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site/en/tutorials/load_data/video.ipynb

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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "ufPx7EiCiqgR"
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},
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"outputs": [],
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"[remotezip](https://github.com/gtsystem/python-remotezip){:.external} to inspect the contents of a ZIP file, [tqdm](https://github.com/tqdm/tqdm){:.external} to use a progress bar, [OpenCV](https://opencv.org/){:.external} to process video files, and [`tensorflow_docs`](https://github.com/tensorflow/docs/tree/master/tools/tensorflow_docs){:.external} for embedding data in a Jupyter notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "kWqHj8uOLeja"
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"To prepare the data to be fed into the model, use batching as shown below. Notice that when working with video data, such as AVI files, the data should be shaped as a five dimensional object. These dimensions are as follows: `[batch_size, number_of_frames, height, width, channels]`. In comparison, an image would have four dimensions: `[batch_size, height, width, channels]`. The image below is an illustration of how the shape of video data is represented.\n",
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"\n",
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"![Video data shape](https://www.tensorflow.org/images/tutorials/video/video_data_shape.png)\n",
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"\n"
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"![Video data shape](https://www.tensorflow.org/images/tutorials/video/video_data_shape.png)\n"
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"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"name": "Video_Loading.ipynb",
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"name": "video.ipynb",
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"toc_visible": true
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},
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"kernelspec": {

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