def build_network(verbose=False, **kwargs):
#network_name, data_augmentation='full', lambda2=0.0005, max_epochs=50, nb_channels=3, crop_size=200,
#activation_function=rectify, batch_size=48, init_learning_rate=0.01, final_learning_rate=0.0001, dataset_ratio=3.8, final_ratio=2., verbose=False):
"""Build nolearn neural network and returns it
:param network: pre-defined network name
:param data_augmentation: type of batch data aug. ('no', 'flip' or 'full')
:return: NeuralNet nolearn object
"""
for key,val in kwargs.items():
exec(key + '=val')
#data_augmentation = kwargs['data_augmentation']
if data_augmentation == 'no':
batch_iterator_train = BatchIterator(batch_size=batch_size)
elif data_augmentation == 'flip':
batch_iterator_train = FlipBatchIterator(batch_size=batch_size)
elif data_augmentation == 'full':
batch_iterator_train = DataAugmentationBatchIterator(batch_size=batch_size, crop_size=crop_size)
elif data_augmentation == 'resampling':
batch_iterator_train = ResamplingBatchIterator(batch_size=batch_size, crop_size=crop_size, scale_delta=scale_delta, max_trans=max_trans, angle_factor=angle_factor,
max_epochs=max_epochs, dataset_ratio=dataset_ratio, final_ratio=final_ratio)
elif data_augmentation == 'resampling-flip':
batch_iterator_train = ResamplingFlipBatchIterator(batch_size=batch_size,
max_epochs=max_epochs, dataset_ratio=dataset_ratio, final_ratio=final_ratio)
else:
raise ValueError(data_augmentation+' is an unknown data augmentation strategy.')
layers = build_layers(network, nb_channels=nb_channels, crop_size=crop_size,
activation_function=activation_function)
conv_net = NeuralNet(
layers,
update=nesterov_momentum,
update_learning_rate=theano.shared(float32(learning_init)),
update_momentum=theano.shared(float32(0.9)),
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=learning_init, stop=learning_final),
AdjustVariable('update_momentum', start=0.9, stop=0.999),
EarlyStopping(patience=patience),
],
batch_iterator_train = batch_iterator_train,
# batch_iterator_test=DataAugmentationBatchIterator(batch_size=31, crop_size=crop_size),
objective=regularization_objective,
objective_lambda2=lambda2,
train_split=TrainSplit(eval_size=0.1, stratify=True),
custom_score=('AUC-ROC', auc_roc),
max_epochs=max_epochs,
verbose=3,
)
if verbose:
print conv_net.__dict__
return conv_net
def setup_net(self, print_out=True):
if print_out:
self.printer = PrintLogSave(out_file=self.out_file)
on_epoch_finished = [self.printer, SaveBestWeights(method=self.method, full_dataset=self.full_dataset),
EarlyStopping(patience=200, out_file=self.out_file)]
on_training_started = [SaveLayerInfo(out_file=self.out_file)]
else:
on_epoch_finished = [EarlyStopping(patience=200, out_file=self.out_file)]
on_training_started = []
if self.flip:
#batch_iterator_train=flip.ResizeBatchIterator(batch_size=128)
batch_iterator_train=flip.FlipBatchIterator(batch_size=128)
else:
batch_iterator_train=flip.ResizeBatchIterator(batch_size=128)
if self.adaptive_learning:
update_learning_rate = theano.shared(utils.float32(0.03))
update_momentum = theano.shared(utils.float32(0.9))
on_epoch_finished.append(AdjustVariable('update_learning_rate', start=0.03, stop=0.0001))
on_epoch_finished.append(AdjustVariable('update_momentum', start=0.9, stop=0.999))
else:
update_learning_rate = 0.01
update_momentum = 0.9
layers, layer_params = my_net.MyNeuralNet.produce_layers(self.num_layers, dropout=self.dropout)
self.net = my_net.MyNeuralNet(
layers=layers,
num_layers=self.num_layers,
input_shape=(None, 3, utils.CROP_SIZE, utils.CROP_SIZE),
output_num_units=3,
output_nonlinearity=lasagne.nonlinearities.softmax,
preproc_scaler = None,
#learning rates
update_learning_rate=update_learning_rate,
update_momentum=update_momentum,
#printing
net_name=self.net_name,
on_epoch_finished=on_epoch_finished,
on_training_started=on_training_started,
#data augmentation
batch_iterator_test= flip.ResizeBatchIterator(batch_size=128),
batch_iterator_train=batch_iterator_train,
max_epochs=self.epochs,
verbose=1,
**layer_params
)
return layer_params
def main():
nottingham = pickle.load(file("Nottingham.pickle"))
train = hot_to_sparse(nottingham['train'], 88)
print len(train)
np.random.shuffle(train)
net = ClockworkRNN((88, (4, 30), 88),
update_fn=adam,
learning_rate=0.001,
cost=quadratic_loss)
batch_size = 700
losses = []
lrs = []
norms, momentum_norms = [], []
norms_thres, momentum_norms_thres = [], []
# X, y, x_series = create_batch_func_params(500, 0.1, 2)
best = np.inf
last_best_index = 0
decrement = float32(0.99)
for i in range(2000):
start = time()
closs, cnorm, cnorm_threshold, cmomentum_norm, cmomentum_norm_threshold = [], [], [], [], []
for g in xrange(0, len(train), batch_size):
loss, norm, norm_theshold, momentum_norm, momentum_norm_threshold = net.bptt(
train[g:g + batch_size, :-1], train[g:g + batch_size, 1:])
closs.append(loss)
cnorm.append(norm)
cnorm_threshold.append(norms_thres)
cmomentum_norm.append(momentum_norm)
cmomentum_norm_threshold.append(momentum_norm_threshold)
losses.append(np.mean(closs))
norms.append(np.mean(cnorm))
momentum_norms.append(np.mean(cnorm_threshold))
norms_thres.append(np.mean(float32(cnorm_threshold)))
momentum_norms_thres.append(
np.mean([float32(cnmt) for cnmt in cmomentum_norm_threshold]))
lrs.append(net.learning_rate.get_value())
epoch_time = time() - start
print i, ':', losses[-1], " took :", epoch_time
if best > losses[-1]:
last_best_index = i
best = losses[-1]
elif i - last_best_index > 20:
best = losses[-1]
new_rate = net.learning_rate.get_value() * decrement
net.learning_rate.set_value(new_rate)
last_best_index = i
print("New learning rate", new_rate)
def __call__(self, nn, train_history):
if self.ls is None:
self.ls = np.linspace(self.start, self.stop, nn.max_epochs)
epoch = train_history[-1]['epoch']
new_value = utils.float32(self.ls[epoch-1])
getattr(nn, self.name).set_value(new_value)
def __call__(self, nn, train_history):
if self.ls is None:
self.ls = np.linspace(self.start, self.stop, nn.max_epochs)
epoch = train_history[-1]['epoch']
new_value = utils.float32(self.ls[epoch - 1])
getattr(nn, self.name).set_value(new_value)
def create_batch_func_params(input_length=300, freq_var=0.1, size=20):
freqs = float32(np.abs(np.random.normal(scale=freq_var, size=size)) + 0.1)
# freqs = np.ones(size, dtype=floatX) * float32(0.1)
X = np.array([np.ones(input_length, dtype=floatX) * freq for freq in freqs], dtype=floatX)[:, :, np.newaxis]
x_series = np.array([np.linspace(0, input_length * freq, num=input_length, dtype=floatX) for freq in freqs],
dtype=floatX)
y = func_to_learn(x_series).astype(floatX)[:, :, np.newaxis]
return X, y, x_series
def __getitem__(self, index, return_manip=False):
x_link = self.X[index]
x = None
if self.lazy:
x = self._load_and_transform(x_link)
if self.train == False:
m = float32(1. if '_manip' in x_link else 0.)
x = (x, m)
return x, self.y[index]
def __call__(self, nn, train_history):
epoch = train_history[-1]['epoch']
if epoch in self.schedule:
new_value = self.schedule[epoch]
if new_value == 'stop':
if self.weights_file is not None:
nn.save_params_to(self.weights_file)
raise StopIteration
getattr(nn, self.name).set_value(utils.float32(new_value))
def main():
nottingham = pickle.load(file("Nottingham.pickle"))
train = hot_to_sparse(nottingham['train'], 88)
print len(train)
np.random.shuffle(train)
net = ClockworkRNN((88, (4, 30), 88), update_fn=adam, learning_rate=0.001, cost=quadratic_loss)
batch_size = 700
losses = []
lrs = []
norms, momentum_norms = [], []
norms_thres, momentum_norms_thres = [], []
# X, y, x_series = create_batch_func_params(500, 0.1, 2)
best = np.inf
last_best_index = 0
decrement = float32(0.99)
for i in range(2000):
start = time()
closs, cnorm, cnorm_threshold, cmomentum_norm, cmomentum_norm_threshold = [], [], [], [], []
for g in xrange(0, len(train), batch_size):
loss, norm, norm_theshold, momentum_norm, momentum_norm_threshold = net.bptt(train[g:g+batch_size, :-1], train[g:g+batch_size, 1:])
closs.append(loss)
cnorm.append(norm)
cnorm_threshold.append(norms_thres)
cmomentum_norm.append(momentum_norm)
cmomentum_norm_threshold.append(momentum_norm_threshold)
losses.append(np.mean(closs))
norms.append(np.mean(cnorm))
momentum_norms.append(np.mean(cnorm_threshold))
norms_thres.append(np.mean(float32(cnorm_threshold)))
momentum_norms_thres.append(np.mean([float32(cnmt) for cnmt in cmomentum_norm_threshold]))
lrs.append(net.learning_rate.get_value())
epoch_time = time() - start
print i, ':', losses[-1], " took :", epoch_time
if best > losses[-1]:
last_best_index = i
best = losses[-1]
elif i - last_best_index > 20:
best = losses[-1]
new_rate = net.learning_rate.get_value() * decrement
net.learning_rate.set_value(new_rate)
last_best_index = i
print("New learning rate", new_rate)
def __init__(self, layer_specs=(1, (2, 20), (2, 10), 1), cost=quadratic_loss, update_fn=adam, learning_rate=0.001,
alpha=1.):
self.alpha = theano.shared(float32(alpha))
self.layer_specs = layer_specs
self.layers = []
previous_size = layer_specs[0]
self.params = []
self.cost = cost
self.update_fn = update_fn
self.learning_rate = theano.shared(float32(learning_rate))
self.training_step = theano.shared(float32(1))
for i, spec in enumerate(layer_specs[1:-1]):
spec = [spec[0], spec[1], previous_size]
self.layers.append(ClockworkLayer(*spec, activation_function=T.tanh))
self.params.extend(self.layers[-1].params)
previous_size = spec[0] * spec[1]
self.layers.append(OutputLayer(layer_specs[-1], previous_size, T.tanh))
self.params.extend(self.layers[-1].params)
def get_estimator(n_features, files, labels, eval_size=0.1):
layers = [
(InputLayer, {
'shape': (None, n_features)
}),
(DenseLayer, {
'num_units': N_HIDDEN_1,
'nonlinearity': rectify,
'W': init.Orthogonal('relu'),
'b': init.Constant(0.01)
}),
(FeaturePoolLayer, {
'pool_size': 2
}),
(DenseLayer, {
'num_units': N_HIDDEN_2,
'nonlinearity': rectify,
'W': init.Orthogonal('relu'),
'b': init.Constant(0.01)
}),
(FeaturePoolLayer, {
'pool_size': 2
}),
(DenseLayer, {
'num_units': 1,
'nonlinearity': None
}),
]
args = dict(
layers=layers,
update=adam,
update_learning_rate=theano.shared(utils.float32(START_LR)),
batch_iterator_train=ResampleIterator(BATCH_SIZE),
batch_iterator_test=BatchIterator(BATCH_SIZE),
objective=nn.get_objective(l1=L1, l2=L2),
# eval_size=eval_size,
custom_score=('kappa', utils.kappa) if eval_size > 0.0 else None,
on_epoch_finished=[
nn.Schedule('update_learning_rate', SCHEDULE),
],
regression=True,
max_epochs=N_ITER,
verbose=1,
)
net = BlendNet(eval_size=eval_size, **args)
net.set_split(files, labels)
return net
def create_net(config, **kwargs):
args = {
'layers':
config.layers,
'batch_iterator_train':
iterator.ResampleIterator(config,
batch_size=config.get('batch_size_train')),
'batch_iterator_test':
iterator.SharedIterator(config,
deterministic=True,
batch_size=config.get('batch_size_test')),
'on_epoch_finished': [
Schedule('update_learning_rate',
config.get('schedule'),
weights_file=config.final_weights_file),
SaveBestWeights(
weights_file=config.weights_file,
loss='kappa',
greater_is_better=True,
),
SaveWeights(config.weights_epoch, every_n_epochs=5),
SaveWeights(config.weights_best, every_n_epochs=1, only_best=True),
],
'objective':
get_objective(),
'use_label_encoder':
False,
'eval_size':
0.1,
'regression':
True,
'max_epochs':
1000,
'verbose':
1,
'update_learning_rate':
theano.shared(utils.float32(config.get('schedule')[0])),
'update':
nesterov_momentum,
'update_momentum':
0.9,
'custom_score': ('kappa', utils.kappa),
}
args.update(kwargs)
net = Net(**args)
return net
def _gen_predict_train_loaders(max_len=500):
X_b = []
y_b = []
manip_b = []
for c in xrange(10):
for b in xrange(N_BLOCKS[c]):
X_block = np.load(
os.path.join(args.data_path,
'X_{0}_{1}.npy'.format(c, b % N_BLOCKS[c])))
X_b += [X_block[i] for i in xrange(len(X_block))]
y_b += np.repeat(c, len(X_block)).tolist()
manip_b += [float32(0.)] * len(X_block)
if len(y_b) >= max_len:
yield _make_predict_train_loader(X_b, manip_b), y_b, manip_b
X_b = []
y_b = []
manip_b = []
for c in xrange(10):
for b in xrange(N_PSEUDO_BLOCKS[c]):
X_pseudo_block = np.load(
os.path.join(
args.data_path,
'X_pseudo_{0}_{1}.npy'.format(c, b % N_PSEUDO_BLOCKS[c])))
X_b += [X_pseudo_block[i] for i in xrange(len(X_pseudo_block))]
y_b += np.repeat(c, len(X_pseudo_block)).tolist()
manip_block = np.load(
os.path.join(
args.data_path,
'manip_pseudo_{0}_{1}.npy'.format(c,
b % N_PSEUDO_BLOCKS[c])))
manip_b += [m for m in manip_block]
if len(y_b) >= max_len:
yield _make_predict_train_loader(X_b, manip_b), y_b, manip_b
X_b = []
y_b = []
manip_b = []
if y_b > 0:
yield _make_predict_train_loader(X_b, manip_b), y_b, manip_b
def _make_predict_train_loader(X_b, manip_b, manip_ratio=0.):
assert len(X_b) == len(manip_b)
# make dataset
rng = RNG(1337)
train_transforms_list = [
transforms.Lambda(lambda (x, m): (Image.fromarray(x), m)),
# if `val` == False
# 972/1982 manip pseudo images
# images : pseudo = approx. 48 : 8 = 6 : 1
# to get unalt : manip = 70 : 30 (like in test metric),
# we manip ~24.7% of non-pseudo images
# else:
# we simply use same ratio as in validation (0.18)
transforms.Lambda(lambda (img, m): (make_random_manipulation(img, rng, crop_policy='center', crop_size=512), float32(1.)) if \
m[0] < 0.5 and rng.rand() < manip_ratio else (center_crop(img, 512), m))
]
train_transforms_list += make_aug_transforms(rng)
if args.crop_size == 512:
train_transforms_list += [
transforms.Lambda(lambda (
img, m): ([img, img.transpose(Image.ROTATE_90)], [m] * 2)),
transforms.Lambda(lambda (crops, ms): (torch.stack([
transforms.Normalize(args.means, args.stds)
(transforms.ToTensor()(crop)) for crop in crops
]), torch.from_numpy(np.asarray(ms))))
]
else:
train_transforms_list += [
transforms.Lambda(lambda (img, m): (transforms.TenCrop(
args.crop_size)(img), [m] * 10)),
transforms.Lambda(lambda (imgs, ms): (list(
imgs) + [img.transpose(Image.ROTATE_90)
for img in imgs], ms + ms)),
transforms.Lambda(lambda (crops, ms): (torch.stack([
transforms.Normalize(args.means, args.stds)
(transforms.ToTensor()(crop)) for crop in crops
]), torch.from_numpy(np.asarray(ms))))
]
train_transform = transforms.Compose(train_transforms_list)
dataset = make_numpy_dataset(X=[(x, m) for x, m in zip(X_b, manip_b)],
y=np.zeros(len(X_b), dtype=np.int64),
transform=train_transform)
# make loader
loader = DataLoader(dataset=dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.n_workers)
return loader
def train(optimizer, train_optimizer=train_optimizer):
# load and crop validation data
print "Loading data ..."
X_val = np.load(os.path.join(args.data_path, 'X_val.npy'))
y_val = np.load(os.path.join(args.data_path, 'y_val.npy'))
manip_val = np.zeros(
(len(y_val), 1), dtype=np.float32
) # np.load(os.path.join(args.data_path, 'manip_with_pseudo.npy')) # 68/480 manipulated
c = args.crop_size
C = X_val.shape[1]
if c < C:
X_val = X_val[:, C / 2 - c / 2:C / 2 + c / 2,
C / 2 - c / 2:C / 2 + c / 2, :]
if args.kernel:
X_val = [conv_K(x) for x in X_val]
# make validation loader
rng = RNG(args.random_seed + 42 if args.random_seed else None)
val_transform = transforms.Compose([
transforms.Lambda(lambda (x, m, y): (Image.fromarray(x), m, y)),
########
# 1 - (480-68-0.3*480)/(480-68) ~ 0.18
########
transforms.Lambda(lambda (img, m, y): (make_random_manipulation(img, rng, crop_policy='center'), float32(1.), y) if\
m[0] < 0.5 and rng.rand() < VAL_MANIP_RATIO else (img, m, y)),
transforms.Lambda(lambda (img, m, y): ([img,
img.transpose(Image.ROTATE_90)][int(rng.rand() < 0.5)], m) if \
KaggleCameraDataset.is_rotation_allowed()[y] else (img, m)),
transforms.Lambda(lambda (img, m): (transforms.ToTensor()(img), m)),
transforms.Lambda(lambda (img, m): (transforms.Normalize(args.means, args.stds)(img), m))
])
np.save(os.path.join(args.model_dirpath, 'y_val.npy'), np.vstack(y_val))
val_dataset = make_numpy_dataset(X=[
(x, m, y) for x, m, y in zip(X_val, manip_val, y_val)
],
y=y_val,
transform=val_transform)
val_loader = DataLoader(dataset=val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.n_workers)
n_runs = args.epochs / args.epochs_per_unique_data + 1
for _ in xrange(n_runs):
train_loader = make_train_loaders(block_index=optimizer.epoch /
args.epochs_per_unique_data)
optimizer.max_epoch = optimizer.epoch + args.epochs_per_unique_data
train_optimizer(optimizer, train_loader, val_loader)
def make_train_loaders(block_index):
# assemble data
X_train = []
y_train = []
manip_train = []
for c in xrange(10):
X_block = np.load(
os.path.join(args.data_path,
'X_{0}_{1}.npy'.format(c, block_index % N_BLOCKS[c])))
X_block = [X_block[i] for i in xrange(len(X_block))]
if args.bootstrap:
X_block = [X_block[i] for i in b_ind[c][block_index % N_BLOCKS[c]]]
X_train += X_block
y_train += np.repeat(c, len(X_block)).tolist()
manip_train += [float32(0.)] * len(X_block)
for c in xrange(10):
X_pseudo_block = np.load(
os.path.join(
args.data_path, 'X_pseudo_{0}_{1}.npy'.format(
c, block_index % N_PSEUDO_BLOCKS[c])))
X_pseudo_block = [
X_pseudo_block[i] for i in xrange(len(X_pseudo_block))
]
if args.bootstrap:
X_pseudo_block = [
X_pseudo_block[i]
for i in b_pseudo_ind[c][block_index % N_PSEUDO_BLOCKS[c]]
]
X_train += X_pseudo_block
y_train += np.repeat(c, len(X_pseudo_block)).tolist()
manip_block = np.load(
os.path.join(
args.data_path, 'manip_pseudo_{0}_{1}.npy'.format(
c, block_index % N_PSEUDO_BLOCKS[c])))
manip_block = [m for m in manip_block]
if args.bootstrap:
manip_block = [
manip_block[i]
for i in b_pseudo_ind[c][block_index % N_PSEUDO_BLOCKS[c]]
]
manip_train += manip_block
shuffle_ind = range(len(y_train))
RNG(seed=block_index).shuffle(shuffle_ind)
X_train = [X_train[i] for i in shuffle_ind]
y_train = [y_train[i] for i in shuffle_ind]
manip_train = [manip_train[i] for i in shuffle_ind]
# make dataset
rng = RNG(args.random_seed)
train_transforms_list = [
transforms.Lambda(lambda (x, m, y): (Image.fromarray(x), m, y)),
######
# 972/1982 manip pseudo images
# images : pseudo = approx. 48 : 8 = 6 : 1
# thus to get 50 : 50 manip : unalt we manip 11965/25874 ~ 46% of non-pseudo images
######
transforms.Lambda(lambda (img, m, y): (make_random_manipulation(img, rng), float32(1.), y) if \
m[0] < 0.5 and rng.rand() < TRAIN_MANIP_RATIO else (make_crop(img, args.crop_size, rng), m, y)),
transforms.Lambda(lambda (img, m, y): ([img,
img.transpose(Image.ROTATE_90)][int(rng.rand() < 0.5)], m) if \
KaggleCameraDataset.is_rotation_allowed()[y] else (img, m)),
]
train_transforms_list += make_aug_transforms(rng)
if args.kernel:
train_transforms_list += [
transforms.Lambda(lambda (img, m):
(conv_K(np.asarray(img, dtype=np.uint8)), m)),
transforms.Lambda(lambda (x, m):
(torch.from_numpy(x.transpose(2, 0, 1)), m))
]
else:
train_transforms_list += [
transforms.Lambda(lambda (img, m): (transforms.ToTensor()(img), m))
]
train_transforms_list += [
transforms.Lambda(lambda (img, m): (transforms.Normalize(
args.means, args.stds)(img), m))
]
train_transform = transforms.Compose(train_transforms_list)
dataset = make_numpy_dataset(X=[
(x, m, y) for x, m, y in zip(X_train, manip_train, y_train)
],
y=y_train,
transform=train_transform)
# make loader
loader = DataLoader(dataset=dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.n_workers,
sampler=StratifiedSampler(
class_vector=np.asarray(y_train),
batch_size=args.batch_size))
return loader
def make_train_loaders(block_index, distill=True):
# assemble data
X_train = []
y_train = []
manip_train = []
soft_logits = []
if distill:
soft_logits_ind = []
for c in xrange(10):
b = block_index % N_BLOCKS[c]
X_block = np.load(
os.path.join(args.data_path, 'X_{0}_{1}.npy'.format(c, b)))
X_block = [X_block[i] for i in xrange(len(X_block))]
if args.bootstrap:
X_block = [X_block[i] for i in b_ind[c][b]]
X_train += X_block
y_train += np.repeat(c, len(X_block)).tolist()
manip_train += [float32(0.)] * len(X_block)
soft_logits_ind_block = SOFT_LOGITS_IND[c][b]
if args.bootstrap:
soft_logits_ind_block = [
soft_logits_ind_block[i] for i in b_ind[c][b]
]
soft_logits_ind += soft_logits_ind_block
for c in xrange(10):
b = N_PSEUDO_BLOCKS_FOR_VALIDATION + block_index % N_PSEUDO_BLOCKS[
c]
X_pseudo_block = np.load(
os.path.join(args.data_path,
'X_pseudo_{0}_{1}.npy'.format(c, b)))
X_pseudo_block = [
X_pseudo_block[i] for i in xrange(len(X_pseudo_block))
]
if args.bootstrap:
X_pseudo_block = [
X_pseudo_block[i] for i in b_pseudo_ind[c][b]
]
X_train += X_pseudo_block
y_train += np.repeat(c, len(X_pseudo_block)).tolist()
manip_block = np.load(
os.path.join(args.data_path,
'manip_pseudo_{0}_{1}.npy'.format(c, b)))
manip_block = [m for m in manip_block]
if args.bootstrap:
manip_block = [manip_block[i] for i in b_pseudo_ind[c][b]]
manip_train += manip_block
soft_logits_ind_block = SOFT_LOGITS_IND[10 + c][b]
if args.bootstrap:
soft_logits_ind_block = [
soft_logits_ind_block[i] for i in b_pseudo_ind[c][b]
]
soft_logits_ind += soft_logits_ind_block
soft_logits = np.load(os.path.join(
args.data_path, 'logits_train.npy')).astype(np.float32)
soft_logits -= soft_logits.mean(axis=1)[:, np.newaxis]
soft_logits = [
soft_logits[i] / max(args.temperature, 1.) for i in soft_logits_ind
]
else:
for c in xrange(10):
X_block = np.load(
os.path.join(args.data_path, G[c][block_index % len(G[c])]))
X_block = [X_block[i] for i in xrange(len(X_block))]
X_train += X_block
y_train += np.repeat(c, len(X_block)).tolist()
manip_train += [float32(0.)] * len(X_block)
soft_logits += [np.zeros((10), dtype=np.float32)] * len(X_block)
shuffle_ind = range(len(y_train))
RNG(seed=block_index * 41).shuffle(shuffle_ind)
X_train = [X_train[i] for i in shuffle_ind]
y_train = [y_train[i] for i in shuffle_ind]
manip_train = [manip_train[i] for i in shuffle_ind]
soft_logits = [soft_logits[i] for i in shuffle_ind]
# make dataset
rng = RNG(args.random_seed)
train_transforms_list = [
transforms.Lambda(lambda (x, m, y): (Image.fromarray(x), m, y)),
######
# 972/1982 manip pseudo images
# images : pseudo = approx. 48 : 8 = 6 : 1
# thus to get 50 : 50 manip : unalt we manip 11965/25874 ~ 46% of non-pseudo images
######
transforms.Lambda(lambda (img, m, y): (make_random_manipulation(img, rng), float32(1.), y) if \
m[0] < 0.5 and rng.rand() < TRAIN_MANIP_RATIO else (make_crop(img, args.crop_size, rng), m, y)),
transforms.Lambda(lambda (img, m, y): ([img,
img.transpose(Image.ROTATE_90)][int(rng.rand() < 0.5)], m) if \
True else (img, m)),
]
train_transforms_list += make_aug_transforms(rng)
if args.kernel:
train_transforms_list += [
transforms.Lambda(lambda (img, m):
(conv_K(np.asarray(img, dtype=np.uint8)), m)),
transforms.Lambda(lambda (x, m):
(torch.from_numpy(x.transpose(2, 0, 1)), m))
]
else:
train_transforms_list += [
transforms.Lambda(lambda (img, m): (transforms.ToTensor()(img), m))
]
train_transforms_list += [
transforms.Lambda(lambda (img, m): (transforms.Normalize(
args.means, args.stds)(img), m))
]
train_transform = transforms.Compose(train_transforms_list)
dataset = make_numpy_dataset(X=[
(x, m, y) for x, m, y in zip(X_train, manip_train, y_train)
],
y=y_train,
transform=train_transform,
soft_logits=soft_logits)
# make loader
loader = DataLoader(dataset=dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.n_workers,
sampler=StratifiedSampler(
class_vector=np.asarray(y_train),
batch_size=args.batch_size))
return loader
def train(optimizer, train_optimizer=train_optimizer):
# load and crop validation data
print "Loading data ..."
X_val = np.load(os.path.join(args.data_path, 'X_val.npy'))
y_val = np.load(os.path.join(args.data_path, 'y_val.npy')).tolist()
manip_val = np.zeros(
(len(y_val), 1), dtype=np.float32
) # np.load(os.path.join(args.data_path, 'manip_with_pseudo.npy')) # 68/480 manipulated
d = args.crop_size * 2
D = X_val.shape[1]
if d < D:
X_val = X_val[:, D / 2 - d / 2:D / 2 + d / 2,
D / 2 - d / 2:D / 2 + d / 2, :]
if args.kernel:
X_val = [conv_K(x) for x in X_val]
X_val = [X_val[i] for i in xrange(len(X_val))]
manip_val = [manip_val[i] for i in xrange(len(manip_val))]
for b in xrange(N_PSEUDO_BLOCKS_FOR_VALIDATION):
for c in xrange(10):
X_block = np.load(
os.path.join(args.data_path,
'X_pseudo_{0}_{1}.npy'.format(c, b)))
y_val += [c] * len(X_block)
d = args.crop_size * 2
D = X_block.shape[1]
if d < D:
X_block = X_block[:, D / 2 - d / 2:D / 2 + d / 2,
D / 2 - d / 2:D / 2 + d / 2, :]
X_val += [X_block[i] for i in xrange(len(X_block))]
manip_block = np.load(
os.path.join(args.data_path,
'manip_pseudo_{0}_{1}.npy'.format(c, b)))
manip_val += [m for m in manip_block]
# make validation loader
rng = RNG(args.random_seed + 42 if args.random_seed else None)
val_transform = transforms.Compose([
transforms.Lambda(lambda (x, m, y): (Image.fromarray(x), m, y)),
########
# 1 - (480-68-0.3*480)/(480-68) ~ 0.18
########
transforms.Lambda(lambda (img, m, y): (make_random_manipulation(img, rng, crop_policy='center'), float32(1.), y) if\
m[0] < 0.5 and rng.rand() < VAL_MANIP_RATIO else (center_crop(img, args.crop_size), m, y)),
# transforms.Lambda(lambda (img, m, y): ([img,
# img.transpose(Image.ROTATE_90)][int(rng.rand() < 0.5)], m) if \
# True else (img, m)),
transforms.Lambda(lambda (img, m, y): (transforms.ToTensor()(img), m)),
transforms.Lambda(lambda (img, m): (transforms.Normalize(args.means, args.stds)(img), m))
])
np.save(os.path.join(args.model_dirpath, 'y_val.npy'), np.vstack(y_val))
val_dataset = make_numpy_dataset(X=[
(x, m, y) for x, m, y in zip(X_val, manip_val, y_val)
],
y=y_val,
transform=val_transform)
val_loader = DataLoader(dataset=val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.n_workers)
for _ in xrange(args.distill_epochs / args.epochs_per_unique_data):
train_loader = make_train_loaders(block_index=optimizer.epoch /
args.epochs_per_unique_data,
distill=True)
optimizer.max_epoch = optimizer.epoch + args.epochs_per_unique_data
train_optimizer(optimizer, train_loader, val_loader)
n_runs = args.epochs / args.epochs_per_unique_data + 1
for _ in xrange(n_runs):
optimizer.distill_cost = 0.
train_loader = make_train_loaders(block_index=optimizer.epoch /
args.epochs_per_unique_data,
distill=False)
optimizer.max_epoch = optimizer.epoch + args.epochs_per_unique_data
train_optimizer(optimizer, train_loader, val_loader)
def main():
net = ClockworkRNN((1, (4, 30), 1), update_fn=adam, learning_rate=0.001)
# ones = np.ones((2, 10, 1), dtype=floatX)
# res = net.fprop(ones)
losses = []
lrs = []
norms, momentum_norms = [], []
norms_thres, momentum_norms_thres = [], []
# X, y, x_series = create_batch_func_params(500, 0.1, 2)
X, y, x_series = create_batch_func_params(410, 0.1, 2000)
best = np.inf
last_best_index = 0
decrement = float32(0.99)
for i in range(8000):
start = time()
loss, norm, norm_theshold, momentum_norm, momentum_norm_threshold = net.bptt(X, y)
losses.append(loss)
norms.append(norm)
momentum_norms.append(momentum_norm)
norms_thres.append(float32(norm_theshold))
momentum_norms_thres.append(float32(momentum_norm_threshold))
lrs.append(net.learning_rate.get_value())
epoch_time = time() - start
print i, ':', losses[-1], " took :", epoch_time
if best > losses[-1]:
last_best_index = i
best = losses[-1]
elif i - last_best_index > 20:
best = losses[-1]
new_rate = net.learning_rate.get_value() * decrement
net.learning_rate.set_value(new_rate)
last_best_index = i
print("New learning rate", new_rate)
with open('rnn.pickle', 'wb') as f:
pickle.dump(net, f)
# plt.figure(figsize=(40, 20), dpi=100)
fig, ax1 = plt.subplots(figsize=(30, 10))
ax2 = ax1.twinx()
ax1.plot(losses, label='loss')
ax1.set_ylabel('Loss')
ax2.plot(lrs, color="red", label='learning rate')
ax2.set_ylabel('Learning rate')
h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
ax1.legend(h1 + h2, l1 + l2, loc='best')
fig.savefig('rnn_learning_rate_and_cost.jpg')
plt.clf()
fig, ax1 = plt.subplots(figsize=(30, 10))
ax2 = ax1.twinx()
ax1.plot(losses[-1000:], label='loss')
ax1.set_ylabel('Loss')
ax2.plot(lrs[-1000:], color="red", label='learning rate')
ax2.set_ylabel('Learning rate')
h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
ax1.legend(h1 + h2, l1 + l2, loc='best')
fig.savefig('rnn_learning_rate_and_cost_last1000.jpg')
plt.clf()
# NORMS
fig, ax1 = plt.subplots(figsize=(30, 10))
ax2 = ax1.twinx()
ax1.plot(norms, label="norm", color='red')
ax1.plot(norms_thres, label="norm threshold", color='blue')
ax1.set_ylabel('Gradient norm')
ax2.plot(momentum_norms, label="momentum norms", color='green')
ax2.plot(momentum_norms_thres, label="momentum norms threshold", color='purple')
ax2.set_ylabel('Momentum norm')
h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
ax1.legend(h1 + h2, l1 + l2, loc='best')
fig.savefig('rnn_norms.jpg')
plt.clf()
# X, y, x_series = X[:10], y[:10], x_series[:10]
X, y, x_series = create_batch_func_params(1500, 0.1, 10)
prediction = net.fprop(X)
# plt.close('all')
fig, axarr = plt.subplots(len(X), sharex=True, figsize=(160, 5 * X.shape[0]))
for i in range(len(X)):
# axarr[i].figure.figure = plt.figure(figsize=(150, 12), dpi=100)
axarr[i].set_title('freq:' + str(X[i][i]))
axarr[i].plot(prediction[i], label='model', color='blue')
axarr[i].plot(y[i], label='actual', color='green')
axarr_twin = axarr[i].twinx()
axarr_twin.plot(np.abs(y[i] - prediction[i]), label='error', color='red')
h1, l1 = axarr[i].get_legend_handles_labels()
h2, l2 = axarr_twin.get_legend_handles_labels()
axarr[i].legend(h1 + h2, l1 + l2, loc=0)
fig.savefig('rnn_prediction_vs_actual.jpg')
print "done"
