Creating a dataset in tensorflow by adding random elements of two datasets










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I have two sets of images, say set A and set B. These are available as numpy arrays. I will choose x random images from A and y random images from B and then add them together (taking the average at the end). These new images will become the input to a CNN.



Now my question is how can I do this using Tensorflow's data pipeline approach?



I can obviously do it outside of TensorFlow using numpy, but this pre-created data will be too big in size. I can also use feed_dicts, but I wanted to know if there's a way to use pipelines to achieve this because that seems to be favoured approach now.










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    I have two sets of images, say set A and set B. These are available as numpy arrays. I will choose x random images from A and y random images from B and then add them together (taking the average at the end). These new images will become the input to a CNN.



    Now my question is how can I do this using Tensorflow's data pipeline approach?



    I can obviously do it outside of TensorFlow using numpy, but this pre-created data will be too big in size. I can also use feed_dicts, but I wanted to know if there's a way to use pipelines to achieve this because that seems to be favoured approach now.










    share|improve this question
























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      0








      I have two sets of images, say set A and set B. These are available as numpy arrays. I will choose x random images from A and y random images from B and then add them together (taking the average at the end). These new images will become the input to a CNN.



      Now my question is how can I do this using Tensorflow's data pipeline approach?



      I can obviously do it outside of TensorFlow using numpy, but this pre-created data will be too big in size. I can also use feed_dicts, but I wanted to know if there's a way to use pipelines to achieve this because that seems to be favoured approach now.










      share|improve this question














      I have two sets of images, say set A and set B. These are available as numpy arrays. I will choose x random images from A and y random images from B and then add them together (taking the average at the end). These new images will become the input to a CNN.



      Now my question is how can I do this using Tensorflow's data pipeline approach?



      I can obviously do it outside of TensorFlow using numpy, but this pre-created data will be too big in size. I can also use feed_dicts, but I wanted to know if there's a way to use pipelines to achieve this because that seems to be favoured approach now.







      python tensorflow dataset tensorflow-datasets






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      share|improve this question











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      share|improve this question










      asked Nov 14 '18 at 2:59









      Epsilon7Epsilon7

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