我创建了一个CNN模型，以使用API序列对恶意软件进行分类。我训练了现在，我想使用我的模型预测恶意软件。但是我遇到了一个错误，该错误说存在一个InvalidArgumentError：[0，295）中没有索引[126,5137] = -2147483648。 谁能帮助我解决这个问题？
train_labels=np.array(train_df.label)
train_seq = pad_sequences(train_df.seq.values, maxlen = 6000)
test_seq = pad_sequences(test_df.seq.values, maxlen = 6000)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
max_len = 6000
max_cnt = 295
embed_size = 256
num_filters = 64
kernel_size = [2,4,6,8,10,12,14]
conv_action = 'relu'
mask_zero = False
TRAIN = True
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
meta_train = np.zeros(shape = (len(train_seq),8))
meta_test = np.zeros(shape = (len(test_seq),8))
FLAG = False
for i,(tr_ind,te_ind) in enumerate(skf.split(train_seq,train_labels)):
print('FOLD: '.format(i))
print(len(te_ind),len(tr_ind))
model = TextCNN(max_len,max_cnt,embed_size,num_filters,kernel_size,conv_action,mask_zero)
model_name = 'benchmark_textcnn_fold_'+str(i)
X_train,X_train_label = train_seq[tr_ind],train_labels[tr_ind]
X_val,X_val_label = train_seq[te_ind],train_labels[te_ind]
model = TextCNN(max_len,max_cnt,embed_size,
num_filters,kernel_size,
conv_action,
mask_zero)
model_save_path = '../model_weight_final/%s_%s.hdf5'%(model_name,embed_size)
early_stopping =EarlyStopping(monitor='val_loss', patience=3)
model_checkpoint = ModelCheckpoint(model_save_path, save_best_only=True, save_weights_only=True)
if TRAIN and FLAG:
model.fit(X_train,X_train_label,
validation_data=(X_val,X_val_label),
epochs=100,batch_size=64,
shuffle=True,
callbacks=[early_stopping,model_checkpoint]
)
model.load_weights(model_save_path)
pred_val = model.predict(X_val,batch_size=128,verbose=1)
pred_test = model.predict(test_seq,batch_size=128,verbose=1)
meta_train[te_ind] = pred_val
meta_test += pred_test
K.clear_session()
meta_test /= 5.0
FOLD:
2780 11107
2780/2780 [==============================] - ETA: 3: - ETA: 4: - ETA: 4: - ETA: 4: - ETA: 3: - ETA: 3: - ETA: 3: - ETA: 3: - ETA: 3: - ETA: 2: - ETA: 2: - ETA: 2: - ETA: 2: - ETA: 1: - ETA: 1: - ETA: 1: - ETA: 1: - ETA: 55s - ETA: 40 - ETA: 25 - ETA: 10 - 327s 118ms/step
1664/12955 [==>...........................] - ETA: 24:2 - ETA: 24:1 - ETA: 23:5 - ETA: 23:4 - ETA: 23:3 - ETA: 23:1 - ETA: 23:0 - ETA: 22:4 - ETA: 22:3 - ETA: 22:1 - ETA: 22:0 - ETA: 21:4 - ETA: 21:33
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-33-d1f608365da1> in <module>
29 model.load_weights(model_save_path)
30 pred_val = model.predict(X_val,batch_size=128,verbose=1)
---> 31 pred_test = model.predict(test_seq,batch_size=128,verbose=1)
32
33 meta_train[te_ind] = pred_val
c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1460 verbose=verbose,
1461 steps=steps,
-> 1462 callbacks=callbacks)
1463
1464 def train_on_batch(self, x, y,
c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\keras\engine\training_arrays.py in predict_loop(model, f, ins, batch_size, verbose, steps, callbacks)
322 batch_logs = {'batch': batch_index, 'size': len(batch_ids)}
323 callbacks._call_batch_hook('predict', 'begin', batch_index, batch_logs)
--> 324 batch_outs = f(ins_batch)
325 batch_outs = to_list(batch_outs)
326 if batch_index == 0:
c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\tensorflow_core\python\keras\backend.py in __call__(self, inputs)
3738 value = math_ops.cast(value, tensor.dtype)
3739 converted_inputs.append(value)
-> 3740 outputs = self._graph_fn(*converted_inputs)
3741
3742 # EagerTensor.numpy() will often make a copy to ensure memory safety.
c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\tensorflow_core\python\eager\function.py in __call__(self, *args, **kwargs)
1079 TypeError: For invalid positional/keyword argument combinations.
1080 """
-> 1081 return self._call_impl(args, kwargs)
1082
1083 def _call_impl(self, args, kwargs, cancellation_manager=None):
c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\tensorflow_core\python\eager\function.py in _call_impl(self, args, kwargs, cancellation_manager)
1119 raise TypeError("Keyword arguments {} unknown. Expected {}.".format(
1120 list(kwargs.keys()), list(self._arg_keywords)))
-> 1121 return self._call_flat(args, self.captured_inputs, cancellation_manager)
1122
1123 def _filtered_call(self, args, kwargs):
c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1222 if executing_eagerly:
1223 flat_outputs = forward_function.call(
-> 1224 ctx, args, cancellation_manager=cancellation_manager)
1225 else:
1226 gradient_name = self._delayed_rewrite_functions.register()
c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
509 inputs=args,
510 attrs=("executor_type", executor_type, "config_proto", config),
--> 511 ctx=ctx)
512 else:
513 outputs = execute.execute_with_cancellation(
c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\six.py in raise_from(value, from_value)
InvalidArgumentError: indices[126,5137] = -2147483648 is not in [0, 295)
[[node embedding_4/embedding_lookup (defined at c:\users\xie07\appdata\local\programs\python\python37\lib\site-packages\tensorflow_core\python\framework\ops.py:1751) ]] [Op:__inference_keras_scratch_graph_2602]
Function call stack:
keras_scratch_graph