Tensorboard Example

tensorboard.py

import tensorflow as tf
import numpy as np

def rmse(y, y_hat):
    """Compute root mean squared error"""
    return tf.sqrt(tf.reduce_mean(tf.square((y - y_hat))))

def forward(x, e):
    """Forward pass for our fuction"""
    # tensorflow has automatic broadcasting 
    # so we do not need to reshape e manually
    return tf.pow(x, e) 

n = 100 # number of examples
learning_rate = 5e-6

# Placeholders for data
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)

# Model parameters
exp = tf.constant(2.0)
exp_hat = tf.Variable(4.0, name='exp_hat')

# Model definition
y_hat = forward(x, exp_hat)

# Optimizer
loss = rmse(y, y_hat)
opt = tf.train.GradientDescentOptimizer(learning_rate)

# Summaries (NEW)
loss_summary = tf.summary.scalar("loss", loss)
exp_summary = tf.summary.scalar("exp", exp_hat)
all_summaries = tf.summary.merge_all()

# We will run this operation to perform a single training step,
# e.g. opt.step() in Pytorch.
# Execution of this operation will also update model parameters
train_op = opt.minimize(loss) 

# Let's generate some training data
x_train = np.random.rand(n) + 10
y_train = x_train ** 2

loss_history = []
exp_history = []

# First, we need to create a Tensorflow session object
with tf.Session() as sess:
    
    # Initialize all defined variables
    tf.global_variables_initializer().run()
    
    summary_writer = tf.summary.FileWriter('./tensorboard', sess.graph)
    
    # Training loop
    for i in range(0, 500):
        print("Iteration %d" % i)
        # Run a single trainig step
        summaries, curr_loss, curr_exp, _ = sess.run([all_summaries, loss, exp_hat, train_op], feed_dict={x: x_train, y: y_train})
        
        print("loss = %s" % curr_loss)
        print("exp = %s" % curr_exp)
        
        # Do some recordings for plots
        loss_history.append(curr_loss)
        exp_history.append(curr_exp)
        
        summary_writer.add_summary(summaries, i)