<jupyter_start><jupyter_text>Visualize the computation using Tensorboard, simple example<jupyter_code>import tensorflow as tf
# Define Placeholders in Tensorflow
X = tf.placeholder(tf.float32, name='X')
Y = tf.placeholder(tf.float32, name = 'Y')
Multiply = tf.multiply(X,Y)<jupyter_output><empty_output><jupyter_text>Now we are creating the session under which we have to run Multiply defined above    Note: We have defined X and Y as place holders unlike constant, so we have provide or assign some value to do the math, :) Let's go<jupyter_code>with tf.Session() as sess:
    result_mul = sess.run(Multiply, feed_dict={X:[10,21,4,2], Y:[12,4,22,4]})
    # Here it gets interesting, we have to add a new line to create a file which document the whole event.
    #I have chosen this directory, you can choose any
    event = tf.summary.FileWriter("logs/multiply-num", sess.graph)
    print(result_mul)<jupyter_output>[ 120.   84.   88.    8.]<jupyter_text>Checkout the file at the destination with Tensorboard command, you can run terminal directly from Jupyter Notebook.<jupyter_code>!Tensorboard --logdir=logs<jupyter_output>TensorBoard 0.1.5 at http://Tanays-MacBook-Air.local:6006 (Press CTRL+C to quit)
{'fraction_non_alphanumeric': 0.09942004971002485, 'fraction_numerical': 0.02568351284175642, 'mean_word_length': 5.131979695431472, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '105921', 'license': 'MIT', 'n_tokens_mistral': 415, 'n_tokens_neox': 383, 'n_words': 131, 'path': 'Basics/Concept of Computational Graph.ipynb', 'repo_name': 'tanay27/Tensorflow-Basics', 'size': '1207'}