docs.scipy tensordot (A B axes = (0 1) (0 1) axes

• ### `numpy.linalg` NumPy

2019-9-29 · # numpy.linalg NumPyBLASLAPACK NumPyC

• ### tensordot — sparse 0.12.0 0.g3297628.dirty documentation

2021-3-19 · tensordot¶ sparse. tensordot (a b axes = 2 return_type = None) source ¶ Perform the equivalent of numpy.tensordot. Parameters. a (Union COO np.ndarray scipy.sparse.spmatrix )The arrays to perform the tensordot operation on. b (Union COO np.ndarray scipy.sparse.spmatrix )The arrays to perform the tensordot operation on.

• ### np.tensordot _-CSDN

2019-5-15 · NumpyTensorFlowtensordot axes NumpyTensorFlow tensordot axes

• ### NumPyeinsum

2018-12-6 ·  einsumNumPy dotinnerBLASeinsum tensordot einsum https //github

• ### Numpy

2018-12-20 · numpy.tensordotNumPy v1.15 Manual docs.scipy tensordot (A B axes = (0 1) (0 1) axes

• ### NumPy tensordot MemoryError

Numpy tensor multiplication. numpy.tensordot tensordot. Compute tensor dot product along specified axes. Given two tensors a and b and an array_like object containing two array_like objects (a_axes b_axes) sum the products of a s and b s elements (components)

• ### Using njit with numpy.tensordotCommunity Support

2020-7-31 · Using njit with numpy.tensordot. Numba. Community Support. camminady July 31 2020 9 55am #1. I m new to numba an struggle with the basics. To optimize my code I d wish to increase the performance of the following operation which sums a tensor (psi) along the first axis but weighted with a vector (qweights).

• ### tf.experimental.numpy.tensordot TensorFlow

• ### Understanding tensordotxspdf

numpy.tensordot(a b axes=2) source ¶ Compute tensor dot product along specified axes for arrays >= 1-D. Given two tensors (arrays of dimension greater than or equal to one) a and b and an array_like object containing two array_like objects (a_axes b_axes) sum the products of a s and b s elements (components) over the axes

• ### numpy.einsum — NumPy v1.21 Manual

2021-6-22 · Tensor contractions numpy.tensordot. Chained array operations in efficient calculation order numpy.einsum_path. The subscripts string is a comma-separated list of subscript labels where each label refers to a dimension of the corresponding operand.

• ### Numpy

2018-12-20 · numpy.tensordotNumPy v1.15 Manual docs.scipy tensordot (A B axes = (0 1) (0 1) axes

• ### juliaNumpy

2018-12-17 · Numpy Numpy einsum tensordot einsum from numpy import from scipy import rand from time import time N1 = 100 Adim = (

• ### jax.numpy.tensordot — JAX documentation

2021-6-17 · jax.numpy.tensordot¶ jax.numpy. tensordot (a b axes = 2 precision = None) source ¶ Compute tensor dot product along specified axes. LAX-backend implementation of tensordot().. In addition to the original NumPy arguments listed below also supports precision for extra control over matrix-multiplication precision on supported devices. precision may be set to None which means

• ### juliaNumpy

2018-12-17 · Numpy Numpy einsum tensordot einsum from numpy import from scipy import rand from time import time N1 = 100 Adim = (

• ### numpy.tensordot Chenxiao Ma

2018-3-7 ·  numpy.tensordot Given two tensors (arrays of dimension greater than or equal to one) a and b and an array_like object containing two array_like objects (a_axes b_axes) sum the products of a s and b s elements (components) over the axes specified by a_axes and b_axes.

• ### juliaNumpy

2018-12-17 · Numpy Numpy einsum tensordot einsum from numpy import from scipy import rand from time import time N1 = 100 Adim = (

• ### numpy.tensordot — NumPy v1.21 Manual

2021-6-22 · numpy.tensordot. ¶. numpy.tensordot(a b axes=2) source ¶. Compute tensor dot product along specified axes. Given two tensors a and b and an array_like object containing two array_like objects (a_axes b_axes) sum the products of a s and b s elements (components) over the axes specified by a_axes and b_axes.

• ### pythonTensordot for vectors in numpyStack Overflow

2019-2-10 · np.tensordot is an attempt to generalize np.dot for 2d arrays like this it can t do anything that a few added transposes can t. Your result isn t a tensordot in that sense. dot involves sum of products you aren t doing any sums.

• ### NumPy 1.14 numpy.tensordotSolved

numpy.tensordot(a b axes=2) source Compute tensor dot product along specified axes for arrays >= 1-D. Given two tensors (arrays of dimension greater than or equal to one) a and b and an array_like object containing two array_like objects (a_axes b_axes) sum the products of a s and b s elements (components) over the axes specified by a_axes and b_axes.

• ### tf.experimental.numpy.tensordot TensorFlow Core v2.5.0

2021-5-14 · TensorFlow variant of NumPy #39s tensordot.

• ### numpy.tensordot — NumPy v1.14 ManualSciPy

2018-1-8 · numpy.tensordot (a b axes=2) source ¶ Compute tensor dot product along specified axes for arrays >= 1-D. Given two tensors (arrays of dimension greater than or equal to one) a and b and an array_like object containing two array_like objects (a_axes b_axes) sum the products of a s and b s elements (components) over the axes specified by a_axes and b_axes .

• ### numpy.tensordot Chenxiao Ma

2018-3-7 ·  numpy.tensordot Given two tensors (arrays of dimension greater than or equal to one) a and b and an array_like object containing two array_like objects (a_axes b_axes) sum the products of a s and b s elements (components) over the axes specified by a_axes and b_axes.

• ### NumPy 1.14.0 Release Notes — NumPy v1.18 Manual

2020-5-24 · Numpy 1.14.0 is the result of seven months of work and contains a large number of bug fixes and new features along with several changes with potential compatibility issues. The major change that users will notice are the stylistic changes in the way numpy arrays and scalars are printed a change that will affect doctests.