def main(data_name, method, dimZ, dimH, n_channel, batch_size, K_mc,
checkpoint, lbd):
# set up dataset specific stuff
from config import config
labels, n_iter, dimX, shape_high, ll = config(data_name, n_channel)
if data_name == 'mnist':
from mnist import load_mnist
if data_name == 'notmnist':
from notmnist import load_notmnist
# import functionalities
if method == 'onlinevi':
from bayesian_generator import generator_head, generator_shared, \
generator, construct_gen
from onlinevi import construct_optimizer, init_shared_prior, \
update_shared_prior, update_q_sigma
if method in ['ewc', 'noreg', 'laplace', 'si']:
from generator import generator_head, generator_shared, generator, construct_gen
if method in ['ewc', 'noreg']:
from vae_ewc import construct_optimizer, lowerbound
if method == 'ewc': from vae_ewc import update_ewc_loss, compute_fisher
if method == 'laplace':
from vae_laplace import construct_optimizer, lowerbound
from vae_laplace import update_laplace_loss, compute_fisher, init_fisher_accum
if method == 'si':
from vae_si import construct_optimizer, lowerbound, update_si_reg
# then define model
n_layers_shared = 2
batch_size_ph = tf.placeholder(tf.int32, shape=(), name='batch_size')
dec_shared = generator_shared(dimX, dimH, n_layers_shared, 'sigmoid',
'gen')
# initialise sessions
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
string = method
if method in ['ewc', 'laplace', 'si']:
string = string + '_lbd%.1f' % lbd
if method == 'onlinevi' and K_mc > 1:
string = string + '_K%d' % K_mc
path_name = data_name + '_%s/' % string
if not os.path.isdir('save/'):
os.mkdir('save/')
if not os.path.isdir('save/' + path_name):
os.mkdir('save/' + path_name)
print 'create path save/' + path_name
filename = 'save/' + path_name + 'checkpoint'
if checkpoint < 0:
print 'training from scratch'
old_var_list = init_variables(sess)
else:
load_params(sess, filename, checkpoint)
checkpoint += 1
# visualise the samples
N_gen = 10**2
path = 'figs/' + path_name
if not os.path.isdir('figs/'):
os.mkdir('figs/')
if not os.path.isdir(path):
os.mkdir(path)
print 'create path ' + path
X_ph = tf.placeholder(tf.float32, shape=(batch_size, dimX), name='x_ph')
# now start fitting
N_task = len(labels)
gen_ops = []
X_valid_list = []
X_test_list = []
eval_func_list = []
result_list = []
if method == 'onlinevi':
shared_prior_params = init_shared_prior()
if method in ['ewc', 'noreg']:
ewc_loss = 0.0
if method == 'laplace':
F_accum = init_fisher_accum()
laplace_loss = 0.0
if method == 'si':
old_params_shared = None
si_reg = None
n_layers_head = 2
n_layers_enc = n_layers_shared + n_layers_head - 1
for task in xrange(1, N_task + 1):
# first load data
if data_name == 'mnist':
X_train, X_test, _, _ = load_mnist(digits=labels[task - 1],
conv=False)
if data_name == 'notmnist':
X_train, X_test, _, _ = load_notmnist(data_path,
digits=labels[task - 1],
conv=False)
N_train = int(X_train.shape[0] * 0.9)
X_valid_list.append(X_train[N_train:])
X_train = X_train[:N_train]
X_test_list.append(X_test)
# define the head net and the generator ops
dec = generator(
generator_head(dimZ, dimH, n_layers_head, 'gen_%d' % task),
dec_shared)
enc = encoder(dimX, dimH, dimZ, n_layers_enc, 'enc_%d' % task)
gen_ops.append(construct_gen(dec, dimZ, sampling=False)(N_gen))
print 'construct eval function...'
eval_func_list.append(construct_eval_func(X_ph, enc, dec, ll, \
batch_size_ph, K=100, sample_W=False))
# then construct loss func and fit func
print 'construct fit function...'
if method == 'onlinevi':
fit = construct_optimizer(X_ph, enc, dec, ll, X_train.shape[0], batch_size_ph, \
shared_prior_params, task, K_mc)
if method in ['ewc', 'noreg']:
bound = lowerbound(X_ph, enc, dec, ll)
fit = construct_optimizer(X_ph, batch_size_ph, bound,
X_train.shape[0], ewc_loss)
if method == 'ewc':
fisher, var_list = compute_fisher(X_ph, batch_size_ph, bound,
X_train.shape[0])
if method == 'laplace':
bound = lowerbound(X_ph, enc, dec, ll)
fit = construct_optimizer(X_ph, batch_size_ph, bound,
X_train.shape[0], laplace_loss)
fisher, var_list = compute_fisher(X_ph, batch_size_ph, bound,
X_train.shape[0])
if method == 'si':
bound = lowerbound(X_ph, enc, dec, ll)
fit, shared_var_list = construct_optimizer(X_ph, batch_size_ph,
bound, X_train.shape[0],
si_reg,
old_params_shared, lbd)
if old_params_shared is None:
old_params_shared = sess.run(shared_var_list)
# initialise all the uninitialised stuff
old_var_list = init_variables(sess, old_var_list)
# start training for each task
if method == 'si':
new_params_shared, w_params_shared = fit(sess, X_train, n_iter, lr)
else:
fit(sess, X_train, n_iter, lr)
# plot samples
x_gen_list = sess.run(gen_ops, feed_dict={batch_size_ph: N_gen})
for i in xrange(len(x_gen_list)):
plot_images(x_gen_list[i], shape_high, path, \
data_name + '_gen_task%d_%d' % (task, i + 1))
x_list = [x_gen_list[i][:1] for i in xrange(len(x_gen_list))]
x_list = np.concatenate(x_list, 0)
tmp = np.zeros([10, dimX])
tmp[:task] = x_list
if task == 1:
x_gen_all = tmp
else:
x_gen_all = np.concatenate([x_gen_all, tmp], 0)
# print test-ll on all tasks
tmp_list = []
for i in xrange(len(eval_func_list)):
print 'task %d' % (i + 1),
test_ll = eval_func_list[i](sess, X_valid_list[i])
tmp_list.append(test_ll)
result_list.append(tmp_list)
# save param values
save_params(sess, filename, checkpoint)
checkpoint += 1
# update regularisers/priors
if method == 'ewc':
# update EWC loss
print 'update ewc loss...'
X_batch = X_train[np.random.permutation(range(
X_train.shape[0]))[:batch_size]]
ewc_loss = update_ewc_loss(sess, ewc_loss, var_list, fisher, lbd,
X_batch)
if method == 'laplace':
# update EWC loss
print 'update laplace loss...'
X_batch = X_train[np.random.permutation(range(
X_train.shape[0]))[:batch_size]]
laplace_loss, F_accum = update_laplace_loss(
sess, F_accum, var_list, fisher, lbd, X_batch)
if method == 'onlinevi':
# update prior
print 'update prior...'
shared_prior_params = update_shared_prior(sess,
shared_prior_params)
# reset the variance of q
update_q_sigma(sess)
if method == 'si':
# update regularisers/priors
print 'update SI big omega matrices...'
si_reg, _ = update_si_reg(sess, si_reg, new_params_shared,
old_params_shared, w_params_shared)
old_params_shared = new_params_shared
plot_images(x_gen_all, shape_high, path, data_name + '_gen_all')
for i in xrange(len(result_list)):
print result_list[i]
# save results
fname = 'results/' + data_name + '_%s.pkl' % string
import pickle
pickle.dump(result_list, open(fname, 'wb'))
print 'test-ll results saved in', fname
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
keras.backend.set_session(sess)
# the data, shuffled and split between train and test sets
data_name = 'notmnist'
if data_name == 'mnist':
from mnist import load_mnist
x_train, x_test, y_train, y_test = load_mnist()
if data_name == 'notmnist':
from notmnist import load_notmnist
data_path = './notMNIST_small'
x_train, x_test, y_train, y_test = [], [], [], []
for i in range(10):
a, b, c, d = load_notmnist(data_path, [i], ratio=0.995)
x_train.append(a); x_test.append(b)
y_train.append(c); y_test.append(d)
x_train = np.concatenate(x_train, 0)
x_test = np.concatenate(x_test, 0)
y_train = np.concatenate(y_train, 0)
y_test = np.concatenate(y_test, 0)
print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)
model = Sequential()
dimH = 1000
n_layer = 3
model.add(Dense(dimH, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
for _ in range(n_layer-1):
model.add(Dense(dimH, activation='relu'))
def main(data_name, method, dimZ, dimH, n_channel, batch_size, K_mc, checkpoint, lbd):
# set up dataset specific stuff
from config import config
labels, n_iter, dimX, shape_high, ll = config(data_name, n_channel)
if data_name == 'mnist':
from mnist import load_mnist
if data_name == 'notmnist':
from notmnist import load_notmnist
# import functionalities
if method == 'onlinevi':
from bayesian_generator import generator_head, generator_shared, \
generator, construct_gen
if method in ['ewc', 'noreg', 'si', 'laplace']:
from generator import generator_head, generator_shared, generator, construct_gen
# then define model
n_layers_shared = 2
batch_size_ph = tf.placeholder(tf.int32, shape=(), name='batch_size')
dec_shared = generator_shared(dimX, dimH, n_layers_shared, 'sigmoid', 'gen')
# initialise sessions
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
string = method
if method in ['ewc', 'laplace', 'si']:
string = string + '_lbd%.1f' % lbd
if method == 'onlinevi' and K_mc > 1:
string = string + '_K%d' % K_mc
path_name = data_name + '_%s/' % string
assert os.path.isdir('save/'+path_name)
filename = 'save/' + path_name + 'checkpoint'
# visualise the samples
N_gen = 10**2
X_ph = tf.placeholder(tf.float32, shape=(batch_size, dimX), name = 'x_ph')
# now start fitting
N_task = len(labels)
gen_ops = []
X_valid_list = []
X_test_list = []
eval_func_list = []
result_list = []
n_layers_head = 2
n_layers_enc = n_layers_shared + n_layers_head - 1
for task in xrange(1, N_task+1):
# first load data
# first load data
if data_name == 'mnist':
X_train, X_test, _, _ = load_mnist(digits = labels[task-1], conv = False)
if data_name == 'notmnist':
X_train, X_test, _, _ = load_notmnist(data_path, digits = labels[task-1], conv = False)
N_train = int(X_train.shape[0] * 0.9)
X_valid_list.append(X_train[N_train:])
X_train = X_train[:N_train]
X_test_list.append(X_test)
# define the head net and the generator ops
dec = generator(generator_head(dimZ, dimH, n_layers_head, 'gen_%d' % task), dec_shared)
enc = encoder(dimX, dimH, dimZ, n_layers_enc, 'enc_%d' % task)
gen_ops.append(construct_gen(dec, dimZ, sampling=False)(N_gen))
eval_func_list.append(construct_eval_func(X_ph, enc, dec, ll, batch_size_ph,
K = 5000, sample_W = False))
# then load the trained model
load_params(sess, filename, checkpoint=task-1, init_all = False)
# plot samples
x_gen_list = sess.run(gen_ops, feed_dict={batch_size_ph: N_gen})
x_list = []
for i in xrange(len(x_gen_list)):
ind = np.random.randint(len(x_gen_list[i]))
x_list.append(x_gen_list[i][ind:ind+1])
x_list = np.concatenate(x_list, 0)
tmp = np.zeros([10, dimX])
tmp[:task] = x_list
if task == 1:
x_gen_all = tmp
else:
x_gen_all = np.concatenate([x_gen_all, tmp], 0)
# print test-ll on all tasks
tmp_list = []
for i in xrange(len(eval_func_list)):
print 'task %d' % (i+1),
test_ll = eval_func_list[i](sess, X_test_list[i])
tmp_list.append(test_ll)
result_list.append(tmp_list)
#x_gen_all = 1.0 - x_gen_all
if not os.path.isdir('figs/visualisation/'):
os.mkdir('figs/visualisation/')
print 'create path figs/visualisation/'
plot_images(x_gen_all, shape_high, 'figs/visualisation/', data_name+'_gen_all_'+method)
for i in xrange(len(result_list)):
print result_list[i]
# save results
fname = 'results/' + data_name + '_%s.pkl' % string
import pickle
pickle.dump(result_list, open(fname, 'wb'))
print 'test-ll results saved in', fname
def main(data_name, method, dimZ, dimH, n_channel, batch_size, K_mc,
checkpoint, lbd):
# set up dataset specific stuff
from config import config
labels, n_iter, dimX, shape_high, ll = config(data_name, n_channel)
# import functionalities
if method == 'onlinevi':
from bayesian_generator import generator_head, generator_shared, \
generator, construct_gen
if method in ['ewc', 'noreg', 'si', 'si2', 'laplace']:
from generator import generator_head, generator_shared, generator, construct_gen
# then define model
n_layers_shared = 2
batch_size_ph = tf.placeholder(tf.int32, shape=(), name='batch_size')
dec_shared = generator_shared(dimX, dimH, n_layers_shared, 'sigmoid',
'gen')
# initialise sessions
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
keras.backend.set_session(sess)
string = method
if method in ['ewc', 'laplace']:
string = string + '_lbd%d' % lbd
if method in ['si', 'si2']:
string = string + '_lbd%.1f' % lbd
if method == 'onlinevi' and K_mc > 1:
string = string + '_K%d' % K_mc
path_name = data_name + '_%s_no_share_enc/' % string
assert os.path.isdir('save/' + path_name)
filename = 'save/' + path_name + 'checkpoint'
# load the classifier
cla = load_model(data_name)
# print test error
X_ph = tf.placeholder(tf.float32, shape=(batch_size, 28**2))
y_ph = tf.placeholder(tf.float32, shape=(batch_size, 10))
y_pred = cla(X_ph)
correct_pred = tf.equal(tf.argmax(y_ph, 1), tf.argmax(y_pred, 1))
acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
y_pred_ = tf.clip_by_value(y_pred, 1e-9, 1.0)
kl = tf.reduce_mean(-tf.reduce_sum(y_ph * tf.log(y_pred_), 1))
for task in range(1):
if data_name == 'mnist':
from mnist import load_mnist
_, X_test, _, Y_test = load_mnist([task])
if data_name == 'notmnist':
from notmnist import load_notmnist
_, X_test, _, Y_test = load_notmnist(data_path, [task], conv=False)
test_acc = 0.0
test_kl = 0.0
N_test = X_test.shape[0]
for i in range(N_test / batch_size):
indl = i * batch_size
indr = min((i + 1) * batch_size, N_test)
tmp1, tmp2 = sess.run(
(acc, kl),
feed_dict={
X_ph: X_test[indl:indr],
y_ph: Y_test[indl:indr],
keras.backend.learning_phase(): 0
})
test_acc += tmp1 / (N_test / batch_size)
test_kl += tmp2 / (N_test / batch_size)
print('classification accuracy on test data', test_acc)
print('kl on test data', test_kl)
# now start fitting
N_task = len(labels)
eval_func_list = []
result_list = []
n_layers_head = 2
n_layers_enc = n_layers_shared + n_layers_head - 1
for task in range(1, N_task + 1):
# define the head net and the generator ops
dec = generator(
generator_head(dimZ, dimH, n_layers_head, 'gen_%d' % task),
dec_shared)
eval_func_list.append(construct_eval_func(dec, cla, batch_size_ph, \
dimZ, task-1, sample_W = True))
# then load the trained model
load_params(sess, filename, checkpoint=task - 1, init_all=False)
# print test-ll on all tasks
tmp_list = []
for i in range(len(eval_func_list)):
print('task %d' % (i + 1))
kl = eval_func_list[i](sess)
tmp_list.append(kl)
result_list.append(tmp_list)
for i in range(len(result_list)):
print(result_list[i])
# save results
fname = 'results/' + data_name + '_%s_gen_class.pkl' % string
import pickle
pickle.dump(result_list, open(fname, 'wb'))
print 'test-ll results saved in', fname
