Comment charger et réapprovisionner le modèle tflean

Question

J'essaie DNN-RNN sur un jeu de données texte. C'est une simple donnée fictive et je pense que le code peut être utilisé avec la plupart des données textuelles. Cependant, je reçois une erreur lorsque j'essaie de charger le modèle entraîné et ensuite le recycler. S'il vous plaît dites-moi si je le fais mal.

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_) 

données de retour, vocab_processor, n_words, MAX_DOCUMENT_LENGTH

Ceci est pour convertir une liste de texte docs dans le format requis

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 

Ici, nous initialize le modèle RNN

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) 

Ceci est pour la formation du modèle .. Je sais que cela pourrait ne pas être la meilleure façon de le faire, mais ce fut une expérience ..

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' 

Cette fonction prédit les nouvelles chaînes

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)) 

Ceci est pour sauver le modèle formé

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) 

Et là, je suis le chargement de la enregistré le modèle et j'obtiens cette erreur.

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

est de créer un graphique et enregistrez-

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) 

Pour recharger et recycler ou l'utiliser pour la prédiction

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 

utiliser le modèle de prévision ou de recyclage. La 1ère ligne et la boucle avec étaient importantes. Pour tous ceux qui pourraient avoir besoin d'aide