Readdatafromfile.py

import tensorflow as tf
filename_queue = tf.train.string_input_producer(['data-01-score-data.csv'], shuffle = False, name = 'filename_queue')

reader = tf.TextLineReader()
key, value = reader.read(filename_queue)

#Default values, in case of empty columns, Also specifies the type of the decoded result.
record_defaults = [[0.], [0.], [0.], [0.]]
xy = tf.decode_csv(value, record_defulats=record_defaults)

#collect batches of csf in
train_x_batch, train_y_batch = \tf.train.batch([xy.[0:-1], xy[-1:]], batch_size = 10)

#placeholder for a tensor that will be always fed
X = tf.placeholder(tf.float32, shape = [None, 3])
Y = tf.placeholder(tf.flaot32, shpae = [None, 1])

W = tf.Variable(tf.random_normal([3, 1]), name = 'weight')
b = tf.Varaible(tf.random_normal([1]), name = 'bias')

#Hypothesis
hypothesis = tf.matmul(X, W) + b

#Simplified cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))

#Minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate = 1e-5)
train = optimizer.minimize(cost)

#Launch the graph in a session
sess = tf.Session()
#Initialize global variables in the graph
sess.run(tf.global_variables_initializer())

#Start populating the filename_queue
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess = sess, coord = coord)

for step in range(2001) :
    x_batch, y_batch = sess.run([train_x_batch, train_y_batch])
    cost_val, hy_val, _ = sess.run([cost, hypothesis, train], feed_dict={X: x_batch, Y: y_batch})
    if step % 10 == 0 :
        print(step, "Cost : ", cost_val, "\nprediction\n", hy_val)

coord.request_stop()
coord.join(threads)