def linear(args,
output_size,
bias,
bias_start=0.0,
scope=None,
squeeze=False,
wd=0.0,
input_keep_prob=1.0,
is_train=None,
kernel_initializer=None):
if args is None or (nest.is_sequence(args) and not args):
raise ValueError("`args` must be specified")
if not nest.is_sequence(args):
args = [args]
flat_args = [flatten(arg, 1)
for arg in args] # flat_args[0] : [N*JX*JQ, d]
if input_keep_prob < 1.0:
assert is_train is not None
flat_args = [
tf.cond(is_train, lambda: tf.nn.dropout(arg, input_keep_prob),
lambda: arg) for arg in flat_args
]
with tf.variable_scope(scope or 'linear'):
flat_out = _linear(flat_args,
output_size,
bias,
kernel_initializer=kernel_initializer)
out = reconstruct(flat_out, args[0], 1)
if squeeze:
out = tf.squeeze(out, [len(args[0].get_shape().as_list()) - 1])
if wd:
add_wd(wd)
return out
def l2_normalize(in_, proj=True, input_keep_prob=1.0, wd=0.0, is_train=None, scope=None):
d = in_.get_shape().as_list()[-1]
with tf.variable_scope(scope or "l2_normalize"):
if proj:
in_ = F(in_, d, scope='in_', input_keep_prob=input_keep_prob, wd=wd, is_train=is_train, use_bias=True)
in_2 = flatten(in_, 1)
out = tf.nn.l2_normalize(in_2, 1)
out = reconstruct(out, in_, 1)
return out
def softmax(logits, mask=None, scope=None, rescale=False, dim=None):
with tf.name_scope(scope or "Softmax"):
if mask is not None:
logits = exp_mask(logits, mask)
if rescale:
assert dim is not None
logits = tf.divide(logits, tf.ones_like(logits, dtype=tf.float32) * tf.sqrt(dim))
flat_logits = flatten(logits, 1)
flat_out = tf.nn.softmax(flat_logits)
out = reconstruct(flat_out, logits, 1)
return out
def collect_clause_values(*criteria):
criteria = [
item.strip() for item in flatten(criteria) if item and item.strip()
]
if not criteria:
return []
if len(criteria) == 1 or '{}' not in criteria[0]:
return criteria
pattern = criteria[0]
criteria.remove(pattern)
return [pattern.format(value) for value in criteria]
def test_normalization(self):
self.assertEqual(flatten(5.0, 0.0, 10.0), 0.5)