DeepLearning | 使用pytorch与tensorflow搭建的深度学习Demo,其中tensorflow包含静态图与动态图两个版本

 by   GISerWang Python Version: Current License: No License

kandi X-RAY | DeepLearning Summary

kandi X-RAY | DeepLearning Summary

DeepLearning is a Python library. DeepLearning has no bugs, it has no vulnerabilities and it has low support. However DeepLearning build file is not available. You can download it from GitHub.


            kandi-support Support

              DeepLearning has a low active ecosystem.
              It has 7 star(s) with 0 fork(s). There are 1 watchers for this library.
              It had no major release in the last 6 months.
              DeepLearning has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of DeepLearning is current.

            kandi-Quality Quality

              DeepLearning has no bugs reported.

            kandi-Security Security

              DeepLearning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              DeepLearning does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              DeepLearning releases are not available. You will need to build from source code and install.
              DeepLearning has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed DeepLearning and discovered the below as its top functions. This is intended to give you an instant insight into DeepLearning implemented functionality, and help decide if they suit your requirements.
            • Compute the forward layer .
            • Initialize layer1 .
            • Softmax loss function .
            • Initialize hidden layers .
            • Returns the length of the array .
            • get item at index
            Get all kandi verified functions for this library.

            DeepLearning Key Features

            No Key Features are available at this moment for DeepLearning.

            DeepLearning Examples and Code Snippets

            Enable mixed precision graph rewrite .
            pythondot img1Lines of Code : 130dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def enable_mixed_precision_graph_rewrite_v1(opt, loss_scale='dynamic'):
              """Enable mixed precision via a graph rewrite.
              Mixed precision is the use of both float32 and float16 data types when
              training a model to improve performance. This is achi  

            Community Discussions


            IndexError: Target 1 is out of bounds
            Asked 2021-Jun-05 at 05:31

            When I run the program below, it gives me an error. The problem seems to be in the loss function but I can't find it. I have read the Pytorch Documentation for nn.CrossEntropyLoss but still can't find the problem.

            Image size is (1 x 256 x 256), Batch size is 1

            I am new to PyTorch, thanks.



            Answered 2021-Jun-05 at 03:06

            Try loss = compute_loss(y_hat, torch.tensor([0]))



            Tuning hyperparameters in mlr does not produce sensible results?
            Asked 2021-May-27 at 15:33

            I am trying to tune the hyperparameters in mlr using the tuneParams function. However, I can't make sense of the results it is giving me (or else Im using it incorrectly).

            For example, if I create some data with a binary response and then create an mlr h2o classification model and then check the accuracy and AUC I will get some values. Then, if I use tuneParams on some parameters and find a better accuracy and AUC and then plug them into my model. The resulting accuracy and AUC (for the model) does not match that found by using tuneParams.

            Hopefully the code below will illustrate my issue:



            Answered 2021-May-27 at 15:33

            You're getting different results because you're evaluating the learner using different train and test data. If I use the same 3-fold CV, I get the same results:



            Issue building from a Dockerfile that used to work
            Asked 2021-May-20 at 14:13

            I'm having an issue building a docker image from a dockerfile that used to work:

            (My dockerfile has more steps, but this is enough to reproduce)



            Answered 2021-May-20 at 14:13

            This is a known issue. Read this for more info.

            You can first add the correct repository GPG key using the following command.



            Docker shared memory size out of bounds or unhandled system error, NCCL version 2.7.8
            Asked 2021-Apr-13 at 05:55

            The following error(s) and solution go for deploying a stack through YAML in portainer but they can surely be applied to docker otherwise.




            Answered 2021-Apr-13 at 05:55

            It seems that by default, the size of the shared memory is limited to 64mb. The solution to this error therefore, as shown in this issue is to increase the size of shared memory.

            Hence, the first idea that comes to mind would be simply defining something like shm_size: 9gb in the YAML file of the stack. However, this might not work as shown for e.g in this issue.

            Therefore, in the end, I had to use the following workaround (also described here, but poorly documented):



            How to Predict a Trend in MATLAB with irregular sampling time?
            Asked 2021-Apr-04 at 06:00

            I'm using MATLAB to predict a trend with a machine learning approach.

            My data file is an .xlsx file containing a timeline in one column (various sampling timestamps, i.e. numbers that represents seconds), and in the other columns I have some integers representing my trend.

            My .xlsx file is pretty much like this:



            Answered 2021-Apr-03 at 20:46

            I would distinguish the forecasting problem from the data sampling time problem. You are dealing substantially with missing data.

            1. Forecasting problem: You may use any machine learning technique just ignoring missing data. If you are not familiar with machine learning, I would suggest you to use LASSO (least absolute shrinkage and selection operator), which has been demonstrated to have predicting power (see "Sparse Signals in the Cross-Section of Returns" by ALEX CHINCO, ADAM D. CLARK-JOSEPH, and MAO YE).

            2. Missing imputation problem: In the first place you should consider the reason why you have missing data. Sometime it makes no sense to impute values because the information that the value is missing is itself important and should not be overridden. Otherwise you have multiple options, other than linear interpolation, to estimate the missing values. For example check the MATLAB function fillmissing.



            "RuntimeError: expected scalar type Double but found Float" in Pytorch CNN training
            Asked 2021-Apr-03 at 14:34

            I just begin to learn Pytorch and create my first CNN. The dataset contains 3360 RGB images and I converted them to a [3360, 3, 224, 224] tensor. The data and label are in the dataset( Below is the training code.



            Answered 2021-Apr-03 at 14:34

            that error is actually refering to the weights of the conv layer which are in float32 by default when the matrix multiplication is called. Since your input is double(float64 in pytorch) while the weights in conv are float
            So the solution in your case is :



            "fatal: unable to auto-detect email address" in DOCKER
            Asked 2021-Mar-11 at 11:15

            My docker run is failing because git complains that I didnt set a user config which I never needed for my older images.



            Answered 2021-Mar-11 at 11:15

            I didn't find why the error occured but I found a solution to remove it. Instead of cloning master then pulling the branch, I directly clone the branch I want to use.

            The cloning line is now :



            Can GCP VM's run while I am offline?
            Asked 2021-Mar-07 at 02:17

            Can GCP VM's run while I am offline? I am using a GCP Deeplearning notebook VM with GPU to train a neural network. When I close the Jupyter notebook tab, the code stops executing while the instance is still alive and I get billed. Is there a way to run the code while I am offline? I think this must be possible.



            Answered 2021-Mar-07 at 00:24

            Thanks to everybody who commented on this question.

            You can run python scripts in GCP Deeplearning notebook VM in the background through nohup.



            Python [Errno 22] Invalid argument when copy a file from one folder to another
            Asked 2021-Mar-03 at 16:29

            I am using the files from a video tutorial. At the beginning, it starts to spread the files of input image data by copying them in various folders. The code works in the tutorial but I wonder why I get the following error:

            [Errno 22] Invalid argument: 'D:\Machine Learning\Deep Learning\SRU-deeplearning-workshop-master\catdogKaggle\train\cat.1.jpg'

            Here is the code. At first it creates the directories.(The catdogKaggle\train contains the input images):



            Answered 2021-Mar-03 at 16:29

            You are on Windows which is why you need to escape the backslashes or use raw strings to store file paths, i.e.:



            Security concerns with the H2O R package
            Asked 2021-Feb-21 at 09:35

            I am using the H2O R package.

            My understanding is, that this package requires you to have an internet connection as well as connect to the the h2o servers? If you use the h2o package run machine learning models on your data, does h2o "see" your data? I turned off my wifi and tried running some machine learning models using h2o :



            Answered 2021-Feb-21 at 09:35

            From the documentation of h2o.init() (emphasis mine):

            This method first checks if H2O is connectible. If it cannot connect and startH2O = TRUE with IP of localhost, it will attempt to start an instance of H2O with IP = localhost, port = 54321. Otherwise, it stops immediately with an error. When initializing H2O locally, this method searches for h2o.jar in the R library resources [...], and if the file does not exist, it will automatically attempt to download the correct version from Amazon S3. The user must have Internet access for this process to be successful. Once connected, the method checks to see if the local H2O R package version matches the version of H2O running on the server. If there is a mismatch and the user indicates she wishes to upgrade, it will remove the local H2O R package and download/install the H2O R package from the server.

            So, h2o.init() with the default setting ip = "", as here, connects the R session with the H2O instance (sometimes referred to as "server") in your local machine. If all the necessary package files are in place and up to date, no internet connection is necessary; the package will attempt to connect to the internet only to download stuff in case something is not present or up to date. No data is uploaded anywhere.


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


            No vulnerabilities reported

            Install DeepLearning

            You can download it from GitHub.
            You can use DeepLearning 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.


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