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Layers.py
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Layers.py
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import theano
import theano.tensor as T
import numpy as np
from initializations import glorot_uniform,zero,alloc_zeros_matrix,glorot_normal
import theano.typed_list
class hidden(object):
def __init__(self,n_in,n_hidden):
self.n_in=int(n_in)
self.n_hidden=int(n_hidden)
self.input= T.tensor3()
self.x_mask=T.matrix()
self.W_hh=glorot_uniform((n_hidden,n_hidden))
self.W_in=glorot_uniform((n_in,n_hidden))
self.bh=zero((n_hidden,))
self.params=[self.W_hh,self.W_in,self.bh]
self.L1 = T.sum(abs(self.W_hh))+T.sum(abs(self.W_in))
self.L2_sqr = T.sum(self.W_hh**2) + T.sum(self.W_in**2)
def set_previous(self,layer):
self.previous = layer
self.input=self.get_input()
def _step(self,x_t, h_tm1):
return T.tanh(T.dot(h_tm1, self.W_hh) + T.dot(x_t, self.W_in) + self.bh)
def set_input(self,x):
self.input=x
def set_mask(self,x_mask):
self.x_mask=x_mask
def get_input(self):
if hasattr(self, 'previous'):
return self.previous.get_output()
else:
return self.input
def get_output(self):
X=self.get_input()
h, _ = theano.scan(self._step,
sequences = X,
outputs_info = alloc_zeros_matrix(self.input.shape[1],
self.n_hidden))
return h
class lstm(object):
def __init__(self,n_in,n_hidden):
self.n_in=int(n_in)
self.n_hidden=int(n_hidden)
self.input= T.tensor3()
self.W_i = glorot_uniform((n_in,n_hidden))
self.U_i = glorot_uniform((n_hidden,n_hidden))
self.b_i = zero((n_hidden,))
self.W_f = glorot_uniform((n_in,n_hidden))
self.U_f = glorot_uniform((n_hidden,n_hidden))
self.b_f = zero((n_hidden,))
self.W_c = glorot_uniform((n_in,n_hidden))
self.U_c = glorot_uniform((n_hidden,n_hidden))
self.b_c = zero((n_hidden,))
self.W_o = glorot_uniform((n_in,n_hidden))
self.U_o = glorot_uniform((n_hidden,n_hidden))
self.b_o = zero((n_hidden,))
self.params = [
self.W_i, self.U_i, self.b_i,
self.W_c, self.U_c, self.b_c,
self.W_f, self.U_f, self.b_f,
self.W_o, self.U_o, self.b_o,
]
self.L1 = T.sum(abs(self.W_i))+T.sum(abs(self.U_i))+\
T.sum(abs(self.W_f))+T.sum(abs(self.U_f))+\
T.sum(abs(self.W_c))+T.sum(abs(self.U_c))+\
T.sum(abs(self.W_o))+T.sum(abs(self.U_o))
self.L2_sqr = T.sum(self.W_i**2) + T.sum(self.U_i**2)+\
T.sum(self.W_f**2) + T.sum(self.U_f**2)+\
T.sum(self.W_c**2) + T.sum(self.U_c**2)+\
T.sum(self.W_o**2) + T.sum(self.U_o**2)
def set_previous(self,layer):
self.previous = layer
self.input=self.get_input()
def _step(self, x_t, h_tm1, c_tm1):
i_t = T.tanh(T.dot(x_t, self.W_i) + self.b_i + T.dot(h_tm1, self.U_i))
f_t = T.tanh(T.dot(x_t, self.W_f) + self.b_f + T.dot(h_tm1, self.U_f))
c_t = f_t * c_tm1 + i_t * T.tanh(T.dot(x_t, self.W_c) + self.b_c + T.dot(h_tm1, self.U_c))
o_t = T.tanh( T.dot(x_t, self.W_o) + self.b_o + T.dot(h_tm1, self.U_o))
h_t = o_t * T.tanh(c_t)
return h_t, c_t
def set_input(self,x):
self.input=x
def get_input(self):
if hasattr(self, 'previous'):
return self.previous.get_output()
else:
return self.input
def get_output(self):
X=self.get_input()
[h,c], _ = theano.scan(self._step,
sequences = X,
outputs_info = [alloc_zeros_matrix(self.input.shape[1],
self.n_hidden),
alloc_zeros_matrix(self.input.shape[1],
self.n_hidden)])
return h
class gru(object):
def __init__(self,n_in,n_hidden):
self.n_in=int(n_in)
self.n_hidden=int(n_hidden)
self.input= T.tensor3()
self.W_z = glorot_uniform((n_in,n_hidden))
self.U_z = glorot_uniform((n_hidden,n_hidden))
self.b_z = zero((n_hidden,))
self.W_r = glorot_uniform((n_in,n_hidden))
self.U_r = glorot_uniform((n_hidden,n_hidden))
self.b_r = zero((n_hidden,))
self.W_h = glorot_uniform((n_in,n_hidden))
self.U_h = glorot_uniform((n_hidden,n_hidden))
self.b_h = zero((n_hidden,))
self.params = [
self.W_z, self.U_z, self.b_z,
self.W_r, self.U_r, self.b_r,
self.W_h, self.U_h, self.b_h,
]
self.L1 = T.sum(abs(self.W_z))+T.sum(abs(self.U_z))+\
T.sum(abs(self.W_r))+T.sum(abs(self.U_r))+\
T.sum(abs(self.W_h))+T.sum(abs(self.U_h))
self.L2_sqr = T.sum(self.W_z**2) + T.sum(self.U_z**2)+\
T.sum(self.W_r**2) + T.sum(self.U_r**2)+\
T.sum(self.W_h**2) + T.sum(self.U_h**2)
def set_previous(self,layer):
self.previous = layer
self.input=self.get_input()
def _step(self,x_t, h_tm1):
z = T.tanh(T.dot(x_t, self.W_z) + self.b_z + T.dot(h_tm1, self.U_z))
r = T.tanh(T.dot(x_t, self.W_r) + self.b_r + T.dot(h_tm1, self.U_r))
hh_t = T.tanh(T.dot(x_t, self.W_h) + self.b_h + T.dot(r * h_tm1, self.U_h))
h_t = z * h_tm1 + (1 - z) * hh_t
return h_t
def set_input(self,x):
self.input=x
def get_input(self):
if hasattr(self, 'previous'):
return self.previous.get_output()
else:
return self.input
def get_output(self):
X=self.get_input()
h, _ = theano.scan(self._step,
sequences = X,
outputs_info = alloc_zeros_matrix(self.input.shape[1],
self.n_hidden))
return h
class BiDirectionLSTM(object):
def __init__(self,n_in,n_hidden,output_mode='concat'):
self.n_in=int(n_in)
n_hidden=int(n_hidden/2)
self.n_hidden=int(n_hidden)
self.output_mode = output_mode
self.input= T.tensor3()
self.x_mask=T.matrix()
# forward weights
self.W_i = glorot_uniform((n_in,n_hidden))
self.U_i = glorot_uniform((n_hidden,n_hidden))
self.b_i = zero((n_hidden,))
self.W_f = glorot_uniform((n_in,n_hidden))
self.U_f = glorot_uniform((n_hidden,n_hidden))
self.b_f = zero((n_hidden,))
self.W_c = glorot_uniform((n_in,n_hidden))
self.U_c = glorot_uniform((n_hidden,n_hidden))
self.b_c = zero((n_hidden,))
self.W_o = glorot_uniform((n_in,n_hidden))
self.U_o = glorot_uniform((n_hidden,n_hidden))
self.b_o = zero((n_hidden,))
# backward weights
self.Wb_i = glorot_uniform((n_in,n_hidden))
self.Ub_i = glorot_uniform((n_hidden,n_hidden))
self.bb_i = zero((n_hidden,))
self.Wb_f = glorot_uniform((n_in,n_hidden))
self.Ub_f = glorot_uniform((n_hidden,n_hidden))
self.bb_f = zero((n_hidden,))
self.Wb_c = glorot_uniform((n_in,n_hidden))
self.Ub_c = glorot_uniform((n_hidden,n_hidden))
self.bb_c = zero((n_hidden,))
self.Wb_o = glorot_uniform((n_in,n_hidden))
self.Ub_o = glorot_uniform((n_hidden,n_hidden))
self.bb_o = zero((n_hidden,))
self.params = [
self.W_i, self.U_i, self.b_i,
self.W_c, self.U_c, self.b_c,
self.W_f, self.U_f, self.b_f,
self.W_o, self.U_o, self.b_o,
self.Wb_i, self.Ub_i, self.bb_i,
self.Wb_c, self.Ub_c, self.bb_c,
self.Wb_f, self.Ub_f, self.bb_f,
self.Wb_o, self.Ub_o, self.bb_o,
]
self.L1 = T.sum(abs(self.W_i))+T.sum(abs(self.U_i))+\
T.sum(abs(self.W_f))+T.sum(abs(self.U_f))+\
T.sum(abs(self.W_c))+T.sum(abs(self.U_c))+\
T.sum(abs(self.W_o))+T.sum(abs(self.U_o))+\
T.sum(abs(self.Wb_i))+T.sum(abs(self.Ub_i))+\
T.sum(abs(self.Wb_f))+T.sum(abs(self.Ub_f))+\
T.sum(abs(self.Wb_c))+T.sum(abs(self.Ub_c))+\
T.sum(abs(self.Wb_o))+T.sum(abs(self.Ub_o))
self.L2_sqr = T.sum(self.W_i**2) + T.sum(self.U_i**2)+\
T.sum(self.W_f**2) + T.sum(self.U_f**2)+\
T.sum(self.W_c**2) + T.sum(self.U_c**2)+\
T.sum(self.W_o**2) + T.sum(self.U_o**2)+\
T.sum(self.Wb_i**2) + T.sum(self.Ub_i**2)+\
T.sum(self.Wb_f**2) + T.sum(self.Ub_f**2)+\
T.sum(self.Wb_c**2) + T.sum(self.Ub_c**2)+\
T.sum(self.Wb_o**2) + T.sum(self.Ub_o**2)
def set_previous(self,layer):
self.previous = layer
self.input=self.get_input()
self.x_mask=self.previous.x_mask
def _fstep(self, x_t, h_tm1, c_tm1):
i_t = T.nnet.sigmoid(T.dot(x_t, self.W_i) + self.b_i + T.dot(h_tm1, self.U_i))
f_t = T.nnet.sigmoid(T.dot(x_t, self.W_f) + self.b_f + T.dot(h_tm1, self.U_f))
c_t = f_t * c_tm1 + i_t * T.tanh(T.dot(x_t, self.W_c) + self.b_c + T.dot(h_tm1, self.U_c))
o_t = T.nnet.sigmoid( T.dot(x_t, self.W_o) + self.b_o + T.dot(h_tm1, self.U_o))
h_t = o_t * T.tanh(c_t)
return h_t, c_t
def _bstep(self, x_t, h_tm1, c_tm1):
i_t = T.nnet.sigmoid(T.dot(x_t, self.Wb_i) + self.bb_i + T.dot(h_tm1, self.Ub_i))
f_t = T.nnet.sigmoid(T.dot(x_t, self.Wb_f) + self.bb_f + T.dot(h_tm1, self.Ub_f))
c_t = f_t * c_tm1 + i_t * T.tanh(T.dot(x_t, self.Wb_c) + self.bb_c + T.dot(h_tm1, self.Ub_c))
o_t = T.nnet.sigmoid( T.dot(x_t, self.Wb_o) + self.bb_o + T.dot(h_tm1, self.Ub_o))
h_t = o_t * T.tanh(c_t)
return h_t, c_t
def set_input(self,x):
self.input=x
def set_mask(self,x_mask):
self.x_mask=x_mask
def get_input(self):
if hasattr(self, 'previous'):
return self.previous.get_output()
else:
return self.input
def get_forward_output(self):
X=self.get_input()
[h,c], _ = theano.scan(self._fstep,
sequences = X,
outputs_info = [alloc_zeros_matrix(self.input.shape[1],
self.n_hidden),
alloc_zeros_matrix(self.input.shape[1],
self.n_hidden)])
return h
def get_backward_output(self):
X=self.get_input()
[h,c], _ = theano.scan(self._bstep,
sequences = X,
outputs_info = [alloc_zeros_matrix(self.input.shape[1],
self.n_hidden),
alloc_zeros_matrix(self.input.shape[1],
self.n_hidden)],
go_backwards = True)
return h
def get_output(self):
forward = self.get_forward_output()
backward = self.get_backward_output()
if self.output_mode is 'sum':
return forward + backward
elif self.output_mode is 'concat':
return T.concatenate([forward, backward], axis=2)
else:
raise Exception('output mode is not sum or concat')
class BiDirectionGRU(object):
def __init__(self,n_in,n_hidden,output_mode='concat'):
self.n_in=int(n_in)
n_hidden=int(n_hidden/2)
self.n_hidden=int(n_hidden)
self.output_mode = output_mode
self.input= T.tensor3()
self.x_mask=T.matrix()
# forward weights
self.W_z = glorot_uniform((n_in,n_hidden))
self.U_z = glorot_uniform((n_hidden,n_hidden))
self.b_z = zero((n_hidden,))
self.W_r = glorot_uniform((n_in,n_hidden))
self.U_r = glorot_uniform((n_hidden,n_hidden))
self.b_r = zero((n_hidden,))
self.W_h = glorot_uniform((n_in,n_hidden))
self.U_h = glorot_uniform((n_hidden,n_hidden))
self.b_h = zero((n_hidden,))
# backward weights
self.Wb_z = glorot_uniform((n_in,n_hidden))
self.Ub_z = glorot_uniform((n_hidden,n_hidden))
self.bb_z = zero((n_hidden,))
self.Wb_r = glorot_uniform((n_in,n_hidden))
self.Ub_r = glorot_uniform((n_hidden,n_hidden))
self.bb_r = zero((n_hidden,))
self.Wb_h = glorot_uniform((n_in,n_hidden))
self.Ub_h = glorot_uniform((n_hidden,n_hidden))
self.bb_h = zero((n_hidden,))
self.params = [
self.W_z, self.U_z, self.b_z,
self.W_r, self.U_r, self.b_r,
self.W_h, self.U_h, self.b_h,
self.Wb_z, self.Ub_z, self.bb_z,
self.Wb_r, self.Ub_r, self.bb_r,
self.Wb_h, self.Ub_h, self.bb_h
]
self.L1 = T.sum(abs(self.W_z))+T.sum(abs(self.U_z))+\
T.sum(abs(self.W_r))+T.sum(abs(self.U_r))+\
T.sum(abs(self.W_h))+T.sum(abs(self.U_h))+\
T.sum(abs(self.Wb_z))+T.sum(abs(self.Ub_z))+\
T.sum(abs(self.Wb_r))+T.sum(abs(self.Ub_r))+\
T.sum(abs(self.Wb_h))+T.sum(abs(self.Ub_h))
self.L2_sqr = T.sum(self.W_z**2) + T.sum(self.U_z**2)+\
T.sum(self.W_r**2) + T.sum(self.U_r**2)+\
T.sum(self.W_h**2) + T.sum(self.U_h**2)+\
T.sum(self.Wb_z**2) + T.sum(self.Ub_z**2)+\
T.sum(self.Wb_r**2) + T.sum(self.Ub_r**2)+\
T.sum(self.Wb_h**2) + T.sum(self.Ub_h**2)
def set_previous(self,layer):
self.previous = layer
self.input=self.get_input()
self.x_mask=self.previous.x_mask
def _fstep(self,x_t,x_m, h_tm1):
z = T.nnet.sigmoid(T.dot(x_t, self.W_z) + self.b_z + T.dot(h_tm1, self.U_z))
r = T.nnet.sigmoid(T.dot(x_t, self.W_r) + self.b_r + T.dot(h_tm1, self.U_r))
hh_t = T.tanh(T.dot(x_t, self.W_h) + self.b_h + T.dot(r * h_tm1, self.U_h))
h_t = z * h_tm1 + (1 - z) * hh_t
h_t=x_m[:,None] * h_t + (1. - x_m)[:,None] * h_tm1
return h_t
def _bstep(self,x_t,x_m, h_tm1):
z = T.nnet.sigmoid(T.dot(x_t, self.Wb_z) + self.bb_z + T.dot(h_tm1, self.Ub_z))
r = T.nnet.sigmoid(T.dot(x_t, self.Wb_r) + self.bb_r + T.dot(h_tm1, self.Ub_r))
hh_t = T.tanh(T.dot(x_t, self.Wb_h) + self.bb_h + T.dot(r * h_tm1, self.Ub_h))
h_t = z * h_tm1 + (1 - z) * hh_t
h_t=x_m[:,None] * h_t + (1. - x_m)[:,None] * h_tm1
return h_t
def set_input(self,x):
self.input=x
def set_mask(self,x_mask):
self.x_mask=x_mask
def get_input(self):
if hasattr(self, 'previous'):
return self.previous.get_output()
else:
return self.input
def get_forward_output(self):
X=self.get_input()
mask_x=self.x_mask
h, _ = theano.scan(self._fstep,
sequences = [X,mask_x],
outputs_info = alloc_zeros_matrix(self.input.shape[1],
self.n_hidden))
return h
def get_backward_output(self):
X=self.get_input()
mask_x=self.x_mask
h, _ = theano.scan(self._bstep,
sequences = [X,mask_x],
outputs_info = alloc_zeros_matrix(self.input.shape[1],
self.n_hidden),
go_backwards = True)
return h
def get_output(self):
forward = self.get_forward_output()
backward = self.get_backward_output()
if self.output_mode is 'sum':
return forward + backward
elif self.output_mode is 'concat':
return T.concatenate([forward, backward], axis=2)
else:
raise Exception('output mode is not sum or concat')
class decoder(object):
def __init__(self,n_in,n_hidden,n_out):
self.n_in=int(n_in)
self.n_hidden=int(n_hidden)
self.n_out=int(n_out)
self.input= T.tensor3()
self.output= T.tensor3()
self.x_mask=T.matrix()
#self.y_mask=T.matrix()
self.W_z = glorot_normal((n_out,n_hidden))
self.U_z = glorot_normal((n_hidden,n_hidden))
self.b_z = zero((n_hidden,))
self.W_r = glorot_normal((n_out,n_hidden))
self.U_r = glorot_normal((n_hidden,n_hidden))
self.b_r = zero((n_hidden,))
self.W_h = glorot_normal((n_out,n_hidden))
self.U_h = glorot_normal((n_hidden,n_hidden))
self.b_h = zero((n_hidden,))
self.U_att= glorot_normal((self.n_in,1))
self.b_att= zero((1,))
self.W_yc=glorot_normal((self.n_out,))
self.W_cy = glorot_normal((self.n_in,self.n_hidden))
self.W_cs= glorot_normal((self.n_in,self.n_hidden))
self.W_ha = glorot_normal((self.n_in,self.n_in))
self.W_sa= glorot_normal((self.n_hidden,self.n_in))
self.W_cl= glorot_normal((self.n_in,self.n_out))
self.W_yl= glorot_normal((self.n_out,self.n_out))
self.W_hl= glorot_normal((self.n_hidden,self.n_out))
self.params=[self.W_z,self.U_z,self.b_z,self.W_r,self.U_r,self.b_r,
self.W_h,self.U_h,self.b_h,self.W_cy,self.W_cs,self.W_ha,self.W_sa
,self.W_cl,self.W_yl,self.W_hl,self.U_att,self.b_att]
self.L1 = T.sum(abs(self.W_z))+T.sum(abs(self.U_z))+\
T.sum(abs(self.W_r))+T.sum(abs(self.U_r))+\
T.sum(abs(self.W_h))+T.sum(abs(self.U_h))+\
T.sum(abs(self.W_cy))+T.sum(abs(self.W_cs))+\
T.sum(abs(self.W_ha))+T.sum(abs(self.W_sa))+\
T.sum(abs(self.W_cl))+T.sum(abs(self.W_yl))+\
T.sum(abs(self.W_hl))+T.sum(abs(self.U_att))
self.L2_sqr = T.sum(self.W_z**2) + T.sum(self.U_z**2)+\
T.sum(self.W_r**2) + T.sum(self.U_r**2)+\
T.sum(self.W_h**2) + T.sum(self.U_h**2)+\
T.sum(self.W_cy**2) + T.sum(self.W_cs**2)+\
T.sum(self.W_ha**2) + T.sum(self.W_sa**2)+\
T.sum(self.W_cl**2) + T.sum(self.W_yl**2)+\
T.sum(self.W_hl**2) + T.sum(self.U_att**2)
def _step(self,y_tm1,y_m,s_tm1,h,x_m):
# attention
pctx__=T.dot(h,self.W_ha)+T.dot(s_tm1,self.W_sa)[None,:,:]
pctx__=T.tanh(pctx__)
e=T.dot(pctx__,self.U_att)+self.b_att
e=T.exp(e.reshape((e.shape[0],e.shape[1])))
e=e/e.sum(0, keepdims=True)
e=e*x_m
c=(h*e[:,:,None]).sum(0)
z = T.nnet.sigmoid(T.dot(y_tm1, self.W_z) + self.b_z + T.dot(s_tm1, self.U_z)+T.dot(c,self.W_cs))
r = T.nnet.sigmoid(T.dot(y_tm1, self.W_r) + self.b_r + T.dot(s_tm1, self.U_r)+T.dot(c,self.W_cs))
hh_t = T.tanh(T.dot(y_tm1, self.W_h) + self.b_h + T.dot(r * s_tm1, self.U_h)+T.dot(c,self.W_cy))
s_t = z * s_tm1 + (1 - z) * hh_t
s_t = (1. - y_m)[:,None] * s_tm1 + y_m[:,None] * s_t
logit=T.tanh(T.dot(s_t, self.W_hl)+T.dot(y_tm1, self.W_yl)+T.dot(c, self.W_cl))
return T.cast(s_t,dtype =theano.config.floatX),T.cast(logit,dtype =theano.config.floatX)
def set_previous(self,layer):
self.previous = layer
self.input=self.get_input()
self.x_mask=self.previous.x_mask
def set_input(self,x):
self.input=x
def get_input(self):
if hasattr(self, 'previous'):
return self.previous.get_output()
else:
return self.input
def get_sample(self,y,s_tm1):
c=self.get_input()
X_mask=self.previous.x_mask
y_mask=T.alloc(1.,y.shape[0])
h,logit=self._step(y,y_mask,s_tm1,c,X_mask)
return h,logit
def get_output(self,y,y_mask,init_state):
X=self.get_input()
X_mask=self.previous.x_mask
### shift 1 sequence backward
y_shifted=T.zeros_like(y)
y_shifted=T.set_subtensor(y_shifted[1:],y[:-1])
y=y_shifted
### shift 1 sequence backward
y_shifted=T.zeros_like(y_mask)
y_shifted=T.set_subtensor(y_shifted[1:],y_mask[:-1])
y_mask=y_shifted
[h,logit], _ = theano.scan(self._step,
sequences = [y,y_mask],
outputs_info = [init_state,
None],
non_sequences=[X,X_mask])
return logit