/
rnn_cell.py
341 lines (280 loc) · 12.4 KB
/
rnn_cell.py
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from operator import iadd
# scipy.special for the sigmoid function expit()
import scipy.special
# library for plotting arrays
import matplotlib.pyplot
import numpy as np
import collections
"""from rnn import zero_state_initializer
from rnn import LSTMStateTuple
from rnn import Cell"""
from org.mk.training.dl.rnn import zero_state_initializer
from org.mk.training.dl.rnn import LSTMStateTuple
from org.mk.training.dl.rnn import Cell
from org.mk.training.dl.common import WeightsInitializer
from org.mk.training.dl import init_ops
from org.mk.training.dl.common import checkdatadim
from org.mk.training.dl.common import checktupledim
from org.mk.training.dl.common import checkarrayshape
from org.mk.training.dl.common import change_internal_state_type
from org.mk.training.dl.common import retrieve_name
from org.mk.training.dl.nn import TrainableVariable
class LSTMCell(Cell):
# initialise the Recurrent Neural Network
def __init__(self, hidden_size, forget_bias=1, debug=False):
super().__init__(hidden_size,debug)
self.forget_bias = forget_bias
self.seqsize=0
# first column is for biases
self.init_function=None
if(WeightsInitializer.initializer is None):
#WeightsInitializer.initializer=init_ops.RandomUniform()
self.init_function=init_ops.RandomUniform()
else:
self.init_function=WeightsInitializer.initializer
self.shape=None
#self.WLSTM=None
self.lstmname=None
self.wf = None
self.wi = None
self.wc = None
self.wo = None
self.bf = None
self.bi = None
self.bc = None
self.bo = None
self.dwi = None
self.dwc = None
self.dwo = None
self.dwf = None
self.dbi = None
self.dbc = None
self.dbo = None
self.dbf = None
self.dht = None
# reverse
self.dh_next = None
self.dC_next = None
self.dx = None
self.Xfacing=True
self.gen_X_Ds=False
def _setinitparams(self,batch, seq, input_size,Xfacing=True,gen_X_Ds=False):
self.input_size=input_size
self.batch_size=batch
self.seqmax=seq
self.shape=(4 * self.hidden_size, self.input_size + self.hidden_size + 1)
if self.lstmname is None:
self.lstmname=str(retrieve_name(self))
WLSTM=TrainableVariable.getInstance(self.lstmname)
if WLSTM is None:
WLSTM=TrainableVariable.getInstance(self.lstmname,self.init_function(self.shape)).value
else:
WLSTM=TrainableVariable.getInstance(self.lstmname).value
self.wf = WLSTM[0:self.hidden_size, 1:]
self.wi = WLSTM[self.hidden_size:self.hidden_size * 2, 1:]
self.wc = WLSTM[self.hidden_size * 2:self.hidden_size * 3, 1:]
self.wo = WLSTM[self.hidden_size * 3:self.hidden_size * 4, 1:]
self.bf = WLSTM[0:self.hidden_size, 0].reshape(self.hidden_size, 1)
self.bi = WLSTM[self.hidden_size:self.hidden_size * 2, 0].reshape(self.hidden_size, 1)
self.bc = WLSTM[self.hidden_size * 2:self.hidden_size * 3, 0].reshape(self.hidden_size, 1)
self.bo = WLSTM[self.hidden_size * 3:self.hidden_size * 4, 0].reshape(self.hidden_size, 1)
"""
self.WLSTM=self.init_function(self.shape)
self.wf = self.WLSTM[0:self.hidden_size, 1:]
self.wi = self.WLSTM[self.hidden_size:self.hidden_size * 2, 1:]
self.wc = self.WLSTM[self.hidden_size * 2:self.hidden_size * 3, 1:]
self.wo = self.WLSTM[self.hidden_size * 3:self.hidden_size * 4, 1:]
self.bf = self.WLSTM[0:self.hidden_size, 0].reshape(self.hidden_size, 1)
self.bi = self.WLSTM[self.hidden_size:self.hidden_size * 2, 0].reshape(self.hidden_size, 1)
self.bc = self.WLSTM[self.hidden_size * 2:self.hidden_size * 3, 0].reshape(self.hidden_size, 1)
self.bo = self.WLSTM[self.hidden_size * 3:self.hidden_size * 4, 0].reshape(self.hidden_size, 1)
"""
self.dwi = np.zeros_like(self.wi)
self.dwc = np.zeros_like(self.wc)
self.dwo = np.zeros_like(self.wo)
self.dwf = np.zeros_like(self.wf)
self.dbi = np.zeros_like(self.bi)
self.dbc = np.zeros_like(self.bc)
self.dbo = np.zeros_like(self.bo)
self.dbf = np.zeros_like(self.bf)
self.dht = np.zeros([self.hidden_size,self.batch_size])
# reverse
self.dh_next = np.zeros_like(self.dht) # dh from the next character
self.dC_next = np.zeros_like(self.dht)
self.dx = np.zeros(self.input_size+self.hidden_size).T
self.setzerostate()
self.ct, self.cprojt, self.coldt, self.ft, self.it, self.ot, self.zt, self.xt= {}, {}, {}, {}, {}, {}, {}, {}
self.dxt=np.zeros((self.seqmax*self.batch_size,self.input_size))
self.Xfacing=Xfacing
self.gen_X_Ds=gen_X_Ds
self.init=True
if self.debug:
print(
"_setinitparams start*******************************************************************************************************")
print("cell:", self)
print("self.lstmname:", self.lstmname)
print("self.weights shape:", WLSTM.shape)
print("self.weights:", WLSTM)
print("self.hidden_size:", self.hidden_size)
print("self.input_size:", self.input_size)
print("self.init:", self.init)
print("self.c:", self.c)
print("self.h:", self.h)
print("self.dh_next:", self.dh_next)
print("self.Xfacing:", self.Xfacing)
print("self.gen_X_Ds:", self.gen_X_Ds)
print(
"_setinitparams End*********************************************************************************************************")
pass
def setstate(self,state):
(c, h) = state
crctc=checkarrayshape(c,(self.hidden_size,self.batch_size))
if(crctc is None):
self.setc(c)
else:
self.setc(crctc)
crcth=checkarrayshape(h,(self.hidden_size,self.batch_size))
if(crcth is None):
self.seth(h)
else:
self.seth(crcth)
def setzerostate(self):
c=zero_state_initializer(self.hidden_size, self.batch_size)
h=zero_state_initializer(self.hidden_size, self.batch_size)
self.setstate((c,h))
def setc(self, c):
self.c=np.copy(c)
def setreverseDs(self,dh_next,dc_next):
checkarrayshape(dh_next,(self.hidden_size,self.batch_size))
checkarrayshape(dc_next,(self.hidden_size,self.batch_size))
self.dh_next=dh_next
self.dC_next=dc_next
def __call__(self, X, state=None):
"""
forward pass of LSTMCell
args:
x-input-size,batch_size
state=hidden-size,batch_size
"""
#sanity checks
checkdatadim(X,2)
if(state is not None):
if (isinstance(state, LSTMStateTuple)):
state=change_internal_state_type(state)
checktupledim(state,2)
#for single call
if not self.init:
input_size=X.shape[0]
self._setinitparams(1, 1, input_size)
if state is not None:
self.setstate(state)
self.ct[-1] = self.c
if(self.Xfacing and self.gen_X_Ds):
self.xt[self.seqsize]=X
z = np.concatenate((X,self.h), 0)
WLSTM=TrainableVariable.getInstance(self.lstmname).value
fico = np.dot(WLSTM[:, 1:], z) + WLSTM[:, 0].reshape(self.hidden_size * 4, 1)
f = self.sigmoid_array(fico[0:self.hidden_size, :] + self.forget_bias)
i = self.sigmoid_array(fico[self.hidden_size * 1:self.hidden_size * 2, :])
cproj = self.tanh_array(fico[self.hidden_size * 2:self.hidden_size * 3, :])
o = self.sigmoid_array(fico[self.hidden_size * 3:self.hidden_size * 4, :])
cnew = (self.c * f) + (cproj * i)
hnew = o * self.tanh_array(cnew)
self.c=cnew
self.h=hnew
state = cnew, hnew
self.coldt[self.seqsize] = self.ct[self.seqsize - 1]
self.ct[self.seqsize] = cnew
self.ht[self.seqsize] = hnew
self.ft[self.seqsize] = f
self.it[self.seqsize] = i
self.ot[self.seqsize] = o
self.cprojt[self.seqsize] = cproj
self.zt[self.seqsize] = z
self.seqsize+=1
tup=LSTMStateTuple(cnew.T,hnew.T)
return hnew.T,tup
def zero_state(self, batch_size, dtype=float):
czero=zero_state_initializer(self.hidden_size,batch_size)
hzero=zero_state_initializer(self.hidden_size,batch_size)
return LSTMStateTuple(czero.T,hzero.T)
def sigmoid_array(self, array):
return 1 / (1 + np.exp(-array))
def tanh_array(self, array):
return np.tanh(array)
def clearStatePerSequence(self,seqmax):
"""
Cleans the state per sequence.
"""
self.ct, self.cprojt, self.coldt, self.ft, self.it, self.ot, self.zt, self.xt = {}, {}, {}, {}, {}, {}, {}, {}
self.seqmax=seqmax
self.seqsize=0
def clearDs(self):
self.dwi = np.zeros_like(self.wi)
self.dwc = np.zeros_like(self.wc)
self.dwo = np.zeros_like(self.wo)
self.dwf = np.zeros_like(self.wf)
self.dbi = np.zeros_like(self.bi)
self.dbc = np.zeros_like(self.bc)
self.dbo = np.zeros_like(self.bo)
self.dbf = np.zeros_like(self.bf)
self.dht = np.zeros([self.hidden_size,self.batch_size])
# reverse
self.dh_next = np.zeros_like(self.dht) # dh from the next character
self.dC_next = np.zeros_like(self.dht)
self.dx = np.zeros(self.input_size+self.hidden_size).T
self.c = zero_state_initializer(self.hidden_size, self.batch_size)
self.h = zero_state_initializer(self.hidden_size, self.batch_size)
self.dxt=np.zeros((self.seqmax*self.batch_size,self.input_size))
def compute_gradients(self,dhtf,dh_nextmlco,t):
#print("dh_next:",self.dh_next," dC_next",self.dC_next)
z = self.zt[t].T
self.dht=dhtf
self.dht = self.dht + self.dh_next
dot = np.multiply(self.dht, self.tanh_array(self.ct[t]) * self.dsigmoid(self.ot[t]))
dct = np.multiply(self.dht, self.ot[t] * self.dtanh(self.ct[t])) + self.dC_next
dcproj = np.multiply(dct, self.it[t] * (1 - self.cprojt[t] * self.cprojt[t]))
dft = np.multiply(dct, self.coldt[t] * self.dsigmoid(self.ft[t]))
dit = np.multiply(dct, self.cprojt[t] * self.dsigmoid(self.it[t]))
self.dwf += np.dot(dft, z)
self.dbf += dft.sum(1,keepdims=True)
WLSTM=TrainableVariable.getInstance(self.lstmname).value
dxf = np.dot(dft.T, WLSTM[0:self.hidden_size, 1:])
self.dwi += np.dot(dit, z)
self.dbi += dit.sum(1,keepdims=True)
dxi = np.dot(dit.T, WLSTM[self.hidden_size * 1:self.hidden_size * 2, 1:])
self.dwc += np.dot(dcproj, z)
self.dbc += dcproj.sum(1,keepdims=True)
dxc = np.dot(dcproj.T, WLSTM[self.hidden_size * 2:self.hidden_size * 3, 1:])
self.dwo += np.dot(dot, z)
self.dbo += dot.sum(1,keepdims=True)
dxo = np.dot(dot.T, WLSTM[self.hidden_size * 3:self.hidden_size * 4, 1:])
dx = dxf + dxi + dxc + dxo
xcomp=dx[:, :self.input_size]
#print("XCOMP:",self.Xfacing, self.gen_X_Ds)
if(self.Xfacing and self.gen_X_Ds):
for bi in range(self.batch_size):
#print("xcomp[bi,:]:",xcomp[bi,:])
self.dxt[t+(bi*self.seqsize)]=xcomp[bi,:]#.reshape((1,-1))
self.dh_next = dx[:, self.input_size:].T
self.dC_next = np.multiply(dct, self.ft[t])
dh_next_recurr=dx[:, :self.hidden_size].T
return np.copy(dh_next_recurr)
def get_Xgradients(self):
if(self.Xfacing and self.gen_X_Ds):
return self.dxt
else:
return None
def get_gradients(self):
"""
For a single cell return a tuple of 8 tuples of ds and ws
|------------------------------------------------------------------|
|((dwi,wi)(dwc,wc)(dwf,wf)(dwo,wo)(dbi,bi)(dbc,bc)(dbf,bf)(dbo,bo))|
|------------------------------------------------------------------|
"""
return (self.dwi,self.wi),(self.dwc,self.wc),(self.dwf,self.wf),(self.dwo,self.wo),(self.dbi,self.bi),(self.dbc,self.bc),(self.dbf,self.bf),(self.dbo,self.bo)
def dsigmoid(self,f):
return f*(1-f)
def dtanh(self,f):
tanhf=np.tanh(f)
return 1 - tanhf * tanhf