keras
TFLearn
keras | TFLearn | |
---|---|---|
3 | 2 | |
54,926 | 9,606 | |
- | 0.0% | |
9.9 | 0.0 | |
about 2 years ago | 5 months ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
keras
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Keras - Difference between categorical_accuracy and sparse_categorical_accuracy
The source code can be found here:
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keras error on predict
Here
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How to get reproducible results in keras
I get different results (test accuracy) every time I run the imdb_lstm.py example from Keras framework (https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py)The code contains np.random.seed(1337) in the top, before any keras imports. It should prevent it from generating different numbers for every run. What am I missing?
TFLearn
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Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
TFLearn – Deep learning library featuring a higher-level API for TensorFlow
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Base ball
Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBI’s, and walk’s are all taken into account and passed through layers. There’s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called TFlearn, documentation available from http://tflearn.org. The program will output the home and away teams as well as their respective score predictions.
What are some alternatives?
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