def train(self, dataset, model=None):
"""Trains the passed model on the given dataset. If no model is passed, `generate_default_model` is used."""
print "[%s] Starting training..." % self.model_name
start = time.time()
# The training will be run on the CPU. If a GPU is available it should be used instead.
backend = gen_backend(backend='cpu',
batch_size=self.batch_size,
rng_seed=self.random_seed,
stochastic_round=False)
cost = GeneralizedCost(
name='cost',
costfunc=CrossEntropyMulti())
optimizer = GradientDescentMomentum(
learning_rate=self.lrate,
momentum_coef=0.9)
# set up the model and experiment
if not model:
model = self.generate_default_model(dataset.num_labels)
args = NeonCallbackParameters()
args.output_file = os.path.join(self.root_path, self.Callback_Store_Filename)
args.evaluation_freq = 1
args.progress_bar = False
args.epochs = self.max_epochs
args.save_path = os.path.join(self.root_path, self.Intermediate_Model_Filename)
args.serialize = 1
args.history = 100
args.model_file = None
callbacks = Callbacks(model, dataset.train(), args, eval_set=dataset.test())
# add a callback that saves the best model state
callbacks.add_save_best_state_callback(self.model_path)
# Uncomment line below to run on GPU using cudanet backend
# backend = gen_backend(rng_seed=0, gpu='cudanet')
model.fit(
dataset.train(),
optimizer=optimizer,
num_epochs=self.max_epochs,
cost=cost,
callbacks=callbacks)
print("[%s] Misclassification error = %.1f%%"
% (self.model_name, model.eval(dataset.test(), metric=Misclassification()) * 100))
print "[%s] Finished training!" % self.model_name
end = time.time()
print "[%s] Duration in seconds", end - start
return model
def train(args, hyper_params, model, opt, data_set):
# setup cost function as CrossEntropy
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
callbacks = Callbacks(model, **args.callback_args)
callbacks.add_callback(EpochEndCallback())
data_set.set_mode('train')
model.fit(data_set, optimizer=opt,
num_epochs=hyper_params.num_epochs, cost=cost, callbacks=callbacks)
return
def main():
# setup the model and run for num_epochs saving the last state only
# this is at the top so that the be is generated
mlp = gen_model(args.backend)
# setup data iterators
(X_train, y_train), (X_test, y_test), nclass = load_mnist(path=args.data_dir)
if args.backend == 'nervanacpu' or args.backend == 'cpu':
# limit data since cpu backend runs slower
train = DataIterator(X_train[:1000], y_train[:1000], nclass=nclass, lshape=(1, 28, 28))
valid = DataIterator(X_test[:1000], y_test[:1000], nclass=nclass, lshape=(1, 28, 28))
else:
train = DataIterator(X_train, y_train, nclass=nclass, lshape=(1, 28, 28))
valid = DataIterator(X_test, y_test, nclass=nclass, lshape=(1, 28, 28))
# serialization related
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
opt_gdm = GradientDescentMomentum(learning_rate=0.1, momentum_coef=0.9)
checkpoint_model_path = os.path.join('./', 'test_oneshot.pkl')
checkpoint_schedule = 1 # save at every step
callbacks = Callbacks(mlp, train)
callbacks.add_serialize_callback(checkpoint_schedule, checkpoint_model_path, history=2)
# run the fit all the way through saving a checkpoint e
mlp.fit(train, optimizer=opt_gdm, num_epochs=num_epochs, cost=cost, callbacks=callbacks)
# setup model with same random seed run epoch by epoch
# serializing and deserializing at each step
mlp = gen_model(args.backend)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
opt_gdm = GradientDescentMomentum(learning_rate=0.1, momentum_coef=0.9)
# reset data iterators
train.reset()
valid.reset()
checkpoint_model_path = os.path.join('./', 'test_manyshot.pkl')
checkpoint_schedule = 1 # save at evey step
callbacks = Callbacks(mlp, train)
callbacks.add_serialize_callback(checkpoint_schedule,
checkpoint_model_path,
history=num_epochs)
for epoch in range(num_epochs):
# _0 points to state at end of epoch 0
mlp.fit(train, optimizer=opt_gdm, num_epochs=epoch+1, cost=cost, callbacks=callbacks)
# load saved file
prts = os.path.splitext(checkpoint_model_path)
fn = prts[0] + '_%d' % epoch + prts[1]
mlp.load_weights(fn) # load the saved weights
# compare test_oneshot_<num_epochs>.pkl to test_manyshot_<num_epochs>.pkl
try:
compare_model_pickles('test_oneshot_%d.pkl' % (num_epochs-1),
'test_manyshot_%d.pkl' % (num_epochs-1))
except:
print 'test failed....'
sys.exit(1)
def train(args, hyper_params, model, opt, data_set):
# setup cost function as CrossEntropy
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
callbacks = Callbacks(model, **args.callback_args)
callbacks.add_callback(EpochEndCallback())
data_set.set_mode('train')
model.fit(data_set,
optimizer=opt,
num_epochs=hyper_params.num_epochs,
cost=cost,
callbacks=callbacks)
return
def run(train, test):
init = Gaussian(scale=0.01)
layers = [
Conv((3, 3, 128),
init=init,
activation=Rectlin(),
strides=dict(str_h=1, str_w=2)),
Conv((3, 3, 256), init=init, batch_norm=True, activation=Rectlin()),
Pooling(2, strides=2),
Conv((2, 2, 512), init=init, batch_norm=True, activation=Rectlin()),
DeepBiRNN(256,
init=init,
activation=Rectlin(),
reset_cells=True,
depth=3),
RecurrentLast(),
Affine(32, init=init, batch_norm=True, activation=Rectlin()),
Affine(nout=common['nclasses'], init=init, activation=Softmax())
]
model = Model(layers=layers)
opt = Adadelta()
metric = Misclassification()
callbacks = Callbacks(model,
eval_set=test,
metric=metric,
**args.callback_args)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
model.fit(train,
optimizer=opt,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
return model
def main(args):
# load up the mnist data set
dataset = MNIST(path=args.data_dir)
# initialize model object
mlp = Model(layers=[
Affine(nout=100,
init=Gaussian(loc=0.0, scale=0.01),
activation=Rectlin()),
Affine(nout=10,
init=Gaussian(loc=0.0, scale=0.01),
activation=Logistic(shortcut=True))
])
# setup optimizer
optimizer = GradientDescentMomentum(0.1,
momentum_coef=0.9,
stochastic_round=args.rounding)
# configure callbacks
callbacks = Callbacks(mlp,
eval_set=dataset.valid_iter,
**args.callback_args)
# run fit
# setup cost function as CrossEntropy
mlp.fit(dataset.train_iter,
optimizer=optimizer,
num_epochs=args.epochs,
cost=GeneralizedCost(costfunc=CrossEntropyBinary()),
callbacks=callbacks)
error_rate = mlp.eval(dataset.valid_iter, metric=Misclassification())
neon_logger.display('Classification accuracy = %.4f' % (1 - error_rate))
def train_model(lrmodel, opt, cost, X, Y, devX, devY, devscores):
"""
Train model, using pearsonr on dev for early stopping
"""
done = False
best = -1.0
r = np.arange(1, 6)
train_set = ArrayIterator(X=X, y=Y, make_onehot=False)
valid_set = ArrayIterator(X=devX, y=devY, make_onehot=False)
eval_epoch = 10
while not done:
callbacks = Callbacks(lrmodel, eval_set=valid_set)
lrmodel.fit(train_set, optimizer=opt, num_epochs=eval_epoch,
cost=cost, callbacks=callbacks)
# Every 10 epochs, check Pearson on development set
yhat = np.dot(lrmodel.get_outputs(valid_set), r)
score = pearsonr(yhat, devscores)[0]
if score > best:
neon_logger.display('Dev Pearson: {}'.format(score))
best = score
bestlrmodel = copy.copy(lrmodel)
else:
done = True
eval_epoch += 10
yhat = np.dot(bestlrmodel.get_outputs(valid_set), r)
score = pearsonr(yhat, devscores)[0]
neon_logger.display('Dev Pearson: {}'.format(score))
return bestlrmodel
def main():
# parse the command line arguments
parser = NeonArgparser(__doc__)
args = parser.parse_args()
logger = logging.getLogger()
logger.setLevel(args.log_thresh)
#Set up batch iterator for training images
train = ImgMaster(repo_dir='dataTmp_optFlow_BW',
set_name='train',
inner_size=240,
subset_pct=100)
val = ImgMaster(repo_dir='dataTmp_optFlow_BW',
set_name='train',
inner_size=240,
subset_pct=100,
do_transforms=False)
test = ImgMaster(repo_dir='dataTestTmp_optFlow_BW',
set_name='train',
inner_size=240,
subset_pct=100,
do_transforms=False)
train.init_batch_provider()
val.init_batch_provider()
test.init_batch_provider()
print "Constructing network..."
#Create AlexNet architecture
model = constuct_network()
# Optimzer for model
opt = Adadelta()
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model,
train,
eval_set=test,
metric=valmetric,
**args.callback_args)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
#flag = input("Press Enter if you want to begin training process.")
print "Training network..."
model.fit(train,
optimizer=opt,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
mets = model.eval(test, metric=valmetric)
print 'Validation set metrics:'
print 'LogLoss: %.2f, Accuracy: %.1f %%0 (Top-1), %.1f %% (Top-5)' % (
mets[0], (1.0 - mets[1]) * 100, (1.0 - mets[2]) * 100)
return
def deserialize(fn, datasets=None, inference=False):
"""
Helper function to load all objects from a serialized file,
this includes callbacks and datasets as well as the model, layers,
etc.
Arguments:
datasets (DataSet, optional): If the dataset is not serialized
in the file it can be passed in
as an argument. This will also
override any dataset in the serialized
file
inference (bool, optional): if true only the weights will be loaded, not
the states
Returns:
Model: the model object
Dataset: the data set object
Callback: the callbacks
"""
config_dict = load_obj(fn)
if datasets is not None:
logger.warn('Ignoring datasets serialized in archive file %s' % fn)
elif 'datasets' in config_dict:
ds_cls = load_class(config_dict['datasets']['type'])
dataset = ds_cls.gen_class(config_dict['datasets']['config'])
datasets = dataset.gen_iterators()
if 'train' in datasets:
data_iter = datasets['train']
else:
key = list(datasets.keys())[0]
data_iter = datasets[key]
logger.warn('Could not find training set iterator'
'using %s instead' % key)
model = Model(config_dict, data_iter)
callbacks = None
if 'callbacks' in config_dict:
# run through the callbacks looking for dataset objects
# replace them with the corresponding data set above
cbs = config_dict['callbacks']['callbacks']
for cb in cbs:
if 'config' not in cb:
cb['config'] = {}
for arg in cb['config']:
if type(cb['config']
[arg]) is dict and 'type' in cb['config'][arg]:
if cb['config'][arg]['type'] == 'Data':
key = cb['config'][arg]['name']
if key in datasets:
cb['config'][arg] = datasets[key]
else:
cb['config'][arg] = None
# now we can generate the callbacks
callbacks = Callbacks.load_callbacks(config_dict['callbacks'], model)
return (model, dataset, callbacks)
def deserialize(fn, datasets=None, inference=False):
"""
Helper function to load all objects from a serialized file,
this includes callbacks and datasets as well as the model, layers,
etc.
Arguments:
datasets (DataSet, optional): If the dataset is not serialized
in the file it can be passed in
as an argument. This will also
override any dataset in the serialized
file
inference (bool, optional): if true only the weights will be loaded, not
the states
Returns:
Model: the model object
Dataset: the data set object
Callback: the callbacks
"""
config_dict = load_obj(fn)
if datasets is not None:
logger.warn('Ignoring datasets serialized in archive file %s' % fn)
elif 'datasets' in config_dict:
ds_cls = load_class(config_dict['datasets']['type'])
dataset = ds_cls.gen_class(config_dict['datasets']['config'])
datasets = dataset.gen_iterators()
if 'train' in datasets:
data_iter = datasets['train']
else:
key = datasets.keys()[0]
data_iter = datasets[key]
logger.warn('Could not find training set iterator'
'using %s instead' % key)
model = Model(config_dict, data_iter)
callbacks = None
if 'callbacks' in config_dict:
# run through the callbacks looking for dataset objects
# replace them with the corresponding data set above
cbs = config_dict['callbacks']['callbacks']
for cb in cbs:
if 'config' not in cb:
cb['config'] = {}
for arg in cb['config']:
if type(cb['config'][arg]) is dict and 'type' in cb['config'][arg]:
if cb['config'][arg]['type'] == 'Data':
key = cb['config'][arg]['name']
if key in datasets:
cb['config'][arg] = datasets[key]
else:
cb['config'][arg] = None
# now we can generate the callbacks
callbacks = Callbacks.load_callbacks(config_dict['callbacks'], model)
return (model, dataset, callbacks)
def main():
parser = get_parser()
args = parser.parse_args()
print('Args:', args)
loggingLevel = logging.DEBUG if args.verbose else logging.INFO
logging.basicConfig(level=loggingLevel, format='')
ext = extension_from_parameters(args)
loader = p1b3.DataLoader(feature_subsample=args.feature_subsample,
scaling=args.scaling,
drug_features=args.drug_features,
scramble=args.scramble,
min_logconc=args.min_logconc,
max_logconc=args.max_logconc,
subsample=args.subsample,
category_cutoffs=args.category_cutoffs)
# initializer = Gaussian(loc=0.0, scale=0.01)
initializer = GlorotUniform()
activation = get_function(args.activation)()
layers = []
reshape = None
if args.convolution and args.convolution[0]:
reshape = (1, loader.input_dim, 1)
layer_list = list(range(0, len(args.convolution), 3))
for l, i in enumerate(layer_list):
nb_filter = args.convolution[i]
filter_len = args.convolution[i+1]
stride = args.convolution[i+2]
# print(nb_filter, filter_len, stride)
# fshape: (height, width, num_filters).
layers.append(Conv((1, filter_len, nb_filter), strides={'str_h':1, 'str_w':stride}, init=initializer, activation=activation))
if args.pool:
layers.append(Pooling((1, args.pool)))
for layer in args.dense:
if layer:
layers.append(Affine(nout=layer, init=initializer, activation=activation))
if args.drop:
layers.append(Dropout(keep=(1-args.drop)))
layers.append(Affine(nout=1, init=initializer, activation=neon.transforms.Identity()))
model = Model(layers=layers)
train_iter = ConcatDataIter(loader, ndata=args.train_samples, lshape=reshape, datatype=args.datatype)
val_iter = ConcatDataIter(loader, partition='val', ndata=args.val_samples, lshape=reshape, datatype=args.datatype)
cost = GeneralizedCost(get_function(args.loss)())
optimizer = get_function(args.optimizer)()
callbacks = Callbacks(model, eval_set=val_iter, **args.callback_args)
model.fit(train_iter, optimizer=optimizer, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
def fit_model(train_set, val_set, num_epochs=50):
relu = Rectlin()
conv_params = {
'strides': 1,
'padding': 1,
'init': Xavier(local=True), # Xavier sqrt(3)/num_inputs [CHECK THIS]
'bias': Constant(0),
'activation': relu
}
layers = []
layers.append(Conv((3, 3, 128), **conv_params)) # 3x3 kernel * 128 nodes
layers.append(Pooling(2))
layers.append(Conv((3, 3, 128), **conv_params))
layers.append(Pooling(2)) # Highest value from 2x2 window.
layers.append(Conv((3, 3, 128), **conv_params))
layers.append(
Dropout(keep=0.5)
) # Flattens Convolution into a flat array, with probability 0.5 sets activation values to 0
layers.append(
Affine(nout=128,
init=GlorotUniform(),
bias=Constant(0),
activation=relu)
) # 1 value per conv kernel - Linear Combination of layers
layers.append(Dropout(keep=0.5))
layers.append(
Affine(nout=2,
init=GlorotUniform(),
bias=Constant(0),
activation=Softmax(),
name="class_layer"))
# initialize model object
cnn = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
optimizer = Adam()
# callbacks = Callbacks(cnn)
# out_fname = 'yarin_fdl_out_data.h5'
callbacks = Callbacks(cnn, eval_set=val_set,
eval_freq=1) # , output_file=out_fname
cnn.fit(train_set,
optimizer=optimizer,
num_epochs=num_epochs,
cost=cost,
callbacks=callbacks)
return cnn
def it_was_correct(last_in, last_out):
# print "naive", replay_memory.clean_values_toone(last_in)[0], last_out
offset_memory.add_episode(last_in, last_out)
print "osize", offset_memory.outputs.size
if offset_memory.outputs is not None and offset_memory.outputs.size % bot_params.batch_size == 0:
X, y = offset_memory.get_dataset()
train = ArrayIterator(X=X, y=y, make_onehot=False)
mlp.fit(train,
optimizer=optimizer,
num_epochs=1,
cost=cost,
callbacks=Callbacks(mlp))
mlp.save_params(bot_params.aim_weights_path)
def train_regressor(orig_wordvecs, w2v_W, w2v_vocab):
"""
Return regressor to map word2vec to RNN word space
Function modified from:
https://github.com/ryankiros/skip-thoughts/blob/master/training/tools.py
"""
# Gather all words from word2vec that appear in wordvecs
d = defaultdict(lambda: 0)
for w in w2v_vocab.keys():
d[w] = 1
shared = OrderedDict()
count = 0
for w in list(orig_wordvecs.keys())[:-2]:
if d[w] > 0:
shared[w] = count
count += 1
# Get the vectors for all words in 'shared'
w2v = np.zeros((len(shared), 300), dtype='float32')
sg = np.zeros((len(shared), 620), dtype='float32')
for w in shared.keys():
w2v[shared[w]] = w2v_W[w2v_vocab[w]]
sg[shared[w]] = orig_wordvecs[w]
train_set = ArrayIterator(X=w2v, y=sg, make_onehot=False)
layers = [
Linear(nout=620, init=Gaussian(loc=0.0, scale=0.1)),
Bias(init=Constant(0.0))
]
clf = Model(layers=layers)
# regression model is trained using default global batch size
cost = GeneralizedCost(costfunc=SumSquared())
opt = GradientDescentMomentum(0.1, 0.9, gradient_clip_value=5.0)
callbacks = Callbacks(clf)
clf.fit(train_set,
num_epochs=20,
optimizer=opt,
cost=cost,
callbacks=callbacks)
return clf
def fit(self, test_set, train_set):
"""
Train and fit the model on the datasets
Args:
test_set (:obj:`neon.data.ArrayIterators`): The test set
train_set (:obj:`neon.data.ArrayIterators`): The train set
args: callback_args and epochs from ArgParser input
"""
# configure callbacks
callbacks = Callbacks(self.model,
eval_set=test_set,
**self.callback_args)
self.model.fit(train_set,
optimizer=self.optimizer,
num_epochs=self.epochs,
cost=self.cost,
callbacks=callbacks)
def run(args, train, test):
init_uni = Uniform(low=-0.1, high=0.1)
opt_gdm = GradientDescentMomentum(learning_rate=0.01,
momentum_coef=0.9,
stochastic_round=args.rounding)
layers = [Conv((5, 5, 16), init=init_uni, activation=Rectlin(), batch_norm=True),
Pooling((2, 2)),
Conv((5, 5, 32), init=init_uni, activation=Rectlin(), batch_norm=True),
Pooling((2, 2)),
Affine(nout=500, init=init_uni, activation=Rectlin(), batch_norm=True),
Affine(nout=10, init=init_uni, activation=Softmax())]
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
mlp = Model(layers=layers)
callbacks = Callbacks(mlp, eval_set=test, **args.callback_args)
mlp.fit(train, optimizer=opt_gdm, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
err = mlp.eval(test, metric=Misclassification())*100
print('Misclassification error = %.2f%%' % err)
return err
layers.append(Dropout(keep=0.5))
layers.append(Affine(nout=1000, init=init1, bias=Constant(-7), activation=Softmax()))
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases})
mlp = Model(layers=layers)
if args.model_file:
import os
assert os.path.exists(args.model_file), '%s not found' % args.model_file
mlp.load_weights(args.model_file)
# configure callbacks
callbacks = Callbacks(mlp, train, output_file=args.output_file)
if args.validation_freq:
class TopKMetrics(Callback):
def __init__(self, valid_set, epoch_freq=args.validation_freq):
super(TopKMetrics, self).__init__(epoch_freq=epoch_freq)
self.valid_set = valid_set
def on_epoch_end(self, epoch):
self.valid_set.reset()
allmetrics = TopKMisclassification(k=5)
stats = mlp.eval(self.valid_set, metric=allmetrics)
print ", ".join(allmetrics.metric_names) + ": " + ", ".join(map(str, stats.flatten()))
callbacks.add_callback(TopKMetrics(test))
# setup cost function as CrossEntropy
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
# setup optimizer
optimizer = GradientDescentMomentum(0.1, momentum_coef=0.9, stochastic_round=args.rounding)
# initialize model object
mlp = Model(layers=layers)
if args.model_file:
assert os.path.exists(args.model_file), '%s not found' % args.model_file
logger.info('loading initial model state from %s' % args.model_file)
mlp.load_weights(args.model_file)
# setup standard fit callbacks
callbacks = Callbacks(mlp, train_set, output_file=args.output_file,
progress_bar=args.progress_bar)
# add a callback ot calculate
if args.validation_freq:
# setup validation trial callbacks
callbacks.add_validation_callback(valid_set, args.validation_freq)
if args.serialize > 0:
# add callback for saving checkpoint file
# every args.serialize epchs
checkpoint_schedule = args.serialize
checkpoint_model_path = args.save_path
callbacks.add_serialize_callback(checkpoint_schedule, checkpoint_model_path)
# run fit
Conv((3, 3, 128), init=gauss, strides=small, **common),
Pooling(2, strides=2),
Dropout(0.4),
Conv((3, 3, 256), init=gauss, strides=small, **common),
Dropout(0.2),
Conv((2, 2, 512), init=gauss, strides=tiny, **common),
Conv((2, 2, 128), init=gauss, strides=tiny, **common),
DeepBiRNN(64, init=glorot, reset_cells=True, depth=5, **common),
RecurrentMean(),
Affine(nout=2, init=gauss, activation=Softmax())
]
}[subj]
model = Model(layers=layers)
opt = Adagrad(learning_rate=rate)
callbacks = Callbacks(model, eval_set=test, **args.callback_args)
if args.validate_mode:
evaluator = Evaluator(subj, data_dir, test)
callbacks.add_callback(evaluator)
preds_name = 'eval.'
else:
preds_name = 'test.'
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
model.fit(tain,
optimizer=opt,
num_epochs=nepochs,
cost=cost,
callbacks=callbacks)
preds = model.get_outputs(test)[:, 1]
preds_file = preds_name + str(subj) + '.npy'
# hyperparameters
num_epochs = args.epochs
(X_train, y_train), (X_test, y_test), nclass = load_mnist(path=args.data_dir)
train_set = DataIterator([X_train, X_train], y_train, nclass=nclass)
valid_set = DataIterator([X_test, X_test], y_test, nclass=nclass)
# weight initialization
init_norm = Gaussian(loc=0.0, scale=0.01)
# initialize model
path1 = Sequential(layers=[Affine(nout=100, init=init_norm, activation=Rectlin()),
Affine(nout=100, init=init_norm, activation=Rectlin())])
path2 = Sequential(layers=[Affine(nout=100, init=init_norm, activation=Rectlin()),
Affine(nout=100, init=init_norm, activation=Rectlin())])
layers = [MergeMultistream(layers=[path1, path2], merge="stack"),
Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))]
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
# fit and validate
optimizer = GradientDescentMomentum(learning_rate=0.1, momentum_coef=0.9)
# configure callbacks
callbacks = Callbacks(model, train_set, args, eval_set=valid_set)
model.fit(train_set, cost=cost, optimizer=optimizer, num_epochs=num_epochs, callbacks=callbacks)
#cost function
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
#final model
mlp = Model(layers=layers)
logger.info("model construction complete...")
"""
model training and classification accurate rate
"""
#model training and results
callbacks = Callbacks(mlp,train, args, eval_set=valid,metric=Misclassification())
#add lost and metric call backs facilitate more diagnostic
callbacks.add_callback(MetricCallback(mlp,eval_set=train,metric=Misclassification(),epoch_freq=args.evaluation_freq))
callbacks.add_callback(MetricCallback(mlp,eval_set=valid,metric=Misclassification(),epoch_freq=args.evaluation_freq))
#run the model
mlp.fit(train, optimizer=opt_gdm, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
#final classification accuracy
t_mis_rate=mlp.eval(train, metric=Misclassification())*100
v_mis_rate=mlp.eval(valid, metric=Misclassification())*100
#test_mis_rate=mlp.eval(test, metric=Misclassification())*100
classifier = TextClassifier(options.word_vectors, options.model_file, optimizer=optimizer,
num_analytics_features=0, num_subject_words=0, num_body_words=60,
overlapping_classes=overlapping_classes, exclusive_classes=exclusive_classes,
network_type=options.network_type)
# we will supercede the email classification function to test the content classification network only
print('loading sentiment data from {}'.format(options.sentiment_path))
sdata = SentimentLoader(classifier, options.sentiment_path)
if options.shuffle_test:
print('SHUFFLE TEST BEGIN... please wait...')
sdata.train.test_shuffle()
sdata.test.test_shuffle()
print('SHUFFLE TEST PASSED... reloading inputs and targets')
sdata = SentimentLoader(classifier, options.sentiment_path)
callbacks = Callbacks(classifier.neuralnet, eval_freq=1, eval_set=sdata.test,
metric=Misclassification())
callbacks.add_callback(MisclassificationTest(sdata.test, Misclassification()))
print('Training neural networks on {} samples for {} epochs'.format(sdata.train.targets[0].shape[1],
options.epochs))
classifier.fit(sdata.train, optimizer, options.epochs, callbacks)
print('finished sentiment classification test, exiting')
exit(0)
classifier = TextClassifier(options.word_vectors, options.model_file, optimizer=optimizer,
num_analytics_features=0 if options.sentiment_path else 4,
overlapping_classes=overlapping_classes, exclusive_classes=exclusive_classes,
network_type=options.network_type, lookup_size=options.lookup_size,
lookup_dim=options.lookup_dim)
# determine if we expect to use a csv file or a maildir as our data source
if os.path.isfile(options.data_path):
# define stopping function
# it takes as input a tuple (State,val[t])
# which describes the cumulative validation state (generated by this function)
# and the validation error at time t
# and returns as output a tuple (State', Bool),
# which represents the new state and whether to stop
# Stop if validation error ever increases from epoch to epoch
def stop_func(s, v):
if s is None:
return (v, False)
return (min(v, s), v > s)
# fit and validate
optimizer = GradientDescentMomentum(learning_rate=0.1, momentum_coef=0.9)
# configure callbacks
if args.callback_args['eval_freq'] is None:
args.callback_args['eval_freq'] = 1
callbacks = Callbacks(mlp, eval_set=valid_set, **args.callback_args)
callbacks.add_early_stop_callback(stop_func)
callbacks.add_save_best_state_callback(os.path.join(args.data_dir, "early_stop-best_state.pkl"))
mlp.fit(train_set,
optimizer=optimizer,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
model, cost = create_network()
rseed = 0 if args.rng_seed is None else args.rng_seed
# setup data provider
assert 'train' in args.manifest, "Missing train manifest"
assert 'val' in args.manifest, "Missing validation manifest"
train = make_alexnet_train_loader(args.manifest['train'], args.manifest_root,
model.be, args.subset_pct, rseed)
valid = make_validation_loader(args.manifest['val'], args.manifest_root,
model.be, args.subset_pct)
sched_weight = Schedule([10], change=0.1)
opt = GradientDescentMomentum(0.01, 0.9, wdecay=0.0005, schedule=sched_weight)
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model,
eval_set=valid,
metric=valmetric,
**args.callback_args)
if args.deconv:
callbacks.add_deconv_callback(train, valid)
model.fit(train,
optimizer=opt,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
Dropout(keep=0.5),
Conv((3, 3, 192), **convp1),
Conv((3, 3, 192), **convp1),
Conv((3, 3, 192), **convp1s2),
Dropout(keep=0.5),
Conv((3, 3, 192), **convp1),
Conv((1, 1, 192), **conv),
Conv((1, 1, 16), **conv),
Pooling(8, op="avg"),
Activation(Softmax()),
]
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
mlp = Model(layers=layers)
if args.model_file:
import os
assert os.path.exists(args.model_file), "%s not found" % args.model_file
mlp.load_params(args.model_file)
# configure callbacks
callbacks = Callbacks(mlp, eval_set=valid_set, **args.callback_args)
if args.deconv:
callbacks.add_deconv_callback(train_set, valid_set)
mlp.fit(train_set, optimizer=opt_gdm, num_epochs=num_epochs, cost=cost, callbacks=callbacks)
print("Misclassification error = %.1f%%" % (mlp.eval(valid_set, metric=Misclassification()) * 100))
parser = NeonArgparser(__doc__, default_config_files=config_files,
default_overrides=dict(batch_size=64))
parser.add_argument('--deconv', action='store_true',
help='save visualization data from deconvolution')
parser.add_argument('--subset_pct', type=float, default=100,
help='subset of training dataset to use (percentage)')
args = parser.parse_args()
model, cost = create_network()
rseed = 0 if args.rng_seed is None else args.rng_seed
# setup data provider
assert 'train' in args.manifest, "Missing train manifest"
assert 'val' in args.manifest, "Missing validation manifest"
train = make_alexnet_train_loader(args.manifest['train'], args.manifest_root,
model.be, args.subset_pct, rseed)
valid = make_validation_loader(args.manifest['val'], args.manifest_root,
model.be, args.subset_pct)
sched_weight = Schedule([10], change=0.1)
opt = GradientDescentMomentum(0.01, 0.9, wdecay=0.0005, schedule=sched_weight)
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model, eval_set=valid, metric=valmetric, **args.callback_args)
if args.deconv:
callbacks.add_deconv_callback(train, valid)
model.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
layers.append(Conv((3, 3, 384), pad=1, init=init2, bias=Constant(0), activation=relu))
layers.append(Conv((3, 3, 256), pad=1, init=init2, bias=Constant(1), activation=relu))
layers.append(Conv((3, 3, 256), pad=1, init=init2, bias=Constant(1), activation=relu))
layers.append(Pooling(3, strides=2))
layers.append(Affine(nout=4096, init=init1, bias=Constant(1), activation=relu))
layers.append(Dropout(keep=0.5))
layers.append(Affine(nout=4096, init=init1, bias=Constant(1), activation=relu))
layers.append(Dropout(keep=0.5))
layers.append(Affine(nout=1000, init=init1, bias=Constant(-7), activation=Softmax()))
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases})
mlp = Model(layers=layers)
# configure callbacks
callbacks = Callbacks(mlp, train, output_file=args.output_file)
if args.validation_freq:
callbacks.add_validation_callback(test, args.validation_freq)
if args.save_path:
checkpoint_schedule = range(1, args.epochs)
callbacks.add_serialize_callback(checkpoint_schedule, args.save_path, history=2)
mlp.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
test.exit_batch_provider()
train.exit_batch_provider()
model = Model(layers=SSD(ssd_config=train_config['ssd_config'], dataset=train_set))
cost = MBoxLoss(num_classes=train_set.num_classes)
if args.model_file is None:
load_vgg_weights(model, cache_dir)
else:
model.load_params(args.model_file)
if args.lr_step is None:
args.lr_step = [40, 80, 120]
base_lr = 0.0001 * be.bsz * args.lr_scale
schedule = Schedule(args.lr_step, 0.1)
opt_w = GradientDescentMomentum(base_lr, momentum_coef=0.9, wdecay=0.0005, schedule=schedule)
opt_b = GradientDescentMomentum(base_lr, momentum_coef=0.9, schedule=schedule)
opt = MultiOptimizer({'default': opt_w, 'Bias': opt_b})
# hijack the eval callback arg here
eval_freq = args.callback_args.pop('eval_freq')
callbacks = Callbacks(model, **args.callback_args)
callbacks.add_callback(MAP_Callback(eval_set=val_set, epoch_freq=eval_freq))
if args.image_sample_dir is not None:
callbacks.add_callback(ssd_image_callback(eval_set=val_set, image_dir=args.image_sample_dir,
epoch_freq=eval_freq, num_images=args.num_images,
classes=val_config['class_names']))
model.fit(train_set, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
validation=False,
remove_history=False,
minimal_set=False,
next_N=3)
valid = HDF5Iterator(filenames,
ndata=(16 * 2014),
validation=True,
remove_history=False,
minimal_set=False,
next_N=1)
out1, out2, out3 = model.layers.get_terminal()
cost = Multicost(costs=[GeneralizedCost(costfunc=CrossEntropyMulti(usebits=True)),
GeneralizedCost(costfunc=CrossEntropyMulti(usebits=True)),
GeneralizedCost(costfunc=CrossEntropyMulti(usebits=True))])
schedule = ExpSchedule(decay=(1.0 / 50)) # halve the learning rate every 50 epochs
opt_gdm = GradientDescentMomentum(learning_rate=0.01,
momentum_coef=0.9,
stochastic_round=args.rounding,
gradient_clip_value=1,
gradient_clip_norm=5,
wdecay=0.0001,
schedule=schedule)
callbacks = Callbacks(model, eval_set=valid, metric=TopKMisclassification(5), **args.callback_args)
callbacks.add_save_best_state_callback(os.path.join(args.workspace_dir, "best_state_h5resnet.pkl"))
model.fit(train, optimizer=opt_gdm, num_epochs=num_epochs, cost=cost, callbacks=callbacks)
model.save_params(os.path.join(args.workspace_dir, "final_state_h5resnet.pkl"))
init = Uniform(low=-0.08, high=0.08)
# model initialization
layers = [
LSTM(hidden_size, init, Logistic(), Tanh()),
Affine(len(train_set.vocab), init, bias=init, activation=Softmax())
]
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyMulti(usebits=True))
optimizer = RMSProp(clip_gradients=clip_gradients, stochastic_round=args.rounding)
# configure callbacks
callbacks = Callbacks(model, train_set, output_file=args.output_file,
progress_bar=args.progress_bar,
valid_set=valid_set, valid_freq=1,
)
callbacks.add_serialize_callback(1, args.save_path)
# fit and validate
model.fit(train_set, optimizer=optimizer, num_epochs=num_epochs, cost=cost, callbacks=callbacks)
def sample(prob):
"""
Sample index from probability distribution
"""
prob = prob / (prob.sum() + 1e-6)
return np.argmax(np.random.multinomial(1, prob, 1))
# Set batch size and time_steps to 1 for generation and reset buffers
common_params = dict(sampling_freq=22050, clip_duration=16000, frame_duration=16)
train_params = AudioParams(**common_params)
valid_params = AudioParams(**common_params)
common = dict(target_size=1, nclasses=10, repo_dir=args.data_dir)
train = DataLoader(set_name='music-train', media_params=train_params,
index_file=train_idx, shuffle=True, **common)
valid = DataLoader(set_name='music-valid', media_params=valid_params,
index_file=valid_idx, shuffle=False, **common)
init = Gaussian(scale=0.01)
layers = [Conv((2, 2, 4), init=init, activation=Rectlin(),
strides=dict(str_h=2, str_w=4)),
Pooling(2, strides=2),
Conv((3, 3, 4), init=init, batch_norm=True, activation=Rectlin(),
strides=dict(str_h=1, str_w=2)),
DeepBiRNN(128, init=GlorotUniform(), batch_norm=True, activation=Rectlin(),
reset_cells=True, depth=3),
RecurrentMean(),
Affine(nout=common['nclasses'], init=init, activation=Softmax())]
model = Model(layers=layers)
opt = Adagrad(learning_rate=0.01, gradient_clip_value=15)
metric = Misclassification()
callbacks = Callbacks(model, eval_set=valid, metric=metric, **args.callback_args)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
model.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
print('Misclassification error = %.1f%%' % (model.eval(valid, metric=metric)*100))
display(model, ['Convolution_0'], 'inputs')
display(model, ['Convolution_0', 'Convolution_1', 'Pooling_0'], 'outputs')
model = Model(layers=layers)
# scale LR by 0.1 every 20 epochs (this assumes batch_size = 256)
weight_sched = Schedule(20, 0.1)
opt_gdm = GradientDescentMomentum(0.01,
0.9,
wdecay=0.0005,
schedule=weight_sched)
opt_biases = GradientDescentMomentum(0.02, 0.9, schedule=weight_sched)
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases})
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model,
eval_set=test,
metric=valmetric,
**args.callback_args)
if args.model_file is not None:
model.load_params(args.model_file)
if not args.test_only:
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
model.fit(train,
optimizer=opt,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
mets = model.eval(test, metric=valmetric)
print 'Validation set metrics:'
print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (
Affine(train_set.vocab_size, init, bias=init2, activation=Softmax())
]
cost = GeneralizedCostMask(costfunc=CrossEntropyMulti(usebits=True))
# configure callbacks
checkpoint_model_path = "~/image_caption2.pickle"
if args.callback_args['save_path'] is None:
args.callback_args['save_path'] = checkpoint_model_path
if args.callback_args['serialize'] is None:
args.callback_args['serialize'] = 1
model = Model(layers=layers)
callbacks = Callbacks(model, **args.callback_args)
opt = RMSProp(decay_rate=0.997,
learning_rate=0.0005,
epsilon=1e-8,
gradient_clip_value=1)
# train model
model.fit(train_set,
optimizer=opt,
num_epochs=num_epochs,
cost=cost,
callbacks=callbacks)
# load model (if exited) and evaluate bleu score on test set
model.load_params(checkpoint_model_path)
# Pooling(fshape=2, strides=2),
# Affine(nout=500, init=init_uni, activation=Rectlin()),
# Affine(nout=10, init=init_uni, activation=Softmax())]
# learning_rate = 0.005
# momentum = 0.9
cnn = Model(layers=layers)
# - cost function
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
# - learning rule
optimizer = GradientDescentMomentum(learning_rate, momentum_coef=momentum)
# Progress bar for each epoch - what's an epoch again? by default 10 Crazy magic - don't even go here!
callbacks = Callbacks(cnn, eval_set=test_set, **args.callback_args)
# put everything together!
cnn.fit(train_set,
optimizer=optimizer,
num_epochs=epochs,
cost=cost,
callbacks=callbacks)
# # Calculate test set results
# results = cnn.get_outputs(test_set)
# dump(cnn, "cnn_0_005.jbl")
# # work out the performance!
# error = cnn.eval(test_set, metric=Misclassification())
LSTM(hidden_size,
init,
activation=Logistic(),
gate_activation=Tanh(),
reset_cells=True),
Affine(train_set.vocab_size, init, bias=init2, activation=Softmax())
]
cost = GeneralizedCostMask(costfunc=CrossEntropyMulti(usebits=True))
checkpoint_model_path = "~/image_caption2.pickle"
checkpoint_schedule = range(num_epochs)
model = Model(layers=layers)
callbacks = Callbacks(model,
train_set,
output_file=args.output_file,
progress_bar=args.progress_bar)
callbacks.add_serialize_callback(checkpoint_schedule, checkpoint_model_path)
opt = RMSProp(decay_rate=0.997,
learning_rate=0.0005,
epsilon=1e-8,
clip_gradients=True,
gradient_limit=1.0)
# train model
model.fit(train_set,
optimizer=opt,
num_epochs=num_epochs,
cost=cost,
callbacks=callbacks)
MergeMultistream(layers=[image_path, sent_path], merge="recurrent"),
Dropout(keep=0.5),
LSTM(hidden_size, init, activation=Logistic(), gate_activation=Tanh(), reset_cells=True),
Affine(train_set.vocab_size, init, bias=init2, activation=Softmax())
]
cost = GeneralizedCostMask(costfunc=CrossEntropyMulti(usebits=True))
# configure callbacks
checkpoint_model_path = "~/image_caption2.pickle"
if args.callback_args['save_path'] is None:
args.callback_args['save_path'] = checkpoint_model_path
if args.callback_args['serialize'] is None:
args.callback_args['serialize'] = 1
model = Model(layers=layers)
callbacks = Callbacks(model, train_set, **args.callback_args)
opt = RMSProp(decay_rate=0.997, learning_rate=0.0005, epsilon=1e-8, gradient_clip_value=1)
# train model
model.fit(train_set, optimizer=opt, num_epochs=num_epochs, cost=cost, callbacks=callbacks)
# load model (if exited) and evaluate bleu score on test set
model.load_params(checkpoint_model_path)
test_set = ImageCaptionTest(path=data_path)
sents, targets = test_set.predict(model)
test_set.bleu_score(sents, targets)
Conv((1, 1, 192), **conv),
Conv((1, 1, 16), **conv),
Pooling(8, op="avg"),
Activation(Softmax())]
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
mlp = Model(layers=layers)
if args.model_file:
import os
assert os.path.exists(args.model_file), '%s not found' % args.model_file
mlp.load_weights(args.model_file)
# configure callbacks
callbacks = Callbacks(mlp, train_set, eval_set=valid_set, **args.callback_args)
if args.deconv:
callbacks.add_deconv_callback(train_set, valid_set)
callbacks.add_callback(
MetricCallback(
valid_set,
Misclassification()
)
)
mlp.fit(train_set, optimizer=opt_gdm, num_epochs=num_epochs, cost=cost, callbacks=callbacks)
import logging
logger = logging.getLogger(__name__)
logger.critical('Misclassification error = %.1f%%' % (mlp.eval(valid_set, metric=Misclassification())*100))
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
model.initialize(train_set, cost)
model.load_params('models/mnist/mnist_cnn.pkl', load_states=False)
# define optimizer
opt_w = GradientDescentMomentum(learning_rate=0.01,
momentum_coef=0.9,
wdecay=0.0005)
opt_b = GradientDescentMomentum(learning_rate=0.01, momentum_coef=0.9)
opt = MultiOptimizer({'default': opt_w, 'Bias': opt_b}, name='multiopt')
# configure callbacks
callbacks = Callbacks(model,
eval_set=valid_set,
metric=Misclassification(),
**args.callback_args)
callbacks.add_callback(
TrainByStageCallback(model, valid_set, Misclassification(),
max_patience=5))
num_prune = [5, 10, 25, 10]
callbacks.add_callback(
FuzzyPruneCallback(num_states=100, num_prune=num_prune, model=model))
print('Original Accuracy = %.2f%%' %
(100. - model.eval(valid_set, metric=Misclassification()) * 100))
logger.info('Training ...')
model.fit(train_set,
optimizer=opt,
num_epochs=250,
Affine(nout=16, linear_name="b1_l1", **normrelu),
Affine(nout=10, linear_name="b1_l2", **normsigm)]
p3 = [b2,
Affine(nout=16, linear_name="b2_l1", **normrelu),
Affine(nout=10, linear_name="b2_l2", **normsigm)]
# setup cost function as CrossEntropy
cost = Multicost(costs=[GeneralizedCost(costfunc=CrossEntropyMulti()),
GeneralizedCost(costfunc=CrossEntropyBinary()),
GeneralizedCost(costfunc=CrossEntropyBinary())],
weights=[1, 0., 0.])
# setup optimizer
optimizer = GradientDescentMomentum(0.1, momentum_coef=0.9, stochastic_round=args.rounding)
# initialize model object
alphas = [1, 0.25, 0.25]
mlp = Model(layers=Tree([p1, p2, p3], alphas=alphas))
# setup standard fit callbacks
callbacks = Callbacks(mlp, train_set, eval_set=valid_set, **args.callback_args)
# run fit
mlp.fit(train_set, optimizer=optimizer, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
logging.getLogger('neon').info("Misclassification error = %.1f%%",
(mlp.eval(valid_set, metric=Misclassification())*100))
print('Misclassification error = %.1f%%' % (mlp.eval(valid_set, metric=Misclassification())*100))
weights=[1, 1, 1])
# setup optimizer
schedule_w = StepSchedule(step_config=[5], change=[0.001 / 10])
schedule_b = StepSchedule(step_config=[5], change=[0.002 / 10])
opt_w = GradientDescentMomentum(0.001, 0.9, wdecay=0.0005, schedule=schedule_w)
opt_b = GradientDescentMomentum(0.002, 0.9, wdecay=0.0005, schedule=schedule_b)
opt_skip = GradientDescentMomentum(0.0, 0.0)
optimizer = MultiOptimizer({'default': opt_w, 'Bias': opt_b,
'skip': opt_skip, 'skip_bias': opt_skip})
# if training a new model, seed the image model conv layers with pre-trained weights
# otherwise, just load the model file
if args.model_file is None:
util.load_vgg_all_weights(model, args.data_dir)
callbacks = Callbacks(model, eval_set=train_set, **args.callback_args)
callbacks.add_callback(TrainMulticostCallback())
# model.benchmark(train_set, optimizer=optimizer, cost=cost)
model.fit(train_set, optimizer=optimizer, cost=cost, num_epochs=args.epochs, callbacks=callbacks)
# Scale the bbox regression branch linear layer weights before saving the model
model = util.scale_bbreg_weights(model, [0.0, 0.0, 0.0, 0.0],
[0.1, 0.1, 0.2, 0.2], train_set.num_classes)
if args.save_path is not None:
save_obj(model.serialize(keep_states=True), args.save_path)
depth=4,
n_extra_layers=4,
batch_norm=True,
dis_iters=5,
wgan_param_clamp=0.01,
wgan_train_sched=True)
# setup optimizer
optimizer = RMSProp(learning_rate=5e-5, decay_rate=0.99, epsilon=1e-8)
# setup data provider
train = make_loader(args.manifest['train'], args.manifest_root, model.be,
args.subset_pct, random_seed)
# configure callbacks
callbacks = Callbacks(model, **args.callback_args)
fdir = ensure_dirs_exist(
os.path.join(os.path.dirname(os.path.realpath(__file__)), 'results/'))
fname = os.path.splitext(os.path.basename(__file__))[0] +\
'_[' + datetime.now().strftime('%Y-%m-%d-%H-%M-%S') + ']'
im_args = dict(filename=os.path.join(fdir, fname),
hw=64,
num_samples=args.batch_size,
nchan=3,
sym_range=True)
callbacks.add_callback(GANPlotCallback(**im_args))
callbacks.add_callback(GANCostCallback())
# model fit
model.fit(train,
optimizer=optimizer,
# Set up the model layers
layers = []
nchan = 128
layers.append(Conv((2, 2, nchan), strides=2, **common))
for idx in range(16):
layers.append(Conv((3, 3, nchan), **common))
if nchan > 16:
nchan /= 2
for idx in range(15):
layers.append(Deconv((3, 3, nchan), **common))
layers.append(Deconv((4, 4, nchan), strides=2, **common))
layers.append(Deconv((3, 3, 1), init=init, activation=Logistic(shortcut=True)))
cost = GeneralizedCost(costfunc=SumSquared())
mlp = Model(layers=layers)
callbacks = Callbacks(mlp, train, **args.callback_args)
evaluator = Evaluator(callbacks.callback_data, mlp, test, imwidth, args.epochs,
args.data_dir, point_num)
callbacks.add_callback(evaluator)
mlp.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost,
callbacks=callbacks)
train.exit_batch_provider()
preds = evaluator.get_outputs()
paths = np.genfromtxt(os.path.join(args.test_data_dir, 'val_file.csv'),
dtype=str)[1:]
basenames = [os.path.basename(path) for path in paths]
filenames = [path.split(',')[0] for path in basenames]
filenames.sort()
content = []
for i, filename in enumerate(filenames):
Conv((1, 1, 192), **conv),
Conv((1, 1, 16), **conv),
Pooling(8, op="avg"),
Activation(Softmax())]
def stop_func(s, v):
if s is None:
return (v, False)
return (min(v, s), v > s)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
mlp = Model(layers=layers)
if args.model_file:
import os
assert os.path.exists(args.model_file), '%s not found' % args.model_file
mlp.load_params(args.model_file)
# configure callbacks
callbacks = Callbacks(mlp, eval_set=valid_set, **args.callback_args)
callbacks.add_early_stop_callback(stop_func)
if args.deconv:
callbacks.add_deconv_callback(train_set, valid_set)
mlp.fit(train_set, optimizer=opt_gdm, num_epochs=50, cost=cost, callbacks=callbacks)
print('Misclassification error = %.1f%%' % (mlp.eval(valid_set, metric=Misclassification())*100))
def train_mlp():
"""
Train data and save scaling and network weights and biases to file
to be used by forward prop phase on test data
"""
parser = NeonArgparser(__doc__)
args = parser.parse_args()
logger = logging.getLogger()
logger.setLevel(args.log_thresh)
# hyperparameters
num_epochs = args.epochs
#preprocessor
std_scale = preprocessing.StandardScaler(with_mean=True,with_std=True)
#std_scale = feature_scaler(type='Standardizer',with_mean=True,with_std=True)
#number of non one-hot encoded features, including ground truth
num_feat = 4
# load up the mnist data set
# split into train and tests sets
#load data from csv-files and rescale
#training
traindf = pd.DataFrame.from_csv('data/train.csv')
ncols = traindf.shape[1]
#tmpmat=std_scale.fit_transform(traindf.as_matrix())
#print std_scale.scale_
#print std_scale.mean_
tmpmat = traindf.as_matrix()
#print tmpmat[:,1:num_feat]
tmpmat[:,:num_feat] = std_scale.fit_transform(tmpmat[:,:num_feat])
X_train = tmpmat[:,1:]
y_train = np.reshape(tmpmat[:,0],(tmpmat[:,0].shape[0],1))
#validation
validdf = pd.DataFrame.from_csv('data/validate.csv')
ncols = validdf.shape[1]
tmpmat = validdf.as_matrix()
tmpmat[:,:num_feat] = std_scale.transform(tmpmat[:,:num_feat])
X_valid = tmpmat[:,1:]
y_valid = np.reshape(tmpmat[:,0],(tmpmat[:,0].shape[0],1))
#test
testdf = pd.DataFrame.from_csv('data/test.csv')
ncols = testdf.shape[1]
tmpmat = testdf.as_matrix()
tmpmat[:,:num_feat] = std_scale.transform(tmpmat[:,:num_feat])
X_test = tmpmat[:,1:]
y_test = np.reshape(tmpmat[:,0],(tmpmat[:,0].shape[0],1))
# setup a training set iterator
train_set = CustomDataIterator(X_train, lshape=(X_train.shape[1]), y_c=y_train)
# setup a validation data set iterator
valid_set = CustomDataIterator(X_valid, lshape=(X_valid.shape[1]), y_c=y_valid)
# setup a validation data set iterator
test_set = CustomDataIterator(X_test, lshape=(X_test.shape[1]), y_c=y_test)
# setup weight initialization function
init_norm = Xavier()
# setup model layers
layers = [Affine(nout=X_train.shape[1], init=init_norm, activation=Rectlin()),
Dropout(keep=0.5),
Affine(nout=X_train.shape[1]/2, init=init_norm, activation=Rectlin()),
Linear(nout=1, init=init_norm)]
# setup cost function as CrossEntropy
cost = GeneralizedCost(costfunc=SmoothL1Loss())
# setup optimizer
#schedule
#schedule = ExpSchedule(decay=0.3)
#optimizer = GradientDescentMomentum(0.0001, momentum_coef=0.9, stochastic_round=args.rounding, schedule=schedule)
optimizer = Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1.e-8)
# initialize model object
mlp = Model(layers=layers)
# configure callbacks
if args.callback_args['eval_freq'] is None:
args.callback_args['eval_freq'] = 1
# configure callbacks
callbacks = Callbacks(mlp, eval_set=valid_set, **args.callback_args)
callbacks.add_early_stop_callback(stop_func)
callbacks.add_save_best_state_callback(os.path.join(args.data_dir, "early_stop-best_state.pkl"))
# run fit
mlp.fit(train_set, optimizer=optimizer, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
#evaluate model
print('Evaluation Error = %.4f'%(mlp.eval(valid_set, metric=SmoothL1Metric())))
print('Test set error = %.4f'%(mlp.eval(test_set, metric=SmoothL1Metric())))
# Saving the model
print 'Saving model parameters!'
mlp.save_params("model/homeapp_model.prm")
# Reloading saved model
# This should go in run.py
mlp=Model("model/homeapp_model.prm")
print('Test set error = %.4f'%(mlp.eval(test_set, metric=SmoothL1Metric())))
# save the preprocessor vectors:
np.savez("model/homeapp_preproc", mean=std_scale.mean_, std=std_scale.scale_)
return 1
Pooling(3, strides=2)]
# Structure of the deep residual part of the network:
# args.depth modules of 2 convolutional layers each at feature map depths
# of 64, 128, 256, 512
nfms = list(itt.chain.from_iterable(
[itt.repeat(2**(x + 6), r) for x, r in enumerate(stages)]))
strides = [-1] + [1 if cur == prev else 2 for cur,
prev in zip(nfms[1:], nfms[:-1])]
for nfm, stride in zip(nfms, strides):
layers.append(module_factory(nfm, stride))
layers.append(Pooling('all', op='avg'))
layers.append(Conv(**conv_params(1, train.nclass, relu=False)))
layers.append(Activation(Softmax()))
model = Model(layers=layers)
weight_sched = Schedule([30, 60], 0.1)
opt = GradientDescentMomentum(0.1, 0.9, wdecay=0.0001, schedule=weight_sched)
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model, eval_set=test, metric=valmetric, **args.callback_args)
callbacks.add_callback(BatchNormTuneCallback(tune), insert_pos=0)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
model.fit(train, optimizer=opt, num_epochs=args.epochs,
cost=cost, callbacks=callbacks)
(X_train, y_train), (X_test, y_test), nclass = load_cifar10(path=args.data_dir)
train = ArrayIterator(X_train, y_train, nclass=nclass, lshape=(3, 32, 32))
test = ArrayIterator(X_test, y_test, nclass=nclass, lshape=(3, 32, 32))
init_uni = Uniform(low=-0.1, high=0.1)
opt_gdm = GradientDescentMomentum(learning_rate=0.01, momentum_coef=0.9)
# set up the model layers
layers = [
Affine(nout=200, init=init_uni, activation=Rectlin()),
Affine(nout=10, init=init_uni, activation=Logistic(shortcut=True))
]
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
mlp = Model(layers=layers)
# configure callbacks
callbacks = Callbacks(mlp, eval_set=test, **args.callback_args)
mlp.fit(train,
optimizer=opt_gdm,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
print('Misclassification error = %.1f%%' %
(mlp.eval(test, metric=Misclassification()) * 100))
print 'Training specialist: ', i
path = EXPERIMENT_DIR + confusion_matrix_name + '_' + clustering_name + '_' + str(num_clusters) + 'clusters/' + 'specialist' + '_' + str(i) + '.prm'
# Create datasets
X_spec, y_spec, spec_out = filter_dataset(X_train, y_train, cluster)
X_spec_test, y_spec_test, spec_out = filter_dataset(
X_test, y_test, cluster)
spec_out = nout
spec_set = DataIterator(
X_spec, y_spec, nclass=spec_out, lshape=(3, 32, 32))
spec_test = DataIterator(
X_spec_test, y_spec_test, nclass=spec_out, lshape=(3, 32, 32))
# Train the specialist
specialist, opt, cost = spec_net(nout=spec_out, archive_path=gene_path)
callbacks = Callbacks(specialist, spec_set, args, eval_set=spec_test)
callbacks.add_early_stop_callback(early_stop)
callbacks.add_save_best_state_callback(path)
specialist.fit(spec_set, optimizer=opt,
num_epochs=specialist.epoch_index + num_epochs, cost=cost, callbacks=callbacks)
# Print results
print 'Specialist Train misclassification error: ', specialist.eval(spec_set, metric=Misclassification())
print 'Specialist Test misclassification error: ', specialist.eval(spec_test, metric=Misclassification())
print 'Generalist Train misclassification error: ', generalist.eval(spec_set, metric=Misclassification())
print 'Generalist Test misclassification error: ', generalist.eval(spec_test, metric=Misclassification())
# specialists.append(specialist)
save_obj(specialist.serialize(), path)
except:
path = confusion_matrix_name + '_' + clustering_name + '_' + str(num_clusters) + 'clusters/'
print 'Failed for ', path
# hyperparameters
num_epochs = args.epochs
(X_train, y_train), (X_test, y_test), nclass = load_mnist(path=args.data_dir)
train_set = ArrayIterator([X_train, X_train], y_train, nclass=nclass, lshape=(1, 28, 28))
valid_set = ArrayIterator([X_test, X_test], y_test, nclass=nclass, lshape=(1, 28, 28))
# weight initialization
init_norm = Gaussian(loc=0.0, scale=0.01)
# initialize model
path1 = Sequential(layers=[Affine(nout=100, init=init_norm, activation=Rectlin()),
Affine(nout=100, init=init_norm, activation=Rectlin())])
path2 = Sequential(layers=[Affine(nout=100, init=init_norm, activation=Rectlin()),
Affine(nout=100, init=init_norm, activation=Rectlin())])
layers = [MergeMultistream(layers=[path1, path2], merge="stack"),
Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))]
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
# fit and validate
optimizer = GradientDescentMomentum(learning_rate=0.1, momentum_coef=0.9)
# configure callbacks
callbacks = Callbacks(model, eval_set=valid_set, **args.callback_args)
model.fit(train_set, cost=cost, optimizer=optimizer, num_epochs=num_epochs, callbacks=callbacks)
output_size = 8
N = 120 # number of memory locations
M = 8 # size of a memory location
# model initialization
layers = [
GRU(hidden_size, init, activation=Tanh(), gate_activation=Logistic()),
Affine(train_set.nout, init, bias=init, activation=Logistic())
]
cost = GeneralizedCostMask(costfunc=CrossEntropyBinary())
model = Model(layers=layers)
optimizer = RMSProp(gradient_clip_value=gradient_clip_value,
stochastic_round=args.rounding)
# configure callbacks
callbacks = Callbacks(model, **args.callback_args)
# we can use the training set as the validation set,
# since the data is tickerally generated
callbacks.add_watch_ticker_callback(train_set)
# train model
model.fit(train_set,
optimizer=optimizer,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
random_seed = args.rng_seed if args.rng_seed else 0
# load up the mnist data set, padding images to size 32
dataset = MNIST(path=args.data_dir, sym_range=True, size=32, shuffle=True)
train = dataset.train_iter
# create a GAN
model, cost = create_model(dis_model=args.dmodel, gen_model=args.gmodel,
cost_type='wasserstein', noise_type='normal',
im_size=32, n_chan=1, n_noise=128,
n_gen_ftr=args.n_gen_ftr, n_dis_ftr=args.n_dis_ftr,
depth=4, n_extra_layers=4,
batch_norm=True, dis_iters=5,
wgan_param_clamp=0.01, wgan_train_sched=True)
# setup optimizer
optimizer = RMSProp(learning_rate=2e-4, decay_rate=0.99, epsilon=1e-8)
# configure callbacks
callbacks = Callbacks(model, **args.callback_args)
fdir = ensure_dirs_exist(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'results/'))
fname = os.path.splitext(os.path.basename(__file__))[0] +\
'_[' + datetime.now().strftime('%Y-%m-%d-%H-%M-%S') + ']'
im_args = dict(filename=os.path.join(fdir, fname), hw=32,
num_samples=args.batch_size, nchan=1, sym_range=True)
callbacks.add_callback(GANPlotCallback(**im_args))
callbacks.add_callback(GANCostCallback())
# model fit
model.fit(train, optimizer=optimizer, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
# setup optimizer
optimizer = {'momentum': [0],
'step_config': 1,
'learning_rate': 0.1,
'weight_decay': 0}
# initialize model object
rbm = RBM(layers=layers)
if args.model_file:
assert os.path.exists(args.model_file), '%s not found' % args.model_file
logger.info('loading initial model state from %s' % args.model_file)
rbm.load_weights(args.model_file)
# setup standard fit callbacks
callbacks = Callbacks(rbm, train_set, output_file=args.output_file,
progress_bar=args.progress_bar)
# add a callback ot calculate
if args.serialize > 0:
# add callback for saving checkpoint file
# every args.serialize epchs
checkpoint_schedule = args.serialize
checkpoint_model_path = args.save_path
callbacks.add_serialize_callback(checkpoint_schedule, checkpoint_model_path)
rbm.fit(train_set, optimizer=optimizer, num_epochs=num_epochs, callbacks=callbacks)
for mb_idx, (x_val, y_val) in enumerate(valid_set):
hidden = rbm.fprop(x_val)
break
layers.append(Conv((3, 3, 384), init=init_uni, activation=relu, strides=1, padding=1))
layers.append(Conv((1, 1, 384), init=init_uni, activation=relu, strides=1))
layers.append(Conv((3, 3, 384), init=init_uni, activation=relu, strides=2, padding=1)) # 12->6
layers.append(Dropout(keep=0.5))
layers.append(Conv((3, 3, 1024), init=init_uni, activation=relu, strides=1, padding=1))
layers.append(Conv((1, 1, 1024), init=init_uni, activation=relu, strides=1))
layers.append(Conv((1, 1, 1000), init=init_uni, activation=relu, strides=1))
layers.append(Pooling(6, op='avg'))
layers.append(Activation(Softmax()))
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
mlp = Model(layers=layers)
if args.model_file:
import os
assert os.path.exists(args.model_file), '%s not found' % args.model_file
mlp.load_weights(args.model_file)
# configure callbacks
callbacks = Callbacks(mlp, train, eval_set=test, **args.callback_args)
if args.deconv:
callbacks.add_deconv_callback(train, test)
mlp.fit(train, optimizer=opt_gdm, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
test.exit_batch_provider()
train.exit_batch_provider()
opt_gdm = GradientDescentMomentum(args.rate_init[0], args.momentum[0], wdecay=args.weight_decay,
schedule=weight_sched, stochastic_round=args.rounding)
opt_biases = GradientDescentMomentum(args.rate_init[1], args.momentum[1],
schedule=weight_sched, stochastic_round=args.rounding)
opt_fixed = GradientDescentMomentum(0.0, 1.0, wdecay=0.0)
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases, 'DOG': opt_fixed})
# configure cost and test metrics
cost = GeneralizedCost(costfunc=(CrossEntropyBinary() \
if train.parser.independent_labels else CrossEntropyMulti()))
metric = EMMetric(oshape=test.parser.oshape, use_softmax=not train.parser.independent_labels) if test else None
# configure callbacks
if not args.neon_progress:
args.callback_args['progress_bar'] = False
callbacks = Callbacks(model, eval_set=test, metric=metric, **args.callback_args)
if not args.neon_progress:
callbacks.add_callback(EMEpochCallback(args.callback_args['eval_freq'],train.nmacrobatches),insert_pos=None)
# xxx - thought of making this an option but not clear that it slows anything down?
#callbacks.add_hist_callback() # xxx - not clear what information this conveys
if args.save_best_path:
callbacks.add_save_best_state_callback(args.save_best_path)
model.fit(train, optimizer=opt, num_epochs=num_epochs, cost=cost, callbacks=callbacks)
print('Model training complete for %d epochs!' % (args.epochs,))
#test.stop(); train.stop()
elif args.write_output:
# write_output mode, must have model loaded
if args.data_config:
