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| %matplotlib widget # Install ipympl https://github.com/matplotlib/ipympl | |
| import matplotlib.pyplot as plt | |
| # Previous steps followed from example notebook at | |
| # https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb | |
| def show_mask(mask, ax): | |
| # From SAM notebook | |
| color = np.array([30/255, 144/255, 255/255, 0.6]) | |
| h, w = mask.shape[-2:] |
jbencina
/ backprop_tutorial.ipynb
Last active
February 19, 2019 09:45
Example for running backprop in numpy
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jbencina
/ tensorflow-simpsons.ipynb
Created
June 1, 2017 03:43
Using CNN TensorFlow to classify Simpsons images http://www.jbencina.com/blog/2017/05/31/getting-started-tensorflow/
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| def topic_coherence(lda_model, corpus, num_words, sort_topics=True, avg_per_word=False): | |
| topics = {} | |
| # Get the top num_words within each model in topic | |
| for idx, topic in enumerate(lda_model.components_): | |
| topics[idx] = [i for i in topic.argsort()[:-num_words -1: -1]] | |
| # Convert to csc for efficient column slicing | |
| D = corpus.tocsc() | |
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jbencina
/ CSPAN-transcript-parser.py
Last active
January 7, 2021 20:09
Quick (and dirty) Python script that takes the transcript information from CSPAN video pages and parses it into a JSON file with the time stamp, speaker name, and speech text. There are probably 100 more elegant ways to write this, but it works for what I needed.
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| from html.parser import HTMLParser | |
| import re | |
| import json | |
| import datetime as dt | |
| class Entry(): | |
| def __init__(self, time): | |
| self.time = '' | |
| self.time_base = time | |
| self.speaker = '' |