我需要帮助，以解决此AlreadyExistsError问题 我是该技术的新手，因此不胜感激。 它在model.fit上抛出错误 ，在互联网上我找不到任何可以帮助我的东西 因此有关此代码的任何信息都是有用的
import tensorflow as tf
from tensorflow import keras
from langdetect import detect
from nltk.tokenize import sent_tokenize
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from keras.utils import np_utils
from keras import optimizers
引发错误的代码
#This trains the model batching from the text file
#every epoch it prints out 300 characters at different "temperatures"
#temperature controls how random the characters sample: more temperature== more crazy (but often better) text
for iteration in range(1, 20):
print()
print('-' * 50)
print('Iteration', iteration)
with open("short_reviews_shuffle.txt",encoding="utf8") as f:
for chunk in iter(lambda: f.read(90000), ""):
X, y = getDataFromChunk(chunk)
model.fit(X, y, batch_size=128, epochs=1, callbacks=None)
# Select a text seed at random
start_index = random.randint(0, len(text) - maxlen - 1)
generated_text = text[start_index: start_index + maxlen]
print('--- Generating with seed: "' + generated_text + '"')
for temperature in [0.5, 0.8, 1.0]:
print('------ temperature:', temperature)
sys.stdout.write(generated_text)
# We generate 300 characters
for i in range(300):
sampled = np.zeros((1, maxlen, len(chars)))
for t, char in enumerate(generated_text):
sampled[0, t, char_indices[char]] = 1.
preds = model.predict(sampled, verbose=0)[0]
next_index = sample(preds, temperature)
next_char = chars[next_index]
generated_text += next_char
generated_text = generated_text[1:]
sys.stdout.write(next_char)
sys.stdout.flush()
print()
这是我得到的错误以及遇到的问题。
AlreadyExistsError Traceback (most recent call last)
<ipython-input-27-80576e87ab39> in <module>
9 for chunk in iter(lambda: f.read(90000), ""):
10 X, y = getDataFromChunk(chunk)
---> 11 model.fit(X, y, batch_size=128, epochs=1, callbacks=None)
12
13 # Select a text seed at random
c:\users\inias somers\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)
1637 initial_epoch=initial_epoch,
1638 steps_per_epoch=steps_per_epoch,
-> 1639 validation_steps=validation_steps)
1640
1641 def evaluate(self,
c:\users\inias somers\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py in fit_loop(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps)
213 ins_batch[i] = ins_batch[i].toarray()
214
--> 215 outs = f(ins_batch)
216 if not isinstance(outs, list):
217 outs = [outs]
c:\users\inias somers\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\keras\backend.py in __call__(self, inputs)
2984
2985 fetched = self._callable_fn(*array_vals,
-> 2986 run_metadata=self.run_metadata)
2987 self._call_fetch_callbacks(fetched[-len(self._fetches):])
2988 return fetched[:len(self.outputs)]
c:\users\inias somers\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args, **kwargs)
1437 ret = tf_session.TF_SessionRunCallable(
1438 self._session._session, self._handle, args, status,
-> 1439 run_metadata_ptr)
1440 if run_metadata:
1441 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
c:\users\inias somers\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
AlreadyExistsError: Resource __per_step_6/training/Adam/gradients/lstm/while/ReadVariableOp_8/Enter_grad/ArithmeticOptimizer/AddOpsRewrite_Add/tmp_var/struct tensorflow::TemporaryVariableOp::TmpVar
[[{{node training/Adam/gradients/lstm/while/ReadVariableOp_8/Enter_grad/ArithmeticOptimizer/AddOpsRewrite_Add/tmp_var}} = TemporaryVariable[dtype=DT_FLOAT, shape=[1024,4096], var_name="training/A...dd/tmp_var", _device="/job:localhost/replica:0/task:0/device:CPU:0"](^training/Adam/gradients/lstm/while/strided_slice_11_grad/StridedSliceGrad)]]
某人可以帮助我与keras融为一体，我现在不该做什么
答案 0 :(得分：2)
我建议您将代码转换为更惯用的语言。
代替
for chunk in iter(lambda: f.read(90000), ""):
X, y = getDataFromChunk(chunk)
model.fit(X, y, batch_size=128, epochs=1, callbacks=None)
创建一个生成数据的生成器并使用fit_generator方法。
与其执行20次迭代的顶部循环，不如使用回调函数评估您的预测，并使用epochs = 20调用fit_generator。