텐서플로우에서 데이터 읽기 | Read data from files on TensorFlow

test.csv :

73,80,75,152

93,88,93,185

89,91,90,180

96,98,100,196

73,66,70,142

53,46,55,101

import tensorflow as tf
filename_queue = tf.train.string_input_producer(\
        ['/Users/sh/Documents/_iPython/TensorFlow/test.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_defaults = record_defaults)

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

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

W = tf.Variable(tf.random_normal([3, 1]), name = 'weight')
b = tf.Variable(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 sesseion.
sess = tf.Session()

# Initializes 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)

RESULT :

2000 Cost:  1.20003 
Prediction:
 [[ 181.07557678]
 [ 195.87307739]
 [ 140.93286133]
 [ 102.22296906]
 [ 153.24723816]
 [ 183.31776428]
 [ 181.07557678]
 [ 195.87307739]
 [ 140.93286133]
 [ 102.22296906]]

-Reference-

https://github.com/hunkim/DeepLearningZeroToAll/