class CommentNetwork:
def __init__(self, n, saveFile, opt, lossFunc, mod=None, use_gpu=False, numDirectIterations=1, defaultOutputTruncation=10):
self.n=n
self.saveFile=saveFile
self.use_gpu=use_gpu
self.lossFunc=lossFunc
self.numDirectIterations=numDirectIterations
self.defaultOutputTruncation=defaultOutputTruncation
if mod==None:
#construct network model
self.model= FunctionSet(
x_to_h = F.Linear(7, n),
h_to_h = F.Linear(n, n),
h_to_y = F.Linear(n, 7)
)
else:
self.model=mod
if self.use_gpu:
self.model.to_gpu()
else:
self.model.to_cpu()
self.optimizer = opt
self.optimizer.setup(self.model)
#constants
self.null_byte=np.array([[0]*7], dtype=np.float32)
if self.use_gpu:
self.null_byte=cuda.to_gpu(self.null_byte)
self.null_byte=Variable(self.null_byte)
def forward_one_step(self, h, x, computeOutput=True):
h=F.sigmoid(self.model.x_to_h(x) + self.model.h_to_h(h))
if computeOutput:
y=F.sigmoid(self.model.h_to_y(h))
return h, y
else:
return h
def forward(self, input_string, output_string, truncateSize=None, volatile=False):
if truncateSize==None:
truncateSize=self.defaultOutputTruncation
#feed variable in, ignoring output until model has whole input string
h=np.zeros((1,self.n),dtype=np.float32)
if self.use_gpu:
h=cuda.to_gpu(h)
h=Variable(h, volatile=volatile)
for c in input_string:
bits=np.array([[bool(ord(c)&(2**i)) for i in range(7)]], dtype=np.float32)
if self.use_gpu:
bits=cuda.to_gpu(bits)
bits=Variable(bits, volatile=volatile) #8 bits, never all 0 for ascii
h=self.forward_one_step(h, bits, computeOutput=False)
#prep for training
self.optimizer.zero_grads()
y='' #output string
nullEnd=False
loss=0
def yc_translation(yc, y, nullEnd, truncateSize):
yc=sum([bool(round(bit))*(2**i_bit) for i_bit, bit in enumerate(cuda.to_cpu(yc.data[0]))]) #translate to int
if not yc: #null byte signifies end of sequence
nullEnd=True
if not nullEnd:
y+=chr(yc) #translate to character
truncateSize-=1
return y, nullEnd, truncateSize
#Read output by prompting with null bytes.; train with training output
for c in output_string:
bits=np.array([[bool(ord(c)&(2**i)) for i in range(7)]], dtype=np.float32)
if self.use_gpu:
bits=cuda.to_gpu(bits)
bits=Variable(bits, volatile=volatile)
h, yc = self.forward_one_step(h, self.null_byte)
loss+=self.lossFunc(yc, bits)
y, nullEnd, truncateSize = yc_translation(yc, y, nullEnd, truncateSize)
#reinforce null byte as end of sequence
h, yc = self.forward_one_step(h, self.null_byte)
loss+=self.lossFunc(yc, self.null_byte)
y, nullEnd, truncateSize = yc_translation(yc, y, nullEnd, truncateSize)
#continue reading out as long as network does not terminate and we have not hit TruncateSize
while not nullEnd and truncateSize>0:
h, yc = self.forward_one_step(h, self.null_byte)
y, nullEnd, truncateSize = yc_translation(yc, y, nullEnd, truncateSize)
#Train
loss.backward()
self.optimizer.update()
return y, nullEnd #nullEnd true if netowrk terminated output sequence. False if output sequence truncated.
def trainTree(self, tree, maxCommentLength=float('inf')): #DFS training
if 'children' in tree:
allPass=True
for child in tree['children']:
self.trainTree(child, maxCommentLength)
prompt=tree['body']
trainResponse=child['body']
if prompt!='[deleted]' and trainResponse!='[deleted]' and prompt and trainResponse and len(prompt)<=maxCommentLength and len(trainResponse)<=maxCommentLength:
for i in range(self.numDirectIterations):
givenResponse, nullEnd=self.forward(prompt, trainResponse)
print '<#'+str(i)+'--prompt--'+str(len(prompt))+'chars-->\n', repr(prompt), '\n<--trainResponse--'+str(len(trainResponse))+'chars-->\n', repr(trainResponse), '\n<--givenResponse--'+str(len(givenResponse))+'chars'+('' if nullEnd else ', truncated')+'-->\n', repr(givenResponse)+'\n'
if givenResponse==trainResponse:
break
else:
allPass=False
return allPass
# loop over lines in a file identifying if they contain a tree after parsing the json
def trainFile(self, openFile, maxCommentLength=float('inf')):
allPass=True
for i, treeText in enumerate(openFile):
#throw away whitespace
if treeText.strip():
#print fileName, treeText
tree=json.loads(treeText.strip())
#it's a tree, let's train
if 'children' in tree:
print 'training #'+str(i)+' '+openFile.name
allPass&=self.trainTree(tree, maxCommentLength)
return allPass
def saveModel(self):
print 'Stopped computation, saving model. Please wait...'
f=open(self.saveFile,'w')
pickle.dump(self.model, f)
f.close()
print 'Saved model'
def sig_exit(self, _1, _2):
self.saveModel()
exit()
