def train(data_dir='data/memes/',
dim_proj=512,
maxlen=30,
batch_size=256,
keep_ratio=1.,
shuffle_data=True,
learning_rate=0.001,
global_steps=50000,
disp_freq=100,
save_freq=1000,
test_freq=1000,
saveto_file='params.npz',
weight_decay=0.0005,
reload_model=False,
train=True):
"""
Topo-LSTM model training.
"""
options = locals().copy()
saveto = data_dir + saveto_file
# loads graph
G, node_index = data_utils.load_graph(data_dir)
print nx.info(G)
options['n_words'] = len(node_index)
print options
# creates and initializes shared variables.
print 'Initializing variables...'
params = init_params(options)
if reload_model:
print 'reusing saved model.'
load_params(saveto, params)
tparams = init_tparams(params)
# builds Topo-LSTM model
print 'Building model...'
model = tprnn_model.build_model(tparams, options)
print 'Loading test data...'
test_examples = data_utils.load_examples(data_dir,
dataset='test',
node_index=node_index,
maxlen=maxlen,
G=G)
test_loader = data_utils.Loader(test_examples, options=options)
print 'Loaded %d test examples' % len(test_examples)
if train:
# prepares training data.
print 'Loading train data...'
train_examples = data_utils.load_examples(
data_dir,
dataset='train',
keep_ratio=options['keep_ratio'],
node_index=node_index,
maxlen=maxlen,
G=G)
train_loader = data_utils.Loader(train_examples, options=options)
print 'Loaded %d training examples.' % len(train_examples)
# compiles updates.
optimizer = downhill.build(algo='adam',
loss=model['cost'],
params=tparams.values(),
inputs=model['data'])
updates = optimizer.get_updates(max_gradient_elem=5.,
learning_rate=learning_rate)
f_update = theano.function(model['data'],
model['cost'],
updates=list(updates))
# training loop.
start_time = timeit.default_timer()
# downhill.minimize(
# loss=cost,
# algo='adam',
# train=train_loader,
# # inputs=input_list + [labels],
# # params=tparams.values(),
# # patience=0,
# max_gradient_clip=1,
# # max_gradient_norm=1,
# learning_rate=learning_rate,
# monitors=[('cost', cost)],
# monitor_gradients=False)
n_examples = len(train_examples)
batches_per_epoch = n_examples // options['batch_size'] + 1
n_epochs = global_steps // batches_per_epoch + 1
global_step = 0
cost_history = []
for _ in range(n_epochs):
for _ in range(batches_per_epoch):
cost = f_update(*train_loader())
cost_history += [cost]
if global_step % disp_freq == 0:
print 'global step %d, cost: %f' % (global_step, cost)
# dump model parameters.
if global_step % save_freq == 0:
params = unzip(tparams)
np.savez(saveto, **params)
pickle.dump(options, open('%s.pkl' % saveto, 'wb'), -1)
# evaluate on test data.
if global_step % test_freq == 0:
scores = evaluate(model['f_prob'], test_loader)
print 'eval scores: ', scores
end_time = timeit.default_timer()
print 'time used: %d seconds.' % (end_time - start_time)
global_step += 1
scores = evaluate(model['f_prob'], test_loader)
pprint.pprint(scores)
def train(data_dir='data/memes/',
dim_proj=256,
dim_att=128,
maxlen=30,
batch_size=256,
keep_ratio=1.,
shuffle_data=True,
learning_rate=0.001,
global_steps=50000,
disp_freq=100,
save_freq=100,
test_freq=100,
saveto_file='params.npz',
tmsaveto_file='timeparams.npz',
weight_decay=0.0005,
sigmasqr=1,
tdim=1.,
reload_model=False,
train=True):
"""
Topo-LSTM model training.
tdim: scale time down by how many times
"""
options = locals().copy()
#savedstep = '0'
saveto = data_dir + saveto_file
tmsaveto = data_dir + tmsaveto_file
# loads graph
Gp, node_index = data_utils.load_graph(data_dir)
#print nx.info(G)
options['n_events'] = len(node_index)
print options
# creates and initializes shared variables.
print 'Initializing variables...'
params = init_params(options)
if reload_model:
print 'reusing saved model.'
load_params(saveto, params)
tparams = init_tparams(params)
timeparams = init_timeparams(options)
if reload_model:
print 'reusing saved model.'
load_params(tmsaveto, timeparams)
timetparams = init_tparams(timeparams)
# builds Topo-LSTM model
print 'Building model...'
model = tpgru_model.build_model(tparams, timetparams, options)
print 'Loading test data...'
test_examples = data_utils.load_examples(data_dir,
dataset='test',
node_index=node_index,
maxlen=maxlen,
Gp=Gp)
test_loader = data_utils.Loader(test_examples, options=options)
print 'Loaded %d test examples' % len(test_examples)
if train:
# prepares training data.
print 'Loading train data...'
train_examples = data_utils.load_examples(
data_dir,
dataset='train',
keep_ratio=options['keep_ratio'],
node_index=node_index,
maxlen=maxlen,
Gp=Gp)
train_loader = data_utils.Loader(train_examples, options=options)
print 'Loaded %d training examples.' % len(train_examples)
# compiles updates.
optimizer = downhill.build(algo='adam',
loss=model['cost'],
params=tparams.values(),
inputs=model['data'])
updates = optimizer.get_updates(max_gradient_elem=5.,
learning_rate=learning_rate)
f_update = theano.function(model['data'],
model['cost'],
updates=list(updates))
toptimizer = downhill.build(algo='adam',
loss=model['timecost'],
params=timetparams.values(),
inputs=model['timedata'])
tupdates = toptimizer.get_updates(max_gradient_elem=5.,
learning_rate=0.005)
f_t_update = theano.function(model['timedata'],
model['timecost'],
updates=list(tupdates))
# training loop.
start_time = timeit.default_timer()
n_examples = len(train_examples)
batches_per_epoch = n_examples // options['batch_size'] + 1
n_epochs = global_steps // batches_per_epoch + 1
global_step = 0
#cost_history = []
for _ in range(n_epochs):
for _ in range(batches_per_epoch):
batch_data = train_loader()
cost = f_update(*(batch_data[:-3] + (batch_data[-2], )))
#cost_history += [cost]
timecost = f_t_update(*(batch_data[:-2] + (batch_data[-1], )))
if global_step % disp_freq == 0:
print 'global step %d, cost: %f' % (global_step, cost)
print 'timecost: %f' % (timecost)
# dump model parameters.
if global_step % save_freq == 0:
params = unzip(tparams)
np.savez(data_dir + saveto_file, **params)
pickle.dump(
options, open('%s.pkl' % (data_dir + saveto_file),
'wb'), -1)
timeparams = unzip(timetparams)
np.savez(data_dir + tmsaveto_file, **timeparams)
# evaluate on test data.
if global_step % test_freq == 0:
scores = evaluate(model['f_prob'], test_loader,
model['f_tprob'], options['tdim'])
print 'eval scores: ', scores
end_time = timeit.default_timer()
print 'time used: %d seconds.' % (end_time - start_time)
global_step += 1
scores = evaluate(model['f_prob'], test_loader, model['f_tprob'],
options['tdim'])
pprint.pprint(scores)