UnboundLocalError:分配前已引用局部变量"batch_index" [英] UnboundLocalError: local variable 'batch_index' referenced before assignment
问题描述
这不是我的代码，因为这是显示问题的行:
This is NOT MY code by here is the line, where it shows a problem:
model.fit(trainX，trainY，batch_size = 2，epochs = 200，verbose = 2)
model.fit(trainX, trainY, batch_size=2, epochs=200, verbose=2)
(正如我现在想的那样，此代码很有可能使用旧版的TF，因为"epochs"被写为"nb_epoch".)
(As I am thinking now, it is very possible this code uses an older version of TF, because 'epochs' was written as 'nb_epoch').
代码的最新更新来自:2017年1月11日！
The last update of the code is from: Jan 11, 2017!
我已经尝试了互联网上的所有内容(还不是很多)，包括在tensorflow/keras的源代码中查找了一些提示.为了清楚起见，我在代码中没有一个名为"batch_index"的变量.
I have tried everything from the internet (which is not so much), including looking inside the source code of tensorflow/keras for some hints. Just to make it clear that I don't have a variable, called 'batch_index' inside the code.
到目前为止，我已经浏览了TF的不同版本(tensorflow/tensorflow/python/keras/engine/training_arrays.py).似乎所有这些内容都来自2018年的版权，但有些以函数fit_loop开头，而其他的则以model_iteration(可能是fit_loop的更新)开头.
So far I have looked inside different versions of TF (tensorflow/tensorflow/python/keras/engine/training_arrays.py). It appears that all are from 2018 copyright, but some start with the function fit_loop, and other with model_iteration (which is probably an update of fit_loop).
因此，此"batch_index"变量只能在第一个函数中看到.
So, this 'batch_index' variable can be seen only in the first function.
我想知道我是否朝着正确的方向前进?！
I wonder if I am going in the right direction at all??!
显示代码没有意义，因为，正如我所解释的那样，代码内部没有这样的变量.
There is no point in showing the code, because, as I explained, there is no such variable in the first place inside the code.
但是，这是函数'stock_prediction'的一些代码，该代码给出错误:
but, here is some code of the function 'stock_prediction', that gives the error:
def stock_prediction():
# Collect data points from csv
dataset = []
with open(FILE_NAME) as f:
for n, line in enumerate(f):
if n != 0:
dataset.append(float(line.split(',')[1]))
dataset = np.array(dataset)
# Create dataset matrix (X=t and Y=t+1)
def create_dataset(dataset):
dataX = [dataset[n+1] for n in range(len(dataset)-2)]
return np.array(dataX), dataset[2:]
trainX, trainY = create_dataset(dataset)
# Create and fit Multilinear Perceptron model
model = Sequential()
model.add(Dense(8, input_dim=1, activation='relu'))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, nb_epoch=200, batch_size=2, verbose=2)
# Our prediction for tomorrow
prediction = model.predict(np.array([dataset[0]]))
result = 'The price will move from %s to %s' % (dataset[0], prediction[0][0])
return result
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-19-3dde95909d6e> in <module>
14
15 # We have our file so we create the neural net and get the prediction
---> 16 print(stock_prediction())
17
18 # We are done so we delete the csv file
<ipython-input-18-8bbf4f61c738> in stock_prediction()
23 model.add(Dense(1))
24 model.compile(loss='mean_squared_error', optimizer='adam')
---> 25 model.fit(trainX, trainY, batch_size=1, epochs=200, verbose=2)
26
27 # Our prediction for tomorrow
~\Anaconda3\lib\site-packages\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, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
1176 steps_per_epoch=steps_per_epoch,
1177 validation_steps=validation_steps,
-> 1178 validation_freq=validation_freq)
1179
1180 def evaluate(self,
~\Anaconda3\lib\site-packages\keras\engine\training_arrays.py in fit_loop(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps, validation_freq)
211 break
212
--> 213 if batch_index == len(batches) - 1: # Last batch.
214 if do_validation and should_run_validation(validation_freq, epoch):
215 val_outs = test_loop(model, val_function, val_inputs,
UnboundLocalError: local variable 'batch_index' referenced before assignment
一些澄清:
我试图查看我的tf/keras版本，就是这样:
I tried to see my version of tf/keras and here is it:
from tensorflow.python import keras
print(keras.__version__)
import keras
print(keras.__version__)
import tensorflow
print(tensorflow.__version__)
2.2.4-tf
2.2.5
1.14.0
为什么喀拉拉邦显示不同的版本?
Why keras shows different versions??
推荐答案
I checked in the training_arrays.py (here) the function in which you got the error and, as I can see, I think the problem could be in these statements (from lines 177 - 205):
batches = make_batches(num_train_samples, batch_size)
for batch_index, (batch_start, batch_end) in enumerate(batches): # the problem is here
# do stuff
...
if batch_index == len(batches) - 1:
# do stuff
...
如果批次是空列表，则可能会出现此错误.可能是您的训练集有问题吗?
If batches is an empty list, you could get this error. Could be that your training set has some problem?
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