tensor | package tensor provides efficient and generic n | Machine Learning library

 by   gorgonia Go Version: v0.9.24 License: Apache-2.0

kandi X-RAY | tensor Summary

kandi X-RAY | tensor Summary

tensor is a Go library typically used in Artificial Intelligence, Machine Learning, Numpy applications. tensor has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

In the data analysis world, Numpy and Matlab currently reign supreme. Both tools rely heavily on having performant n-dimensional arrays, or tensors. There is an obvious need for multidimensional arrays in Go. While slices are cool, a large majority of scientific and numeric computing work relies heavily on matrices (two-dimensional arrays), three dimensional arrays and so on. In Go, the typical way of getting multidimensional arrays is to use something like [][]T. Applications that are more math heavy may opt to use the very excellent Gonum matrix package. What then if we want to go beyond having a float64 matrix? What if we wanted a 3-dimensional float32 array?. It comes to reason then there should be a data structure that handles these things. The tensor package fits in that niche.
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            kandi-support Support

              tensor has a low active ecosystem.
              It has 304 star(s) with 44 fork(s). There are 20 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 27 open issues and 22 have been closed. On average issues are closed in 62 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of tensor is v0.9.24

            kandi-Quality Quality

              tensor has 0 bugs and 0 code smells.

            kandi-Security Security

              tensor has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              tensor code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              tensor is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              tensor releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.

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            tensor Key Features

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            tensor Examples and Code Snippets

            Locate the tensor element of the given indices .
            pythondot img1Lines of Code : 122dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def locate_tensor_element(formatted, indices):
              """Locate a tensor element in formatted text lines, given element indices.
            
              Given a RichTextLines object representing a tensor and indices of the sought
              element, return the row number at which the   
            r Solve tensor product .
            pythondot img2Lines of Code : 94dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def lu_solve(lower_upper, perm, rhs, validate_args=False, name=None):
              """Solves systems of linear eqns `A X = RHS`, given LU factorizations.
            
              Note: this function does not verify the implied matrix is actually invertible
              nor is this condition ch  
            Wrap input tensor into a function .
            pythondot img3Lines of Code : 84dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _wrap_2d_function(inputs, compute_op, dim=-1, name=None):
              """Helper function for ops that accept and return 2d inputs of same shape.
            
              It reshapes and transposes the inputs into a 2-D Tensor and then invokes
              the given function. The output wo  

            Community Discussions

            QUESTION

            module 'numpy.distutils.__config__' has no attribute 'blas_opt_info'
            Asked 2022-Mar-17 at 10:50

            I'm trying to study the neural-network-and-deep-learning (http://neuralnetworksanddeeplearning.com/chap1.html). Using the updated version for Python 3 by MichalDanielDobrzanski (https://github.com/MichalDanielDobrzanski/DeepLearningPython). Tried to run it in my command console and it gives an error below. I've tried uninstalling and reinstalling setuptools, theano, and numpy but none have worked thus far. Any help is very appreciated!!

            Here's the full error log:

            ...

            ANSWER

            Answered 2022-Feb-17 at 14:12

            I had the same issue and solved it downgrading numpy to version 1.20.3 by:

            Source https://stackoverflow.com/questions/70839312

            QUESTION

            Colab: (0) UNIMPLEMENTED: DNN library is not found
            Asked 2022-Feb-08 at 19:27

            I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:

            ...

            ANSWER

            Answered 2022-Feb-07 at 09:19

            It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason

            Source https://stackoverflow.com/questions/71000120

            QUESTION

            Saving model on Tensorflow 2.7.0 with data augmentation layer
            Asked 2022-Feb-04 at 17:25

            I am getting an error when trying to save a model with data augmentation layers with Tensorflow version 2.7.0.

            Here is the code of data augmentation:

            ...

            ANSWER

            Answered 2022-Feb-04 at 17:25

            This seems to be a bug in Tensorflow 2.7 when using model.save combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format model.save("test", save_format='h5'):

            Source https://stackoverflow.com/questions/69955838

            QUESTION

            Difference between autograd.grad and autograd.backward?
            Asked 2022-Jan-30 at 22:41

            Suppose I have my custom loss function and I want to fit the solution of some differential equation with help of my neural network. So in each forward pass, I am calculating the output of my neural net and then calculating the loss by taking the MSE with the expected equation to which I want to fit my perceptron.

            Now my doubt is: should I use grad(loss) or should I do loss.backward() for backpropagation to calculate and update my gradients?

            I understand that while using loss.backward() I have to wrap my tensors with Variable and have to set the requires_grad = True for the variables w.r.t which I want to take the gradient of my loss.

            So my questions are :

            • Does grad(loss) also requires any such explicit parameter to identify the variables for gradient computation?
            • How does it actually compute the gradients?
            • Which approach is better?
            • what is the main difference between the two in a practical scenario.

            It would be better if you could explain the practical implications of both approaches because whenever I try to find it online I am just bombarded with a lot of stuff that isn't much relevant to my project.

            ...

            ANSWER

            Answered 2021-Sep-12 at 12:57

            TLDR; Both are two different interfaces to perform gradient computation: torch.autograd.grad is non-mutable while torch.autograd.backward is.

            Descriptions

            The torch.autograd module is the automatic differentiation package for PyTorch. As described in the documentation it only requires minimal change to code base in order to be used:

            you only need to declare Tensors for which gradients should be computed with the requires_grad=True keyword.

            The two main functions torch.autograd provides for gradient computation are torch.autograd.backward and torch.autograd.grad:

            torch.autograd.backward (source) torch.autograd.grad (source) Description Computes the sum of gradients of given tensors with respect to graph leaves. Computes and returns the sum of gradients of outputs with respect to the inputs. Header torch.autograd.backward(
            tensors,
            grad_tensors=None,
            retain_graph=None,
            create_graph=False,
            grad_variables=None,
            inputs=None) torch.autograd.grad(
            outputs,
            inputs,
            grad_outputs=None,
            retain_graph=None,
            create_graph=False,
            only_inputs=True,
            allow_unused=False) Parameters - tensors – Tensors of which the derivative will be computed.
            - grad_tensors – The "vector" in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors.
            - retain_graph – If False, the graph used to compute the grad will be freed. [...]
            - inputs – Inputs w.r.t. which the gradient be will be accumulated into .grad. All other Tensors will be ignored. If not provided, the gradient is accumulated into all the leaf Tensors that were used [...]. - outputs – outputs of the differentiated function.
            - inputs – Inputs w.r.t. which the gradient will be returned (and not accumulated into .grad).
            - grad_tensors – The "vector" in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors.
            - retain_graph – If False, the graph used to compute the grad will be freed. [...]. Usage examples

            In terms of high-level usage, you can look at torch.autograd.grad as a non-mutable function. As mentioned in the documentation table above, it will not accumulate the gradients on the grad attribute but instead return the computed partial derivatives. In contrast torch.autograd.backward will be able to mutate the tensors by updating the grad attribute of leaf nodes, the function won't return any value. In other words, the latter is more suitable when computing gradients for a large number of parameters.

            In the following, we will take two inputs (x1 and, x2), calculate a tensor y with them, and then compute the partial derivatives of the result w.r.t both inputs, i.e. dL/dx1 and dL/dx2:

            Source https://stackoverflow.com/questions/69148622

            QUESTION

            How to use fn_map to map each row in an array C to its coresponding one in the array B
            Asked 2022-Jan-03 at 18:53

            Since I am working with TensorFlow, I would like to know how to map my rows from a tensor C to the index of its corresponding row in matrix B.

            Here is the code I wrote:

            ...

            ANSWER

            Answered 2022-Jan-03 at 18:53

            You do not have to use tf.map_fn. Maybe try something like this:

            Source https://stackoverflow.com/questions/70559051

            QUESTION

            partial tucker decomposition
            Asked 2021-Dec-28 at 21:06

            I want to apply a partial tucker decomposition algorithm to minimize MNIST image tensor dataset of (60000,28,28), in order to conserve its features when applying another machine algorithm afterwards like SVM. I have this code that minimizes the second and third dimension of the tensor

            ...

            ANSWER

            Answered 2021-Dec-28 at 21:05

            So if you look at the source code for tensorly linked here you can see that the documentation for the function in question partial_tucker says:

            Source https://stackoverflow.com/questions/70466992

            QUESTION

            AssertionError: Tried to export a function which references untracked resource
            Asked 2021-Sep-07 at 11:23

            I wrote a unit-test in order to safe a model after noticing that I am not able to do so (anymore) during training.

            ...

            ANSWER

            Answered 2021-Sep-06 at 13:25

            Your issue is not related to 'transformer_transducer/transducer_encoder/inputs_embedding/ convolution_stack/conv2d/kernel:0'.
            The error code tells you this element is referring to a non trackable element. It seems the non-trackable object is not directly assigned to an attribute of this conv2d/kernel:0.

            To solve your issue, we need to localize Tensor("77040:0", shape=(), dtype=resource) from this error code:

            Source https://stackoverflow.com/questions/69040420

            QUESTION

            Error while trying to fine-tune the ReformerModelWithLMHead (google/reformer-enwik8) for NER
            Asked 2021-Aug-22 at 21:36

            I'm trying to fine-tune the ReformerModelWithLMHead (google/reformer-enwik8) for NER. I used the padding sequence length same as in the encode method (max_length = max([len(string) for string in list_of_strings])) along with attention_masks. And I got this error:

            ValueError: If training, make sure that config.axial_pos_shape factors: (128, 512) multiply to sequence length. Got prod((128, 512)) != sequence_length: 2248. You might want to consider padding your sequence length to 65536 or changing config.axial_pos_shape.

            • When I changed the sequence length to 65536, my colab session crashed by getting all the inputs of 65536 lengths.
            • According to the second option(changing config.axial_pos_shape), I cannot change it.

            I would like to know, Is there any chance to change config.axial_pos_shape while fine-tuning the model? Or I'm missing something in encoding the input strings for reformer-enwik8?

            Thanks!

            Question Update: I have tried the following methods:

            1. By giving paramteres at the time of model instantiation:

            model = transformers.ReformerModelWithLMHead.from_pretrained("google/reformer-enwik8", num_labels=9, max_position_embeddings=1024, axial_pos_shape=[16,64], axial_pos_embds_dim=[32,96],hidden_size=128)

            It gives me the following error:

            RuntimeError: Error(s) in loading state_dict for ReformerModelWithLMHead: size mismatch for reformer.embeddings.word_embeddings.weight: copying a param with shape torch.Size([258, 1024]) from checkpoint, the shape in current model is torch.Size([258, 128]). size mismatch for reformer.embeddings.position_embeddings.weights.0: copying a param with shape torch.Size([128, 1, 256]) from checkpoint, the shape in current model is torch.Size([16, 1, 32]).

            This is quite a long error.

            1. Then I tried this code to update the config:

            model1 = transformers.ReformerModelWithLMHead.from_pretrained('google/reformer-enwik8', num_labels = 9)

            Reshape Axial Position Embeddings layer to match desired max seq length ...

            ANSWER

            Answered 2021-Aug-15 at 06:11

            The Reformer model was proposed in the paper Reformer: The Efficient Transformer by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya. The paper contains a method for factorization gigantic matrix which is resulted of working with very long sequences! This factorization is relying on 2 assumptions

            1. the parameter config.axial_pos_embds_dim is set to a tuple (d1,d2) which sum has to be equal to config.hidden_size
            2. config.axial_pos_shape is set to a tuple (n1s,n2s) which product has to be equal to config.max_embedding_size (more on these here!)

            Finally your question ;)

            • I'm almost sure your session crushed duo to ram overflow
            • you can change any config parameter during model instantiation like the official documentation!

            Source https://stackoverflow.com/questions/68742863

            QUESTION

            Can Java generics be parameterized with values instead of types?
            Asked 2021-Aug-05 at 17:50

            Assume I want to define types that are similar in structure, but differ in a parameter that could be an integer or could be something else.

            Is it possible in Java to define a family of classes parameterized by an integer or even an arbitrary object?

            Consider the following pseudocode (which does not compile):

            ...

            ANSWER

            Answered 2021-Aug-04 at 14:42

            As the name suggests, a "type parameter" is a type. Not 'a length of a string'.

            To be specific: One can imagine the concept of the type fixed length string, and one can imagine this concept has a parameter, whose type is int; one could have FixedString<5> myID = "HELLO"; and that would compile, but FixedString<5> myID = "GOODBYE"; would be an error, hopefully a compile-time one.

            Java does not support this concept whatsoever. If that's what you're looking for, hack it together; you can of course make this work with code, but it means all the errors and checking occurs at runtime, nothing special would occur at compile time.

            Instead, generics are to give types the ability to parameterize themselves, but only with a type. If you want to convey the notion of 'A List... but not just any list, nono, a list that stores Strings' - you can do that, that's what generics are for. That concept applies only to types and not to anything else though (such as lengths).

            Furthermore, javac will be taking care of applying the parameter. So you can't hack it together by making some faux hierarchy such as:

            Source https://stackoverflow.com/questions/68653064

            QUESTION

            Why does Pytorch autograd need a scalar?
            Asked 2021-Jul-26 at 21:46

            I am working through "Deep Learning for Coders with fastai & Pytorch". Chapter 4 introduces the autograd function from the PyTorch library on a trivial example.

            ...

            ANSWER

            Answered 2021-Jul-26 at 21:46

            TLDR; the derivative of a sum of functions is the sum of their derivatives

            Let x be your input vector made of x_i (where i in [0,n]), y = x**2 and L = sum(y_i). You are looking to compute dL/dx, a vector of the same size as x whose components are the dL/dx_j (where j in [0,n]).

            For j in [0,n], dL/dx_j is simply dy_j/dx_j (derivative of the sum is the sum of derivates and only one of them is different to zero), which is d(x_j**2)/dx_j, i.e. 2*x_j. Therefore, dL/dx = [2*x_j where j in [0,n]].

            This is the result you get in x.grad when either computing the gradient of x as:

            Source https://stackoverflow.com/questions/68536392

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            You can download it from GitHub.

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