def train(self, shallow, optimizer, epochs, callbacks, chk_period=-1, chk_monitor=None):
if chk_monitor == None:
chk_monitor = self.chk_mon
shallow_path = common.shallow_path(shallow.name, self.trainset_name, self.feat_net_name, ext=False)
my_callbacks = callbacks[:]
if chk_period > 0:
name = shallow_path + '.weights.{epoch:02d}.h5'
checkpoint = ModelCheckpoint(name, monitor=chk_monitor, save_weights_only=True, period=chk_period)
my_callbacks.append(checkpoint)
bestpoint = ModelCheckpoint(shallow_path + '.weights.best.h5', monitor='val_loss', save_best_only=True, save_weights_only=True)
my_callbacks.append(bestpoint)
model = shallow.model()
if model is None:
RuntimeError("You have to initialize the model with init() before training the network")
model.compile(optimizer=copy.deepcopy(optimizer), loss=self.loss, metrics=self.metric)
#model.summary()
#print("Valid split: " + str(valid_split))
model.fit(self.trainset.data, self.trainset.getLabelsVec(),
validation_data=self.valdata, validation_split=self.validsplit,
nb_epoch=epochs, batch_size=self.batch_size,
callbacks=my_callbacks,
shuffle=True)
save_model_json(model, shallow_path + '.json')
model.save_weights(shallow_path + '.weights.last.h5')
def for_resnet50():
early_stopping = EarlyStopping('val_loss',
min_delta=0.01,
patience=7,
verbose=1)
reduceLR = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=2,
verbose=1,
epsilon=0.01,
cooldown=0,
min_lr=0)
callbacks = [early_stopping, reduceLR]
A = new_model(in_shape, out_shape)
optimizer = SGD(lr=0.01, momentum=0.9, decay=1e-6, nesterov=True)
addestra(A,
"A_5ep",
optimizer,
100,
callbacks,
chk_period=1,
loss_in_name=True)
shallow_path = common.shallow_path("A_5ep",
trainset_name,
feat_net,
ext=False)
early_stopping = EarlyStopping('val_loss',
min_delta=0.001,
patience=10,
verbose=1)
reduceLR = ReduceLROnPlateau('val_loss',
factor=0.1,
patience=4,
verbose=1,
epsilon=0.0001)
callbacks = [early_stopping, reduceLR]
LF = new_model(in_shape, out_shape, lf=True, lf_decay=0.03)
LF.load_weights(shallow_path + '.weights.best.h5', by_name=True)
optimizer = SGD(lr=0.001, momentum=0.9, decay=1e-6, nesterov=True)
addestra(LF,
"LF_FT_A",
optimizer,
epochs=100,
callbacks=callbacks,
chk_period=1)
def load(self, shallow, weight_index, alt_net_load=None, alt_name_load=None, extra_name=''):
if alt_net_load is not None:
name = alt_net_load.name + extra_name
elif alt_name_load is not None:
name = alt_name_load + extra_name
else:
name = shallow.name + extra_name
shallow_path = common.shallow_path(name, self.trainset_name, self.feat_net_name, ext=False)
model = shallow.model()
if model is None:
raise RuntimeError("You have to initialize the model with init() before loading the weights")
model.load_weights(shallow_path + '.weights.' + str(weight_index) + '.h5', by_name=True)
shallow.weights_loaded_from=name
shallow.weights_loaded_index=weight_index
return self
def for_resnet50_HL():
early_stopping = EarlyStopping('val_acc',
min_delta=0.01,
patience=5,
verbose=1)
callbacks = [early_stopping]
HL8K = new_model(in_shape, out_shape, hiddens=[Hidden(8000, 0.5)])
optimizer = SGD(lr=0.01, momentum=0.9, decay=1e-6, nesterov=True)
addestra(HL8K,
"HL8K",
optimizer,
5,
callbacks,
chk_period=1,
loss_in_name=True)
shallow_path = common.shallow_path("HL8K",
trainset_name,
feat_net,
ext=False)
early_stopping = EarlyStopping('val_loss',
min_delta=0.001,
patience=10,
verbose=1)
reduceLR = ReduceLROnPlateau('val_loss',
factor=0.1,
patience=4,
verbose=1,
epsilon=0.0001)
callbacks = [early_stopping, reduceLR]
LF = new_model(in_shape,
out_shape,
hiddens=[Hidden(8000, 0.5)],
lf=True,
lf_decay=0.03)
LF.load_weights(shallow_path + '.weights.best.h5', by_name=True)
optimizer = SGD(lr=0.0001, momentum=0.9, decay=1e-6, nesterov=True)
addestra(LF,
"LF_FT_HL8K",
optimizer,
epochs=100,
callbacks=callbacks,
chk_period=1)
def addestra(model, name, optimizer, epochs, callbacks, chk_period=-1, loss_in_name=False):
shallow_path = common.shallow_path(name, trainset_name, feat_net, ext=False)
if chk_period > 0:
name = shallow_path + '.weights.{epoch:02d}' + ('-{val_loss:.2f}.h5' if loss_in_name else '.h5')
checkpoint = ModelCheckpoint(name, monitor='val_loss', save_weights_only=True, period=chk_period)
callbacks.append(checkpoint)
bestpoint = ModelCheckpoint(shallow_path + '.weights.best.h5', monitor='val_loss', save_best_only=True, save_weights_only=True)
callbacks.append(bestpoint)
model.compile(optimizer=optimizer, loss=LOSS, metrics=METRIC)
#model.summary()
#print("Valid split: " + str(valid_split))
model.fit(trainset.data, trainset.getLabelsVec(), nb_epoch=epochs, batch_size=BATCH, callbacks=callbacks,
shuffle=True, validation_data=valid_data, validation_split=valid_split)
save_model_json(model, shallow_path + '.json')
model.save_weights(shallow_path + '.weights.last.h5')
