Tensorflow Alternatives

Similar projects and alternatives to tensorflow

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better tensorflow alternative or higher similarity.

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Reviews and mentions

Posts with mentions or reviews of tensorflow. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-09.
  • Running tensorflow/etc. inside vms...? Is it workable for performance?
  • I'm going to start with the tulpa thing. I want to know about the tulpa thing. Can you tell me about the creation process and how you did it?
    There's something called 'tulpas' in Python called Tensorflow.
  • Tensorflow GPU yields a lower accuracy then Tensorflow CPU
    Hello! My group is training a model based on ResNet50 architecture pre-trained with the ImageNet dataset. We have noticed that when training the model on tensorflow-cpu 2.6.0, it yields higher accuracy than tensorflow-gpu 2.0.0. I am unaware as to why this happens. I have found this link: https://stackoverflow.com/questions/43221730/tensorflow-same-code-but-get-different-result-from-cpu-device-to-gpu-device and this: reduce_mean is not numerically accurate on GPU · Issue #3775 · tensorflow/tensorflow · GitHub . It is still unclear to me as to why such happens. If I install a higher version of tensorflow-gpu, like tensorflow-gpu 2.6.0, will there be changes in the resulting accuracy?
  • The State of Machine Learning Frameworks in 2019 (2019)
    news.ycombinator.com | 2021-09-13
  • Tensorflow 1 vs Tensorflow 2
    Have you read the release notes?
  • Top 10 Python Libraries for Machine Learning
    dev.to | 2021-09-09
    Website: https://www.tensorflow.org/ GitHub Repository: https://github.com/tensorflow/tensorflow Developed By: Google Brain Team Primary Purpose: Deep Neural Networks
  • Load Faster R-CNN Object Detection net with Tensorflow 2.5.0 and OpenCV 4.3 DNN package
    [1]: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph_test.py [2]: https://leimao.github.io/blog/Save-Load-Inference-From-TF2-Frozen-Graph/
  • Why does Microsoft have a higher P/E ratio that Google and Apple? Is Microsoft expected to grow more than the other two?
    reddit.com/r/stocks | 2021-09-01
  • Should you learn Julia or Python for Machine Learning?
    But, now we have to get used to Python's library of Machine Learning packages: tensorflow, numpy, matplotlib, and finally pandas
  • Release TensorFlow 2.6
    reddit.com/r/keras | 2021-08-11
  • TensorFlow 2.6
    news.ycombinator.com | 2021-08-11
  • Dynamical Isometry and Mean Field Theory: How to Train 10k-Layer Vanilla CNNs [pdf]
    news.ycombinator.com | 2021-08-10
    This paper is one of my all time favorites.

    It shows that extremely deep vanilla CNNs - without the use of batch normalization or residual connections - can be trained simply by using a Delta-Orthogonal weight initialization scheme and appropriate activation function.

    The Delta-Orthogonal initialization scheme is derived theoretically by developing a mean field theory for signal propagation which characterizes the conditions for dynamical isometry. Ultra-deep CNNs can train faster and perform better if their input-output Jacobians exhibit dynamical isometry, namely the property that the entire distribution of singular values is close to 1. Put another way, dynamical isometry is a necessary condition for signals to flow both forward and backward through the network without attenuation. A variety of pathologies such as vanishing/exploding gradients make training such deep networks challenging - mean field theory is a powerful tool that offers solutions to these challenges.

    The authors demonstrate experimentally that Delta-Orthogonal kernels outperform existing initialization schemes for very deep vanilla convolutional networks. They also find strikingly good agreement between theoretical and experimental results. One of the most astonishing findings IMO is that for networks initialized using this scheme the learning time measured in number of training epochs is independent of depth.

    > Our results indicate that we have removed all the major fundamental obstacles to training arbitrarily deep vanilla convolutional networks. In doing so, we have layed the groundwork to begin addressing some outstanding questions in the deep learning community, such as whether depth alone can deliver enhanced generalization performance. Our initial results suggest that past a certain depth, on the order of tens or hundreds of layers, the test performance for vanilla convolutional architecture saturates. These observations suggest that architectural features such as residual connections and batch normalization are likely to play an important role in defining a good model class, rather than simply enabling efficient training.

    Here is a link to the ConvolutionDeltaOrthogonal initializer in tensorflow [1].

    [1] https://github.com/tensorflow/tensorflow/blob/d287ff3d95c06b...

  • TensorFlow: Distutils
    If you run python -c 'print "Python Error"' then you can find the bug, just like this: https://github.com/tensorflow/tensorflow/issues/4929
    You can install tensorflow.py from github: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tensorflow.py


Basic tensorflow repo stats
about 21 hours ago

tensorflow/tensorflow is an open source project licensed under Apache License 2.0 which is an OSI approved license.

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