def train(
out_path=None, name='', model_to_load=None, save_images=True, test_every=None,
dim_h=None, rec_args=None, gen_args=None, prior='gaussian',
preprocessing=None,
learning_args=None,
dataset_args=None):
# ========================================================================
if preprocessing is None: preprocessing = []
if learning_args is None: learning_args = dict()
if dataset_args is None: raise ValueError('Dataset args must be provided')
learning_args = init_learning_args(**learning_args)
print 'Dataset args: %s' % pprint.pformat(dataset_args)
print 'Learning args: %s' % pprint.pformat(learning_args)
# ========================================================================
print_section('Setting up data')
batch_size = learning_args.pop('batch_size')
valid_batch_size = learning_args.pop('valid_batch_size')
train, valid, test = load_data(
train_batch_size=batch_size,
valid_batch_size=valid_batch_size,
**dataset_args)
# ========================================================================
print_section('Setting model and variables')
dim_in = train.dims[train.name]
X = T.matrix('x', dtype=floatX)
X.tag.test_value = np.zeros((batch_size, dim_in), dtype=X.dtype)
trng = get_trng()
preproc = Preprocessor(preprocessing)
X_i = preproc(X, data_iter=train)
inps = [X]
# ========================================================================
print_section('Loading model and forming graph')
def create_model():
model = Helmholtz.factory(
dim_h, train,
prior=prior,
rec_args=rec_args,
gen_args=gen_args)
models = OrderedDict()
models[model.name] = model
return models
models = set_model(create_model, model_to_load, unpack)
model = next((v for k, v in models.iteritems() if k in ['sbn', 'gbn', 'lbn']), None)
posterior = model.posterior
if not posterior.distribution.is_continuous:
raise ValueError('Cannot perform VAE with posterior with distribution '
'%r' % type(posterior.distribution))
tparams = model.set_tparams()
print_profile(tparams)
# ==========================================================================
print_section('Getting cost')
constants = []
updates = theano.OrderedUpdates()
n_posterior_samples = learning_args.pop('n_posterior_samples')
reweight = learning_args.pop('reweight')
results, samples, constants, updates = model(
X_i, X, qk=None, pass_gradients=True,
n_posterior_samples=n_posterior_samples, reweight=reweight)
cost = results['cost']
extra_outs = []
extra_outs_keys = ['cost']
l2_decay = learning_args.pop('l2_decay')
if l2_decay is not False and l2_decay > 0.:
print 'Adding %.5f L2 weight decay' % l2_decay
l2_rval = model.l2_decay(l2_decay)
cost += l2_rval.pop('cost')
extra_outs += l2_rval.values()
extra_outs_keys += l2_rval.keys()
# ==========================================================================
print_section('Test functions')
f_test_keys = results.keys()
f_test = theano.function([X], results.values())
prior_samples, p_updates = model.sample_from_prior()
f_prior = theano.function([], prior_samples, updates=p_updates)
latent_vis = model.visualize_latents()
f_latent = theano.function([], latent_vis)
py = samples['py']
f_py_h = theano.function([X], py)
# ========================================================================
print_section('Setting final tparams and save function')
excludes = learning_args.pop('excludes')
tparams, all_params = set_params(
tparams, updates, excludes=excludes)
def save(tparams, outfile):
d = dict((k, v.get_value()) for k, v in all_params.items())
d.update(
dim_h=dim_h,
rec_args=rec_args,
gen_args=gen_args
)
np.savez(outfile, **d)
def save_images():
p_samples = f_prior()
p_samples = p_samples.reshape(
(p_samples.shape[0] // 10, 10, p_samples.shape[1]))
train.save_images(p_samples, path.join(out_path, 'prior_samples.png'))
l_vis = f_latent()
l_vis = l_vis.reshape((l_vis.shape[0] // 10, 10, l_vis.shape[1]))
train.save_images(l_vis, path.join(out_path, 'latent_vis.png'))
py_h = f_py_h(train.X[:100])
train.save_images(py_h, path.join(out_path, 'py_h.png'))
# ========================================================================
print_section('Getting gradients and building optimizer.')
f_grad_shared, f_grad_updates, learning_args = set_optimizer(
inps, cost, tparams, constants, updates, extra_outs, **learning_args)
# ========================================================================
print_section('Actually running (main loop)')
monitor = SimpleMonitor()
main_loop(
train, valid, tparams,
f_grad_shared, f_grad_updates, f_test, f_test_keys,
test_every=test_every,
save=save,
save_images=save_images,
monitor=monitor,
out_path=out_path,
name=name,
extra_outs_keys=extra_outs_keys,
**learning_args)
def train(
out_path=None, name='', model_to_load=None, save_images=True, test_every=None,
dim_h=None, preprocessing=None,
learning_args=None,
inference_args=None,
dataset_args=None):
# ========================================================================
if preprocessing is None: preprocessing = []
if learning_args is None: learning_args = dict()
if inference_args is None: inference_args = dict()
if dataset_args is None: raise ValueError('Dataset args must be provided')
learning_args = init_learning_args(**learning_args)
inference_args = init_inference_args(**inference_args)
print 'Dataset args: %s' % pprint.pformat(dataset_args)
print 'Learning args: %s' % pprint.pformat(learning_args)
print 'Inference args: %s' % pprint.pformat(inference_args)
# ========================================================================
print_section('Setting up data')
batch_size = learning_args.pop('batch_size')
valid_batch_size = learning_args.pop('valid_batch_size')
train, valid, test = load_data(
train_batch_size=batch_size,
valid_batch_size=valid_batch_size,
**dataset_args)
# ========================================================================
print_section('Setting model and variables')
dim_in = train.dims[train.name]
X = T.matrix('x', dtype=floatX)
X.tag.test_value = np.zeros((batch_size, dim_in), dtype=X.dtype)
trng = get_trng()
preproc = Preprocessor(preprocessing)
X_i = preproc(X, data_iter=train)
inps = [X]
# ========================================================================
print_section('Loading model and forming graph')
def create_model():
model = RBM(dim_in, dim_h, v_dist=train.distributions[train.name],
mean_image=train.mean_image)
models = OrderedDict()
models[model.name] = model
return models
models = set_model(create_model, model_to_load, unpack)
model = models['rbm']
tparams = model.set_tparams()
print_profile(tparams)
# ==========================================================================
print_section('Getting cost')
persistent = inference_args.pop('persistent')
if persistent:
H_p = theano.shared(
np.zeros((inference_args['n_chains'], model.h_dist.dim)).astype(floatX),
name='h_p')
else:
H_p = None
results, samples, updates, constants = model(
X_i, h_p=H_p, **inference_args)
updates = theano.OrderedUpdates()
if persistent:
updates += theano.OrderedUpdates([(H_p, samples['hs'][-1])])
cost = results['cost']
extra_outs = [results['free_energy']]
extra_outs_keys = ['cost', 'free_energy']
# ==========================================================================
print_section('Test functions')
f_test_keys = results.keys()
f_test = theano.function([X], results.values())
try:
_, z_updates = model.update_partition_function(K=1000)
f_update_partition = theano.function([], [], updates=z_updates)
except NotImplementedError:
f_update_partition = None
H0 = model.trng.binomial(size=(10, model.h_dist.dim), dtype=floatX)
s_outs, s_updates = model.sample(H0, n_steps=100)
f_chain = theano.function(
[], model.v_dist.get_center(s_outs['pvs']), updates=s_updates)
# ========================================================================
print_section('Setting final tparams and save function')
excludes = learning_args.pop('excludes')
tparams, all_params = set_params(tparams, updates, excludes=excludes)
def save(tparams, outfile):
d = dict((k, v.get_value()) for k, v in all_params.items())
d.update(
dim_in=dim_in,
dim_h=dim_h
)
np.savez(outfile, **d)
def save_images():
w = model.W.get_value().T
w = w.reshape((10, w.shape[0] // 10, w.shape[1]))
train.save_images(w, path.join(out_path, 'weights.png'))
chain = f_chain()
train.save_images(chain, path.join(out_path, 'chain.png'))
# ========================================================================
print_section('Getting gradients and building optimizer.')
f_grad_shared, f_grad_updates, learning_args = set_optimizer(
[X], cost, tparams, constants, updates, extra_outs, **learning_args)
# ========================================================================
print_section('Actually running (main loop)')
monitor = SimpleMonitor()
main_loop(
train, valid, tparams,
f_grad_shared, f_grad_updates, f_test, f_test_keys,
f_extra=f_update_partition,
test_every=test_every,
save=save,
save_images=save_images,
monitor=monitor,
out_path=out_path,
name=name,
extra_outs_keys=extra_outs_keys,
**learning_args)
def train(
out_path=None, name='', model_to_load=None, test_every=None,
classifier=None, preprocessing=None,
learning_args=None,
dataset_args=None):
'''Basic training script.
Args:
out_path: str, path for output directory.
name: str, name of experiment.
test_every: int (optional), if not None, test every n epochs instead of
every 1 epoch.
classifier: dict, kwargs for MLP factory.
learning_args: dict or None, see `init_learning_args` above for options.
dataset_args: dict, arguments for Dataset class.
'''
# ========================================================================
if preprocessing is None: preprocessing = []
if learning_args is None: learning_args = dict()
if dataset_args is None: raise ValueError('Dataset args must be provided')
learning_args = init_learning_args(**learning_args)
print 'Dataset args: %s' % pprint.pformat(dataset_args)
print 'Learning args: %s' % pprint.pformat(learning_args)
# ========================================================================
print_section('Setting up data')
input_keys = dataset_args.pop('keys')
batch_size = learning_args.pop('batch_size')
valid_batch_size = learning_args.pop('valid_batch_size')
train, valid, test = load_data(
train_batch_size=batch_size,
valid_batch_size=valid_batch_size,
**dataset_args)
# ========================================================================
print_section('Setting model and variables')
dim_in = train.dims[input_keys[0]]
dim_out = train.dims[input_keys[1]]
X = T.matrix('x', dtype=floatX) # Input data
Y = T.matrix('y', dtype=floatX) # Lables
X.tag.test_value = np.zeros((batch_size, dim_in), dtype=X.dtype)
Y.tag.test_value = np.zeros((batch_size, dim_out), dtype=X.dtype)
trng = get_trng()
preproc = Preprocessor(preprocessing)
X_i = preproc(X, data_iter=train)
inps = [X, Y]
# ========================================================================
print_section('Loading model and forming graph')
dropout = learning_args.pop('dropout')
def create_model():
model = MLP.factory(dim_in=dim_in, dim_out=dim_out,
distribution=train.distributions[input_keys[1]],
dropout=dropout,
**classifier)
models = OrderedDict()
models[model.name] = model
return models
def unpack(dim_in=None, dim_out=None, mlp=None, **model_args):
model = MLP.factory(dim_in=dim_in, dim_out=dim_out, **mlp)
models = [model]
return models, model_args, None
models = set_model(create_model, model_to_load, unpack)
model = models['MLP']
tparams = model.set_tparams()
print_profile(tparams)
# ==========================================================================
print_section('Getting cost')
outs = model(X_i)
p = outs['p']
base_cost = model.neg_log_prob(Y, p).sum(axis=0)
cost = base_cost
updates = theano.OrderedUpdates()
l2_decay = learning_args.pop('l2_decay')
if l2_decay > 0.:
print 'Adding %.5f L2 weight decay' % l2_decay
l2_rval = model.l2_decay(l2_decay)
l2_cost = l2_rval.pop('cost')
cost += l2_cost
constants = []
extra_outs = []
extra_outs_keys = ['cost']
# ==========================================================================
print_section('Test functions')
error = (Y * (1 - p)).sum(axis=1).mean()
f_test_keys = ['error', 'cost']
f_test_vals = [error, base_cost]
if l2_decay > 0.:
f_test_keys.append('L2 cost')
f_test_vals.append(l2_cost)
f_test = theano.function([X, Y], f_test_vals)
# ========================================================================
print_section('Setting final tparams and save function')
tparams, all_params = set_params(tparams, updates)
def save(tparams, outfile):
d = dict((k, v.get_value()) for k, v in all_params.items())
d.update(
dim_in=dim_in,
dim_out=dim_out,
mlp=classifier
)
np.savez(outfile, **d)
# ========================================================================
print_section('Getting gradients and building optimizer.')
f_grad_shared, f_grad_updates, learning_args = set_optimizer(
inps, cost, tparams, constants, updates, extra_outs, **learning_args)
# ========================================================================
print_section('Actually running (main loop)')
monitor = SimpleMonitor()
main_loop(
train, valid, tparams,
f_grad_shared, f_grad_updates, f_test, f_test_keys,
input_keys=input_keys,
test_every=test_every,
save=save,
monitor=monitor,
out_path=out_path,
name=name,
extra_outs_keys=extra_outs_keys,
**learning_args)
def train(out_path=None,
name='',
model_to_load=None,
save_images=True,
test_every=None,
dim_h=None,
preprocessing=None,
learning_args=None,
inference_args=None,
dataset_args=None):
# ========================================================================
if preprocessing is None: preprocessing = []
if learning_args is None: learning_args = dict()
if inference_args is None: inference_args = dict()
if dataset_args is None: raise ValueError('Dataset args must be provided')
learning_args = init_learning_args(**learning_args)
inference_args = init_inference_args(**inference_args)
print 'Dataset args: %s' % pprint.pformat(dataset_args)
print 'Learning args: %s' % pprint.pformat(learning_args)
print 'Inference args: %s' % pprint.pformat(inference_args)
# ========================================================================
print_section('Setting up data')
batch_size = learning_args.pop('batch_size')
valid_batch_size = learning_args.pop('valid_batch_size')
train, valid, test = load_data(train_batch_size=batch_size,
valid_batch_size=valid_batch_size,
**dataset_args)
# ========================================================================
print_section('Setting model and variables')
dim_in = train.dims[train.name]
X = T.matrix('x', dtype=floatX)
X.tag.test_value = np.zeros((batch_size, dim_in), dtype=X.dtype)
trng = get_trng()
preproc = Preprocessor(preprocessing)
X_i = preproc(X, data_iter=train)
inps = [X]
# ========================================================================
print_section('Loading model and forming graph')
def create_model():
model = RBM(dim_in,
dim_h,
v_dist=train.distributions[train.name],
mean_image=train.mean_image)
models = OrderedDict()
models[model.name] = model
return models
models = set_model(create_model, model_to_load, unpack)
model = models['rbm']
tparams = model.set_tparams()
print_profile(tparams)
# ==========================================================================
print_section('Getting cost')
persistent = inference_args.pop('persistent')
if persistent:
H_p = theano.shared(np.zeros(
(inference_args['n_chains'], model.h_dist.dim)).astype(floatX),
name='h_p')
else:
H_p = None
results, samples, updates, constants = model(X_i,
h_p=H_p,
**inference_args)
updates = theano.OrderedUpdates()
if persistent:
updates += theano.OrderedUpdates([(H_p, samples['hs'][-1])])
cost = results['cost']
extra_outs = [results['free_energy']]
extra_outs_keys = ['cost', 'free_energy']
# ==========================================================================
print_section('Test functions')
f_test_keys = results.keys()
f_test = theano.function([X], results.values())
try:
_, z_updates = model.update_partition_function(K=1000)
f_update_partition = theano.function([], [], updates=z_updates)
except NotImplementedError:
f_update_partition = None
H0 = model.trng.binomial(size=(10, model.h_dist.dim), dtype=floatX)
s_outs, s_updates = model.sample(H0, n_steps=100)
f_chain = theano.function([],
model.v_dist.get_center(s_outs['pvs']),
updates=s_updates)
# ========================================================================
print_section('Setting final tparams and save function')
excludes = learning_args.pop('excludes')
tparams, all_params = set_params(tparams, updates, excludes=excludes)
def save(tparams, outfile):
d = dict((k, v.get_value()) for k, v in all_params.items())
d.update(dim_in=dim_in, dim_h=dim_h)
np.savez(outfile, **d)
def save_images():
w = model.W.get_value().T
w = w.reshape((10, w.shape[0] // 10, w.shape[1]))
train.save_images(w, path.join(out_path, 'weights.png'))
chain = f_chain()
train.save_images(chain, path.join(out_path, 'chain.png'))
# ========================================================================
print_section('Getting gradients and building optimizer.')
f_grad_shared, f_grad_updates, learning_args = set_optimizer(
[X], cost, tparams, constants, updates, extra_outs, **learning_args)
# ========================================================================
print_section('Actually running (main loop)')
monitor = SimpleMonitor()
main_loop(train,
valid,
tparams,
f_grad_shared,
f_grad_updates,
f_test,
f_test_keys,
f_extra=f_update_partition,
test_every=test_every,
save=save,
save_images=save_images,
monitor=monitor,
out_path=out_path,
name=name,
extra_outs_keys=extra_outs_keys,
**learning_args)
def train(out_path=None,
name='',
model_to_load=None,
save_images=True,
test_every=None,
dim_h=None,
rec_args=None,
gen_args=None,
prior='gaussian',
preprocessing=None,
learning_args=None,
dataset_args=None):
# ========================================================================
if preprocessing is None: preprocessing = []
if learning_args is None: learning_args = dict()
if dataset_args is None: raise ValueError('Dataset args must be provided')
learning_args = init_learning_args(**learning_args)
print 'Dataset args: %s' % pprint.pformat(dataset_args)
print 'Learning args: %s' % pprint.pformat(learning_args)
# ========================================================================
print_section('Setting up data')
batch_size = learning_args.pop('batch_size')
valid_batch_size = learning_args.pop('valid_batch_size')
dataset = dataset_args['dataset']
dataset_class = resolve_dataset(dataset)
train, valid, test, idx = load_data_split(
dataset_class,
train_batch_size=batch_size,
valid_batch_size=valid_batch_size,
**dataset_args)
dataset_args['idx'] = idx
# ========================================================================
print_section('Setting model and variables')
dim_in = train.dims[train.name]
X = T.matrix('x', dtype=floatX)
X.tag.test_value = np.zeros((batch_size, dim_in), dtype=X.dtype)
trng = get_trng()
preproc = Preprocessor(preprocessing)
X_i = preproc(X, data_iter=train)
inps = [X]
# ========================================================================
print_section('Loading model and forming graph')
def create_model():
model = Helmholtz.factory(dim_h,
train,
prior=prior,
rec_args=rec_args,
gen_args=gen_args)
models = OrderedDict()
models[model.name] = model
return models
models = set_model(create_model, model_to_load, unpack)
model = next(
(v
for k, v in models.iteritems() if k in ['sbn', 'gbn', 'lbn', 'labn']),
None)
posterior = model.posterior
if not posterior.distribution.is_continuous:
raise ValueError('Cannot perform VAE with posterior with distribution '
'%r' % type(posterior.distribution))
tparams = model.set_tparams()
print_profile(tparams)
# ==========================================================================
print_section('Getting cost')
constants = []
updates = theano.OrderedUpdates()
n_posterior_samples = learning_args.pop('n_posterior_samples')
results, samples, updates, constants = model(
X_i,
X,
qk=None,
pass_gradients=True,
n_posterior_samples=n_posterior_samples)
cost = results['cost']
extra_outs = []
extra_outs_keys = ['cost']
l2_decay = learning_args.pop('l2_decay')
if l2_decay is not False and l2_decay > 0.:
print 'Adding %.5f L2 weight decay' % l2_decay
l2_rval = model.l2_decay(l2_decay)
cost += l2_rval.pop('cost')
extra_outs += l2_rval.values()
extra_outs_keys += l2_rval.keys()
# ==========================================================================
print_section('Test functions')
f_test_keys = results.keys()
f_test = theano.function([X], results.values())
prior_samples, p_updates = model.sample_from_prior()
f_prior = theano.function([],
model.get_center(prior_samples),
updates=p_updates)
latent_vis = model.visualize_latents()
f_latent = theano.function([], latent_vis)
py = model.get_center(samples['py'])
f_py_h = theano.function([X], py)
# ========================================================================
print_section('Setting final tparams and save function')
excludes = learning_args.pop('excludes')
tparams, all_params = set_params(tparams, updates, excludes=excludes)
def save(tparams, outfile):
d = dict((k, v.get_value()) for k, v in all_params.items())
d.update(dim_h=dim_h, rec_args=rec_args, gen_args=gen_args)
np.savez(outfile, **d)
def save_images():
p_samples = f_prior()
train.save_images(p_samples, path.join(out_path, 'prior_samples.png'))
l_vis = f_latent()
train.save_images(l_vis, path.join(out_path, 'latent_vis.png'))
py_h = f_py_h(train.X[:100])
train.save_images(py_h, path.join(out_path, 'py_h.png'))
# ========================================================================
print_section('Getting gradients and building optimizer.')
f_grad_shared, f_grad_updates, learning_args = set_optimizer(
inps, cost, tparams, constants, updates, extra_outs, **learning_args)
# ========================================================================
print_section('Actually running (main loop)')
monitor = SimpleMonitor()
main_loop(train,
valid,
tparams,
f_grad_shared,
f_grad_updates,
f_test,
f_test_keys,
test_every=test_every,
save=save,
save_images=save_images,
monitor=monitor,
out_path=out_path,
name=name,
extra_outs_keys=extra_outs_keys,
**learning_args)