print("F1 score %.2f"%f) file_=open('run1.txt','a') str1="act3={}, input_neurons={}, batchsize={}, loss={}, nb_filter={}, filter_length={}, optimizer={}".format(act3, input_neurons, batchsize, loss,nb_filter, filter_length, optimizer) str2="EER={0:.2f} Precision={1:.2f} Recall={2:.2f} F1 score={3:.2f}".format(eer,p,r,f) file_.write(str1+'\n'+str2+'\n') file_.close() """ #file_logger.close() miz = aud_model.Functional_Model(input_neurons=input_neurons, dropout=dropout, num_classes=num_classes, model=model, dimx=dimx, dimy=dimy, nb_filter=nb_filter, act1=act1, act2=act2, act3=act3, filter_length=filter_length, pool_size=pool_size, optimizer=optimizer, loss=loss) #fit the model #tr_y=to_categorical(tr_y,len(cfg.labels)) #v_y=to_categorical(v_y,len(cfg.labels)) #fold_='saved_models_3' #if os.path.exists(fold_): # os.rmdir(fold_) #os.mkdir(fold_) #filepath=fold_+"/weights-improvement1-{epoch:02d}-{val_acc:.2f}.hdf5" #checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
#aud_utils.check_dimension(feature[i],dimy[i],'defaults.yaml') #tr_X=aud_utils.equalise(tr_X) for i in range(len(feature)): tr_X[i]=aud_utils.mat_3d_to_nd(model,tr_X[i]) print(tr_X[i].shape) dimx=tr_X[0].shape[-2] if prep=='dev': cross_validation=True else: cross_validation=False miz=aud_model.Functional_Model(input_neurons=input_neurons,cross_validation=cross_validation,dropout1=dropout1, act1=act1,act2=act2,act3=act3,nb_filter = nb_filter, filter_length=filter_length, num_classes=num_classes, model=model,dimx=dimx,dimy=dimy) np.random.seed(68) if cross_validation: kf = KFold(len(tr_X[0]),folds,shuffle=True,random_state=42) results=[] for train_indices, test_indices in kf: train_x = list(np.zeros(len(feature),dtype='int')) test_x = list(np.zeros(len(feature),dtype='int')) for i in range(len(feature)): train_x[i] = [tr_X[i][ii] for ii in train_indices] test_x[i] = [tr_X[i][ii] for ii in test_indices] train_x[i] = np.array(train_x[i]) test_x[i] = np.array(test_x[i]) train_y = [tr_y[ii] for ii in train_indices]
print(tr_y.shape) dimx = tr_X.shape[-2] dimy = tr_X.shape[-1] tr_X = aud_utils.mat_3d_to_nd(model, tr_X) print(tr_X.shape) if prep == 'dev': cross_validation = True else: cross_validation = False miz = aud_model.Functional_Model(input_neurons=input_neurons, dropout=0.2, num_classes=num_classes, model=model, dimx=dimx, dimy=dimy, loss=loss, optimizer=optimizer) np.random.seed(68) if cross_validation: kf = KFold(len(tr_X), folds, shuffle=True, random_state=42) results = [] for train_indices, test_indices in kf: train_x = [tr_X[ii] for ii in train_indices] train_y = [tr_y[ii] for ii in train_indices] test_x = [tr_X[ii] for ii in test_indices] test_y = [tr_y[ii] for ii in test_indices] train_y = to_categorical(train_y, num_classes=len(labels)) test_y = to_categorical(test_y, num_classes=len(labels))
print(tr_X.shape) print(tr_y.shape) tr_X = aud_utils.mat_3d_to_nd(model, tr_X) print(tr_X.shape) dimx = tr_X.shape[-2] dimy = tr_X.shape[-1] if prep == 'dev': print "Number of folds", folds cross_validation = True else: cross_validation = False miz = aud_model.Functional_Model(num_classes=num_classes, model=model, dimx=dimx, dimy=dimy) np.random.seed(68) if cross_validation: kf = KFold(len(tr_X), folds, shuffle=True, random_state=42) results = [] for train_indices, test_indices in kf: train_x = [tr_X[ii] for ii in train_indices] train_y = [tr_y[ii] for ii in train_indices] test_x = [tr_X[ii] for ii in test_indices] test_y = [tr_y[ii] for ii in test_indices] #train_y = to_categorical(train_y,num_classes=len(labels)) #test_y = to_categorical(test_y,num_classes=len(labels)) train_x = np.array(train_x)
print(tr_X.shape) dimx = tr_X.shape[-2] dimy = tr_X.shape[-1] if prep == 'dev': print "Number of folds", folds cross_validation = True else: cross_validation = False rnn_units = [20, 20] miz = aud_model.Functional_Model(num_classes=num_classes, model=model, dimx=dimx, dimy=dimy, dropout=dropout1, filter_lenght=filter_length, act1=act1, act2=act2, nb_filter=nb_filter, rnn_units=rnn_units, act3=act3) np.random.seed(68) if cross_validation: kf = KFold(len(tr_X), folds, shuffle=True, random_state=42) results = [] for train_indices, test_indices in kf: train_x = [tr_X[ii] for ii in train_indices] train_y = [tr_y[ii] for ii in train_indices] test_x = [tr_X[ii] for ii in test_indices] test_y = [tr_y[ii] for ii in test_indices]
aud_audio.extract(feature, cfg.wav_eva_fd, cfg.eva_fd + '/' + feature, 'parameters.yaml', dataset=dataset) ############# LOAD DATA ########################### tr_X, tr_y = seth.get_train_data() dimx = tr_X.shape[-2] dimy = tr_X.shape[-1] tr_X = aud_utils.mat_3d_to_nd(model, tr_X) miz = aud_model.Functional_Model(input_neurons=input_neurons, dropout=dropout, num_classes=num_classes, model=model, dimx=dimx, nb_filter=nb_filter, pool_size=pool_size, dimy=dimy, loss=loss, optimizer=optimizer) if prep == 'dev': kf = KFold(len(tr_X), folds, shuffle=True, random_state=42) results = [] for train_indices, test_indices in kf: train_x = [tr_X[ii] for ii in train_indices] train_y = [tr_y[ii] for ii in train_indices] test_x = [tr_X[ii] for ii in test_indices] test_y = [tr_y[ii] for ii in test_indices] train_y = to_categorical(train_y, num_classes=len(cfg.labels)) test_y = to_categorical(test_y, num_classes=len(cfg.labels))
tr_X = aud_utils.mat_3d_to_nd(model, tr_X) print(tr_X.shape) dimx = tr_X.shape[-2] dimy = tr_X.shape[-1] if prep == 'dev': print "Number of folds", folds cross_validation = True else: cross_validation = False ## In case of Functional CRNN miz = aud_model.Functional_Model(model=model, dimx=dimx, dimy=dimy, num_classes=num_classes, act1=act1, act2=act2, act3=act3) np.random.seed(68) if cross_validation: kf = KFold(len(tr_X), folds, shuffle=True, random_state=42) results = [] for train_indices, test_indices in kf: train_x = [tr_X[ii] for ii in train_indices] train_y = [tr_y[ii] for ii in train_indices] test_x = [tr_X[ii] for ii in test_indices] test_y = [tr_y[ii] for ii in test_indices] train_x = np.array(train_x)
cfg.wav_eva_fd, cfg.eva_fd + '/' + feature, 'parameters.yaml', dataset=dataset) ############# LOAD DATA ########################### tr_X, tr_y = seth.get_train_data() dimx = tr_X.shape[-2] dimy = tr_X.shape[-1] tr_X = aud_utils.mat_3d_to_nd(model, tr_X) miz = aud_model.Functional_Model(input_neurons=input_neurons, dropout=dropout, num_classes=num_classes, model=model, dimx=dimx, nb_filter=nb_filter, act1=act1, act2=act2, act3=act3, filter_length=filter_length, dimy=dimy) if prep == 'dev': kf = KFold(len(tr_X), folds, shuffle=True, random_state=42) results = [] for train_indices, test_indices in kf: train_x = [tr_X[ii] for ii in train_indices] train_y = [tr_y[ii] for ii in train_indices] test_x = [tr_X[ii] for ii in test_indices] test_y = [tr_y[ii] for ii in test_indices] train_y = to_categorical(train_y, num_classes=len(cfg.labels))
print(tr_X.shape) print(tr_y.shape) tr_X=aud_utils.mat_3d_to_nd(model,tr_X) print(tr_X.shape) dimx=tr_X.shape[-2] dimy=tr_X.shape[-1] if prep=='dev': print "Number of folds",folds cross_validation=True else: cross_validation=False nb_filter = [50 , 100] miz=aud_model.Functional_Model(model=model,dimx=dimx,dimy=dimy,num_classes=num_classes,act1=act1,act2=act2,act3=act3,nb_filter = nb_filter,dropout=dropout1) np.random.seed(68) if cross_validation: kf = KFold(len(tr_X),folds,shuffle=True,random_state=42) results=[] for train_indices, test_indices in kf: train_x = [tr_X[ii] for ii in train_indices] train_y = [tr_y[ii] for ii in train_indices] test_x = [tr_X[ii] for ii in test_indices] test_y = [tr_y[ii] for ii in test_indices] #train_y = to_categorical(train_y,num_classes=len(labels)) #test_y = to_categorical(test_y,num_classes=len(labels)) train_x=np.array(train_x) train_y=np.array(train_y)