Python Callbacks.add_early_stop_callback方法代码示例

# 需要导入模块: from neon.callbacks.callbacks import Callbacks [as 别名]
# 或者: from neon.callbacks.callbacks.Callbacks import add_early_stop_callback [as 别名]
# 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)

# 需要导入模块: from neon.callbacks.callbacks import Callbacks [as 别名]
# 或者: from neon.callbacks.callbacks.Callbacks import add_early_stop_callback [as 别名]
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())))
#.........这里部分代码省略.........

# 需要导入模块: from neon.callbacks.callbacks import Callbacks [as 别名]
# 或者: from neon.callbacks.callbacks.Callbacks import add_early_stop_callback [as 别名]
                        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
                    failed.append(path)