def __init__(self, n_classes, width, depth, get_bow, rho=1e-5, eta=0.005,
eps=1e-6, update_step='adadelta'):
nn_shape = tuple([width] + [width] * depth + [n_classes])
NeuralNet.__init__(self, nn_shape, embed=((width,), (0,)),
rho=rho, eta=eta, eps=eps,
update_step=update_step)
self.get_bow = get_bow
def __init__(self,
n_classes,
width,
depth,
get_bow,
rho=1e-5,
eta=0.005,
eps=1e-6,
update_step='adadelta'):
nn_shape = tuple([width] + [width] * depth + [n_classes])
NeuralNet.__init__(self,
nn_shape,
embed=((width, ), (0, )),
rho=rho,
eta=eta,
eps=eps,
update_step=update_step)
self.get_bow = get_bow
def __init__(self,
n_classes,
width,
depth,
get_bow,
rho=1e-5,
eta=0.005,
eps=1e-6,
batch_norm=False,
update_step='sgd_cm',
noise=0.001):
unigram_width = width
bigram_width = 0
nn_shape = tuple([unigram_width + bigram_width] + [width] * depth +
[n_classes])
NeuralNet.__init__(self,
nn_shape,
embed=((width, bigram_width), (0, 1)),
rho=rho,
eta=eta,
update_step=update_step,
batch_norm=batch_norm,
noise=noise)
self.get_bow = get_bow
