tflean 모델로드 및 재교육 방법

Question

DNN- RNN을 텍스트 데이터 세트로 사용하려고합니다. 그것은 단순한 더미 데이터이고 나는이 코드가 대부분의 텍스트 데이터와 함께 사용될 수 있다고 생각합니다. 그러나 훈련 된 모델을로드하고 다시 테스트하려고 할 때 오류가 발생합니다. 내가 잘못하고있다면 말해줘.

def convert_docs(documents,no_class=2,MAX_DOCUMENT_LENGTH=200): 
    '''Takes list of docs and associated clas list as input. 
    Prepares it for the tflearn library. documents should be a list of strings and 
    clas should be a numbered list of classes encoded into 0,1,2 etc. 
    no_classes is the number of classes that are going to be used in the model 
    this is defaulted to 2''' 

    if MAX_DOCUMENT_LENGTH is None: 
     list_docs = [] 
     for x in documents: 
      list_docs.append(x.split()) 

     MAX_DOCUMENT_LENGTH = max(len(l) for l in list_docs) 
     print(MAX_DOCUMENT_LENGTH) 
    else: 
     MAX_DOCUMENT_LENGTH=MAX_DOCUMENT_LENGTH 

    vocab_processor = VocabularyProcessor(MAX_DOCUMENT_LENGTH,min_frequency=5,vocabulary=None) 
    data = np.array(list(vocab_processor.fit_transform(documents))) 
    n_words = len(vocab_processor.vocabulary_) 

반환 데이터, 여기

def model_RNN(MAX_DOCUMENT_LENGTH,n_words): 
     net = input_data(shape=[None, MAX_DOCUMENT_LENGTH]) 
     net = embedding(net, input_dim=n_words, output_dim=128) 
     net = bidirectional_rnn(net, BasicLSTMCell(128), BasicLSTMCell(128)) 
     net = dropout(net, 0.5) 
     net = fully_connected(net, 2, activation='softmax') 
     net = regression(net, optimizer='adam', loss='categorical_crossentropy') 
     model = tflearn.DNN(net, clip_gradients=0., tensorboard_verbose=2) 
     return model 

필요 이것은 형식으로 텍스트 문서의 목록을 변환하는 것입니다 vocab_processor, n_words, MAX_DOCUMENT_LENGTH

우리는 RNN 모델을 초기화

def classify_DNN(data,clas,model): 
    from sklearn.cross_validation import StratifiedKFold 
    folds = 10 #number of folds for the cv 
    skf = StratifiedKFold(n_folds=folds,y=clas) 
    fold = 1 
    cms = np.array([[0,0],[0,0]]) 
    accs = [] 
    aucs=[] 
    for train_index, test_index in skf: 
     X_train, X_test = data[train_index], data[test_index] 
     y_train, y_test = clas[train_index], clas[test_index] 
     trainy= to_categorical(y_train, nb_classes=2) 
     model.fit(X_train, trainy, n_epoch = 10, shuffle=True) 
     prediction = model.predict(X_test) 
     pred=np.argmax(prediction,axis=1) 
     acc = accuracy_score(pred, y_test) 
     cm = confusion_matrix(y_test,pred) 
     fpr, tpr, thresholds = metrics.roc_curve(y_test, pred) 
     print('Test Accuracy for fold {} : {}'.format(fold,round((acc*100),2))) 
     au = metrics.auc(fpr, tpr) 
     #au=roc_auc_score(testY, pred) 
     print('AUC for fold {} : {}'.format(fold,round((au*100),2))) 
     fold +=1 
     cms += cm 
     accs.append(acc) 
     aucs.append(au) 
    #print('CV test accuracy: {}\n{}'.format(round((np.mean(accs)*100),2),cms)) 
    #print('\nCV AUC: {}'.format(round(np.mean(aucs)*100),2)) 
    print('\nCV accuracy: %.3f +/- %.3f' % (round((np.mean(accs)*100),2),round((np.std(accs)*100),2))) 
    print('\nCV ROC AUC: %.3f +/- %.3f' % (round((np.mean(aucs)*100),2),round((np.std(aucs)*100),2))) 
    return model, round(np.mean(accs)*100,2), round(np.mean(aucs)*100,2) 

이것은 모델을 훈련하기위한 것입니다. 이것이 최선의 방법이 아닐 수도 있습니다.

def pred_user_dnn(user_transformed, clf, y=None): 
    ''' 
    Used for predicting the class of the user string given the transformed user input and the pretrained classifier 
    Arguments: 
     user_transformed= the transformed doc using the one used on the training data.. Must have same dimension as the training data 
     clf= classifier pre trained on the training data of the one returned from cros_val() 
     y= the training labels 
    returns: 
     string- Yes if the predicted label is 0 
     No is the predicted label is 1 
    ''' 
    usr_p = clf.predict(user_transformed) 
    usr_p= np.argmax(usr_p,1) 
    print('\nUser class'+str(usr_p)) 
    for x in usr_p: 
     if x==0: 
      print("Case recovery eligibility is: Yes") 
      return 'Yes' 
     elif x==1: 
      print("Case recovery eligibility is: No") 
      return 'No' 

이 기능은 새로운 문자열

tf.reset_default_graph()  
data,vocab_processor, n_words, MAX_DOCUMENT_LENGTH = convert_docs(documents,no_class=2,MAX_DOCUMENT_LENGTH=200) 
model = model_RNN(MAX_DOCUMENT_LENGTH,n_words) 
clf, acc, roc_auc =classify_DNN(data,clas,model) 
final_name = 'LSTM'.lower()+'_'+now+'.clf' 
clf.save(os.path.join(trained,final_name)) 

이 내가로드하고

tf.reset_default_graph() 
model_name=model_name.lower() 
data,vocab_processor, n_words, MAX_DOCUMENT_LENGTH = convert_docs(documents,no_class=2,MAX_DOCUMENT_LENGTH=200) 
model = model_RNN(MAX_DOCUMENT_LENGTH,n_words) 
path_clf= #path where the model is saved 
model.load(os.path.join(trained,path_clf)) 
user_transformed = np.array(list(vocab_processor.transform(clean_user_list))) 
#using it for prediction 
user_transformed =pad_sequences(sequences=user_transformed,maxlen=MAX_DOCUMENT_LENGTH, value=0.) 
result = pred_user_dnn(user_transformed, model) 

그리고 여기에 훈련 모델을 저장하기위한 예측 ... 그것을하지만 실험 하였다 저장된 모델 이 오류가 발생합니다.

model.load(os.path.join(trained,path_clf)) 
Traceback (most recent call last): 

    File "<ipython-input-28-d4cf3784bb15>", line 1, in <module> 
    model.load(os.path.join(trained,path_clf)) 

    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tflearn\models\dnn.py", line 260, in load 
    self.trainer.restore(model_file, weights_only, **optargs) 

    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tflearn\helpers\trainer.py", line 449, in restore 
    self.restorer.restore(self.session, model_file) 

    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 1388, in restore 
    {self.saver_def.filename_tensor_name: save_path}) 

    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 766, in run 
    run_metadata_ptr) 

    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 964, in _run 
    feed_dict_string, options, run_metadata) 

    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1014, in _do_run 
    target_list, options, run_metadata) 

    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1034, in _do_call 
    raise type(e)(node_def, op, message) 

NotFoundError: Key val_loss_2 not found in checkpoint 
    [[Node: save_5/RestoreV2_122 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_5/Const_0, save_5/RestoreV2_122/tensor_names, save_5/RestoreV2_122/shape_and_slices)]] 

Caused by op 'save_5/RestoreV2_122', defined at: 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 223, in <module> 
    main() 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 219, in main 
    kernel.start() 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 474, in start 
    ioloop.IOLoop.instance().start() 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\zmq\eventloop\ioloop.py", line 162, in start 
    super(ZMQIOLoop, self).start() 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tornado\ioloop.py", line 887, in start 
    handler_func(fd_obj, events) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper 
    return fn(*args, **kwargs) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events 
    self._handle_recv() 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv 
    self._run_callback(callback, msg) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback 
    callback(*args, **kwargs) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper 
    return fn(*args, **kwargs) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 276, in dispatcher 
    return self.dispatch_shell(stream, msg) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 228, in dispatch_shell 
    handler(stream, idents, msg) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 390, in execute_request 
    user_expressions, allow_stdin) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute 
    res = shell.run_cell(code, store_history=store_history, silent=silent) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 501, in run_cell 
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2717, in run_cell 
    interactivity=interactivity, compiler=compiler, result=result) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2821, in run_ast_nodes 
    if self.run_code(code, result): 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code 
    exec(code_obj, self.user_global_ns, self.user_ns) 
    File "<ipython-input-18-395d2873044e>", line 2, in <module> 
    model = model_bi_LSTM(MAX_DOCUMENT_LENGTH,n_words) 
    File "C:\Users\kkothari\Desktop\text_mining\deep_learning.py", line 112, in model_bi_LSTM 
    model = tflearn.DNN(net, clip_gradients=0., tensorboard_verbose=2) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tflearn\models\dnn.py", line 63, in __init__ 
    best_val_accuracy=best_val_accuracy) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tflearn\helpers\trainer.py", line 145, in __init__ 
    keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 1000, in __init__ 
    self.build() 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 1030, in build 
    restore_sequentially=self._restore_sequentially) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 624, in build 
    restore_sequentially, reshape) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 361, in _AddRestoreOps 
    tensors = self.restore_op(filename_tensor, saveable, preferred_shard) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 200, in restore_op 
    [spec.tensor.dtype])[0]) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_io_ops.py", line 441, in restore_v2 
    dtypes=dtypes, name=name) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 759, in apply_op 
    op_def=op_def) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2240, in create_op 
    original_op=self._default_original_op, op_def=op_def) 
    File "C:\Users\kkothari\AppData\Local\Continuum\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1128, in __init__ 
    self._traceback = _extract_stack() 

NotFoundError (see above for traceback): Key val_loss_2 not found in checkpoint 
    [[Node: save_5/RestoreV2_122 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_5/Const_0, save_5/RestoreV2_122/tensor_names, save_5/RestoreV2_122/shape_and_slices)]] 

Answer 1

이 그래프를 생성하고이

graph1 = tf.Graph() 
with graph1.as_default(): 
    network = input_data(shape=[None, MAX_DOCUMENT_LENGTH]) 
    network = tflearn.embedding(network, input_dim=n_words, output_dim=128) 
    branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") 
    branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") 
    branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") 
    network = merge([branch1, branch2, branch3], mode='concat', axis=1) 
    network = tf.expand_dims(network, 2) 
    network = global_max_pool(network) 
    network = dropout(network, 0.5) 
    network = fully_connected(network, 2, activation='softmax') 
    network = regression(network, optimizer='adam', learning_rate=0.001,loss='categorical_crossentropy', name='target') 
    model = tflearn.DNN(network, tensorboard_verbose=0) 
    clf, acc, roc_auc,fpr,tpr =classify_DNN(data,clas,model) 
    clf.save(model_path) 

다시로드하기 위해 저장하고 재교육 또는

MODEL = None 
with tf.Graph().as_default(): 
## Building deep neural network 
    network = input_data(shape=[None, MAX_DOCUMENT_LENGTH]) 
    network = tflearn.embedding(network, input_dim=n_words, output_dim=128) 
    branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") 
    branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") 
    branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") 
    network = merge([branch1, branch2, branch3], mode='concat', axis=1) 
    network = tf.expand_dims(network, 2) 
    network = global_max_pool(network) 
    network = dropout(network, 0.5) 
    network = fully_connected(network, 2, activation='softmax') 
    network = regression(network, optimizer='adam', learning_rate=0.001,loss='categorical_crossentropy', name='target') 
    new_model = tflearn.DNN(network, tensorboard_verbose=3) 
    new_model.load(model_path) 
    MODEL = new_model 

를 사용하여 예측 또는 재교육을위한 모델 예측

을 위해 그것을 사용하는 것입니다. 첫 번째 줄과 with 루프가 중요했습니다. 도움이 필요할 수있는 사람에게