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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)
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.
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?
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