def ff(self, x):
self.check_dimension(x)
n_ins,_ = x.shape
n_layer = len(self.w)
tmp = x
for i in xrange(n_layer):
if i == 0 and sp.isspmatrix(x):
tmp = tmp * self.w[i]
else:
tmp = np.dot(tmp, self.w[i])
tmp += np.tile(self.b[i], [n_ins,1] )
tmp = active( tmp, self.hidden_active)
#print tmp.shape
#print self.lw.shape
output = np.dot( tmp, self.lw )
output += np.tile(self.lb,[n_ins, 1])
#output = active( output, self.output_active )
#output = np.zeros((n_ins,self.num_label))
return output
def ff(self, x):
self.check_dimension(x)
n_ins, _ = x.shape
n_layer = len(self.w)
tmp = x
for i in xrange(n_layer):
if i == 0 and sp.isspmatrix(x):
tmp = tmp * self.w[i]
else:
tmp = np.dot(tmp, self.w[i])
tmp += np.tile(self.b[i], [n_ins, 1])
tmp = active(tmp, self.hidden_active)
#print tmp.shape
#print self.lw.shape
output = np.dot(tmp, self.lw)
output += np.tile(self.lb, [n_ins, 1])
#output = active( output, self.output_active )
#output = np.zeros((n_ins,self.num_label))
return output
def bp(self, x, y, idx):
#import cProfile, pstats, StringIO
#pr = cProfile.Profile()
#pr.enable()
self.check_dimension(x, y)
#-------------------------------------------------------
#ff for train
#-------------------------------------------------------
#compute the instance factor
hidden_output = []
n,d = x.shape
n_layer = len(self.w)
#print "n_layer",n_layer
hidden = []
tmp = x
for i in xrange( n_layer ):
if 0 == i and sp.isspmatrix(x):#type(x) == type(sp.csr_matrix([[0]])):
tmp = tmp * self.w[i]
else:
tmp = np.dot( tmp, self.w[i] )
tmp += np.tile(self.b[i], [n,1] )
tmp = active( tmp, self.hidden_active )
hidden_output.append(tmp)
ins_factor = tmp
xy = idx.nonzero()
mo,no = idx.shape
#print "sparity of label", len(xy[0]) * 1.0 / (mo * no)
if len(xy[0]) * 1.0 / (mo * no) < self.sparse_thr:
row = ins_factor[xy[0],:]
col = self.lw[:,xy[1]]
data = np.einsum('ik,ki->i',row,col)
data += self.lb[xy[1]]
#data = np.zeros(len(xy[0]))
#for k in xrange(len(xy[0])):
# i = xy[0][k]
# j = xy[1][k]
# data[k] = np.dot(ins_factor[i,:], self.lw[:,j]) + self.lb[j]
output = sp.csr_matrix((data,xy), idx.shape)
else:
output = np.zeros(idx.shape)
for k in xrange(len(xy[0])):
i = xy[0][k]
j = xy[1][k]
output[i,j] = np.dot( ins_factor[i:i+1, :], self.lw[:, j:j+1]) \
+ self.lb[j]
output = active( output, self.output_active )
#---------------------------------------------------
#compute the grad
#---------------------------------------------------
grad_type = self.output_active \
+ "_" + self.loss
output_grad = grad( output, y, grad_type )
#print "type(output_grad",type(output_grad),sp.isspmatrix(output_grad)
num_rates, _ = idx.shape
if sp.isspmatrix(output_grad):
#print "enter"
self.grad_lw = sp.csr_matrix(np.transpose(ins_factor)) * output_grad
self.grad_lb = np.asarray(output_grad.sum(0))[0,:]
#print "sp.issmatrix(self.grad_lw)", sp.isspmatrix(self.grad_lw)
else:
self.grad_lw = self.lw - self.lw
self.grad_lb = self.lb - self.lb
xy = idx.nonzero()
for k in xrange(len(xy[0])):
i = xy[0][k]
j = xy[1][k]
self.grad_lw[:,j] += output_grad[i,j] * ins_factor[i,:]
self.grad_lb[j] += output_grad[i,j]
self.grad_lw /= num_rates
self.grad_lb /= num_rates
#self.grad_lw = np.asarray(self.grad_lw.todense())
#self.grad_lb = self.grad_lb.todense()
## compute grad of instance factor
xy = idx.nonzero()
ins_factor_grad = ins_factor - ins_factor
#ins_factor_grad = np.zeros(ins_factor.shape)
if sp.isspmatrix(output_grad):
for i,j,v in zip(xy[0], xy[1], output_grad.data):
ins_factor_grad[i,:] += v * self.lw[:,j]
else:
for k in xrange(len(xy[0])):
i = xy[0][k]
j = xy[1][k]
ins_factor_grad[i,:] += output_grad[i,j] * self.lw[:, j]
# self.grad_lw = np.asarray(self.grad_lw.todense())
#import cProfile, pstats, StringIO
#pr = cProfile.Profile()
#pr.enable()
tmp = ins_factor_grad
for i in xrange( len(self.w) - 1, -1, -1):
tmp = tmp * grad(tmp, grad_type = self.hidden_active )
t1, t2 = x.shape
if 0 == i and sp.isspmatrix(x):
#print "sparity of input", len(x.nonzero()[0]) * 1.0 / (t1 * t2)
if len(x.nonzero()[0]) * 1.0 /(t1 * t2) < self.sparse_thr:
self.grad_w[i] = np.transpose(x) * sp.csr_matrix(tmp) / num_rates
#self.grad_w[i] = np.asarray(self.grad_w[i].todense())
self.grad_w[i] = self.grad_w[i].tocsr()
else:
self.grad_w[i] = np.transpose(x) * tmp / num_rates
#self.grad_w[i] = self.grad_w[i].tocsr()
elif 0 == i:
self.grad_w[i] = np.dot( np.transpose(x), tmp ) / num_rates
else:
self.grad_w[i] = np.dot( np.transpose(hidden_output[i-1]), tmp )\
/ num_rates
self.grad_b[i] = np.sum(tmp, 0) / num_rates
if 0 == i: continue
tmp = np.dot( tmp, np.transpose(self.w[i]) )
def bp(self, x, y, idx):
#import cProfile, pstats, StringIO
#pr = cProfile.Profile()
#pr.enable()
self.check_dimension(x, y)
#-------------------------------------------------------
#ff for train
#-------------------------------------------------------
#compute the instance factor
hidden_output = []
n, d = x.shape
n_layer = len(self.w)
#print "n_layer",n_layer
hidden = []
tmp = x
for i in xrange(n_layer):
if 0 == i and sp.isspmatrix(
x): #type(x) == type(sp.csr_matrix([[0]])):
tmp = tmp * self.w[i]
else:
tmp = np.dot(tmp, self.w[i])
tmp += np.tile(self.b[i], [n, 1])
tmp = active(tmp, self.hidden_active)
hidden_output.append(tmp)
ins_factor = tmp
if None == idx:
output = np.dot(ins_factor, self.lw)
else:
xy = idx.nonzero()
mo, no = idx.shape
#print "sparity of label", len(xy[0]) * 1.0 / (mo * no)
if len(xy[0]) * 1.0 / (mo * no) < self.sparse_thr:
row = ins_factor[xy[0], :]
col = self.lw[:, xy[1]]
data = np.einsum('ik,ki->i', row, col)
data += self.lb[xy[1]]
#data = np.zeros(len(xy[0]))
#for k in xrange(len(xy[0])):
# i = xy[0][k]
# j = xy[1][k]
# data[k] = np.dot(ins_factor[i,:], self.lw[:,j]) + self.lb[j]
output = sp.csr_matrix((data, xy), idx.shape)
else:
output = np.zeros(idx.shape)
for k in xrange(len(xy[0])):
i = xy[0][k]
j = xy[1][k]
output[i,j] = np.dot( ins_factor[i:i+1, :], self.lw[:, j:j+1]) \
+ self.lb[j]
output = active(output, self.output_active, idx)
#---------------------------------------------------
#compute the grad
#---------------------------------------------------
#grad_type = self.output_active \
# + "_" + self.loss
grad_type = actlo2grad(self.output_active, self.loss)
output_grad = grad(output, y, grad_type)
#print "type(output_grad",type(output_grad),sp.isspmatrix(output_grad)
num_rates, _ = y.shape
if sp.isspmatrix(output_grad):
#print "enter"
self.grad_lw = sp.csr_matrix(
np.transpose(ins_factor)) * output_grad
self.grad_lb = np.asarray(output_grad.sum(0))[0, :]
#print "sp.issmatrix(self.grad_lw)", sp.isspmatrix(self.grad_lw)
else:
if None == idx:
self.grad_lw = np.asarray(
np.dot(np.transpose(ins_factor), output_grad))
self.grad_lb = np.asarray(output_grad.sum(0))[0, :]
else:
self.grad_lw = self.lw - self.lw
self.grad_lb = self.lb - self.lb
xy = idx.nonzero()
for k in xrange(len(xy[0])):
i = xy[0][k]
j = xy[1][k]
self.grad_lw[:, j] += output_grad[i, j] * ins_factor[i, :]
self.grad_lb[j] += output_grad[i, j]
self.grad_lw /= num_rates
self.grad_lb /= num_rates
#self.grad_lw = np.asarray(self.grad_lw.todense())
#self.grad_lb = self.grad_lb.todense()
## compute grad of instance factor
if sp.isspmatrix(output_grad):
ins_factor_grad = ins_factor - ins_factor
xy = output_grad.nonzero()
for i, j, v in zip(xy[0], xy[1], output_grad.data):
ins_factor_grad[i, :] += v * self.lw[:, j]
else:
if None == idx:
ins_factor_grad = np.asarray(
np.dot(output_grad, np.transpose(self.lw)))
else:
ins_factor_grad = ins_factor - ins_factor
xy = idx.nonzero()
for k in xrange(len(xy[0])):
i = xy[0][k]
j = xy[1][k]
ins_factor_grad[i, :] += output_grad[i, j] * self.lw[:, j]
# self.grad_lw = np.asarray(self.grad_lw.todense())
#import cProfile, pstats, StringIO
#pr = cProfile.Profile()
#pr.enable()
tmp = ins_factor_grad
for i in xrange(len(self.w) - 1, -1, -1):
tmp = tmp * grad(tmp, grad_type=self.hidden_active)
t1, t2 = x.shape
if 0 == i and sp.isspmatrix(x):
#print "sparity of input", len(x.nonzero()[0]) * 1.0 / (t1 * t2)
if len(x.nonzero()[0]) * 1.0 / (t1 * t2) < self.sparse_thr:
self.grad_w[i] = np.transpose(x) * sp.csr_matrix(
tmp) / num_rates
#self.grad_w[i] = np.asarray(self.grad_w[i].todense())
self.grad_w[i] = self.grad_w[i].tocsr()
else:
self.grad_w[i] = np.transpose(x) * tmp / num_rates
#self.grad_w[i] = self.grad_w[i].tocsr()
elif 0 == i:
self.grad_w[i] = np.dot(np.transpose(x), tmp) / num_rates
else:
self.grad_w[i] = np.dot( np.transpose(hidden_output[i-1]), tmp )\
/ num_rates
self.grad_b[i] = np.sum(tmp, 0) / num_rates
if 0 == i: continue
tmp = np.dot(tmp, np.transpose(self.w[i]))
