def run( ):
RS = RandomState((seed, "to p_rs"))
data = loadData.loadMnist()
train_data, tests_data = loadData.load_data_as_dict(data, classNum)
train_data = random_partition(train_data, RS, [N_train]).__getitem__(0)
tests_data = random_partition(tests_data, RS, [ N_tests]).__getitem__(0)
print "training samples {0}: testing samples: {1}".format(N_train,N_tests)
w_parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weights = w_parser.vect.size
init_scales = w_parser.new_vect(np.zeros(N_weights))
for i in range(N_layers):
init_scales[('weights', i)] = 1 / np.sqrt(layer_sizes[i])
init_scales[('biases', i)] = 1.0
init_scales = init_scales.vect
def regularization(w_vect, reg):
return np.dot(w_vect, w_vect * np.exp(reg))
def constrain_reg(t_vect, name):
all_r = w_parser.new_vect(t_vect)
for i in range(N_layers):
all_r[('biases', i)] = 0.0
if name == 'universal':
r_mean = np.mean([np.mean(all_r[('weights', i)]) for i in range(N_layers)])
for i in range(N_layers):
all_r[('weights', i)] = r_mean
elif name == 'layers':
for i in range(N_layers):
all_r[('weights', i)] = np.mean(all_r[('weights', i)])
elif name == 'units':
for i in range(N_layers):
all_r[('weights', i)] = np.mean(all_r[('weights', i)], axis=1, keepdims=True)
else:
raise Exception
return all_r.vect
def process_reg(t_vect):
# Remove the redundancy due to sharing regularization within units
all_r = w_parser.new_vect(t_vect)
new_r = np.zeros((0,))
for i in range(N_layers):
layer = all_r[('weights', i)]
assert np.all(layer[:, 0] == layer[:, 1])
cur_r = layer[:, 0]
new_r = np.concatenate((new_r, cur_r))
return new_r
def train_z(data, w_vect_0, reg):
N_data = data['X'].shape[0]
def primal_loss(w_vect, reg, i_primal, record_results=False):
RS = RandomState((seed, i_primal, "primal"))
idxs = RS.randint(N_data, size=batch_size)
minibatch = dictslice(data, idxs)
loss = loss_fun(w_vect, **minibatch)
reg = regularization(w_vect, reg)
if record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}".format(i_primal, getval(loss))
return loss + reg
return sgd(grad(primal_loss), reg, w_vect_0, alpha, beta, N_iters)
all_regs, all_tests_loss = [], []
def train_reg(reg_0, constraint, N_meta_iter, i_top):
def hyperloss(reg, i_hyper, cur_train_data, cur_valid_data):
RS = RandomState((seed, i_top, i_hyper, "hyperloss"))
w_vect_0 = RS.randn(N_weights) * init_scales
w_vect_final = train_z(cur_train_data, w_vect_0, reg)
return loss_fun(w_vect_final, **cur_valid_data)
hypergrad = grad(hyperloss)
cur_reg = reg_0
for i_hyper in range(N_meta_iter):
if i_hyper % N_meta_thin == 0:
tests_loss = hyperloss(cur_reg, i_hyper, train_data, tests_data)
all_tests_loss.append(tests_loss)
all_regs.append(cur_reg.copy())
print "Hyper iter {0}, test loss {1}".format(i_hyper, all_tests_loss[-1])
print "Cur_reg", cur_reg
# print "Cur_reg", np.mean(cur_reg)
RS = RandomState((seed, i_top, i_hyper, "hyperloss"))
cur_split = random_partition(train_data, RS, [N_train - N_valid, N_valid])
# print("calculate hypergradients")
raw_grad = hypergrad(cur_reg, i_hyper, *cur_split)
constrained_grad = constrain_reg(raw_grad, constraint)
# print "constrained_grad",constrained_grad
print "\n"
# cur_reg -= constrained_grad / np.abs(constrained_grad + 1e-8) * meta_alpha
cur_reg -= constrained_grad * meta_alpha
# cur_reg -= np.sign(constrained_grad) * meta_alpha
return cur_reg
def new_hyperloss(reg, i_hyper, cur_train_data, cur_valid_data):
RS = RandomState((seed, i_hyper, "hyperloss"))
w_vect_0 = RS.randn(N_weights) * init_scales
w_vect_final = train_z(cur_train_data, w_vect_0, reg)
return loss_fun(w_vect_final, **cur_valid_data)
# t_scale = [-1, 0, 1]
# cur_split = random_partition(train_data, RS, [N_train - N_valid, N_valid])
# for s in t_scale:
# reg = np.ones(N_weights) * log_L2_init + s
# loss = new_hyperloss(reg, 0, *cur_split)
# print "Results: s= {0}, loss = {1}".format(s, loss)
reg = np.ones(N_weights) * log_L2_init
constraints = ['universal', 'layers', 'units']
for i_top, (N_meta_iter, constraint) in enumerate(zip(all_N_meta_iter, constraints)):
print "Top level iter {0}".format(i_top)
reg = train_reg(reg, constraint, N_meta_iter, i_top)
all_L2_regs = np.array(zip(*map(process_reg, all_regs)))
return all_L2_regs, all_tests_loss
def run( subClassIndexList):
RS = RandomState((seed, "to p_rs"))
data = loadData.loadMnist()
train_data,tests_data = loadData.load_data_as_dict(data, classNum, subClassIndexList.__getitem__(0))
train_data_subclass = []
train_data_subclass= loadSubsetData(train_data,RS, N_train, clientNum)
print "training samples {0}: testing samples: {1}".format(N_train,N_tests)
w_parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weights = w_parser.vect.size
init_scales = w_parser.new_vect(np.zeros(N_weights))
for i in range(N_layers):
init_scales[('weights', i)] = 1 / np.sqrt(layer_sizes[i])
init_scales[('biases', i)] = 1.0
init_scales = init_scales.vect
all_regs, all_tests_loss = [], []
def train_reg(reg_0, constraint, N_meta_iter, i_top):
def hyperloss(reg, i_hyper, cur_train_data, cur_valid_data):
RS = RandomState((seed, i_top, i_hyper, "hyperloss"))
w_vect_0 = RS.randn(N_weights) * init_scales
w_vect_final = train_z(loss_fun, cur_train_data, w_vect_0, reg)
return loss_fun(w_vect_final, **cur_valid_data)
hypergrad = grad(hyperloss)
#reg is the list of hyperparameters
cur_reg = reg_0
for i_hyper in range(N_meta_iter):
if i_hyper % N_meta_thin == 0:
tests_loss = hyperloss(cur_reg, i_hyper, train_data, tests_data)
all_tests_loss.append(tests_loss)
all_regs.append(cur_reg.copy())
print "Hyper iter {0}, test loss {1}".format(i_hyper, all_tests_loss[-1])
# print "Cur_reg", np.mean(cur_reg)
print "Cur_reg", cur_reg
for client_i in range (0,clientNum):
RS = RandomState((seed, i_top, i_hyper, "hyperloss"))
cur_split = random_partition(train_data_subclass.__getitem__(client_i), RS, [N_train - N_valid, N_valid])
# print("calculate hypergradients")
raw_grad = hypergrad(cur_reg, i_hyper, *cur_split)
constrained_grad = constrain_reg(w_parser, raw_grad, constraint)
# cur_reg -= constrained_grad / np.abs(constrained_grad + 1e-8) * meta_alpha/clientNum
cur_reg -= constrained_grad * meta_alpha/clientNum
print "\n"
return cur_reg
def new_hyperloss(reg, i_hyper, cur_train_data, cur_valid_data):
RS = RandomState((seed, i_hyper, "hyperloss"))
w_vect_0 = RS.randn(N_weights) * init_scales
w_vect_final = train_z(loss_fun, cur_train_data, w_vect_0, reg)
return loss_fun(w_vect_final, **cur_valid_data)
# t_scale = [-1, 0, 1]
# cur_split = random_partition(train_data, RS, [N_train - N_valid, N_valid])
# for s in t_scale:
# reg = np.ones(N_weights) * log_L2_init + s
# loss = new_hyperloss(reg, 0, *cur_split)
# print "Results: s= {0}, loss = {1}".format(s, loss)
reg = np.ones(N_weights) * log_L2_init
constraints = ['universal', 'layers', 'units']
for i_top, (N_meta_iter, constraint) in enumerate(zip(all_N_meta_iter, constraints)):
print "Top level iter {0}".format(i_top)
reg = train_reg(reg, constraint, N_meta_iter, i_top)
all_L2_regs = np.array(zip(*map(w_parser, process_reg, all_regs)))
return all_L2_regs, all_tests_loss
