我尝试在 Google Colab 上为此项目运行此源代码。在我的文件夹中，除了 Multiheadtrain.ipynb 和 Multiheadpredict.ipynb 这 2 个文件外，其他文件没有发生任何错误。
这是源代码。
import datetime
import itertools
from collections import OrderedDict
import argparse
import os
import sys
import tensorflow.compat.v1 as tf
import plac
import easydict
tf.disable_v2_behavior()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
basedir='./'
sys.path.append(basedir)
#sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import tensorflow as tf
gpu_options = tf.compat.v1.GPUOptions()
gpu_options.allow_growth = True
config = tf.compat.v1.ConfigProto(gpu_options=gpu_options)
sess = tf.compat.v1.Session(config=config)
from tensorflow.python.keras.backend import set_session
import tensorflow.keras.backend as K
set_session(session=sess)
from multihead_attention_model import *
from Genedata import Gene_data
from keras.preprocessing.sequence import pad_sequences
from keras.layers import MaxPooling1D
from sklearn.model_selection import KFold, StratifiedKFold
encoding_seq = OrderedDict([
('UNK', [0, 0, 0, 0]),
('A', [1, 0, 0, 0]),
('C', [0, 1, 0, 0]),
('G', [0, 0, 1, 0]),
('T', [0, 0, 0, 1]),
('N', [0.25, 0.25, 0.25, 0.25]), # A or C or G or T
])
seq_encoding_keys = list(encoding_seq.keys())
seq_encoding_vectors = np.array(list(encoding_seq.values()))
gene_ids = None
def calculating_class_weights(y_true):
from sklearn.utils.class_weight import compute_class_weight
number_dim = np.shape(y_true)[1]
weights = np.empty([number_dim, 2])
for i in range(number_dim):
weights[i] = compute_class_weight('balanced', [0., 1.], y_true[:, i])
return weights
def get_id_label_seq_Dict(gene_data):
id_label_seq_Dict = OrderedDict()
for gene in gene_data:
label = gene.label
gene_id = gene.id.strip()
id_label_seq_Dict[gene_id] = {}
id_label_seq_Dict[gene_id][label]= (gene.seqleft,gene.seqright)
return id_label_seq_Dict
def get_label_id_Dict(id_label_seq_Dict):
label_id_Dict = OrderedDict()
for eachkey in id_label_seq_Dict.keys():
label = list(id_label_seq_Dict[eachkey].keys())[0]
label_id_Dict.setdefault(label,set()).add(eachkey)
return label_id_Dict
def typeicalSampling(ids, k):
kf = KFold(n_splits=k, shuffle=True, random_state=1234)
folds = kf.split(ids)
train_fold_ids = OrderedDict()
val_fold_ids = OrderedDict()
test_fold_ids=OrderedDict()
for i, (train_indices, test_indices) in enumerate(folds):
size_all = len(train_indices)
train_fold_ids[i] = []
val_fold_ids[i] = []
test_fold_ids[i] =[]
train_indices2 = train_indices[:int(size_all * 0.8)]
val_indices = train_indices[int(size_all * 0.8):]
for s in train_indices2:
train_fold_ids[i].append(ids[s])
for s in val_indices:
val_fold_ids[i].append(ids[s])
for s in test_indices:
test_fold_ids[i].append(ids[s])
return train_fold_ids,val_fold_ids,test_fold_ids
def group_sample(label_id_Dict,datasetfolder,foldnum=5):
Train = OrderedDict()
Test = OrderedDict()
Val = OrderedDict()
for i in range(foldnum):
Train.setdefault(i,list())
Test.setdefault(i,list())
Val.setdefault(i,list())
for eachkey in label_id_Dict:
label_ids = list(label_id_Dict[eachkey])
if len(label_ids)<foldnum:
for i in range(foldnum):
Train[i].extend(label_ids)
continue
[train_fold_ids, val_fold_ids,test_fold_ids] = typeicalSampling(label_ids, foldnum)
for i in range(foldnum):
Train[i].extend(train_fold_ids[i])
Val[i].extend(val_fold_ids[i])
Test[i].extend(test_fold_ids[i])
print('label:%s finished sampling! Train length: %s, Test length: %s, Val length:%s'%(eachkey, len(train_fold_ids[i]), len(test_fold_ids[i]),len(val_fold_ids[i])))
for i in range(foldnum):
print('Train length: %s, Test length: %s, Val length: %s'%(len(Train[i]),len(Test[i]),len(Val[i])))
#print(type(Train[i]))
#print(Train[0][:foldnum])
np.savetxt(datasetfolder+'/Train'+str(i)+'.txt', np.asarray(Train[i]),fmt="%s")
np.savetxt(datasetfolder+'/Test'+str(i)+'.txt', np.asarray(Test[i]),fmt="%s")
np.savetxt(datasetfolder+'/Val'+str(i)+'.txt', np.asarray(Val[i]),fmt="%s")
return Train, Test, Val
def label_dist(dist):
#assert (len(dist) == 4)
return [int(x) for x in dist]
def maxpooling_mask(input_mask,pool_length=3):
#input_mask is [N,length]
max_index = int(input_mask.shape[1]/pool_length)-1
max_all=np.zeros([input_mask.shape[0],int(input_mask.shape[1]/pool_length)])
for i in range(len(input_mask)):
index=0
for j in range(0,len(input_mask[i]),pool_length):
if index<=max_index:
max_all[i,index] = np.max(input_mask[i,j:(j+pool_length)])
index+=1
return max_all
def preprocess_data(left, right,dataset,padmod='center',pooling_size=3):
gene_data = Gene_data.load_sequence(dataset, left, right)
id_label_seq_Dict = get_id_label_seq_Dict(gene_data)
label_id_Dict = get_label_id_Dict(id_label_seq_Dict)
Train=OrderedDict()
Test=OrderedDict()
Val=OrderedDict()
datasetfolder=os.path.dirname(dataset)
if os.path.exists(datasetfolder+'/Train'+str(0)+'.txt'):
for i in range(5):
Train[i] = np.loadtxt(datasetfolder+'/Train'+str(i)+'.txt',dtype='str')#HDF5Matrix(os.path.join('../mRNA_multi_data_keepnum_code/', 'datafold'+str(i)+'.h5'), 'Train')[:]
Test[i] = np.loadtxt(datasetfolder+'/Test'+str(i)+'.txt',dtype='str')#HDF5Matrix(os.path.join('../mRNA_multi_data_keepnum_code/', 'datafold'+str(i)+'.h5'), 'Test')[:]
Val[i] = np.loadtxt(datasetfolder+'/Val'+str(i)+'.txt',dtype='str')#HDF5Matrix(os.path.join('../mRNA_multi_data_keepnum_code/', 'datafold'+str(i)+'.h5'), 'Val')[:]
else:
[Train, Test,Val] = group_sample(label_id_Dict,datasetfolder)
Xtrain={}
Xtest={}
Xval={}
Ytrain={}
Ytest={}
Yval={}
Train_mask_label={}
Test_mask_label={}
Val_mask_label={}
maxpoolingmax = int((left+right)/pooling_size)
for i in range(5):
#if i <2:
# continue
print('padding and indexing data')
encoding_keys = seq_encoding_keys
encoding_vectors = seq_encoding_vectors
#train
#padd center
X_left = [[encoding_keys.index(c) for c in list(id_label_seq_Dict[id].values())[0][0]] for id in Train[i]]
X_right = [[encoding_keys.index(c) for c in list(id_label_seq_Dict[id].values())[0][1]] for id in Train[i]]
if padmod =='center':
mask_label_left = np.array([np.concatenate([np.ones(len(gene)),np.zeros(left-len(gene))]) for gene in X_left],dtype='float32')
mask_label_right = np.array([np.concatenate([np.zeros(right-len(gene)),np.ones(len(gene))]) for gene in X_right],dtype='float32')
mask_label = np.concatenate([mask_label_left,mask_label_right],axis=-1)
Train_mask_label[i]=maxpooling_mask(mask_label,pool_length=pooling_size)
X_left = pad_sequences(X_left,maxlen=left,
dtype=np.int8, value=encoding_keys.index('UNK'),padding='post') #padding after sequence
X_right = pad_sequences(X_right,maxlen=right,
dtype=np.int8, value=encoding_keys.index('UNK'),padding='pre')# padding before sequence
Xtrain[i] = np.concatenate([X_left,X_right],axis = -1)
else:
#merge left and right and padding after sequence
Xall = [np.concatenate([x,y],axis=-1) for x,y in zip(X_left,X_right)]
Xtrain[i] = pad_sequences(Xall,maxlen=left+right,dtype=np.int8, value=encoding_keys.index('UNK'),padding='post')
#mask_label = np.array([np.concatenate([np.ones(len(gene)),np.zeros(left+right-len(gene))]) for gene in Xall],dtype='float32')
#Train_mask_label[i]=maxpooling_mask(mask_label,pool_length=pooling_size)
Train_mask_label[i]=np.array([np.concatenate([np.ones(int(len(gene)/pooling_size)),np.zeros(maxpoolingmax-int(len(gene)/pooling_size))]) for gene in Xall],dtype='float32')
Ytrain[i] = np.array([label_dist(list(id_label_seq_Dict[id].keys())[0]) for id in Train[i]])
print("training shapes"+str(Xtrain[i].shape)+" "+str(Ytrain[i].shape))
#test
X_left = [[encoding_keys.index(c) for c in list(id_label_seq_Dict[id].values())[0][0]] for id in Test[i]]
X_right = [[encoding_keys.index(c) for c in list(id_label_seq_Dict[id].values())[0][1]] for id in Test[i]]
if padmod =='center':
mask_label_left = np.array([np.concatenate([np.ones(len(gene)),np.zeros(left-len(gene))]) for gene in X_left],dtype='float32')
mask_label_right = np.array([np.concatenate([np.zeros(right-len(gene)),np.ones(len(gene))]) for gene in X_right],dtype='float32')
mask_label = np.concatenate([mask_label_left,mask_label_right],axis=-1)
Test_mask_label[i]=maxpooling_mask(mask_label,pool_length=pooling_size)
X_left = pad_sequences(X_left,maxlen=left,
dtype=np.int8, value=encoding_keys.index('UNK'),padding='post') #padding after sequence
X_right = pad_sequences(X_right,maxlen=right,
dtype=np.int8, value=encoding_keys.index('UNK'),padding='pre')# padding before sequence
Xtest[i] = np.concatenate([X_left,X_right],axis = -1)
else:
#merge left and right and padding after sequence
Xall = [np.concatenate([x,y],axis=-1) for x,y in zip(X_left,X_right)]
Xtest[i] = pad_sequences(Xall,maxlen=left+right,dtype=np.int8, value=encoding_keys.index('UNK'),padding='post')
#mask_label = np.array([np.concatenate([np.ones(len(gene)),np.zeros(left+right-len(gene))]) for gene in Xall],dtype='float32')
#Test_mask_label[i]=maxpooling_mask(mask_label,pool_length=pooling_size)
Test_mask_label[i]=np.array([np.concatenate([np.ones(int(len(gene)/pooling_size)),np.zeros(maxpoolingmax-int(len(gene)/pooling_size))]) for gene in Xall],dtype='float32')
Ytest[i] = np.array([label_dist(list(id_label_seq_Dict[id].keys())[0]) for id in Test[i]])
#validation
X_left = [[encoding_keys.index(c) for c in list(id_label_seq_Dict[id].values())[0][0]] for id in Val[i]]
X_right = [[encoding_keys.index(c) for c in list(id_label_seq_Dict[id].values())[0][1]] for id in Val[i]]
if padmod=='center':
mask_label_left = np.array([np.concatenate([np.ones(len(gene)),np.zeros(left-len(gene))]) for gene in X_left],dtype='float32')
mask_label_right = np.array([np.concatenate([np.zeros(right-len(gene)),np.ones(len(gene))]) for gene in X_right],dtype='float32')
mask_label = np.concatenate([mask_label_left,mask_label_right],axis=-1)
Val_mask_label[i]=maxpooling_mask(mask_label,pool_length=pooling_size)
X_left = pad_sequences(X_left,maxlen=left,
dtype=np.int8, value=encoding_keys.index('UNK'),padding='post') #padding after sequence
X_right = pad_sequences(X_right,maxlen=right,
dtype=np.int8, value=encoding_keys.index('UNK'),padding='pre')# padding before sequence
Xval[i] = np.concatenate([X_left,X_right],axis = -1)
else:
#merge left and right and padding after sequence
Xall = [np.concatenate([x,y],axis=-1) for x,y in zip(X_left,X_right)]
Xval[i] = pad_sequences(Xall,maxlen=left+right,dtype=np.int8, value=encoding_keys.index('UNK'),padding='post')
#mask_label = np.array([np.concatenate([np.ones(len(gene)),np.zeros(left+right-len(gene))]) for gene in Xall],dtype='float32')
#Val_mask_label[i]=maxpooling_mask(mask_label,pool_length=pooling_size)
Val_mask_label[i]=np.array([np.concatenate([np.ones(int(len(gene)/pooling_size)),np.zeros(maxpoolingmax-int(len(gene)/pooling_size))]) for gene in Xall],dtype='float32')
Yval[i] = np.array([label_dist(list(id_label_seq_Dict[id].keys())[0]) for id in Val[i]])
return Xtrain,Ytrain,Train_mask_label,Xtest, Ytest,Test_mask_label,Xval,Yval,Val_mask_label, encoding_keys, encoding_vectors
# starts training in CNN model
def run_model(lower_bound, upper_bound, max_len, dataset, **kwargs):
pooling_size = kwargs['pooling_size'] #
#pooling_size = int(kwargs['pooling_size']*kwargs['num_encoder']*2)
print("pooling_size")
print(pooling_size)
Xtrain,Ytrain,Train_mask_label,Xtest, Ytest,Test_mask_label,Xval,Yval,Val_mask_label, encoding_keys, encoding_vectors = preprocess_data(kwargs['left'], kwargs['right'], dataset,padmod = kwargs['padmod'],pooling_size=pooling_size)
max_len = kwargs['left']+kwargs['right']
# model mode maybe overridden by other parameter settings
for i in range(foldnum):
print(Xtrain[i].shape)
print(Train_mask_label[i].shape)
print('Evaluating KFolds {}/10'.format(i + 1))
model = multihead_attention(max_len, kwargs['nb_classes'], OUTPATH, kfold_index=i) # initialize
model.build_model_multihead_attention_multiscaleCNN4_covermore(
load_weights = kwargs['load_pretrain'],
weight_dir = kwargs['weights_dir'],
dim_attention=kwargs['dim_attention'],
headnum=kwargs['headnum'],
embedding_vec=encoding_vectors,
nb_filters=kwargs['nb_filters'],
filters_length1=kwargs['filters_length1'],
filters_length2=kwargs['filters_length2'],
filters_length3=kwargs['filters_length3'],
pooling_size=kwargs['pooling_size'],
drop_input=kwargs['drop_input'],
drop_cnn=kwargs['drop_cnn'],
drop_flat=kwargs['drop_flat'],
W1_regularizer=kwargs['W1_regularizer'],
W2_regularizer=kwargs['W2_regularizer'],
Att_regularizer_weight=kwargs['Att_regularizer_weight'],
BatchNorm=kwargs['BatchNorm'],
fc_dim = kwargs['fc_dim'],
fcnum = kwargs['fcnum'],
posembed=kwargs['posembed'],
pos_dmodel=kwargs['pos_dmodel'],
pos_nwaves = kwargs['pos_nwaves'],
posmod = kwargs['posmod'],
regularfun = kwargs['regularfun'],
huber_delta=kwargs['huber_delta'],
activation = kwargs['activation'],
activationlast = kwargs['activationlast'],
add_avgpooling = kwargs['add_avgpooling'],
poolingmod = kwargs['poolingmod'], #1 maxpooling 2 avgpooling
normalizeatt=kwargs['normalizeatt'],
attmod=kwargs['attmod'],
sharp_beta=kwargs['sharp_beta'],
lr = kwargs['lr']
)
if kwargs['nb_classes'] == 6:
class_weights={0:1,1:1,2:1,3:3,4:5,5:8}
model.train(Xtrain[i], Ytrain[i],Train_mask_label[i], kwargs['batch_size'], kwargs['epochs'],Xval[i],Yval[i],Val_mask_label[i],loadFinal=kwargs['loadFinal'],classweight = kwargs['classweight'],class_weights=class_weights)
model.evaluate(Xtest[i], Ytest[i],Test_mask_label[i])
K.clear_session()
if __name__ == "__main__":
args = easydict.EasyDict({
"lower_bound": 0,
"upper_bound": 4000,
"max_len": 4000,
"left": 4000,
"right": 4000,
"dim_attention": 80,
"headnum": 5,
"dim_capsule": 4,
"drop_rate": 0.1,
"drop_input": 0.06,
"drop_cnn": 0.25,
"drop_flat": 0.26,
"W1_regularizer": 0.001,
"W2_regularizer": 0.001,
"Att_regularizer_weight": 0.001,
"dataset": "/content/gdrive/MyDrive/ColabNotebooks/DM3Loc-master/DM3Loc-master/testdata/Train_fold1.fasta",
"epochs": 50,
"nb_filters": 64,
"filters_length1": 9,
"filters_length2": 20,
"filters_length3": 49,
"pooling_size": 8 ,
"att_weight": 1,
"BatchNorm": "store_true",
"loadFinal": "store_true",
"fc_dim": 100,
"fcnum": 1,
"sigmoidatt": 0 ,
"message": "" ,
"load_pretrain": "store_true",
"weights_dir": "",
"randomization": None ,
"posembed": "store_true",
"pos_dmodel": 40,
"pos_nwaves": 20,
"posmod": concatenate,
"regularfun": 1,
"huber_delta": 1.0,
"activation": "gelu",
"add_avgpooling": "store_true",
"poolingmod": 1,
"classweight": "store_true" ,
"batch_size": 256,
"padmod":"after",
"num_encoder": 1 ,
"lastCNN_length": 1,
"lastCNN_filter": 128 ,
"attmod":"smooth" ,
"sharp_beta": 1,
"lr": 0.001,
"nb_classes": 6,
"foldnum": 5
})
# parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
# args =parser.parse_known_args()[0]
# print(args)
# parser = argparse.ArgumentParser()
#'''Model parameters'''
# parser.add_argument('--lower_bound', type=int, default=0, help='set lower bound on sample sequence length')
# parser.add_argument('--upper_bound', type=int, default=4000, help='set upper bound on sample sequence length')
# parser.add_argument('--max_len', type=int, default=4000,
# help="pad or slice sequences to a fixed length in preprocessing")
#
# parser.add_argument('--left', type=int, default=4000, help='set left on sample sequence length')
# parser.add_argument('--right', type=int, default=4000, help='set left on sample sequence length')
# parser.add_argument('--dim_attention', type=int, default=80, help='dim_attention')
# parser.add_argument('--headnum', type=int, default=5, help='number of multiheads') #select one from 3
# parser.add_argument('--dim_capsule', type=int, default=4, help='capsule dimention')
# parser.add_argument('--drop_rate', type=float, default=0.1, help='dropout ratio')
# parser.add_argument('--drop_input', type=float, default=0.06, help='dropout ratio')
# parser.add_argument('--drop_cnn', type=float, default=0.25, help='dropout ratio')
# parser.add_argument('--drop_flat', type=float, default=0.26, help='dropout ratio')
# parser.add_argument('--W1_regularizer', type=float, default=0.001, help='W1_regularizer')
# parser.add_argument('--W2_regularizer', type=float, default=0.001, help='W2_regularizer')
# parser.add_argument('--Att_regularizer_weight', type=float, default=0.001, help='Att_regularizer_weight')
# parser.add_argument('--dataset', type=str, default='../../mRNAsubloci_train.fasta', help='input sequence data')
# parser.add_argument('--epochs', type=int, default=50, help='')
# parser.add_argument('--nb_filters', type=int, default=64, help='number of CNN filters')
# parser.add_argument('--filters_length1', type=int, default=9, help='kernel length for CNN filters1')
# parser.add_argument('--filters_length2', type=int, default=20, help='kernel length for CNN filters2')
# parser.add_argument('--filters_length3', type=int, default=49, help='kernel length for CNN filters3')
# parser.add_argument('--pooling_size', type=int, default=8, help='pooling_size')
# parser.add_argument('--att_weight', type=float, default=1, help='number of att_weight') #select one from 3
# parser.add_argument("--BatchNorm", action="store_true",help="use BatchNorm")
# parser.add_argument("--loadFinal", action="store_true",help="whether loadFinal model")
# parser.add_argument('--fc_dim', type=int, default=100, help='fc_dim')
# parser.add_argument('--fcnum', type=int, default=1, help='fcnum')
# parser.add_argument('--sigmoidatt', type=int, default=0, help='whether sigmoidatt 0 no 1 yes') #select one from 3
# parser.add_argument("--message", type=str, default="", help="append to the dir name")
# parser.add_argument("--load_pretrain", action="store_true",
# help="load pretrained CNN weights to the first convolutional layers")
# parser.add_argument("--weights_dir", type=str, default="",
# help="Must specificy pretrained weights dir, if load_pretrain is set to true. Only enter the relative path respective to the root of this project.")
# parser.add_argument("--randomization", type=int, default=None,
# help="Running randomization test with three settings - {1,2,3}.") #use default none
# parser.add_argument("--posembed", action="store_true",help="use posembed")
# parser.add_argument("--pos_dmodel", type=int,default=40,help="pos_dmodel")
# parser.add_argument("--pos_nwaves", type=int,default=20,help="pos_nwaves")
# parser.add_argument("--posmod", type=str,default='concat',help="posmod")
# parser.add_argument("--regularfun",type=int,default=1,help = 'regularfun for l1 or l2 3 for huber_loss')
# parser.add_argument("--huber_delta",type=float,default=1.0,help = 'huber_delta')
# parser.add_argument("--activation",type=str,default='gelu',help = 'activation')
# parser.add_argument("--activationlast",type=str,default='gelu',help = 'activationlast')
# parser.add_argument("--add_avgpooling", action="store_true",help="add_avgpooling")
# parser.add_argument('--poolingmod',type=int,default=1,help = '1:maxpooling 2:avgpooling')
# parser.add_argument('--classweight', action="store_true", help='classweight')
# parser.add_argument('--batch_size', type=int, default=256, help='batch_size')
# parser.add_argument("--padmod", type=str,default='after',help="padmod: center, after")
# parser.add_argument("--normalizeatt", action="store_true",help="normalizeatt")
# parser.add_argument('--num_encoder', type=int, default=1, help='num_encoder')
# parser.add_argument('--lastCNN_length', type=int, default=1, help='lastCNN_length')
# parser.add_argument('--lastCNN_filter', type=int, default=128, help='lastCNN_filter')
# parser.add_argument("--attmod", type=str, default="smooth",help="attmod")
# parser.add_argument("--sharp_beta", type=int, default=1,help="sharp_beta")
# parser.add_argument("--lr",type=float,default=0.001,help = 'lr')
# parser.add_argument("--nb_classes",type=int,default=6,help = 'nb_classes')
# parser.add_argument('--foldnum', type=int, default=5, help='number of cross-validation folds')
# args = parser.parse_args()
OUTPATH = os.path.join(basedir,'Results/'+args.message + '/')
if not os.path.exists(OUTPATH):
os.makedirs(OUTPATH)
print('OUTPATH:', OUTPATH)
del args.message
args.weights_dir = os.path.join(basedir, args.weights_dir)
for k, v in vars(args).items():
print(k, ':', v)
run_model(**vars(args))
#python3 Multihead_train.py --normalizeatt --classweight --dataset ./testdata/modified_mutlilabel_seq.fasta --epochs 500 --message cnn64_smooth_l1
但是发生的错误是我不知道这个错误是什么，我不知道如何修复它。所以有人可以帮我解决这个问题吗？
这是 multiheadtrain.ipynb 的错误
<块引用>警告：tensorflow：来自/usr/local/lib/python3.7/dist-packages/tensorflow/python/compat/v2_compat.py:96：disable_resource_variables（来自tensorflow.python.ops.variable_scope）已被弃用，并将在以后的版本中删除。 更新说明： 长期不支持非资源变量 输出路径：./结果// 下界：0 pooling_size 8 导入数据集 /content/gdrive/MyDrive/ColabNotebooks/DM3Loc-master/DM3Loc-master/testdata/Train_fold1.fasta 样本总数：11064 填充和索引数据
KeyError Traceback（最近一次调用最后一次） 在 （） 第469话 470 --> 471 run_model(**vars(args)) 472 473
<块引用>2 帧 在 (.0) 第189话 190 #padd中心 --> 191 X_left = [[encoding_keys.index(c) for c in list(id_label_seq_Dict[id].values())[0][0]] for id in Train[i]] 192 X_right = [[encoding_keys.index(c) for c in list(id_label_seq_Dict[id].values())[0][1]] for id in Train[i]] 第193话 <块引用>
KeyError: '>010000,ACCNUM:NM_001256339,Gene_ID:4824,Gene_Name:NKX3-1'