def __init__(self,
vocab=None,
mask_special_tokens=True,
trainable=True,
**kwargs):
super().__init__(trainable=trainable, **kwargs)
self._vocab = vocabulary.get_default() if vocab is None else vocab
self._mask_special_tokens = mask_special_tokens
def __init__(self,
vocab=None,
kernel_init=tf.initializers.GlorotUniform(),
bias_init=tf.initializers.Zeros(),
**kwargs):
super().__init__(**kwargs)
self._vocab = vocabulary.get_default() if vocab is None else vocab
self._kernel_init = kernel_init
self._bias_init = bias_init
def __init__(self,
vocab = None,
kernel_init='GlorotUniform',
bias_init='Zeros',
**kwargs):
super().__init__(**kwargs)
self._vocab = vocabulary.get_default() if vocab is None else vocab
self._kernel_init = tf.keras.initializers.get(kernel_init)
self._bias_init = tf.keras.initializers.get(bias_init)
def __init__(self,
vocab=None,
matrix_str=BLOSUM_62,
pad_penalty=-1e9,
**kwargs):
super().__init__(**kwargs)
self._vocab = vocabulary.get_default() if vocab is None else vocab
self._matrix_str = matrix_str
self._pad_penalty = pad_penalty
def __init__(self,
on='seq_len',
vocab=None,
max_len=512,
precomputed=True):
self._on = (on, ) if isinstance(on, str) else on
self._vocab = vocabulary.get_default() if vocab is None else vocab
self._max_len = max_len
self._precomputed = precomputed
def __init__(self,
vocab=None,
use_hidden_layer=True,
activation=activations.approximate_gelu,
kernel_init=tf.initializers.GlorotUniform(),
bias_init=tf.initializers.Zeros(),
logits_bias_init=tf.initializers.Zeros(),
norm_logits=True,
**kwargs):
super().__init__(**kwargs)
self._vocab = vocabulary.get_default() if vocab is None else vocab
self._use_hidden_layer = use_hidden_layer
self._activation = activation
self._kernel_init = kernel_init
self._bias_init = bias_init
self._logits_bias_init = logits_bias_init
self._norm_logits = norm_logits
def __init__(self,
max_len = 512,
tau = 0.01,
alpha = 0.05,
eta = 0.7,
vocab = None):
self._max_len = max_len
vocab = vocabulary.get_default() if vocab is None else vocab
self._sampler = vocabulary.Sampler(vocab=vocab)
self._eos = vocab.get(vocab.specials[-1])
self._pad = vocab.padding_code
# Transition look-up table (excluding special initial transition).
look_up = {
(self.MATCH, self.MATCH): 1,
(self.GAP_IN_X, self.MATCH): 2,
(self.GAP_IN_Y, self.MATCH): 3,
(self.MATCH, self.GAP_IN_X): 4,
(self.GAP_IN_X, self.GAP_IN_X): 5,
(self.GAP_IN_Y, self.GAP_IN_X): 9, # "forbidden" transition.
(self.MATCH, self.GAP_IN_Y): 6,
(self.GAP_IN_X, self.GAP_IN_Y): 7,
(self.GAP_IN_Y, self.GAP_IN_Y): 8,
}
# Builds data structures for efficiently encoding transitions.
self._hash_fn = lambda d0, d1: 3 * (d1 + 1) + (d0 + 1)
hashes = [self._hash_fn(d0, d1) for (d0, d1) in look_up]
trans_encoder = tf.scatter_nd(
indices=[[x] for x in hashes],
updates=list(look_up.values()),
shape=[max(hashes) + 1])
self._trans_encoder = tf.cast(trans_encoder, tf.int32)
self._init_trans = tf.convert_to_tensor([self.INIT_TRANS], dtype=tf.int32)
cond_probs = tf.convert_to_tensor(
[[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 1.0 - 2.0 * alpha - tau, alpha, alpha, tau],
[0.0, eta, 1.0 - eta - alpha, alpha, 0.0],
[0.0, eta, 0.0, 1.0 - eta, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0]],
tf.float32)
self._logits = tf.where(cond_probs > 0.0, tf.math.log(cond_probs), -np.inf)
self._delta_len_x = tf.convert_to_tensor([0, 1, 0, 1, 0])
self._delta_len_y = tf.convert_to_tensor([0, 1, 1, 0, 0])
def __init__(self, on='sequence', out=None, vocab=None):
self._on = (on, ) if isinstance(on, str) else on
out = self._on if out is None else out
self._out = (out, ) if isinstance(out, str) else out
self._vocab = vocabulary.get_default() if vocab is None else vocab
