parameters_mllib = {}
parameters_output = {}
dd.put_service(sname,model,description,mllib,
parameters_input,parameters_mllib,parameters_output,'unsupervised')
# training
train_data = [training_repo]
parameters_input = {'id':'','separator':',','label':'label'}
parameters_mllib = {'iterations':500}
parameters_output = {}
predout = dd.post_train(sname,train_data,parameters_input,parameters_mllib,parameters_output,async=True)
time.sleep(1)
train_status = ''
while True:
train_status = dd.get_train(sname,job=1,timeout=3)
if train_status['head']['status'] == 'running':
print train_status['body']['measure']
else:
print train_status
predout = train_status
break
predictions = predout['body']['predictions']
N = len(predictions)
points = np.empty((N,2),dtype=np.float)
i = 0
for p in predictions:
points[i,0] = p['vals'][0]
points[i,1] = p['vals'][1]
i = i + 1
# training
train_data = [training_repo]
parameters_input = {'id': '', 'separator': ',', 'label': 'label'}
parameters_mllib = {'iterations': 500}
parameters_output = {}
predout = dd.post_train(sname,
train_data,
parameters_input,
parameters_mllib,
parameters_output,
async=True)
time.sleep(1)
train_status = ''
while True:
train_status = dd.get_train(sname, job=1, timeout=3)
if train_status['head']['status'] == 'running':
print train_status['body']['measure']
else:
print train_status
predout = train_status
break
predictions = predout['body']['predictions']
N = len(predictions)
points = np.empty((N, 2), dtype=np.float)
i = 0
for p in predictions:
points[i, 0] = p['vals'][0]
points[i, 1] = p['vals'][1]
i = i + 1
#Start training the service
iterations = int(service['iterations'])
solver_type = service['solver_type']
base_lr = float(service['base_lr'])
parameters_input_training = {'shuffle':True,'test_split':test_split,'min_count':min_count,'min_word_length':min_word_length,'count':False}
parameters_mllib_training = {'gpu':True,'solver':{'iterations':iterations,'test_interval':test_interval,'base_lr':base_lr,'solver_type':solver_type},'net':{'batch_size':batch_size}}
parameters_output_training = {'measure':['mcll','f1','cmdiag','cmfull']}
train_data = [root_repository+'dataset/']
training_service = dd.post_train(service_name.lower(),train_data,parameters_input_training,parameters_mllib_training,parameters_output_training,async=True)
job_number = training_service['head']['job']
#Get training data while the service is running
sleep(20)
status_code = 200
count_job_data = 1
while status_code == 200:
job_data = dd.get_train(service_name.lower(),job=job_number, measure_hist=True)
status_code = job_data['status']['code']
if not 'accp' in job_data['body']['measure']:
sleep(20)
continue
if job_data['head']['status'] == 'running':
log_file.write("job running time "+str(job_data['head']['time'])+"\n")
log_file.write("Iteration number "+str(job_data['body']['measure']['iteration'])+"\n")
log_file.flush()
running_time = job_data['head']['time']
accp = job_data['body']['measure']['accp']
recall = job_data['body']['measure']['recall']
iteration = job_data['body']['measure']['iteration']
precision = job_data['body']['measure']['precision']
mcll = job_data['body']['measure']['mcll']
f1 = job_data['body']['measure']['f1']
class ModelTrainer:
""" Prediction Model trainer class
binary char-based model training class
"""
def __init__(self,structure,logger,config):
""" Instanciate a model trainer
:param dic structure: Model Trainer specific settings
eg: {"model-repo":"../models/mymodel","training-repo":"../training/mytraining","sname":"MyTrainer","test_split":0.01,"base-lr":0.01,"clevel":False,"sequence":140,"iterations":50000,"test_interval":1000,"stepsize":15000,"destroy":True,"resume":False,"finetune":False,"weights":"","nclasses":2,"documents":True,"batch-size":128,"test-batch-size":16,"gpuid":0,"mllib":"xgboost","lregression":False}
*model-repo* location of the model
*training-repo* location of the training files
*sname* service name
*test_plit* training split between 0 and < 1,type=float,default=0.01
*base_lr* initial learning rate,default=0.01,type=float
*clevel* character-level convolutional net,type=boolean
*sequence* sequence length for character level models,default=140,type=int
*iterations* number of iterations,default=50000,type=int
*test_interval* test interval',default=1000,type=int
*stepsize* lr policy stepsize',default=15000,type=int
*destroy* whether to destroy model',type=boolean
*resume* whether to resume training,type=boolean
*finetune* whether to finetune,type=boolean
*weights* pre-trained weight file, when finetuning
*nclasses* number of classes,type=int,default=2
*documents* whether to train from text documents (as opposed to sentences in one doc),type=boolean
*batch_size* batch size,type=int,default=128
*test_batch_size* test batch size,type=int,default=16
*gpu* enable gpu usage is True, default=False
*gpuid* specify gpu id,type=int,default=0
*mllib* caffe or xgboost,default='caffe'
*lregression* whether to use logistic regression,type=boolean
:param obj logger: DFM logger object
:param obj storage: DFM storage object
:param obj config: DFM global config object
:returns: ModelTrainer object (instance of a modeltrainer class)
"""
self.config=config
self.structure=structure
self.logger=logger
self.nclasses = self.structure['nclasses']
self.description = 'classifier'
self.sname=self.structure['sname']
self.mllib = self.structure['mllib']
self.dd = DD(config['DEEP_DETECT_URI'],config['DEEP_DETECT_PORT'])
self.dd.set_return_format(self.dd.RETURN_PYTHON)
def createMLTrainerService(self):
""" Create ML Trainer service in DeepDetect """
if self.structure['lregression']:
self.template = 'lregression'
else:
self.template = 'mlp'
layers = [800,500,200]
if self.structure['clevel']:
self.template = 'convnet'
self.layers = ['1CR256','1CR256','4CR256','1024','1024']
self.model = {'templates':'../templates/caffe/','repository':self.structure['model-repo']}
self.parameters_input = {'connector':'txt','sentences':False,'characters':self.structure['clevel'],'read_forward':True}
if self.structure['documents']:
self.parameters_input['sentences'] = False
if self.structure['clevel']:
self.parameters_input['sequence'] = self.sequence
#parameters_input['alphabet'] = 'abcdef0123456789' # hex
# parameters_input['alphabet'] = '_-,:?/.(){}*%0123456789abcdefghijklmnopqrstuvwxyz' # opcode
#parameters_input['alphabet'] = "abcdefghijklmnopqrstuvwxyz0123456789,;.!?'"#\"/\\|_@#$%^&*~`+-=<>"
self.parameters_mllib = {'template':self.template,'nclasses':self.nclasses,'db':True,'dropout':0.5}
if self.mllib == 'xgboost':
self.parameters_mllib['db'] = False
if not self.template == 'lregression':
self.parameters_mllib['layers'] = layers
#parameters_mllib = {'nclasses':nclasses,'db':True}
if self.structure['finetune']:
self.parameters_mllib['finetuning'] = True
if not self.structure['weights']:
logger.error('Finetuning requires weights file') # server will fail on service creation anyways
else:
self.parameters_mllib['weights'] = self.structure['weights']
self.parameters_output = {}
self.logger.debug("dd.put_service("+str(self.structure['sname'])+","+str(self.model)+","+str(self.description)+","+str(self.mllib)+","+str(self.parameters_input)+","+str(self.parameters_mllib)+","+str(self.parameters_output)+")")
return self.dd.put_service(self.structure['sname'],self.model,self.description,self.mllib,self.parameters_input,self.parameters_mllib,self.parameters_output)
def trainModel(self):
""" Train the model. """
self.train_data = [self.structure['training-repo']]
self.parameters_input = {'test_split':self.structure['test_split'],'shuffle':True,'db':True}
if not self.structure['clevel']:
self.parameters_input['min_word_length'] = 5
self.parameters_input['min_count'] = 10
self.parameters_input['count'] = False
if self.mllib == 'xgboost':
self.parameters_input['tfidf'] = True
self.parameters_input['db'] = False
else:
self.parameters_input['sentences'] = True
self.parameters_input['characters'] = True
self.parameters_input['sequence'] = self.sequence
if self.structure['documents']:
self.parameters_input['sentences'] = False
if self.mllib == 'caffe':
self.parameters_input['db']=True
self.parameters_mllib = {
'gpu':self.structure['gpu'],
'gpuid':self.structure['gpuid'],
'resume':self.structure['resume'],
'net':{
'batch_size':self.structure['batch_size']
},
'solver':{
'test_interval':self.structure['test_interval'],
'test_initialization':False,
'base_lr':self.structure['base_lr'],
'solver_type':'ADAM',
'iterations':self.structure['iterations']
}
}#,'lr_policy':'step','stepsize':self.structure['stepsize'],'gamma':0.5,'weight_decay':0.0001}}
elif self.mllib == 'xgboost':
self.parameters_mllib = {
'iterations':self.structure['iterations'],
'objective':'multi:softprob',
'booster_params':{'max_depth':50}
}
self.parameters_output = {'measure':['mcll','f1','cmdiag','cmfull']}
if self.nclasses == 2:
self.parameters_output['measure'].append('auc')
self.logger.debug("dd.post_train("+self.structure['sname']+","+str(self.train_data)+","+str(self.parameters_input)+","+str(self.parameters_mllib)+","+str(self.parameters_output)+",async="+str(True)+")")
self.dd.post_train(self.structure['sname'],self.train_data,self.parameters_input,self.parameters_mllib,self.parameters_output,async=True)
time.sleep(1)
train_status = ''
while True:
train_status = self.dd.get_train(self.sname,job=1,timeout=10)
if train_status['head']['status'] == 'running':
self.logger.debug(train_status['body']['measure'])
else:
self.logger.debug(train_status)
break
return train_status
def clearMLTrainerService(self,clear=''):
""" delete the service, keeping the model
:param str clear: use clear='lib' to clear the model as well, default empty.
:returns: DeepDetect delete result
"""
return self.dd.delete_service(self.sname,clear=clear)
