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# pylint: disable=g-bad-import-order,unused-import
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.models.rnn import linear
class LinearTest(tf.test.TestCase):
def testLinear(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(1.0)):
x = tf.zeros([1, 2])
l = linear.linear([x], 2, False)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run([l], {x.name: np.array([[1., 2.]])})
self.assertAllClose(res[0], [[3.0, 3.0]])
# Checks prevent you from accidentally creating a shared function.
with self.assertRaises(ValueError) as exc:
l1 = linear.linear([x], 2, False)
self.assertEqual(exc.exception.message[:12], "Over-sharing")
# But you can create a new one in a new scope and share the variables.
with tf.variable_scope("l1") as new_scope:
l1 = linear.linear([x], 2, False)
with tf.variable_scope(new_scope, reuse=True):
linear.linear([l1], 2, False)
self.assertEqual(len(tf.trainable_variables()), 2)
if __name__ == "__main__":
tf.test.main()
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