I'm trying to decide whether to use the existing keras.utils.sequence module or to switch to tf.data. From what I understand, tf.data optimizes performance by overlapping training on GPU with pre-processing on the CPU. But how does that compare to keras.utils.sequence and the keras data generator? From what I read here it seems that it's doing the same thing. Is there anything to gain by switching to tf.data ?
- No module named 'sleep'
- PermissionError: [WinError 32]
- How do i loop a python script?
- Speech Recognition UnknownValueError
- Rename Columns of several dataframes
- What is "--snip--' in python?
- Name 'Actor' is not defined
- Python, beyond the basics
- Python Telnet script to Cisco switch
- how to fix error of my compiled python code
- Pulling info from Json data
- Interesting 'takes exactly 1 argument (2 given)' Python error
- How to use wiktextract
- how to ignore specific error in pyre-check python package for the whole project
- Farey sequence length
- Partially applied generic function "cannot be cast to Nothing"
- Agar.io style ripple effect for canvas arcs
- What is the difference between [ValidateModel] and a check of valid state in ASP.NET?
- Passing shared_ptr to std::function (member function)
- UWP location tracking even when the app was suspended
- Dynamic partition in hive
- Woocommerce Different Products Different Currency
- Rails render js file but can't execute it
- My rotated TextView is cut off. What i have to do?
- Store object created by gson in greenDao