We would like to forward a particular 'key' column which is part of the features to appear alongside the predictions - this is to be able to identify to which set of features a particular prediction belongs to. Here is an example of predictions output using the tensorflow.contrib.estimator.multi_class_head:
I have the same hardware envs, same network, but I could not get the result as you, almost half as you. Any best practices and experience? thanks very much! for bytePS with 1 instance and 8 GPU, I have similar testing result.
Dynamic training with Apache MXNet reduces cost and time for training deep neural networks by leveraging AWS cloud elasticity and scale. The system reduces training cost and time by dynamically updating the training cluster size during training, with minimal impact on model training accuracy.
Currently the allocator triggers its allocation policy at a fixed time interval (default 60s). This is useful for periodically re-optimizing the resource allocations, but new jobs also need to wait for the next allocation cycle to start. When there are enough resources available for the new job, it should be possible to immediately schedule it.
This repository is a tutorial targeting how to train a deep neural network model in a higher efficient way. In this repository, we focus on two main frameworks that are Keras and Tensorflow.
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Please can you train ghostnet.
(i don't have the imagenet dataset)