def get_solver(param):
solver = SolverParameter()
solver.net = param["file_train_val_net"]
solver.test_interval = param["solver_test_interval"]
solver.base_lr = param["solver_base_lr"]
solver.weight_decay = param["solver_weight_decay"]
solver.lr_policy = param["solver_lr_policy"]
solver.display = param["solver_display"]
solver.max_iter = param["solver_max_iter"]
solver.clip_gradients = param["solver_clip_gradients"]
solver.snapshot = param["solver_snapshot"]
solver.lr_policy = param["solver_lr_policy"]
solver.stepsize = param["solver_stepsize"]
solver.gamma = param["solver_gamma"]
solver.snapshot_prefix = param["solver_snapshot_prefix"]
solver.random_seed = param["solver_random_seed"]
solver.solver_mode = param["solver_solver_mode"]
solver.test_iter.append(param["solver_test_iter"])
return solver
def get_solver(param):
solver = SolverParameter()
solver.net = param['file_train_val_net']
solver.test_interval = param['solver_test_interval']
solver.base_lr = param['solver_base_lr']
solver.weight_decay = param['solver_weight_decay']
solver.lr_policy = param['solver_lr_policy']
solver.display = param['solver_display']
solver.max_iter = param['solver_max_iter']
solver.clip_gradients = param['solver_clip_gradients']
solver.snapshot = param['solver_snapshot']
solver.lr_policy = param['solver_lr_policy']
solver.stepsize = param['solver_stepsize']
solver.gamma = param['solver_gamma']
solver.snapshot_prefix = param['solver_snapshot_prefix']
solver.random_seed = param['solver_random_seed']
solver.solver_mode = param['solver_solver_mode']
solver.test_iter.append(param['solver_test_iter'])
return solver
def get_solver(self, param):
solver = SolverParameter()
solver.net = param['base_dir'] + param['net_name'] + "/net.prototxt"
solver.base_lr = 0.01
solver.weight_decay = 0.0005
solver.lr_policy = "poly"
solver.power = 1
solver.momentum = 0.9
solver.type = "SGD"
solver.clip_gradients = 10
solver.display = 100
solver.max_iter = param['max_iter']
solver.average_loss = 100
solver.snapshot = param['solver_snapshot_interval']
solver.snapshot_prefix = param['snapshot_dir'] + param[
'net_name'] + "/lstm"
solver.snapshot_format = solver.HDF5
solver.random_seed = param['random_seed']
solver.iter_size = 1
solver.layer_wise_reduce = False
return solver
def get_solver(param):
solver = SolverParameter()
solver.net = param['file_train_val_net']
solver.test_interval = param['solver_test_interval']
solver.base_lr = param['solver_base_lr']
solver.weight_decay = param['solver_weight_decay']
solver.lr_policy = param['solver_lr_policy']
solver.display = param['solver_display']
solver.max_iter = param['solver_max_iter']
solver.clip_gradients = param['solver_clip_gradients']
solver.snapshot = param['solver_snapshot']
solver.lr_policy = param['solver_lr_policy']
solver.stepsize = param['solver_stepsize']
solver.gamma = param['solver_gamma']
solver.snapshot_prefix = param['solver_snapshot_prefix']
solver.random_seed = param['solver_random_seed']
solver.solver_mode = param['solver_solver_mode']
solver.test_iter.append(param['solver_test_iter'])
return solver
