#!/usr/bin/env python
# coding: utf-8

# In[1]:


FN = 'tf-projector'


# Using TensorFlow's TenosrBoard to project numpy embedding matrix to 3D/2D

# https://www.tensorflow.org/versions/master/how_tos/embedding_viz/

# In[2]:


import os
LOG_DIR = os.path.join('data', FN)


# In[3]:


get_ipython().system('mkdir -p {LOG_DIR}')


# In[4]:


import numpy as np
my_embedding = np.random.random((10000,100)).astype(np.float32)
vocabulary_size, embedding_size = my_embedding.shape
vocabulary_size, embedding_size, my_embedding.dtype


# In[5]:


# You dont need to normalize because tensorboard will do it for you
# embedding /= np.sqrt((embedding*embedding).sum(axis=-1,keepdims=1))


# In[6]:


import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
    embedding_var = tf.Variable(
        tf.constant(my_embedding))
    init = tf.global_variables_initializer()


# In[7]:


with tf.Session(graph=graph) as session:
    init.run()
    saver = tf.train.Saver()
    saver.save(session, LOG_DIR+"/model.ckpt", 0)


# In[8]:


from tensorflow.contrib.tensorboard.plugins import projector
# Use the same LOG_DIR where you stored your checkpoint.
summary_writer = tf.summary.FileWriter(LOG_DIR)


# In[9]:


# Format: tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto
config = projector.ProjectorConfig()

# You can add multiple embeddings. Here we add only one.
embedding = config.embeddings.add()
embedding.tensor_name = embedding_var.name
# Link this tensor to its metadata file (e.g. labels).
embedding.metadata_path = os.path.join(LOG_DIR, 'metadata.tsv')

# Saves a configuration file that TensorBoard will read during startup.
projector.visualize_embeddings(summary_writer, config)


# documentation say you dont need a header line for a single column file, but code say otherwise

# In[10]:


from __future__ import print_function
with open(embedding.metadata_path,'w') as fp:
    print('Name', file=fp)
    for i in xrange(vocabulary_size):
        print('label%d'%i, file=fp)


# In[11]:


get_ipython().system('head {embedding.metadata_path}')


# In[12]:


get_ipython().system('wc -l {embedding.metadata_path}')


# In[13]:


get_ipython().system('ls data/{FN}')


# In[ ]:


get_ipython().system('tensorboard --logdir=data/{FN}')


# click on [this link](http://localhost:6006) and click on `EMBEDDING` on the top right