def eval(self):
self.learn_sharedmodel()
model_utils.calculateFisherMatrix(self)
model_utils.UpdateMultiTaskWeightWithAlphas(self.global_index, self.modelMain)
self.modelMaintainMode = Model_Structure()
model_utils.CalculateModeParametes(self)
model_utils.CalculateModePerClassParametes(self)
# pdb.set_trace()
for label_test in range(self.opt.num_class):
self.learn_sharedmodel_w(label_test)
self.modelMain[label_test].eval()
self.modelMain[label_test].eval_flag = True
print("============== Train task #%d (Mean-IMM) ==============" %
no_of_task)
LW = model_utils.UpdateMultiTaskLwWithAlphas(L_copy[0], alpha_list,
no_of_task)
model_utils.AddMultiTaskLayers(sess, L_copy, mlp.Layers, LW,
no_of_task)
ret = mlp.TestTasks(sess, x, y, x_, y_, debug=False)
utils.PrintResults(alpha, ret)
mlp.TestAllTasks(sess, x_, y_)
######################### Mode-IMM ##########################
if mode_imm:
print("")
print("Main experiment on Drop-transfer + Mode-IMM, shuffled MNIST")
print("============== Train task #%d (Mode-IMM) ==============" %
no_of_task)
LW = model_utils.UpdateMultiTaskWeightWithAlphas(
FM, alpha_list, no_of_task)
model_utils.AddMultiTaskLayers(sess, L_copy, mlp.Layers, LW,
no_of_task)
ret = mlp.TestTasks(sess, x, y, x_, y_, debug=False)
utils.PrintResults(alpha, ret)
mlp.TestAllTasks(sess, x_, y_)
print("")
print("Time: %.4f s" % (time.time() - start))
