def task_train_filter(args):
ff, mapper = getattr(__import__('dataset'), args.dataset)()
dataset = Dataset(root_path=args.path,
verbose=args.verbose,
name='base-model',
mapper=mapper,
filename_filter=ff,
rotation=False)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base
TT.debug("Compile base model.")
model = model_base(args.lr)
model_saved_weights_path = os.path.join(args.path,
'base-model.weights.npy')
if os.path.exists(model_saved_weights_path):
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
train_start = time.time()
log = LearnLog("filter", args.path)
for epoch in xrange(args.epoch):
TT.debug(epoch + 1, "of", args.epoch, "epochs")
log.on_dataset_epoch_begin(epoch + 1)
for x, y in dataset_batches:
model.fit(x,
y,
batch_size=args.mini_batch,
nb_epoch=1,
validation_split=.1,
callbacks=[log],
show_accuracy=True,
shuffle=True)
log.on_dataset_epoch_end(epoch + 1)
log.on_dataset_train_end()
TT.success("Training finished in %.2f hours." %
((time.time() - train_start) / 3600.))
def task_train_filter(args):
ff, mapper = getattr(__import__('dataset'), args.dataset)()
dataset = Dataset(root_path=args.path, verbose=args.verbose, name='base-model',
mapper=mapper, filename_filter=ff, rotation=False)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base
TT.debug("Compile base model.")
model = model_base(args.lr)
model_saved_weights_path = os.path.join(args.path, 'base-model.weights.npy')
if os.path.exists(model_saved_weights_path):
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
train_start = time.time()
log = LearnLog("filter", args.path)
for epoch in xrange(args.epoch):
TT.debug(epoch + 1, "of", args.epoch, "epochs")
log.on_dataset_epoch_begin(epoch + 1)
for x, y in dataset_batches:
model.fit(x, y, batch_size=args.mini_batch, nb_epoch=1, validation_split=.1,
callbacks=[log], show_accuracy=True, shuffle=True)
log.on_dataset_epoch_end(epoch + 1)
log.on_dataset_train_end()
TT.success("Training finished in %.2f hours." % ((time.time() - train_start) / 3600.))
def task_train_cnn(args):
ff, mapper = getattr(__import__('dataset'), args.dataset)()
dataset = Dataset(root_path=args.path, verbose=args.verbose, name='cnn',
mapper=mapper, filename_filter=ff, ratio=9)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base, model_1, model_2
TT.debug("Compile base model.")
model = model_base(lr=0)
TT.debug("Compile model 1.")
model1 = model_1(args.lr)
TT.debug("Compile model 2.")
model2 = model_2(args.lr)
model_saved_weights_path = os.path.join(args.path, 'base-model.weights.npy')
model1_saved_weights_path = os.path.join(args.path, 'model1.weights.npy')
model2_saved_weights_path = os.path.join(args.path, 'model2.weights.npy')
if os.path.exists(model_saved_weights_path):
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
if os.path.exists(model1_saved_weights_path):
TT.info("Loading weights from %s" % model1_saved_weights_path)
model1.load_weights(model1_saved_weights_path)
if os.path.exists(model2_saved_weights_path):
TT.info("Loading weights from %s" % model2_saved_weights_path)
model2.load_weights(model2_saved_weights_path)
train_start = time.time()
log1 = LearnLog("model1", args.path)
log2 = LearnLog("model2", args.path)
for epoch in xrange(args.epoch):
TT.debug(epoch + 1, "of", args.epoch, "epochs")
log1.on_dataset_epoch_begin(epoch + 1)
log2.on_dataset_epoch_begin(epoch + 1)
for x, y in dataset_batches:
outputs = model.predict(x, batch_size=args.mini_batch, verbose=args.verbose)
# Multiply each window with it's prediction and then pass it to the next layer
# x_new = []
# y_new = []
x = 1. - x
for i in range(len(outputs)):
if y[i][0] < 1.:
# x_new.append(x[i])
# y_new.append(y[i])
x[i] *= outputs[i][0]
TT.debug("Model 1 on epoch %d" % (epoch + 1))
model1.fit(numpy.asarray(x), numpy.asarray(y), batch_size=args.mini_batch, nb_epoch=1, validation_split=.1,
callbacks=[log1], show_accuracy=True, shuffle=True)
TT.debug("Model 2 on epoch %d" % (epoch + 1))
model2.fit(numpy.asarray(x), numpy.asarray(y), batch_size=args.mini_batch, nb_epoch=1, validation_split=.1,
callbacks=[log2], show_accuracy=True, shuffle=True)
log1.on_dataset_epoch_end(epoch + 1)
log2.on_dataset_epoch_end(epoch + 1)
log1.on_dataset_train_end()
log2.on_dataset_train_end()
TT.success("Training finished in %.2f hours." % ((time.time() - train_start) / 3600.))
def task_train_cnn(args):
ff, mapper = getattr(__import__('dataset'), args.dataset)()
dataset = Dataset(root_path=args.path,
verbose=args.verbose,
name='cnn',
mapper=mapper,
filename_filter=ff,
ratio=9)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base, model_1, model_2
TT.debug("Compile base model.")
model = model_base(lr=0)
TT.debug("Compile model 1.")
model1 = model_1(args.lr)
TT.debug("Compile model 2.")
model2 = model_2(args.lr)
model_saved_weights_path = os.path.join(args.path,
'base-model.weights.npy')
model1_saved_weights_path = os.path.join(args.path, 'model1.weights.npy')
model2_saved_weights_path = os.path.join(args.path, 'model2.weights.npy')
if os.path.exists(model_saved_weights_path):
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
if os.path.exists(model1_saved_weights_path):
TT.info("Loading weights from %s" % model1_saved_weights_path)
model1.load_weights(model1_saved_weights_path)
if os.path.exists(model2_saved_weights_path):
TT.info("Loading weights from %s" % model2_saved_weights_path)
model2.load_weights(model2_saved_weights_path)
train_start = time.time()
log1 = LearnLog("model1", args.path)
log2 = LearnLog("model2", args.path)
for epoch in xrange(args.epoch):
TT.debug(epoch + 1, "of", args.epoch, "epochs")
log1.on_dataset_epoch_begin(epoch + 1)
log2.on_dataset_epoch_begin(epoch + 1)
for x, y in dataset_batches:
outputs = model.predict(x,
batch_size=args.mini_batch,
verbose=args.verbose)
# Multiply each window with it's prediction and then pass it to the next layer
# x_new = []
# y_new = []
x = 1. - x
for i in range(len(outputs)):
if y[i][0] < 1.:
# x_new.append(x[i])
# y_new.append(y[i])
x[i] *= outputs[i][0]
TT.debug("Model 1 on epoch %d" % (epoch + 1))
model1.fit(numpy.asarray(x),
numpy.asarray(y),
batch_size=args.mini_batch,
nb_epoch=1,
validation_split=.1,
callbacks=[log1],
show_accuracy=True,
shuffle=True)
TT.debug("Model 2 on epoch %d" % (epoch + 1))
model2.fit(numpy.asarray(x),
numpy.asarray(y),
batch_size=args.mini_batch,
nb_epoch=1,
validation_split=.1,
callbacks=[log2],
show_accuracy=True,
shuffle=True)
log1.on_dataset_epoch_end(epoch + 1)
log2.on_dataset_epoch_end(epoch + 1)
log1.on_dataset_train_end()
log2.on_dataset_train_end()
TT.success("Training finished in %.2f hours." %
((time.time() - train_start) / 3600.))
