# numpy tensor productdot product of two tensors

• ### torch.matmul — PyTorch 1.9.0 documentation

2021-7-22 · torch.matmul(input other out=None) → Tensor. Matrix product of two tensors. The behavior depends on the dimensionality of the tensors as follows If both tensors are 1-dimensional the dot product (scalar) is returned. If both arguments are 2-dimensional the matrix-matrix product is returned. If the first argument is 1-dimensional and

• ### torch.tensordot — PyTorch 1.9.0 documentation

2021-7-22 · 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 lists or Tensor

• ### numpy.tensordot — NumPy v1.7 Manual (DRAFT)

2020-6-27 · numpy.tensordot¶ 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.

• ### 1 Introduction to the Tensor ProductMIT

2020-12-30 · The tensor product V ⊗ W is thus deﬁned to be the vector space whose elements are (complex) linear combinations of elements of the form v ⊗ w with v ∈ V w ∈ W with the above rules for manipulation. The tensor product V ⊗ W is the complex vector space of states of the two

• ### Tensors I Basic Operations and Representations

2012-11-8 · Tensors Vectors matrices and so on Definition . Operators . x y = x y seen as a two-leg tensor Tensor as data hive of different form ( ) 1 2 1 2 1 1 1 1 i i i i Tensor product gives tensor with more legs. i. 2. i. 3. Operations between tensors are defined by contracted indices.

• ### torch.dot — PyTorch 1.9.0 documentation

2021-7-21 · torch.dot. torch.dot(input other out=None) → Tensor. Computes the dot product of two 1D tensors. Note. Unlike NumPy s dot torch.dot intentionally only supports computing the dot product of two 1D tensors with the same number of elements. Parameters. input ( Tensor)first tensor in the dot product must be 1D.

• ### A Gentle Introduction to Tensors for Machine Learning with

2019-12-6 · The tensor product can be implemented in NumPy using the tensordot() function. The function takes as arguments the two tensors to be multiplied and the axis on which to sum the products over called the sum reduction. To calculate the tensor product also called the tensor dot product in NumPy the axis must be set to 0.

• ### linear algebraHow does tensor product/multiplication

2021-6-5 · How does tensor product/multiplication work in TensorFlow Ask Question Asked 2 In Tensorflow I saw the following example import tensorflow as tf import numpy as np mat_a = tfnstant(np.arange(1 13 dtype=npt32) shape= 2 2 3 ) mat_b = tfnstant(np.arange(12 24 dtype=npt32) shape= 2 3 2 ) mul_c = tf.matmul(mat_a mat_b) with

• ### 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 .

• ### Basic Tensor Functionality — Theano 1.1.2 29.g8b2825658

2021-1-29 · This is equivalent to numpy.isclose. theano.tensor.allclose (a b rtol = 1e-05 atol = 1e-08 equal_nan = False) source ¶ Returns a symbolic int8 value representing if all elements in two tensors are equal within a tolerance. See notes in isclose for determining values equal within a tolerance. This is equivalent to numpy.allclose.

• ### numpy.tensordot — NumPy v1.10 ManualSciPy

2015-10-18 · 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.

• ### torch.matmul — PyTorch 1.9.0 documentation

2021-7-22 · torch.matmul(input other out=None) → Tensor. Matrix product of two tensors. The behavior depends on the dimensionality of the tensors as follows If both tensors are 1-dimensional the dot product (scalar) is returned. If both arguments are 2-dimensional the matrix-matrix product is returned. If the first argument is 1-dimensional and

• ### torch.dot — PyTorch 1.9.0 documentation

2021-7-21 · torch.dot. torch.dot(input other out=None) → Tensor. Computes the dot product of two 1D tensors. Note. Unlike NumPy s dot torch.dot intentionally only supports computing the dot product of two 1D tensors with the same number of elements. Parameters. input ( Tensor)first tensor in the dot product must be 1D.

• ### From numpy to xtensor — xtensor documentation

2021-6-22 · Linear algebra¶. Many functions found in the numpy.linalg module are implemented in xtensor-blas a separate package offering BLAS and LAPACK bindings as well as a convenient interface replicating the linalg module.. Please note however that while we re trying to be as close to NumPy as possible some features are not implemented yet.

• ### torch.tensordot — PyTorch 1.9.0 documentation

2021-7-22 · 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 lists or Tensor

• ### torch.matmul — PyTorch 1.9.0 documentation

2021-7-22 · torch.matmul(input other out=None) → Tensor. Matrix product of two tensors. The behavior depends on the dimensionality of the tensors as follows If both tensors are 1-dimensional the dot product (scalar) is returned. If both arguments are 2-dimensional the matrix-matrix product is returned. If the first argument is 1-dimensional and

• ### linear algebraHow does tensor product/multiplication

2021-6-5 · How does tensor product/multiplication work in TensorFlow Ask Question Asked 2 In Tensorflow I saw the following example import tensorflow as tf import numpy as np mat_a = tfnstant(np.arange(1 13 dtype=npt32) shape= 2 2 3 ) mat_b = tfnstant(np.arange(12 24 dtype=npt32) shape= 2 3 2 ) mul_c = tf.matmul(mat_a mat_b) with

• ### torch.matmul — PyTorch 1.9.0 documentation

2021-7-22 · torch.matmul(input other out=None) → Tensor. Matrix product of two tensors. The behavior depends on the dimensionality of the tensors as follows If both tensors are 1-dimensional the dot product (scalar) is returned. If both arguments are 2-dimensional the matrix-matrix product is returned. If the first argument is 1-dimensional and

• ### Tensors I Basic Operations and Representations

2012-11-8 · Tensors Vectors matrices and so on Definition . Operators . x y = x y seen as a two-leg tensor Tensor as data hive of different form ( ) 1 2 1 2 1 1 1 1 i i i i Tensor product gives tensor with more legs. i. 2. i. 3. Operations between tensors are defined by contracted indices.

• ### numpytheanouse tensordot compute dot product of two

2015-12-16 · I want to use tensordot to compute the dot product of a specific dim of two tensors. Like A is a tensor whose shape is (3 4 5) B is a tensor whose shape is (3 5) I want to do a dot use A s third dim and B s second dim and get a output whose dims is (3 4) Like below for i in range(3) C i = dot(A i B i ) How to do it by tensordot

• ### Appendix D Numerical Tensor Products

2019-7-9 · Tensor products are implemented by the numpy.kron function which performs an opera-tion called a Kronecker product. The function takes two inputs which can be 1D arrays 2D arrays or even higher-dimensional arrays (which we won t discuss). It returns a new array representing the tensor product of the inputs whose dimensionality depends on

• ### numpy.tensordot — NumPy v1.15 ManualSciPy

2018-7-24 · 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.

• ### torch.vdot — PyTorch 1.9.0 documentation

2021-7-20 · Unlike NumPy s vdot torch.vdot intentionally only supports computing the dot product of two 1D tensors with the same number of elements. input ( Tensor)first tensor in the dot product must be 1D. Its conjugate is used if it s complex. other ( Tensor)second tensor in the dot product must be 1D. out ( Tensor optional)the

• ### numpy.tensordot — NumPy v1.9 Manual

2014-11-12 · 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.

• ### torch.dot — PyTorch 1.9.0 documentation

2021-7-21 · torch.dot. torch.dot(input other out=None) → Tensor. Computes the dot product of two 1D tensors. Note. Unlike NumPy s dot torch.dot intentionally only supports computing the dot product of two 1D tensors with the same number of elements. Parameters. input ( Tensor)first tensor in the dot product must be 1D.

• ### Appendix D Numerical Tensor Products

2019-7-9 · Tensor products are implemented by the numpy.kron function which performs an opera-tion called a Kronecker product. The function takes two inputs which can be 1D arrays 2D arrays or even higher-dimensional arrays (which we won t discuss). It returns a new array representing the tensor product of the inputs whose dimensionality depends on

• ### tensorflow => Dot Product

tensorflow TutorialThe dot product between two tensors can be performed using tf.matmul(a b)A full example is given below # Build a graphgraph =

• ### numpy.tensordot — NumPy v1.10 ManualSciPy

2015-10-18 · numpy.tensordot¶ 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 — NumPy v1.11 ManualSciPy

2020-6-27 · numpy.tensordot¶ 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.

• ### SVD in scipy and numpy for tensorsMathematics Stack

2021-3-16 · Thus I use the reshape option to unfold the tensor and compute the svd using scipy.linalg as follows Xreshape = np.reshape (X (9 3)) S2 = scipy.linalg.svd (X) Now S2 here is a tuple containing U 9x9 Sigma 3x1 and Vh 3x3. The elements of S1 and S2 are not the same.

• ### numpy.tensordot — NumPy v1.15 ManualSciPy

2018-7-24 · numpy.tensordot¶ 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.

• ### linear algebra4th order tensors double dot product and

2017-5-11 · and for the double dot product between a fourth order tensor and a second order tensor (A s)_ ij = A_ ijkl s_ kl Using the convention of sommation over repeating indices. What I call the identity of the fourth order tensors is the only tensor such that A I = I A = A

• ### numpy.tensordot — NumPy v1.9 Manual

2014-11-12 · numpy.tensordot¶ 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 — NumPy v1.9 Manual

2014-11-12 · numpy.tensordot¶ 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.

• ### Introduction to the Tensor ProductUC Santa Barbara

2012-3-11 · When described as multilinear maps the tensor product simply multiplies the two tensors i.e ˝ ˙2Tk l(V) and ˝ ˙(v 1 v kv k 1 v k l) = ˝(v 1 v k)˙(v k 1 v k l) which again produces a map that is linear in all its arguments. On components the e ect similarly is to multiply the components of the two input tensors.

• ### linear algebraHow does tensor product/multiplication

2021-6-5 · How does tensor product/multiplication work in TensorFlow Ask Question Asked 2 In Tensorflow I saw the following example import tensorflow as tf import numpy as np mat_a = tfnstant(np.arange(1 13 dtype=npt32) shape= 2 2 3 ) mat_b = tfnstant(np.arange(12 24 dtype=npt32) shape= 2 3 2 ) mul_c = tf.matmul(mat_a mat_b) with

• ### torch.matmul — PyTorch 1.9.0 documentation

2021-7-22 · torch.matmul(input other out=None) → Tensor. Matrix product of two tensors. The behavior depends on the dimensionality of the tensors as follows If both tensors are 1-dimensional the dot product (scalar) is returned. If both arguments are 2-dimensional the matrix-matrix product is returned. If the first argument is 1-dimensional and

• ### Vector and Tensor AlgebraTU/e

2010-8-31 · 1.1.6 Tensor product The tensor product of two vectors represents a dyad which is a linear vector transformation. A dyad is a special tensorto be discussed later which explains the name of this product. Because it is often denoted without a symbol between the two vectors it is also referred to as the open product.

• ### Multiply Tensors with Different Shapes in TensorFlow

2020-8-28 · Understand NumPy np.multiply() np.dot() and Operation A Beginner GuideNumPy Tutorial Computing Hadamard Product of Two Tensors in TensorFlowTensorFlow Example TensorFlow tf.split() Splits a Tensor into Sub TensorsTensorFlow Tutorial

• ### Multiply Tensors with Different Shapes in TensorFlow

2020-8-28 · Understand NumPy np.multiply() np.dot() and Operation A Beginner GuideNumPy Tutorial Computing Hadamard Product of Two Tensors in TensorFlowTensorFlow Example TensorFlow tf.split() Splits a Tensor into Sub TensorsTensorFlow Tutorial