def abel_sigm_abel_tf(x):
abel1 = abel_fracexpn_f(x,
T.shape_padright(self.abel_in_n(lyr)))
sigm = T.nnet.sigmoid(abel1) + 0.05
abel2 = abel_fracexpn_f(sigm,
T.shape_padright(self.abel_out_n(lyr)))
return abel2
def make_transfer_func(self, lyr, tf):
if tf == 'tanh':
return T.tanh
elif tf == 'sigm':
return T.nnet.sigmoid
elif tf == 'softmax':
return T.nnet.softmax
elif tf == 'linear':
return lambda x: x
elif tf == 'relu':
return T.nnet.relu
elif tf == 'exp':
return T.exp
elif tf == 'log':
return lambda x: T.log(T.maximum(0.0001, x))
elif tf == 'sin':
return T.sin
elif tf == 'psi':
from addiplication.nnet.abelpsi_f import abel_psi_f
return abel_psi_f
elif tf == 'psiinv':
from addiplication.nnet.abelpsi_f import abel_psi_inv_f
return abel_psi_inv_f
elif tf == 'abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: abel_fracexpn_f(x, T.shape_padright(self.abel_n(lyr)))
elif tf == 'relu_abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: abel_fracexpn_f(T.nnet.relu(x), T.shape_padright(self.abel_n(lyr)))
elif tf == 'sigm_abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: abel_fracexpn_f(T.nnet.sigmoid(x), T.shape_padright(self.abel_n(lyr)))
elif tf == 'abel_sigm':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: T.nnet.sigmoid(abel_fracexpn_f(x, T.shape_padright(self.abel_n(lyr))))
elif tf == 'abel_tanh':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: T.tanh(abel_fracexpn_f(x, T.shape_padright(self.abel_n(lyr))))
elif tf == 'tanh_abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: abel_fracexpn_f(T.tanh(x), T.shape_padright(self.abel_n(lyr)))
elif tf == 'abel_sigm_abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
def abel_sigm_abel_tf(x):
abel1 = abel_fracexpn_f(x, T.shape_padright(self.abel_in_n(lyr)))
sigm = T.nnet.sigmoid(abel1) + 0.05
abel2 = abel_fracexpn_f(sigm, T.shape_padright(self.abel_out_n(lyr)))
return abel2
return abel_sigm_abel_tf
else:
raise ValueError("unknown transfer function %s for layer %s" % (tf, str(lyr)))
def abel_sigm_abel_tf(x):
abel1 = abel_fracexpn_f(x, T.shape_padright(self.abel_in_n(lyr)))
sigm = T.nnet.sigmoid(abel1) + 0.05
abel2 = abel_fracexpn_f(sigm, T.shape_padright(self.abel_out_n(lyr)))
return abel2
def make_transfer_func(self, lyr, tf):
if tf == 'tanh':
return T.tanh
elif tf == 'sigm':
return T.nnet.sigmoid
elif tf == 'softmax':
return T.nnet.softmax
elif tf == 'linear':
return lambda x: x
elif tf == 'relu':
return T.nnet.relu
elif tf == 'exp':
return T.exp
elif tf == 'log':
return lambda x: T.log(T.maximum(0.0001, x))
elif tf == 'sin':
return T.sin
elif tf == 'psi':
from addiplication.nnet.abelpsi_f import abel_psi_f
return abel_psi_f
elif tf == 'psiinv':
from addiplication.nnet.abelpsi_f import abel_psi_inv_f
return abel_psi_inv_f
elif tf == 'abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: abel_fracexpn_f(
x, T.shape_padright(self.abel_n(lyr)))
elif tf == 'relu_abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: abel_fracexpn_f(
T.nnet.relu(x), T.shape_padright(self.abel_n(lyr)))
elif tf == 'sigm_abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: abel_fracexpn_f(
T.nnet.sigmoid(x), T.shape_padright(self.abel_n(lyr)))
elif tf == 'abel_sigm':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: T.nnet.sigmoid(
abel_fracexpn_f(x, T.shape_padright(self.abel_n(lyr))))
elif tf == 'abel_tanh':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: T.tanh(
abel_fracexpn_f(x, T.shape_padright(self.abel_n(lyr))))
elif tf == 'tanh_abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
return lambda x: abel_fracexpn_f(
T.tanh(x), T.shape_padright(self.abel_n(lyr)))
elif tf == 'abel_sigm_abel':
from addiplication.nnet.abelpsi_f import abel_fracexpn_f
def abel_sigm_abel_tf(x):
abel1 = abel_fracexpn_f(x,
T.shape_padright(self.abel_in_n(lyr)))
sigm = T.nnet.sigmoid(abel1) + 0.05
abel2 = abel_fracexpn_f(sigm,
T.shape_padright(self.abel_out_n(lyr)))
return abel2
return abel_sigm_abel_tf
else:
raise ValueError("unknown transfer function %s for layer %s" %
(tf, str(lyr)))
