def _ragged_nn_dropout_v2(x, rate, noise_shape=None, seed=None, name=None):
if noise_shape is not None:
raise ValueError('noise_shape is not supported yet for RaggedTensor x')
with ops.name_scope(name, 'RaggedNNDropout', [x, rate]):
x = ragged_tensor.convert_to_tensor_or_ragged_tensor(x, name='x')
return x.with_flat_values(
nn_ops.dropout_v2(x.flat_values, rate=rate, seed=seed))
def testNoDropoutFast(self):
x = array_ops.zeros((5,))
y = nn_ops.dropout(x, keep_prob=1)
self.assertTrue(x is y)
y = nn_ops.dropout_v2(x, rate=0)
self.assertTrue(x is y)
def testNoDropoutFast(self):
x = array_ops.zeros((5,))
y = nn_ops.dropout(x, keep_prob=1)
self.assertTrue(x is y)
y = nn_ops.dropout_v2(x, rate=0)
self.assertTrue(x is y)
def dropout(i, do_dropout, v):
if not isinstance(do_dropout, bool) or do_dropout:
return nn_ops.dropout_v2(v,
rate=1. - keep_prob,
seed=self._gen_seed(
salt_prefix, i))
else:
return v
def testInvalidRate(self):
x_dim = 40
y_dim = 30
t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
with self.assertRaises(ValueError):
nn_ops.dropout_v2(t, -1.0)
with self.assertRaises(ValueError):
nn_ops.dropout_v2(t, 1.1)
with self.assertRaises(ValueError):
nn_ops.dropout_v2(t, [0.0, 1.0])
def testInvalidRate(self):
x_dim = 40
y_dim = 30
t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
with self.assertRaises(ValueError):
nn_ops.dropout_v2(t, -1.0)
with self.assertRaises(ValueError):
nn_ops.dropout_v2(t, 1.1)
with self.assertRaises(ValueError):
nn_ops.dropout_v2(t, [0.0, 1.0])