tensor | The swiss-army-knife of monitoring agents

 by   calston Python Version: 0.3.6 License: MIT

kandi X-RAY | tensor Summary

kandi X-RAY | tensor Summary

tensor is a Python library. tensor has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install tensor' or download it from GitHub, PyPI.

Tensor is now known as Duct.

            kandi-support Support

              tensor has a highly active ecosystem.
              It has 24 star(s) with 7 fork(s). There are 4 watchers for this library.
              It had no major release in the last 12 months.
              There are 0 open issues and 7 have been closed. On average issues are closed in 5 days. There are no pull requests.
              It has a positive sentiment in the developer community.
              The latest version of tensor is 0.3.6

            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 MIT 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.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              tensor saves you 2126 person hours of effort in developing the same functionality from scratch.
              It has 4662 lines of code, 361 functions and 61 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed tensor and discovered the below as its top functions. This is intended to give you an instant insight into tensor implemented functionality, and help decide if they suit your requirements.
            • Receive a counter
            • Process a queue of flow information
            • Resolve an IP address
            • Create an Event object
            • Parse format string
            • Return field name
            • Start the watcher
            • Create an object source
            • Get stats from ADSL
            • Walk through a message and return a deferred function
            • Send a ping request
            • Check the queue and remove stale events
            • Encode the packet
            • Called when a connection failed
            • Called when a connection is lost
            • Setup sources
            • Return the value of a key
            • Returns a list of rows
            • Decode a packet
            • Called when a string is received
            • Create a connection to the server
            • Receive a flow
            • Send events to source
            • Trigger the next event
            • Send events to Elasticsearch
            • Decode the Flow VCF packet
            Get all kandi verified functions for this library.

            tensor Key Features

            No Key Features are available at this moment for tensor.

            tensor Examples and Code Snippets

            Pytorch RuntimeError: expected scalar type Double but found Float
            Pythondot img1Lines of Code : 2dot img1License : Strong Copyleft (CC BY-SA 4.0)
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             return torch.tensor(batch_x).float(), torch.tensor(batch_t)
            Pytorch: How to transform image patches into matrix of feature vectors?
            Pythondot img2Lines of Code : 15dot img2License : Strong Copyleft (CC BY-SA 4.0)
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            #example on one image
            mnist_train = torchvision.datasets.FashionMNIST(
                root="../data", train=True, 
            transform=transforms.Compose([transforms.ToTensor()]), download=True)
            x = mnist_train[0][0][-1, :, :]
            x = x.unfold(0, 7, 7).unfold(1, 7
            How can I find multiple maximum indices of a torch tensor?
            Pythondot img3Lines of Code : 2dot img3License : Strong Copyleft (CC BY-SA 4.0)
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            (x == torch.max(x)).nonzero()
            Failing During Training MobileNetSSD Object Detection on a Custom Dataset Google Colab
            Pythondot img4Lines of Code : 4dot img4License : Strong Copyleft (CC BY-SA 4.0)
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            !pip install numpy==1.19.5
            !pip uninstall -y pycocotools
            !pip install pycocotools --no-binary pycocotools
            speeding up 1d convolution in PyTorch
            Pythondot img5Lines of Code : 3dot img5License : Strong Copyleft (CC BY-SA 4.0)
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            out = torch.conv1d(x_batch.unsqueeze(0), y_batch.unsqueeze(1).flip(2), padding=y_batch.size(1)-1, groups=x_batch.size(0))
            print(torch.allclose(out, res1))  # True
            What does \* mean in the function signature of PyTorch?
            Pythondot img6Lines of Code : 7dot img6License : Strong Copyleft (CC BY-SA 4.0)
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                    'randint': ['def randint(low: _int, high: _int, size: _size, *,'
                                ' generator: Optional[Generator]=None, {}) -> Tensor: ...'
                                'def randint(high: 
            How to use pt file
            Pythondot img7Lines of Code : 6dot img7License : Strong Copyleft (CC BY-SA 4.0)
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            file = "model.pt"
            model = your_model()
            # this will automatically load the file and load the parameters into the model.
            Lookup table in TensorFlow with key is string and value is list of strings
            Pythondot img8Lines of Code : 32dot img8License : Strong Copyleft (CC BY-SA 4.0)
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            class TensorLookup:
              def __init__(self, keys, strings):
                self.keys = keys
                self.strings = strings
                self.table = tf.lookup.StaticHashTable(
                tf.lookup.KeyValueTensorInitializer(self.keys, tf.range(tf.shape(self.keys)[0])),
            Multiply a [3, 2, 3] by a [3, 2] tensor in pytorch (dot product along dimension)
            Pythondot img9Lines of Code : 8dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # all equivalent
            x * y.unsqueeze(2)
            x * y[..., None]
            x * y[:, :, None]
            x * y.reshape(3, 2, 1)
            torch.einsum('abc,ab->abc', x, y)
            didn't match because some of the keywords were incorrect: dtype
            Pythondot img10Lines of Code : 6dot img10License : Strong Copyleft (CC BY-SA 4.0)
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            components = torch.IntTensor(torch.argmax(torch.cat( self.compose_stick_segments(v_means),1) ,1), dtype=torch.long, device=self.device)
            components = torch.argmax(torch.cat( self.compose_stick_segments(v_means),1) ,

            Community Discussions


            attribute error and key error in the join operation of string
            Asked 2021-Jun-15 at 21:50

            There is a function given as follows



            Answered 2021-Jun-15 at 21:34

            Your code doesn’t attempt to not fail if w isn’t a key in id2word, so it shouldn’t be too much of a surprise when it does fail. You could try changing

            Source https://stackoverflow.com/questions/67993679


            I'm using bert pre-trained model for question and answering. It's returning correct result but with lot of spaces between the text
            Asked 2021-Jun-15 at 17:14

            I'm using bert pre-trained model for question and answering. It's returning correct result but with lot of spaces between the text

            The code is below :



            Answered 2021-Jun-15 at 17:14

            You can just use the tokenizer decode function:

            Source https://stackoverflow.com/questions/67990545


            Problem with FULLY_CONNECTED op in TF Lite
            Asked 2021-Jun-15 at 13:22

            I'd like to run a simple neural network model which uses Keras on a Rasperry microcontroller. I get a problem when I use a layer. The code is defined like this:



            Answered 2021-May-25 at 01:08

            I had the same problem, man. I want to transplant tflite to the development board of CEVA. There is no problem in compiling. In the process of running, there is also an error in AddBuiltin(full_connect). At present, the only possible situation I guess is that some devices can not support tflite.

            Source https://stackoverflow.com/questions/67677228


            SHAP DeepExplainer with TensorFlow 2.4+ error
            Asked 2021-Jun-14 at 14:52

            I'm trying to compute shap values using DeepExplainer, but I get the following error:

            keras is no longer supported, please use tf.keras instead

            Even though i'm using tf.keras?



            Answered 2021-Jun-14 at 14:52


            • Add tf.compat.v1.disable_v2_behavior() at the top for TF 2.4+
            • calculate shap values on numpy array, not on df

            Full reproducible example:

            Source https://stackoverflow.com/questions/66814523


            What does Tensorflow LSTM return?
            Asked 2021-Jun-14 at 14:38

            I'm writing a German->English translator using an encoder/decoder pattern, where the encoder connects to the decoder by passing the state output of its last LSTM layer as the input state of the decoder's LSTM.

            I'm stuck, though, because I don't know how to interpret the output of the encoder's LSTM. A small example:



            Answered 2021-Jun-14 at 14:38

            An LSTM cell in Keras gives you three outputs:

            • an output state o_t (1st output)
            • a hidden state h_t (2nd output)
            • a cell state c_t (3rd output)

            and you can see an LSTM cell here:

            The output state is generally passed to any upper layers, but not to any layers to the right. You would use this state when predicting your final output.

            The cell state is information that is transported from previous LSTM cells to the current LSTM cell. When it arrives in the LSTM cell, the cell decides whether information from the cell state should be deleted, i.e. we will "forget" some states. This is done by a forget gate: This gate takes the current features x_t as an input and the hidden state from the previous cell h_{t-1}. It outputs a vector of probabilities that we multiply with the last cell state c_{t-1}. After determining what information we want to forget, we update the cell state with the input gate. This gate takes the current features x_t as an input and the hidden state from the previous cell h_{t-1} and produces an input which is added to the last cell state (from which we have already forgotten information). This sum is the new cell state c_t. To get the new hidden state, we combine the cell state with a hidden state vector, which is again a vector of probabilities that determines which information from the cell state should be kept and which should be discarded.

            As you have correctly interpreted, the first tensor is the output of all hidden states.

            The second tensor is the hidden output, i.e. $h_t$, which acts as the short-term memory of the neural network The third tensor is the cell output, i.e. $c_t$, which acts as the long-term memory of the neural network

            In the keras-documentation it is written that

            Source https://stackoverflow.com/questions/67970519


            Error failed to convert a NumPy array to a Tensor
            Asked 2021-Jun-14 at 02:18

            I have the data in the following format. I am using a neural network to predict three parameters downtime, latency and accuracy using neural network regression.



            Answered 2021-Jun-14 at 00:47

            I can't run your code so I created something similar and I get this error when pre_norms has values NaN.

            I get pre_norms with NaN because predictors has columns No_Model,Technique which have strings and predictors-predictors.mean()/predictors.std()) convert them to NaN

            Solution could be removing columns No_Model,Technique but this create empty data - so it is useless.

            I don't know you full code but you should check what you have in variables and if you have NaN then you have wrong calculations.

            Source https://stackoverflow.com/questions/67962668


            AttributeError: 'numpy.ndarray' object has no attribute 'op'
            Asked 2021-Jun-13 at 18:26

            I am have a time series data and I am trying to build and train an LSTM model over it. I have 1 input and 1 Output corresponding to my model. I am trying to build a Many to Many model where Input length is exactly equal to output length.

            The shape of my inputs are X --> (1700,70,401) (examples, Timestep, Features)

            Shape of my output is Y_1-->(1700,70,3) (examples, Timestep, Features)

            Now When I am trying to approach this problem via sequential API everything is running fine.



            Answered 2021-Jun-13 at 18:26

            I made a mistake in the code itself while executing the Model part of in the functional API version.

            Source https://stackoverflow.com/questions/67880921


            Force BERT transformer to use CUDA
            Asked 2021-Jun-13 at 09:57

            I want to force the Huggingface transformer (BERT) to make use of CUDA. nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0 or .to(cuda:0).

            The code below is basically a customized part from german sentiment BERT working example



            Answered 2021-Jun-12 at 16:19

            You can make the entire class inherit torch.nn.Module like so:

            Source https://stackoverflow.com/questions/67948945


            UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach()
            Asked 2021-Jun-12 at 23:00

            I'm new on PyTorch and I'm trying to code with it so I have a function called OH which tack a number and return a vector like this



            Answered 2021-Apr-30 at 23:19

            the problem is that you are receiving a tensor on the act function on the Network and then save it as a tensor just remove the tensor in the action like this

            Source https://stackoverflow.com/questions/67341208


            Selecting values from tensor based on an index tensor
            Asked 2021-Jun-12 at 15:56

            I have two matrices. Matrix A is contains some values and matrix B contains indices. The shape of matrix A and B is (batch, values) and (batch, indices), respectively.

            My goal is to select values from matrix A based on indices of matrix B along the batch dimension.

            For example:



            Answered 2021-Jun-12 at 15:56

            You can achieve this with the tf.gather function.

            Source https://stackoverflow.com/questions/67950502

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install tensor

            You can install using 'pip install tensor' or download it from GitHub, PyPI.
            You can use tensor like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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