def visActProbs(self):
GaussianRBM.visActProbs(
self) #now self.negVis holds the net input to the visible units
#self.negVis has shape (self.numVis, self.mbsz)
self.negVis.reshape((self.k, self.mbsz * self.numVis / self.k))
singleSoftmax(self.negVis, self.tempRow)
self.negVis.reshape((self.numVis, self.mbsz))
def fprop(self):
cm.dot( self.W.T, self.vis, target = self.hActProbs)
self.hActProbs.add_col_vec(self.hidBias)
self.U.copy_to_host()
labHid = self.U.numpy_array
Utrans = cm.CUDAMatrix(labHid.transpose())
self.tempLabelMB.assign_scalar(0.0)
self.tempLabelMB.add_col_vec(self.labBias)
for c in range(self.numClasses): #183 of these
self.discacchids[c].assign_scalar(0.0) #numHid by mbsz
self.discacchids[c].add(self.hActProbs)
self.discacchids[c].add_col_vec(Utrans.slice(c,c+1))
self.tempHidMB.assign(self.discacchids[c])
cm.exp(self.tempHidMB)
self.tempHidMB.add_scalar(1.0)
cm.log(self.tempHidMB)
self.tempHidMB.sum(0, self.tempRow)
self.tempLabelMB.set_row_slice(c,c+1, self.tempRow)
#singleSoftmax(self.tempLabelMB, self.tempLabelCol, self.tempRow)
singleSoftmax(self.tempLabelMB, self.tempRow)
self.tempLabelMB.copy_to_host()
self.acts = self.tempLabelMB.numpy_array.copy() #self.tempLabelMB.numpy_array #we may not need the copy here
return self.acts
def fprop(self):
cm.dot(self.W.T, self.vis, target=self.hActProbs)
self.hActProbs.add_col_vec(self.hidBias)
self.U.copy_to_host()
labHid = self.U.numpy_array
Utrans = cm.CUDAMatrix(labHid.transpose())
self.tempLabelMB.assign_scalar(0.0)
self.tempLabelMB.add_col_vec(self.labBias)
for c in range(self.numClasses): #183 of these
self.discacchids[c].assign_scalar(0.0) #numHid by mbsz
self.discacchids[c].add(self.hActProbs)
self.discacchids[c].add_col_vec(Utrans.slice(c, c + 1))
self.tempHidMB.assign(self.discacchids[c])
cm.exp(self.tempHidMB)
self.tempHidMB.add_scalar(1.0)
cm.log(self.tempHidMB)
self.tempHidMB.sum(0, self.tempRow)
self.tempLabelMB.set_row_slice(c, c + 1, self.tempRow)
#singleSoftmax(self.tempLabelMB, self.tempLabelCol, self.tempRow)
singleSoftmax(self.tempLabelMB, self.tempRow)
self.tempLabelMB.copy_to_host()
self.acts = self.tempLabelMB.numpy_array.copy(
) #self.tempLabelMB.numpy_array #we may not need the copy here
return self.acts
def visActProbs(self, reconstructLabels):
#since we always have labels clamped, we have no need for a negLabels variable
cm.dot( self.W, self.hActs, target = self.negVis)
self.negVis.add_col_vec(self.visBias)
self.negVis.apply_sigmoid()
if reconstructLabels:
cm.dot( self.U, self.hActs, target = self.negLabels)
self.negLabels.add_col_vec(self.labBias)
#singleSoftmax(self.negLabels, self.tempLabelCol, self.tempRow)
singleSoftmax(self.negLabels, self.tempRow)
def visActProbs(self, reconstructLabels):
#since we always have labels clamped, we have no need for a negLabels variable
cm.dot(self.W, self.hActs, target=self.negVis)
self.negVis.add_col_vec(self.visBias)
self.negVis.apply_sigmoid()
if reconstructLabels:
cm.dot(self.U, self.hActs, target=self.negLabels)
self.negLabels.add_col_vec(self.labBias)
#singleSoftmax(self.negLabels, self.tempLabelCol, self.tempRow)
singleSoftmax(self.negLabels, self.tempRow)
def visActProbs(self):
GaussianRBM.visActProbs(self) #now self.negVis holds the net input to the visible units
#self.negVis has shape (self.numVis, self.mbsz)
self.negVis.reshape((self.k, self.mbsz*self.numVis/self.k))
singleSoftmax(self.negVis, self.tempRow)
self.negVis.reshape((self.numVis, self.mbsz))