{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convert gensim word2vec to tensorboard visualized model\n", "\n", "detail: https://eliyar.biz/using-pre-trained-gensim-word2vector-in-a-keras-model-and-visualizing/" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'tensorflow'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgensim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mWord2Vec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensorboard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplugins\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprojector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'" ] } ], "source": [ "import sys\n", "import os\n", "from gensim.models import Word2Vec\n", "import tensorflow as tf\n", "import numpy as np\n", "from tensorflow.contrib.tensorboard.plugins import projector" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def visualize(model, output_path):\n", " meta_file = \"w2x_metadata.tsv\"\n", " placeholder = np.zeros((len(model.wv.index2word), 100))\n", "\n", " with open(os.path.join(output_path,meta_file), 'wb') as file_metadata:\n", " for i, word in enumerate(model.wv.index2word):\n", " placeholder[i] = model[word]\n", " # temporary solution for https://github.com/tensorflow/tensorflow/issues/9094\n", " if word == '':\n", " print(\"Emply Line, should replecaed by any thing else, or will cause a bug of tensorboard\")\n", " file_metadata.write(\"{0}\".format('').encode('utf-8') + b'\\n')\n", " else:\n", " file_metadata.write(\"{0}\".format(word).encode('utf-8') + b'\\n')\n", "\n", " # define the model without training\n", " sess = tf.InteractiveSession()\n", "\n", " embedding = tf.Variable(placeholder, trainable = False, name = 'w2x_metadata')\n", " tf.global_variables_initializer().run()\n", "\n", " saver = tf.train.Saver()\n", " writer = tf.summary.FileWriter(output_path, sess.graph)\n", "\n", " # adding into projector\n", " config = projector.ProjectorConfig()\n", " embed = config.embeddings.add()\n", " embed.tensor_name = 'w2x_metadata'\n", " embed.metadata_path = meta_file\n", "\n", " # Specify the width and height of a single thumbnail.\n", " projector.visualize_embeddings(writer, config)\n", " saver.save(sess, os.path.join(output_path,'w2x_metadata.ckpt'))\n", " print('Run `tensorboard --logdir={}` to run visualize result on tensorboard'.format(output_path))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def arg_parser():\n", " pass" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'Word2Vec' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please provice model path and output path\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWord2Vec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mvisualize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'Word2Vec' is not defined" ] } ], "source": [ "if __name__ == \"__main__\":\n", " \"\"\"\n", " Just run `python w2v_visualizer.py word2vec.model visualize_result`\n", " \"\"\"\n", " try:\n", " model_path = sys.argv[1]\n", " output_path = sys.argv[2]\n", " except:\n", " print(\"Please provide model path and output path\")\n", " model = Word2Vec.load(model_path)\n", " visualize(model, output_path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }