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February 3, 2017 14:38
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A simple example to demonstrate how to link embedding metadata to word embeddings in tensorflow / tensorboard
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| """ | |
| Simple example to demostrate the embedding visualization for word embeddings in tensorflow / tensorboard | |
| https://www.tensorflow.org/how_tos/embedding_viz/ | |
| """ | |
| import tensorflow as tf | |
| import os | |
| assert tf.__version__ == '1.0.0-rc0' # if code breaks, check tensorflow version | |
| from tensorflow.contrib.tensorboard.plugins import projector | |
| """ | |
| Hyperparameter | |
| """ | |
| checkpoint_path = "checkpoints" | |
| if not os.path.exists(checkpoint_path): os.mkdir(checkpoint_path) | |
| vocabulary_size = 20 # we have 20 words in our vocabulary | |
| word_embedding_dim = 10 # each word is represented by a [1,10] dimensional row vector. | |
| """ | |
| Create Word Embedding | |
| """ | |
| # create word embeddings, fill randomly | |
| word_embeddings = tf.Variable(tf.random_uniform([vocabulary_size, word_embedding_dim], -1.0, 1.0), name='word_embeddings') | |
| """ | |
| Save Word embedding checkpoint | |
| """ | |
| # Saver | |
| saver = tf.train.Saver(tf.global_variables()) | |
| # Start session | |
| session = tf.Session() | |
| summary_writer = tf.summary.FileWriter(checkpoint_path, graph=session.graph) | |
| session.run([tf.global_variables_initializer()]) # init variables | |
| #... do stuff with session | |
| # save checkpoints periodically | |
| chkpoint_out_filename = os.path.join(checkpoint_path, "word_embedding_sample") | |
| saver.save(session, chkpoint_out_filename, global_step=1) | |
| print("\nword_embeddings checkpoint saved") | |
| """ | |
| Write metadata file | |
| """ | |
| tsv_row_template = "{}\t{}\t{}\n" | |
| with open(os.path.join(checkpoint_path, 'word_embeddings.tsv'), "w") as f: | |
| header_row = tsv_row_template.format("Name", "Category", "Type") | |
| f.write(header_row) | |
| for w_id in xrange(vocabulary_size): | |
| # get metadat for each word | |
| word = "word %0.2d"%w_id | |
| category = w_id%5 | |
| word_type = "type %i"%(w_id%3) | |
| data_row = tsv_row_template.format(word,category,word_type) | |
| f.write(data_row) | |
| print("word_embeddings.tsv written.") | |
| """ | |
| Link metadata tsv file to embedding | |
| """ | |
| config = projector.ProjectorConfig() | |
| embedding = config.embeddings.add() # could add more metadata files here | |
| embedding.tensor_name = word_embeddings.name | |
| embedding.metadata_path = os.path.join(checkpoint_path, 'word_embeddings.tsv') | |
| projector.visualize_embeddings(summary_writer, config) | |
| print("Metadata linked to checkpoint\n") | |
| print("run: tensorboard --logdir checkpoints/") |
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