CNN | pytorch implementation of several CNNs | Computer Vision library

 by   syt2 Python Version: Current License: No License

kandi X-RAY | CNN Summary

kandi X-RAY | CNN Summary

CNN is a Python library typically used in Artificial Intelligence, Computer Vision, Pytorch applications. CNN has no bugs, it has no vulnerabilities and it has low support. However CNN build file is not available. You can download it from GitHub.

Train CNNs for image classification from scratch.

            kandi-support Support

              CNN has a low active ecosystem.
              It has 10 star(s) with 1 fork(s). There are 1 watchers for this library.
              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of CNN is current.

            kandi-Quality Quality

              CNN has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              CNN 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

              CNN releases are not available. You will need to build from source code and install.
              CNN has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed CNN and discovered the below as its top functions. This is intended to give you an instant insight into CNN implemented functionality, and help decide if they suit your requirements.
            • Train the model
            • Returns an instance of the model class
            • Returns the appropriate loader function
            • Return a loss function based on the configuration
            • Validate the model
            • Convert a dict to OrderedDict
            • Get a logger for the given time
            • Construct a ResNet
            • Generate a sknet model
            • ResNeXT
            Get all kandi verified functions for this library.

            CNN Key Features

            No Key Features are available at this moment for CNN.

            CNN Examples and Code Snippets

            No Code Snippets are available at this moment for CNN.

            Community Discussions


            unable to mmap 1024 bytes - Cannot allocate memory - even though there is more than enough ram
            Asked 2021-Jun-14 at 11:16

            I'm currently working on a seminar paper on nlp, summarization of sourcecode function documentation. I've therefore created my own dataset with ca. 64000 samples (37453 is the size of the training dataset) and I want to fine tune the BART model. I use for this the package simpletransformers which is based on the huggingface package. My dataset is a pandas dataframe. An example of my dataset:

            My code:



            Answered 2021-Jun-08 at 08:27

            While I do not know how to deal with this problem directly, I had a somewhat similar issue(and solved). The difference is:

            • I use fairseq
            • I can run my code on google colab with 1 GPU
            • Got RuntimeError: unable to mmap 280 bytes from file : Cannot allocate memory (12) immediately when I tried to run it on multiple GPUs.

            From the other people's code, I found that he uses python -m torch.distributed.launch -- ... to run fairseq-train, and I added it to my bash script and the RuntimeError is gone and training is going.

            So I guess if you can run with 21000 samples, you may use torch.distributed to make whole data into small batches and distribute them to several workers.



            Is it possible to combine 2 neural networks?
            Asked 2021-Jun-13 at 00:55

            I have a NET like (exemple from here)



            Answered 2021-Jun-07 at 14:26

            The most naive way to do it would be to instantiate both models, sum the two predictions and compute the loss with it. This will backpropagate through both models:



            Input_shape in 3D CNN
            Asked 2021-Jun-11 at 21:50

            I have a dataset of 100000 binary 3D arrays of shape (6, 4, 4) so the shape of my input is (10000, 6, 4, 4). I'm trying to set up a 3D Convolutional Neural Network (CNN) using Keras; however, there seems to be a problem with the input_shape that I enter. My first layer is:



            Answered 2021-Jun-11 at 21:50

            Example with dummy data:



            Formula to split JSON inside a Google Sheet cell into multiple rows
            Asked 2021-Jun-11 at 19:32

            I have the below JSON in a Google Sheet cell that I would like to split into multiple rows. Can anyone suggest a way to do this via a formula?



            Answered 2021-Jun-11 at 06:11

            I think the next formula can help you




            Pytorch CPU CUDA device load without gpu
            Asked 2021-Jun-11 at 12:41

            I found this nice code Pytorch mobilenet which I cant get running on a CPU.

            I am new to Pytorch so I am not shure what to do.

            In line 174 of the module the device is set:



            Answered 2021-Jun-11 at 08:55

            In you'll see there's a map_location keyword argument to send weights to the proper device:



            Pytorch Inferencing form the model is giving me different results every time
            Asked 2021-Jun-11 at 09:55

            I have created and trained one very simple network in pytorch as shown below:



            Answered 2021-Jun-11 at 09:55

            I suspect this is due to you not having set the model to inference mode with



            Failed to convert a NumPy array ((the whole sequence is a string)) to a Tensor, in genome sequence classification for CNN?
            Asked 2021-Jun-10 at 21:54

            The data is basically in CSV format, which is a fasta/genome sequence, basically the whole sequence is a string. To pass this data into a CNN model I convert the data into numeric. The genome/fasta sequence, which I want to change into tensor acceptable format so I convert this string into float e.g., "AACTG,...,AAC.." to [[0.25,0.25,0.50,1.00,0.75],....,[0.25,0.25,0.50.....]]. But the conversion data shows like this (see #data show 2). But, when I run tf.convert_to_tensor(train_data) it gives me an error of Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray). But in order to pass the data into CNN model, it has to be a tensor, but I don't know why it gives an error! What will be the solution to it?



            Answered 2021-Jun-10 at 21:47

            The problem is probably in your numpy array dtype.

            Using array with dtype float32 should fix problem: tf.convert_to_tensor(train_data.astype(np.float32))



            Difficulty with stacking MNIST and Fashion_MNIST
            Asked 2021-Jun-10 at 03:15

            I know it's basic and too easy for you people, but I'm a beginner who needs your help. I'm struggling to make binary classifier with CNN. My final goal is to check accuracy over 0.99

            I import both MNIST and FASHION_MNIST to identify if it's number or clothing. So there are 2 category. I want to categorize 0-60000 as 0, and 60001-120000 as 1. I will use binary_crossentropy.

            but I dont know how to start from the beginning. How can I use vstack hstack at first to combine MNIST and FASHION_MNIST?

            This is how I tried so far



            Answered 2021-Jun-10 at 03:15
            First of all

            They're images so better treat them as images and don't reshape them to vectors.

            Now the answer of the question. Suppose you have mnist_train_image and fashion_train_image, both have (60000, 28, 28) input shape.

            What you want to do is consist of 2 parts, combining inputs and making the targets.

            First the inputs

            As you've already wrote in the question, you can use np.vstack like this



            Which Shap-Explainer fits my requirements
            Asked 2021-Jun-08 at 00:11

            i have created a CNN with Keras and Tensorflow as my backend and my data consists of 2D images which represent EEG (Electroencephalography)-data from the preprocessed DEAP-Dataset.

            I have considered to use SHAP as the model explainer but since there are several shap-explainers (kernel, deep, linear, gradient...) I am not sure which one fits my needs the most or if even SHAP could be helpful in my case. Since my images (Dimensions:40x100x1, the third dimension comes from np.expand_dims, since keras needs 3D images) have no colors, is SHAP even a considerable approach?

            Snippet of one item in my dataset



            Answered 2021-Jun-08 at 00:11

            There are no limitations to the use of SHAP for model explanations as they are literally meant to

            explain the output of any machine learning model

            (compare with the docs)

            There are indeed several core explainers available. But they are optimized for different kinds of models. Since your case consists of a CNN model built with TensorFlow and Keras, you might want to consider the following two as your primary options:

            • DeepExplainer: meant to approximate SHAP values for deep learning models.

            • GradientExplainer: explains a model using expected gradients (an extension of integrated gradients).

            Both of these core explainers are meant to be used with Deep Learning models, in particular such built in TensorFlow and Keras. The difference between them is how they approximate the SHAP values internally (you can read more about the underlying methods by following the respective link). Because of this, they will very likely not return the exact same result. However, it is fair to assume that there won't be any dramatic differences (although there is no guarantee).

            While the former two are the primary options, you might still want to check the other core explainers as well. For example, the KernelExplainer can be used on any model and thus, could also be an option on Deep Learning models. But as mentioned earlier, the former two are particularly meant for Deep Learning models and should therefore (probably) be preferred.

            Since you are using pictures as input, you might find the image_plot useful. Usage examples can be found on the GitHub repository you already linked to. You also do not need to worry about colors as they do not matter (see the DeepExplainer MNIST example).



            Unicode not translating correctly for Right-to-left languages (Hebrew and Arabic)
            Asked 2021-Jun-06 at 21:51

            The bigquery code below provided by Mikhail Berlyant (thank you again!) works well on left-to-right languages such as Russian. However, it fails on right-to-left languages such as Arabic and Hebrew whenever there is a double quotation mark <" "> inside the text to be translated. The expected result should show all input text-to-be-translated without unicode letters inside the translation. Thanks!



            Answered 2021-Jun-06 at 21:51

            Consider below example


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


            No vulnerabilities reported

            Install CNN

            You can download it from GitHub.
            You can use CNN 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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