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4eeb178856
- the tensorboard standalone project expects a different set of files than the plugin to TensorFlow.
89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""Export spaCy model vectors for use in TensorBoard's standalone embedding projector.
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https://github.com/tensorflow/embedding-projector-standalone
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Usage:
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python vectors_tensorboard_standalone.py ./myVectorModel ./output [name]
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This outputs two files that have to be copied into the "oss_data" of the standalone projector:
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[name]_labels.tsv - metadata such as human readable labels for vectors
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[name]_tensors.bytes - numpy.ndarray of numpy.float32 precision vectors
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"""
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from __future__ import unicode_literals
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import json
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import math
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from os import path
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import numpy
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import plac
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import spacy
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import tqdm
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@plac.annotations(
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vectors_loc=("Path to spaCy model that contains vectors", "positional", None, str),
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out_loc=("Path to output folder writing tensors and labels data", "positional", None, str),
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name=("Human readable name for tsv file and vectors tensor", "positional", None, str),
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)
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def main(vectors_loc, out_loc, name="spaCy_vectors"):
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# A tab-separated file that contains information about the vectors for visualization
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#
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# Learn more: https://www.tensorflow.org/programmers_guide/embedding#metadata
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meta_file = "{}_labels.tsv".format(name)
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out_meta_file = path.join(out_loc, meta_file)
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print('Loading spaCy vectors model: {}'.format(vectors_loc))
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model = spacy.load(vectors_loc)
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print('Finding lexemes with vectors attached: {}'.format(vectors_loc))
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voacb_strings = [
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w for w in tqdm.tqdm(model.vocab.strings, total=len(model.vocab.strings), leave=False)
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if model.vocab.has_vector(w)
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]
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vector_count = len(voacb_strings)
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print('Building Projector labels for {} vectors: {}'.format(vector_count, out_meta_file))
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vector_dimensions = model.vocab.vectors.shape[1]
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tf_vectors_variable = numpy.zeros((vector_count, vector_dimensions), dtype=numpy.float32)
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# Write a tab-separated file that contains information about the vectors for visualization
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#
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# Reference: https://www.tensorflow.org/programmers_guide/embedding#metadata
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with open(out_meta_file, 'wb') as file_metadata:
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# Define columns in the first row
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file_metadata.write("Text\tFrequency\n".encode('utf-8'))
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# Write out a row for each vector that we add to the tensorflow variable we created
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vec_index = 0
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for text in tqdm.tqdm(voacb_strings, total=len(voacb_strings), leave=False):
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# https://github.com/tensorflow/tensorflow/issues/9094
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text = '<Space>' if text.lstrip() == '' else text
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lex = model.vocab[text]
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# Store vector data and metadata
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tf_vectors_variable[vec_index] = numpy.float64(model.vocab.get_vector(text))
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file_metadata.write("{}\t{}\n".format(text, math.exp(lex.prob) * len(voacb_strings)).encode('utf-8'))
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vec_index += 1
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# Write out "[name]_tensors.bytes" file for standalone embeddings projector to load
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tensor_path = '{}_tensors.bytes'.format(name)
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tf_vectors_variable.tofile(path.join(out_loc, tensor_path))
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print('Done.')
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print('Add the following entry to "oss_data/oss_demo_projector_config.json"')
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print(json.dumps({
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"tensorName": name,
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"tensorShape": [vector_count, vector_dimensions],
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"tensorPath": 'oss_data/{}'.format(tensor_path),
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"metadataPath": 'oss_data/{}'.format(meta_file)
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}, indent=2))
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if __name__ == '__main__':
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plac.call(main)
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