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TensorFlow Introduction & Training Data Using A Python Example

TensorFlow is one ofthe most efficientopen-source librariesthat hasan excellence in numerical computing. And this numerical computing is an important factor for our neural network calculations. It is being accompanied by a huge cluster of application interfaces for many of the major languages that are being used in learning field. we will be learning many more in this TensorFlow Introduction.

https://www.blogsden.com/tensorflow-introduction/

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TensorFlow is an open-source library that is commonly used for data flow programming. It also includes a symbolic math library that can be used for machine learning applications and neural networking. TensorFlow was built by the Google Brain Team for their internal development needs on AI and ML, before it was released to the public.

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A neural network for generating motion for quadruped characters in video games has been developed. It's demonstrated with what looks like a wolf. The system works by giving it motion-capture data for walking, pacing, trotting, cantering, jumping, turning, and various other movements, and the data can be unstructured, meaning the data doesn't need to be labeled as "trot," "canter," etc. Previous neural networks generated sliding artifacts and other unnatural movements, especially when interpolating between modes. This system works by using two neural networks where the first blends weights, that are previously calculated for the various locomotion modes, that are subsequently used in the second, which actually predicts the motion. The result is very smooth transitions between modes of motion.

Why tflearn DNN softmax regression model's prediction is changing when executed on two different machines?

I am working on the text multi-label classification using TFlearn softmax regression. I have training data and labels as in the class of that text data. There are some labels which have 40% similar data. But the model fails to predict the same label on the two different machine. The model has trained on same epoch and same batch size but why the prediction of the labels which are 40% same is changing on two different machines. while the accuracy shows same on both machine.

Does Model prediction accuracy depend upon the processing power and speed of both machines?

I seek advice on your question.

Thank you.

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#TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. Read More!

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