if pos < 0:
filename = model + '.theta0'
else:
filename = model[0:pos] + '.theta0' + model[pos:]
with Writer(filename) as theta0_writer:
pickle.dump(theta0, theta0_writer)
theta0_saving_time = timer.toc()
print >> stderr, 'optimizing...'
callback = ThetaSaver(model, every)
func = compute_cost_and_grad
args = (instances, total_internal_node, word_vectors, embsize, lambda_reg)
theta_opt = None
try:
theta_opt = lbfgs.optimize(func, theta0, maxiter, verbose, checking_grad,
args, callback=callback)
except GridentCheckingFailedError:
send_terminate_signal()
print >> stderr, 'Gradient checking failed, exit'
exit(-1)
send_terminate_signal()
opt_time = timer.toc()
timer.tic()
# pickle form
print >> stderr, 'saving parameters to %s' % model
with Writer(model) as model_pickler:
pickle.dump(theta_opt, model_pickler)
# pure text form
with Writer(model+'.txt') as writer:
pickle.dump(trg_theta0, theta0_writer)
theta0_saving_time = timer.toc()
# 每隔every步就存储一次模型参数
src_callback = ThetaSaver( model + '_source', every )
trg_callback = ThetaSaver( model + '_target', every )
func = compute_cost_and_grad
src_args = ( src_instances, src_total_internal_node, src_word_vectors, src_embsize, lambda_reg, lambda_reg_L )
trg_args = ( trg_instances, trg_total_internal_node, trg_word_vectors, trg_embsize, lambda_reg, lambda_reg_L )
src_theta_opt = None
trg_theta_opt = None
print >> stderr, 'optimizing...'
try:
# 开始优化
src_theta_opt = lbfgs.optimize( func, src_theta0, maxiter, verbose, checking_grad,
src_args, callback = src_callback )
except GridentCheckingFailedError:
print >> stderr, 'Gradient checking failed, exit'
exit(-1)
try:
# 开始优化
trg_theta_opt = lbfgs.optimize( func, trg_theta0, maxiter, verbose, checking_grad,
trg_args, callback = trg_callback )
except GridentCheckingFailedError:
print >> stderr, 'Gradient checking failed, exit'
exit(-1)
send_terminate_signal()
with Writer(filename) as theta0_writer:
pickle.dump(theta0, theta0_writer)
theta0_saving_time = timer.toc()
print >> stderr, 'optimizing...'
callback = ThetaSaver(model, every)
func = compute_cost_and_grad
args = (instances, total_internal_node, word_vectors, embsize,
lambda_reg)
theta_opt = None
try:
theta_opt = lbfgs.optimize(func,
theta0,
maxiter,
verbose,
checking_grad,
args,
callback=callback)
except GridentCheckingFailedError:
send_terminate_signal()
print >> stderr, 'Gradient checking failed, exit'
exit(-1)
send_terminate_signal()
opt_time = timer.toc()
timer.tic()
# pickle form
print >> stderr, 'saving parameters to %s' % model
with Writer(model) as model_pickler:
theta0_la_saving_time = timer.toc()
# 每隔every步就存储一次模型参数
callback_la = ThetaSaver( model_la, every )
theta_opt_la = None
func = compute_cost_and_grad_la
args_la = ( src_instances, src_total_internal_node, src_word_vectors, src_embsize,
trg_instances, trg_total_internal_node, trg_word_vectors, trg_embsize, lambda_reg_L,
lambda_reg_rec,lambda_reg_sem, alpha, bad_src_instances, bad_trg_instances )
print >> stderr, 'Start Supervised Optimizing...'
try:
# Optimizing with label
theta_opt_la = lbfgs.optimize( func, theta0_la, maxiter_la, verbose, checking_grad,
args_la, callback = callback_la )
except GridentCheckingFailedError:
print >> stderr, 'Gradient checking failed, exit'
exit(-1)
send_terminate_signal()
opt_la_time = timer.toc()
timer.tic()
# pickle form
print >> stderr, 'saving parameters to %s' % model_la
with Writer( model_la ) as model_pickler:
pickle.dump( theta_opt_la, model_pickler)
# pure text form
with Writer(model_la+'.txt') as writer:
[writer.write('%20.8f\n' % v) for v in theta_opt_la]
filename = model + '.theta0'
else:
filename = model[0:pos] + '.theta0' + model[pos:]
with Writer(filename) as theta0_writer:
pickle.dump(theta0, theta0_writer)
theta0_saving_time = timer.toc()
print >> stderr, 'optimizing...'
callback = ThetaSaver(model, every)
func = preTrain
args = (instances, total_internal_node, word_vectors, embsize, lambda_reg)
theta_opt = None
try:
theta_opt = lbfgs.optimize(func, theta0[0:4 * embsize * embsize + 3 * embsize], maxiter, verbose,
checking_grad,
args, callback=callback)
except GridentCheckingFailedError:
send_terminate_signal()
print >> stderr, 'Gradient checking failed, exit'
exit(-1)
send_terminate_signal()
opt_time = timer.toc()
timer.tic()
print >> stderr, 'Prepare training data...'
instances, instances_of_Unlabel, _ = prepare_data(word_vectors, instances_files, instances_file_of_Unlabel)
func = compute_cost_and_grad
args = (instances, instances_of_Unlabel, word_vectors, embsize, total_labeled_internal_node, lambda_rec, lambda_reg, lambda_reo, lambda_unlabel, instances_of_News, is_Test)
