deeplearning-benchmark | Benchmark Suite for Deep Learning | Machine Learning library

by lambdal Shell Version: Current License: No License

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kandi X-RAY | deeplearning-benchmark Summary

deeplearning-benchmark is a Shell library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. deeplearning-benchmark has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Benchmark Suite for Deep Learning

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deeplearning-benchmark has a low active ecosystem.

It has 147 star(s) with 27 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 0 have been closed. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deeplearning-benchmark is current.

Quality

deeplearning-benchmark has no bugs reported.

Security

deeplearning-benchmark has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

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

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deeplearning-benchmark Key Features

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deeplearning-benchmark Examples and Code Snippets

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Community Discussions

Trending Discussions on deeplearning-benchmark

TensorFlow: Train model on a custom image dataset

QUESTION

TensorFlow: Train model on a custom image dataset

Asked 2017-Sep-12 at 18:47

I am interested in training and evaluating a convolutional neural net model on my own set of images. I want to use the tf.layers module for my model definition, along with a tf.learn.Estimator object to train and evaluate the model using the fit() and evaluate() methods, respectively.

Here is the tutorial that I have been following, which is helpful for showcasing the tf.layers module and the tf.learn.Estimator class. However, the dataset that it uses (MNIST) is simply imported and loaded (as NumPy arrays). See the following main function from the tutorial script:

...

ANSWER

Answered 2017-Sep-12 at 18:47

The file that you have referenced, cnn_mnist.py, and specifically the following function mnist_classifier.fit, requires Numpy arrays as input for x and y. Therefore, I will address your second and third questions as TFRecords may not be easily incorporated into the referenced code.

however, it is not clear how the mnist.train.images and mnist.train.validation are formatted

mnist.train.images is a Numpy array with shape (55000, 784), where 55000 is the number of images and 784 is the dimension of each flattened image (28 x 28). mnist.validation.images is also a Numpy array with shape (5000, 784).

Does anyone have any experience with converting jpg files and labels to NumPy arrays that this Estimator class expects as inputs?

The following code reads in one JPEG image as a three-dimensional Numpy array:

Source https://stackoverflow.com/questions/44974203

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

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Install deeplearning-benchmark

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