TensorFlow-Input-Pipeline | TensorFlow Input Pipeline Examples | Dataset library
kandi X-RAY | TensorFlow-Input-Pipeline Summary
kandi X-RAY | TensorFlow-Input-Pipeline Summary
TensorFlow Input Pipeline Examples based on multi-thread and FIFOQueue
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Top functions reviewed by kandi - BETA
- Write data to file .
- Reads examples from a tensorflow file .
- Create a tf . train . Feature .
- Create a tf . train . Feature .
- Initialize path .
TensorFlow-Input-Pipeline Key Features
TensorFlow-Input-Pipeline Examples and Code Snippets
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Trending Discussions on TensorFlow-Input-Pipeline
QUESTION
I am trying to get a simple CNN to train for the past 3 days.
First, I have setup an input pipeline/queue configuration that reads images from a directory tree and prepares batches.
I got the code for this at this link. So, I now have train_image_batch and train_label_batch that I need to feed to my CNN.
...ANSWER
Answered 2017-Jun-29 at 10:55The image from your input pipeline is of type 'uint8', you need to type cast it to 'float32', You can do this after the image jpeg decoder:
QUESTION
So, I've been playing around with the TensorFlow dataset API for loading images, and segmentation masks (for a semantic segmentation project), I would like to be able to generate batches of images and masks, with each image having randomly gone through any combination of pre-processing functions like brightness changes, contrast changes, cropping, saturation changes etc. So, the first image in my batch may have no pre-processing, second may have saturation changes, third may have brightness and saturation and so on.
I tried the following:
...ANSWER
Answered 2017-Dec-13 at 13:23So the problem was indeed that the control flow with the if statements are with Python variables, and are only executed once when the graph is created, to do what I want to do, I had to define a placeholder that contains the boolean values of whether to apply a function or not (and feed in a new boolean tensor per iteration to change the augmentation), and control flow is handled by tf.cond. I pushed the new code to the GitHub link I posted in the question above if anyone is interested.
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Install TensorFlow-Input-Pipeline
You can use TensorFlow-Input-Pipeline like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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