def testCreatePhasesWithControlFlowOpsNotWrappedInApplyFunction(self):
int_placeholder = tf.placeholder(tf.int64, shape=(None, ))
int_placeholder_minus_10 = _subtract_ten(int_placeholder)
# We need to call an analyzer after the loop because only the transitive
# parents of analyzers are inspected by create_phases
mappers.scale_to_0_1(int_placeholder_minus_10)
with self.assertRaisesRegexp(ValueError, 'Cycle detected'):
impl_helper.create_phases()
def testCreatePhasesWithControlFlowOpsWrappedInApplyFunction(self):
int_placeholder = tf.placeholder(tf.int64, shape=(None, ))
int_placeholder_minus_10 = api.apply_function(_subtract_ten,
int_placeholder)
# We need to call an analyzer after the loop because only the transitive
# parents of analyzers are inspected by create_phases
mappers.scale_to_0_1(int_placeholder_minus_10)
phases = impl_helper.create_phases()
self.assertEqual(len(phases), 1)
# tft.scale_to_0_1 uses a single analyzer: analyzers._min_and_max.
self.assertEqual(len(phases[0].analyzer_infos), 1)
def test_create_phases_with_tf_while(self):
int_placeholder = tf.placeholder(tf.int64, shape=(None,))
int_placeholder_minus_10 = _subtract_ten_with_tf_while(int_placeholder)
# We need to call an analyzer after the loop because only the transitive
# parents of analyzers are inspected by create_phases.
mappers.scale_to_0_1(int_placeholder_minus_10)
phases = impl_helper.create_phases({'x': int_placeholder})
self.assertEqual(len(phases), 1)
# tft.scale_to_0_1 uses a single analyzer: analyzers._min_and_max.
self.assertEqual(len(phases[0].analyzer_infos), 1)
def test_create_phases_with_assert_equal(self):
# Create a graph with a assert_equal, which tests the case when an op has
# control flow inputs that are ops (not tensors).
x = tf.placeholder(tf.float32, shape=(None,))
y = tf.placeholder(tf.float32, shape=(None,))
x = control_flow_ops.with_dependencies([tf.assert_equal(x, y)], x)
# We need to call an analyzer after the loop because only the transitive
# parents of analyzers are inspected by create_phases
mappers.scale_to_0_1(x)
phases = impl_helper.create_phases({'x': x, 'y': y})
self.assertEqual(len(phases), 1)
# tft.scale_to_0_1 uses a single analyzer: analyzers._min_and_max.
self.assertEqual(len(phases[0].analyzer_infos), 1)
def test_create_phases_with_tf_cond(self):
int_placeholder = tf.placeholder(tf.int64, shape=(None,))
abs_int_placeholder = tf.cond(
tf.reduce_sum(int_placeholder) > 0,
lambda: int_placeholder,
lambda: -int_placeholder)
# We need to call an analyzer after the tf.cond because only the transitive
# parents of analyzers are inspected by create_phases.
mappers.scale_to_0_1(abs_int_placeholder)
phases = impl_helper.create_phases({'x': int_placeholder})
self.assertEqual(len(phases), 1)
# tft.scale_to_0_1 uses a single analyzer: analyzers._min_and_max.
self.assertEqual(len(phases[0].analyzer_infos), 1)
def preprocessing_fn(inputs):
def _subtract_ten(x):
i = tf.constant(0)
c = lambda i, x: tf.less(i, 10)
b = lambda i, x: (tf.add(i, 1), tf.add(x, -1))
return tf.while_loop(c, b, [i, x])[1]
scaled_to_0_1 = mappers.scale_to_0_1(_subtract_ten(inputs['x']))
return {'x_scaled': scaled_to_0_1}
def preprocessing_fn(inputs):
return {
'dense_out':
mappers.scale_to_0_1(inputs['dense_1']),
'sparse_out':
api.map(lambda x: tf.sparse_reshape(x, (1, 10)),
inputs['sparse'])
}
def preprocessing_fn(inputs):
return {
'dense_out': mappers.scale_to_0_1(inputs['dense_1']),
'sparse_out': tf.sparse_reshape(inputs['sparse'], (1, 10)),
}
