<reponame>littlealexchen/Deep-Learning-with-TensorFlow-master<gh_stars>100-1000
import tensorflow as tf

weight = tf.Variable(1.0, name="weight")
input_value = tf.constant(0.5, name="input_value")
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)

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)

import tensorflow as tf

weight = tf.Variable(1.0, name="weight")
input_value = tf.constant(0.5, name="input_value")
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)

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)