cnn_model | pytorch 0 | Machine Learning library

by liuyuemaicha Python Version: Current License: No License

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kandi X-RAY | cnn_model Summary

cnn_model is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. cnn_model has no vulnerabilities and it has low support. However cnn_model has 1 bugs and it build file is not available. You can download it from GitHub.

pytorch 0.3.1 python 2.7 blog:

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cnn_model has a low active ecosystem.

It has 154 star(s) with 61 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 427 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cnn_model is current.

Quality

cnn_model has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 145 code smells.

Security

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

cnn_model code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cnn_model 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.

Reuse

cnn_model releases are not available. You will need to build from source code and install.

cnn_model has no build file. You will be need to create the build yourself to build the component from source.

cnn_model saves you 521 person hours of effort in developing the same functionality from scratch.

It has 1222 lines of code, 82 functions and 10 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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cnn_model Key Features

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cnn_model Examples and Code Snippets

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Community Discussions

Trending Discussions on cnn_model

"AttributeError: Layer cnn_model has no inbound nodes" when making a model from a subclassed model for Keras GradCam tutorial

Replace stride layers in MobileNet application in Keras

Stuck at this error "RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu"

How to reshape input data correctly for input in CNN model?

Is the number of second convolution layer parameters correct?

Using ResNet50 multiples times with different inputs (weights shared)

how to reducing validation loss and improving the test result in CNN Model

Difference between the input shape for a 1D CNN, 2D CNN and 3D CNN

Concatenation of ResNet (2D images) with fully-connected network (1D input)

Don't include an operation for gradient computation in PyTorch

QUESTION

"AttributeError: Layer cnn_model has no inbound nodes" when making a model from a subclassed model for Keras GradCam tutorial

Asked 2021-May-20 at 20:08

I'm trying to follow this GradCam Tutorial with my own model. Here is its architecture:

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ANSWER

Answered 2021-May-19 at 18:07

I faced a similar issue regarding the subclassed API model and further trying to use it in grad-cam by incorporating it into functional API. Later, the thing that worked for me that time was to build a subclassed model separately for grad-cam either and build desired output model in __init__.

Source https://stackoverflow.com/questions/67605715

QUESTION

Replace stride layers in MobileNet application in Keras

Asked 2021-May-09 at 06:39

I would like to apply in Keras MobileNetV2 on images of size 39 x 39 to classify 3 classes. My images represent heat maps (e.g. what keys have been pressed on the keyboard). I think MobileNet was designed to work on images of size 224 x 224. I will not use transfer learning but train the model from scratch.

To make MobileNet work on my images, I would like to replace the first three stride 2 convolutions with stride 1. I have the following code:

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ANSWER

Answered 2021-May-09 at 06:24

Here is one workaround for your need but I think probably it's possible to have a more general approach. However, in the MobileNetV2, there is only one conv layer with strides 2. If you follow the source code, here

Source https://stackoverflow.com/questions/67453434

QUESTION

Stuck at this error "RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu"

Asked 2021-Apr-25 at 12:11

I am running the following code. If I try to run it on CPU only it runs fine but it takes too much time to train. So I thought to change the runtime to GPU and made appropriate changes. Now it is stuck.

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ANSWER

Answered 2021-Apr-25 at 11:16

The problem is exactly as the error says, Pytorch expects all operations to be done in the same device but the two tensors you are adding are in different places.

You need to add .to(device) to these variables

Source https://stackoverflow.com/questions/67251758

QUESTION

How to reshape input data correctly for input in CNN model?

Asked 2021-Apr-20 at 05:29

I have a CNN model, which input image size is (150, 150). I want to feed array-like data for predict function (tensorflow) like this:

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ANSWER

Answered 2021-Apr-20 at 05:29

Just add a new dimension to your array data:

Source https://stackoverflow.com/questions/67172698

QUESTION

Is the number of second convolution layer parameters correct?

Asked 2021-Mar-18 at 16:51

I have simple CNN for the MNIST data problem.

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ANSWER

Answered 2021-Mar-18 at 16:51

The number of parameters for a convolutional layer is

Source https://stackoverflow.com/questions/66694699

QUESTION

Using ResNet50 multiples times with different inputs (weights shared)

Asked 2021-Mar-07 at 16:14

I would like to use the same ResNet50 multiple times with different inputs, i.e. weights shared. Below is my coce but I'm getting the error message AttributeError: 'Tensor' object has no attribute 'output' for the line resnet_x = resnet_x.output.

What do I have to change to make it work?

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ANSWER

Answered 2021-Mar-06 at 19:44

simply removing the lines resnet_XXX = resnet_XXX.output does the job. pay attention to the name of the variables (below resnet_z layer)

Source https://stackoverflow.com/questions/66509839

QUESTION

how to reducing validation loss and improving the test result in CNN Model

Asked 2021-Mar-06 at 15:26

How may I improve the valid accuracy? Besides that, my test accuracy is also low. I am trying to do categorical image classification on pictures about weeds detection in the agriculture field.

Dataset: The total number of images is 5539 with 12 classes where 70% (3870 images) of Training set 15% (837 images) of Validation and 15% (832 images) of Testing set

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ANSWER

Answered 2021-Mar-06 at 00:30

I would adjust the number of filters to size to 32, then 64, 128, 256. Then I would replace the flatten layer with

Source https://stackoverflow.com/questions/66500806

QUESTION

Difference between the input shape for a 1D CNN, 2D CNN and 3D CNN

Asked 2021-Feb-16 at 16:28

I'm first time building a CNN model for image classification and i'm a little bit confused about what would be the input shape for each type (1D CNN, 2D CNN, 3D CNN) and how to fix the number of filters in the convolution layer. My data is 100x100x30 where 30 are features. Here is my essay for the 1D CNN using the Functional API Keras:

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ANSWER

Answered 2021-Feb-16 at 09:44

By a 100x100x30 input shape, are you saying the batch size is 100? Or is each data in a shape of 100x100x30? In the second case, you must use a Conv2D layer instead. Input shapes of each layer are supposed to be:

Conv1D: (size1, channel_number), Conv2D: (size1, size2, channel_number) , Conv3D: (size1, size2, size3, channel_number)

The 1DCNN, 2DCNN, 3DCNN denotes the dimension of each kernel and channel of the convolution layer.

Source https://stackoverflow.com/questions/66220774

QUESTION

Concatenation of ResNet (2D images) with fully-connected network (1D input)

Asked 2021-Jan-19 at 06:11

I'm using a pre-built ResNet in Keras (TensorFlow 2) in the following way:

...

ANSWER

Answered 2021-Jan-18 at 01:15

Yes both options make sense and are possible with Keras. For #2 you could define another model which takes the 20D vector as the input and passes it through a fully connected layer, then concatenate that output with the output of your pooling layer. And for both options you'd have to adjust your final model inputs to include both the base_model input and your 20D vector.

Source https://stackoverflow.com/questions/65767416

QUESTION

Don't include an operation for gradient computation in PyTorch

Asked 2021-Jan-17 at 13:08

I have a custom layer. Let the layer be called 'Gaussian'

...

ANSWER

Answered 2021-Jan-15 at 20:12

The gradient calculation has sense when there are parameters to optimise.

If your module do not have any parameters, then no gradient will be stored, because there are no parameters to associate it.

Source https://stackoverflow.com/questions/65741865

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

Vulnerabilities

No vulnerabilities reported

Install cnn_model

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

Support

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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