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"""Functional tests for Pack Op."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.kernel_tests import gradient_checker
class PackOpTest(tf.test.TestCase):
def testSimple(self):
np.random.seed(7)
for use_gpu in False, True:
with self.test_session(use_gpu=use_gpu):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape)
# Convert [data[0], data[1], ...] separately to tensorflow
xs = map(tf.constant, data)
# Pack back into a single tensorflow tensor
c = tf.pack(xs)
self.assertAllEqual(c.eval(), data)
def testGradients(self):
np.random.seed(7)
for use_gpu in False, True:
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape)
shapes = [shape[1:]] * shape[0]
with self.test_session(use_gpu=use_gpu):
xs = map(tf.constant, data)
c = tf.pack(xs)
err = gradient_checker.ComputeGradientError(xs, shapes, c, shape)
self.assertLess(err, 1e-6)
def testZeroSize(self):
# Verify that pack doesn't crash for zero size inputs
for use_gpu in False, True:
with self.test_session(use_gpu=use_gpu):
for shape in (0,), (3,0), (0, 3):
x = np.zeros((2,) + shape)
p = tf.pack(list(x)).eval()
self.assertAllEqual(p, x)
if __name__ == "__main__":
tf.test.main()
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