Convert gensim word2vec to tensorboard visualized model, detail: https://eliyar.biz/using-pre-trained-gensim-word2vector-in-a-keras-model-and-visualizing/ This version contains small edits done by Niko Partanen

w2v_visualizer.py

# encoding: utf-8
"""
@author: BrikerMan 
@contact: eliyar917@gmail.com
@blog: https://eliyar.biz
@version: 1.0
@license: Apache Licence
@file: w2v_visualizer.py
@time: 2017/7/30 上午9:37
@comment: Modified by Niko Partanen in 30.11.2017
"""
import sys, os
from gensim.models import Word2Vec
import tensorflow as tf
import numpy as np
from tensorflow.contrib.tensorboard.plugins import projector
import gensim

def visualize(model, output_path):
    meta_file = "w2x_metadata.tsv"
    placeholder = np.zeros((len(model.wv.index2word), 300))

    with open(os.path.join(output_path,meta_file), 'wb') as file_metadata:
        for i, word in enumerate(model.wv.index2word):
            placeholder[i] = model[word]
            # temporary solution for https://github.com/tensorflow/tensorflow/issues/9094
            if word == '':
                print("Emply Line, should replecaed by any thing else, or will cause a bug of tensorboard")
                file_metadata.write("{0}".format('<Empty Line>').encode('utf-8') + b'\n')
            else:
                file_metadata.write("{0}".format(word).encode('utf-8') + b'\n')

    # define the model without training
    sess = tf.InteractiveSession()

    embedding = tf.Variable(placeholder, trainable = False, name = 'w2x_metadata')
    tf.global_variables_initializer().run()

    saver = tf.train.Saver()
    writer = tf.summary.FileWriter(output_path, sess.graph)

    # adding into projector
    config = projector.ProjectorConfig()
    embed = config.embeddings.add()
    embed.tensor_name = 'w2x_metadata'
    embed.metadata_path = meta_file

    # Specify the width and height of a single thumbnail.
    projector.visualize_embeddings(writer, config)
    saver.save(sess, os.path.join(output_path,'w2x_metadata.ckpt'))
    print('Run `tensorboard --logdir={0}` to run visualize result on tensorboard'.format(output_path))

if __name__ == "__main__":
    """
    Just run `python w2v_visualizer.py word2vec.model visualize_result`
    """
    try:
        model_path = sys.argv[1]
        output_path  = sys.argv[2]
    except:
        print("Please provice model path and output path")
    model = gensim.models.KeyedVectors.load_word2vec_format(model_path)
    visualize(model, output_path)