Tukukkk

I got an error ,IndexError: list index out of range.Traceback says

Run id: P0W5X0

Log directory: /tmp/tflearn_logs/

Exception in thread Thread-2:

Traceback (most recent call last):

  File "/usr/local/Cellar/python@2/2.7.15/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py", line 801, in __bootstrap_inner

    self.run()

  File "/usr/local/Cellar/python@2/2.7.15/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py", line 754, in run

    self.__target(*self.__args, **self.__kwargs)

  File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tflearn/data_flow.py", line 201, in fill_batch_ids_queue

    ids = self.next_batch_ids()

  File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tflearn/data_flow.py", line 215, in next_batch_ids

    batch_start, batch_end = self.batches[self.batch_index]

IndexError: list index out of range

I wrote codes,

# coding: utf-8

import tensorflow as tf

import tflearn



from tflearn.layers.core import input_data,dropout,fully_connected

from tflearn.layers.conv import conv_2d, max_pool_2d

from tflearn.layers.normalization import local_response_normalization

from tflearn.layers.estimator import regression



tf.reset_default_graph()

net = input_data(shape=[None,20000, 4, 42])

net = conv_2d(net, 4, 16, activation='relu')

net = max_pool_2d(net, 1)

net = tflearn.activations.relu(net)

net = dropout(net, 0.5)

net = tflearn.fully_connected(net, 2, activation='softmax')

net = tflearn.regression(net, optimizer='adam', learning_rate=0.5, loss='categorical_crossentropy')



model = tflearn.DNN(net)



model.fit(np.array(trainDataSet).reshape(1,20000, 4, 42), np.array(trainLabel), n_epoch=400, batch_size=32, validation_set=0.1, show_metric=True)





pred = np.array(model.predict(np.array(testDataSet).reshape(1,20000, 4, 42)).argmax(axis=1))



label = np.array(testLabel).argmax(axis=0)

accuracy = np.mean(pred == label, axis=0)



print(accuracy)

I really cannot understand why such an error happens.I tried to rewrite into

model.fit(np.array(trainDataSet).reshape(1,20000, 4, 42), np.array(trainLabel), n_epoch=400, batch_size=1, validation_set=0.1, show_metric=True) 

because bach causes this error,but same error happens.I rewrite another number in this part but same error happens.What is wrong in my codes?How should I fix this?

I also had the same problem with you. My solution is making the number of n_epoch equal to your row's number of the dataset. For example, my array's shape is 461*5, the value of the n_epoch is 461. you can also make the value a little bit bigger or shorter than your row's number. In my code, 500 or 400 is also useful.