TensorBoard

import tensorflow as tf

input_value = tf.constant(0.5,name="input_value")
weight = tf.Variable(1.0,name="weight")
expected_output = tf.constant(0.0,name="expected_output")
model = tf.multiply(input_value,weight,"model")
loss_function =tf.pow(expected_output - model,2,name="loss_function")
optimizer =tf.train.GradientDescentOptimizer(0.025).minimize(loss_function)

for value in [input_value,weight,expected_output,model,loss_function]:
    tf.summary.scalar(value.op.name,value)

Each value to display is passed to tf.scalar_summary function. It provides the following two arguments:

---

• value.op.name: This is a tag for the summary.
• value: A real numeric tensor. This is a value for the summary.

summaries = tf.summary.merge_all()
sess = tf.Session()
summary_writer = tf.summary.FileWriter('log_simple_stats',sess.graph)
sess.run(tf.global_variables_initializer())

for i in range(100):
    summary_writer.add_summary(sess.run(summaries),i)
    sess.run(optimizer)

After running the code, we can see the log file created with TensorBoard. Running the TensorBoard is very simple; open a terminal and digit the following:

• $tensorboard –logdir= log_simple_stats