Regression

gistfile1.txt

import tensorflow as tf

learning_rate = 0.00475

x_ph = tf.placeholder("float")
y_ph = tf.placeholder("float")

w = tf.Variable(0.0, name="weights")
b = tf.Variable(0.0, name="b")

init_op = tf.global_variables_initializer()

y_model = tf.nn.xw_plus_b(tf.expand_dims([x_ph], 0), tf.expand_dims([w], 0), [b]) # tf.add(tf.multiply(x_ph, w), b)

cost = tf.reduce_mean(tf.square(y_ph - y_model))

cost_summary = tf.summary.scalar("cost", cost)

my_optimizer = tf.train.GradientDescentOptimizer(learning_rate)

train_op = my_optimizer.minimize(cost)

with tf.Session() as sess:
    sess.run(init_op)
    my_writer = tf.summary.FileWriter("./logs", sess.graph)

    data = {x_ph: 2.1, y_ph: 5.0}
    sess.run(train_op, feed_dict=data)
    summary = sess.run(cost_summary, feed_dict=data)
    my_writer.add_summary(summary, 1)

    data = {x_ph: 3., y_ph: 7.1}
    sess.run(train_op, feed_dict=data)
    summary = sess.run(cost_summary, feed_dict=data)
    my_writer.add_summary(summary, 2)

    data = {x_ph: 10, y_ph: 21}
    sess.run(train_op, feed_dict=data)
    summary = sess.run(cost_summary, feed_dict=data)
    my_writer.add_summary(summary, 3)

    w_val = sess.run(w)
    b_val = sess.run(b)
    print(w_val, b_val)