preprocess_data with tensorflow is slow

[adeagle]

def test_tf():
with tf.Session() as sess:
array=tf.ones([1024,5],dtype=tf.float32)
t0=time.clock()
out=0
for i in range(array.shape[0]):
out+=array[i]
out=sess.run([out])
t1=time.clock()
print("test_tf:",out,t1-t0)

def test_np():
array=np.ones((1024,5),dtype=np.float32)
print array.shape
t0=time.clock()
out=0
for i in range(array.shape[0]):
out+=array[i]
t1=time.clock()
print("test_np:",out,t1-t0)

console output:
('test_tf:', [array([ 1024., 1024., 1024., 1024., 1024.], dtype=float32)], 2.395962)
(1024, 5)
('test_np:', array([ 1024., 1024., 1024., 1024., 1024.], dtype=float32), 0.0008499999999997954)

why not prepare data with numpy?

[PaulChongPeng]

1. The data got from TFRecord is tensor.
   2.The way you evaluate the speed is not appropriate for my code. It's more reasonable that we evaluate the speed with vectorization.