def test_identity_with_unordered_dict(self):
with tf.Graph().as_default() as graph:
c1 = {'foo': tf.constant(10, dtype=tf.int32, shape=[])}
c2 = tf_computation_utils.identity(c1)
self.assertIsNot(c2, c1)
with tf.compat.v1.Session(graph=graph) as sess:
result = sess.run(c2['foo'])
self.assertEqual(result, 10)
def test_identity_with_no_nesting(self):
with tf.Graph().as_default() as graph:
c1 = tf.constant(10, dtype=tf.int32, shape=[])
c2 = tf_computation_utils.identity(c1)
self.assertIsNot(c2, c1)
with tf.compat.v1.Session(graph=graph) as sess:
result = sess.run(c2)
self.assertEqual(result, 10)
def _mnist_batch_train(model, batch):
optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.01)
model_vars = tf_computation_utils.create_variables('v', _mnist_model_type)
assign_vars_op = tf_computation_utils.assign(model_vars, model)
with tf.control_dependencies([assign_vars_op]):
train_op = optimizer.minimize(_mnist_batch_loss(model_vars, batch))
with tf.control_dependencies([train_op]):
return tf_computation_utils.identity(model_vars)
def test_identity_with_structure(self):
with tf.Graph().as_default() as graph:
c1 = structure.Struct([('foo', tf.constant(10, dtype=tf.int32,
shape=[]))])
c2 = tf_computation_utils.identity(c1)
self.assertIsNot(c2, c1)
with tf.compat.v1.Session(graph=graph) as sess:
result = sess.run(c2.foo)
self.assertEqual(result, 10)
def test_identity_with_anonymous_tuple(self):
with tf.Graph().as_default() as graph:
c1 = anonymous_tuple.AnonymousTuple([('foo',
tf.constant(10,
dtype=tf.int32,
shape=[]))])
c2 = tf_computation_utils.identity(c1)
self.assertIsNot(c2, c1)
with tf.Session(graph=graph) as sess:
result = sess.run(c2.foo)
self.assertEqual(result, 10)
