def __init__(self,
output_dim,
vocab_size1,
emb1_dim,
vocab_size2,
emb2_dim,
batch_size,
max_seq_length,
n_hidden,
n_hidden_layers,
learning_rate,
keep_prob,
test_inputs1,
test_inputs2,
test_seq_lengths,
test_indices_wsd,
test_labels,
wsd_classifier=True,
pos_classifier=False,
pos_classes=0,
test_pos_labels=None):
"""See docstring for AbstratModel"""
AbstractModel.__init__(self, output_dim, vocab_size1, emb1_dim,
vocab_size2, emb2_dim, batch_size,
max_seq_length, n_hidden, n_hidden_layers,
learning_rate, keep_prob, test_inputs1,
test_inputs2, test_seq_lengths,
test_indices_wsd, test_labels, wsd_classifier,
pos_classifier, pos_classes, test_pos_labels)
self.run_neural_model()
def __init__(self):
AbstractModel.__init__(self)
parser = argparse.ArgumentParser()
parser.add_argument('--noise_n', type=int, default=128)
parser.add_argument('--G_last_act', type=str, default="tanh")
parser.add_argument('--G_pretrained_model', type=str, default="weights/generator.pth")
config = parser.parse_args()
self.solver = Solver(config)
def __init__(self,
output_dim,
vocab_size1,
emb1_dim,
vocab_size2,
emb2_dim,
batch_size,
max_seq_length,
n_hidden,
n_hidden_layers,
learning_rate,
keep_prob,
test_inputs1,
test_inputs2,
test_seq_lengths,
test_indices_wsd,
test_labels_wsd,
test_labels_wsd_context,
wsd_classifier=True,
pos_classifier=False,
pos_classes=0,
test_pos_labels=None):
"""See docstring for AbstractModel for most of the parameters
Additional args:
test_labels_wsd_context: An array of floats, the gold data embeddings for the embedding pathway
"""
AbstractModel.__init__(
self, output_dim, vocab_size1, emb1_dim, vocab_size2, emb2_dim,
batch_size, max_seq_length, n_hidden, n_hidden_layers,
learning_rate, keep_prob, test_inputs1, test_inputs2,
test_seq_lengths, test_indices_wsd, test_labels_wsd,
wsd_classifier, pos_classifier, pos_classes, test_pos_labels)
self.weights_wsd_context = tf.get_variable(
name="context_wsd-w",
shape=[2 * n_hidden, emb1_dim],
dtype=tf.float32)
self.biases_wsd_context = tf.get_variable(name="context_wsd-b",
shape=[emb1_dim],
dtype=tf.float32)
self.train_labels_wsd_context = tf.placeholder(
tf.float32,
shape=[None, emb1_dim],
name="train_labels_wsd_context")
# self.train_labels_wsd = (self.train_labels_wsd, self.train_labels_wsd_context)
self.test_labels_wsd_context = tf.constant(test_labels_wsd_context,
tf.float32)
# self.test_labels_wsd = (self.test_labels_wsd, self.test_labels_wsd_context)
self.run_neural_model()
def __init__(self):
AbstractModel.__init__(self)
self.classfier = cv2.CascadeClassifier(
r"weights/haarcascade_frontalface_alt.xml")
def __init__(self, packing=None, name='circle', sigma=1.0):
AbstractModel.__init__(self, name, sigma)
if packing == None: packing = 0.0
self.__packing__ = packing
return
def __init__(self):
AbstractModel.__init__(self)
self.solver = Solver("database", "weights/recognition.pth")
def __init__(self,name='lorentz',metric=lambda x: numpysum(x*x),sigma=1.0):
AbstractModel.__init__(self,name,metric,sigma)
return
def __init__(self,packing=None,name='circle',sigma=1.0):
AbstractModel.__init__(self,name,sigma)
if packing == None: packing = 0.0
self.__packing__ = packing
return
def __init__(self,name='decay',metric=lambda x: numpysum(x*x)):
AbstractModel.__init__(self,name,metric)
return
def __init__(self,name='decay',metric=lambda x: numpysum(x*x)):
AbstractModel.__init__(self,name,metric)
return
