Tensorflow loss minimization is increasing loss - tensorflow

Question

I implemented the linear regression model shown on Tensorflow's main page: https://www.tensorflow.org/get_started/get_started
import numpy as np
import tensorflow as tf
# Model parameters
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
x_train = [1,2,3,4]
y_train = [0,-1,-2,-3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
sess.run(train, {x:x_train, y:y_train})
# evaluate training accuracy
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))
However, when I change the training data to x_train=[2,4,6,8] and y_train=[3,4,5,6],
the loss starts to increase over time until it reaches 'nan'

Answer 1

As suggested by Steven, you should probably use reduce_mean(), which seems to fix the problem of the increasing loss function. Note that I also increased the number of training steps since reduce_mean() appears to need a bit longer to converge. Be careful with increasing the learning rate, since this may reproduce the problem. Instead, if training time is not a critical factor, you might want to decrease the learning rate and increase the number of training iterations further.
With the reduce_sum() function it worked well for me after decreasing the learning rate from 0.01 to 0.001. Again, thanks to Steven for the suggestion.
import numpy as np
import tensorflow as tf
# Model parameters
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_mean(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
x_train = [2,4,6,8]
y_train = [0,3,4,5]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(5000):
sess.run(train, {x:x_train, y:y_train})
# evaluate training accuracy
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))