def logreg_cost(self, label, output):
if self.cost.shape[0] != self.batch_size:
self.cost = gpuarray.zeros((self.batch_size, 1), dtype=np.float32)
maxid = gpuarray.zeros((self.batch_size, 1), dtype=np.float32)
find_col_max_id(maxid, output)
self.batchCorrect = same_reduce(label , maxid)
logreg_cost_col_reduce(output, label, self.cost)
def logreg_cost_multiview(self, label, output, num_view):
unit = self.batch_size / num_view
if self.cost.shape[0] != unit:
self.cost = gpuarray.zeros((unit, 1), dtype = np.float32)
maxid = gpuarray.zeros((self.batch_size, 1), dtype = np.float32)
find_col_max_id(maxid, output)
self.batchCorrect = same_reduce_multiview(label, maxid, num_view)
tmp = gpuarray.zeros((output.shape[0], unit), dtype = np.float32)
gpu_partial_copy_to(output, tmp, 0, output.shape[0], 0, unit)
logreg_cost_col_reduce(tmp, label, self.cost)
def logreg_cost_multiview(self, label, output, num_view):
unit = self.batch_size / num_view
if self.cost.shape[0] != unit:
self.cost = gpuarray.zeros((unit, 1), dtype = np.float32)
maxid = gpuarray.zeros((self.batch_size, 1), dtype = np.float32)
find_col_max_id(maxid, output)
self.batchCorrect = same_reduce_multiview(label, maxid, num_view)
tmp = gpuarray.zeros((output.shape[0], unit), dtype = np.float32)
gpu_partial_copy_to(output, tmp, 0, output.shape[0], 0, unit)
logreg_cost_col_reduce(tmp, label, self.cost)