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.square(expected_output - model, name='loss_function')

optimizer = tf.train.GradientDescentOptimizer(0.0001).minimize(loss_function)

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

summaries = tf.summary.merge_all()

sess = tf.Session()

summary_writer = tf.summary.FileWriter('logs/simple_test_logs', sess.graph)

sess.run(tf.global_variables_initializer())

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