youtube-8m | My try with Kaggle deep-learning competition | Machine Learning library
kandi X-RAY | youtube-8m Summary
kandi X-RAY | youtube-8m Summary
This repo contains starter code for training and evaluating machine learning models over the YouTube-8M dataset. The code gives an end-to-end working example for reading the dataset, training a TensorFlow model, and evaluating the performance of the model. Out of the box, you can train several model architectures over either frame-level or video-level features. The code can easily be extended to train your own custom-defined models. It is possible to train and evaluate on YouTube-8M in two ways: on Google Cloud or on your own machine. This README provides instructions for both.
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Top functions reviewed by kandi - BETA
- Prepare a TFRecord reader
- Resize a tensor
- Gets feature matrix
- Prepare examples from serialized examples
- Evaluate the model
- Run evaluation loop
- Get epoch info
- Returns the map at the given position
- Runs the model
- Returns the heap at the given index
- Calculate the gap between predictions
- Calculate the average precision of predictions
- Creates a model
- Sample a random sequence of random samples
- Performs pooling
- Builds inputs and outputs
- Builds a prediction graph
- Run inference
- Yields the lines of the prediction
- Get a dictionary of statistics
- Convert a dict to a CSV row
- Read a TFRecord reader from a file
- Get a reader for YT8M frame features
- Start the parameter server
youtube-8m Key Features
youtube-8m Examples and Code Snippets
Community Discussions
Trending Discussions on youtube-8m
QUESTION
I am getting started with Google's Audioset. While the dataset is extensive, I find the information with regards to the audio feature extraction very vague. The website mentions
128-dimensional audio features extracted at 1Hz. The audio features were extracted using a VGG-inspired acoustic model described in Hershey et. al., trained on a preliminary version of YouTube-8M. The features were PCA-ed and quantized to be compatible with the audio features provided with YouTube-8M. They are stored as TensorFlow Record files.
Within the paper, the authors discuss using mel spectrograms on 960 ms chunks to get a 96x64 representation. It is then unclear to me how they get to the 1x128 format representation used in the Audioset. Does anyone know more about this??
...ANSWER
Answered 2018-Aug-13 at 08:52They use the 96*64 data as input for a modified VGG network.The last layer of VGG is FC-128, so its output will be 1*128, and that is the reason.
The architecture of VGG can be found here: https://github.com/tensorflow/models/blob/master/research/audioset/vggish_slim.py
QUESTION
The Youtube-8m download webpage provides the following curl instructions:
...ANSWER
Answered 2017-Nov-02 at 15:50That script is intended to run in a *nix (Unix or linux or ...) environment.
Do you have the bash for windows installed? If so, that is the quick solution, just run the script/cmds in that environment (and make sure that which python returns the correct /path/to/preferred/version_of/python).
To explain/expand on what that code does, *nix allows setting env vars specific to the command being run at the end of the line. An alternate way to "say" the same thing as the code you have included in *nix is
QUESTION
As denoted here youtube-8m tf-records are saved with the format comes at the end of my question.I write a code to extract features. but there is a problem. the code can read all elements in features successfully but it is not able to read feature_lists. in fact, the example does not include features_list at all and I get an error while I try to access it. How can I read the feauures_list. I attach Data format, My code and the output :
...ANSWER
Answered 2017-Sep-14 at 12:23Instead of
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Install youtube-8m
You can use youtube-8m 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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