simple-CNN | A homework of convolutional neural network | Machine Learning library

by sjm1992st Python Version: Current License: No License

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kandi X-RAY | simple-CNN Summary

simple-CNN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. simple-CNN has no bugs, it has no vulnerabilities and it has low support. However simple-CNN build file is not available. You can download it from GitHub.

A homework of convolutional neural network

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simple-CNN has a low active ecosystem.

It has 24 star(s) with 1 fork(s). There are 2 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 102 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of simple-CNN is current.

Quality

simple-CNN has 0 bugs and 0 code smells.

Security

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

simple-CNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

simple-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.

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simple-CNN releases are not available. You will need to build from source code and install.

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

simple-CNN saves you 463 person hours of effort in developing the same functionality from scratch.

It has 1092 lines of code, 110 functions and 20 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed simple-CNN and discovered the below as its top functions. This is intended to give you an instant insight into simple-CNN implemented functionality, and help decide if they suit your requirements.

Generate the network .
Train the model .
forward computation .
Load MNIST dataset .
Compute the gradient .
Load training data .
Calculate the cfi field
Reshape the data
Generates y - axis labels
Compute the difference between two points .

Get all kandi verified functions for this library.

simple-CNN Key Features

No Key Features are available at this moment for simple-CNN.

simple-CNN Examples and Code Snippets

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Community Discussions

Trending Discussions on simple-CNN

What is the use of validation data?

how to input image for class's prediction in keras using python

'Error when checking target' message when trying to train with VGG16 model

QUESTION

What is the use of validation data?

Asked 2019-Dec-11 at 18:28

I am following this tutorial to build a classifier: https://towardsdatascience.com/a-simple-cnn-multi-image-classifier-31c463324fa In this part of the code:

...

ANSWER

Answered 2019-Dec-11 at 18:24

The idea is to avoid overfitting, or in other words learning more about specific examples than general characteristics. If you just tested your model on the training data, it could easily just learn the (lets say) 1000 cat pics you wanted to distinguish from the 100 dog pics, by 'memorizing' those 100 pics - a CNN could easily have an amount of memory in its weights equivalent to 100 pics. And obviously its not the entire pic that needs to be memorized but only something that distinguishes those particular cat pics from those particular dog pics. This can happen anytime the number of free params in the model can compete with the amount of information in the training set. To avoid this the test should be on another set of data, the validation set. But the same thing can happen with the validation set ! If the network is set to minimize error on the validation set that's what it will do, and thus the validation set may itself become overfit. So a third test is used (in principle only once, to avoid yet again overfitting on this data, and so on ) for final evaluation.

Source https://stackoverflow.com/questions/59291741

QUESTION

how to input image for class's prediction in keras using python

Asked 2019-May-04 at 14:12

I am trying to predict the result for a single image but it is giving an irrelevant result. I have trained the model on cifar 10 dataset I have used keras and tensorflow to train this model. I suppose the input which I am providing is not of the correct size.

here is the gist of training code :https://github.com/09rohanchopra/cifar10/blob/master/cifar10-simple-cnn.ipynb Code for predicting simgle image

# ...

ANSWER

Answered 2017-Oct-17 at 18:28

I got the answer

Source https://stackoverflow.com/questions/46751503

QUESTION

'Error when checking target' message when trying to train with VGG16 model

Asked 2019-Mar-23 at 19:32

I am a beginner to transfer learning, In this project i aimed to use VGG16 and add some more layers to do a classification between 2 classes: class0 and class1

I have dataframe named 'train' with 'id' column contains file names while label contains class of that image

Images and prepared through ImageDataGenerator() and flow.from_dataframe

To summarise, the last layer of mine was Dense(2,activation='softmax')

The input image to VGG16 has shape of (32,32,3)

However, it kepts being error:

ValueError: Error when checking target: expected dense_55 to have 2 dimensions, but got array with shape (1, 32, 32, 3)

A summary of my model:

Here was my jupyter notebook for training.

what is wrong with my coding here?

...

ANSWER

Answered 2019-Mar-23 at 19:32

The error means that your network's output has two dimensions (as the summary shows, output shape is (None, 2)), but your label has shape (1, 32, 32, 3).

In your validation data generation, you set class_mode="input". This means that your labels will also be images of the same shape as your input (see doc https://keras.io/preprocessing/image/#flow_from_dataframe) instead of being 2 dimensional classification labels. This is the problem. Use class_mode="categorical" as you used for the training data.

Source https://stackoverflow.com/questions/55312650

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

Vulnerabilities

No vulnerabilities reported

Install simple-CNN

You can download it from GitHub.
You can use simple-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.

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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