MNIST-cnn | Convolutional neural networks with Python | Machine Learning library
kandi X-RAY | MNIST-cnn Summary
kandi X-RAY | MNIST-cnn Summary
MNIST-cnn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Keras applications. MNIST-cnn has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However MNIST-cnn build file is not available. You can download it from GitHub.
Convolutional neural networks with Python 3
Convolutional neural networks with Python 3
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Quality
Security
License
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Support
MNIST-cnn has a low active ecosystem.
It has 18 star(s) with 15 fork(s). There are 6 watchers for this library.
It had no major release in the last 6 months.
MNIST-cnn has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of MNIST-cnn is current.
Quality
MNIST-cnn has 0 bugs and 0 code smells.
Security
MNIST-cnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
MNIST-cnn code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
MNIST-cnn is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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MNIST-cnn releases are not available. You will need to build from source code and install.
MNIST-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.
MNIST-cnn saves you 177 person hours of effort in developing the same functionality from scratch.
It has 439 lines of code, 45 functions and 8 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed MNIST-cnn and discovered the below as its top functions. This is intended to give you an instant insight into MNIST-cnn implemented functionality, and help decide if they suit your requirements.
- Train the network
- Prints string to stdout
- Compute the accuracy of the neural network
- Return a progress bar string
- Load MNIST dataset
- Backpropagate the loss function
- Apply feedforward to all layers
- Prints a string to stdout
- Connect to previous layer
- Create a zeros array
- Softmax function
- Sigmoid function
Get all kandi verified functions for this library.
MNIST-cnn Key Features
No Key Features are available at this moment for MNIST-cnn.
MNIST-cnn Examples and Code Snippets
No Code Snippets are available at this moment for MNIST-cnn.
Community Discussions
Trending Discussions on MNIST-cnn
QUESTION
Error in load a model saved by callbakcs.ModelCheckpoint() in Keras
Asked 2019-Apr-29 at 17:09
I saved my model automatically by callbacks.ModelCheckpoint() with a HDF5 file.
ANSWER
Answered 2018-Oct-29 at 15:55I hit a similar problem that yields the same error message, but the cause might be different than yours:
Code: (Tensorflow 1.11 and tf.keras.version: 2.1.6-tf)
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install MNIST-cnn
You can download it from GitHub.
You can use MNIST-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.
You can use MNIST-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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