Keras fit_generator() & Input array should have the same as target samples

manpreet Best Answer 2 years ago

So far I'm trying to implement the fit-generator for sentiment analysis as I only have a small PGU and big dataset. But I keep getting this error

Using Theano backend.
Can not use cuDNN on context None: cannot compile with cuDNN. We got this error:
b'In file included from C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\include/driver_types.h:53:0,\r\n                 from C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\include/cudnn.h:63,\r\n                 from C:\\Users\\Def\\AppData\\Local\\Temp\\try_flags_p2iwer2o.c:4:\r\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\include/host_defines.h:84:0: warning: "__cdecl" redefined\r\n #define __cdecl\r\n ^\r\n: note: this is the location of the previous definition\r\nd000029.o:(.idata$5+0x0): multiple definition of `__imp___C_specific_handler\'\r\nd000026.o:(.idata$5+0x0): first defined here\r\nC:/Users/Def/Anaconda3/envs/Final/Library/mingw-w64/bin/../lib/gcc/x86_64-w64-mingw32/5.3.0/../../../../x86_64-w64-mingw32/lib/../lib/crt2.o: In function `__tmainCRTStartup\':\r\nC:/repo/mingw-w64-crt-git/src/mingw-w64/mingw-w64-crt/crt/crtexe.c:285: undefined com/tag/reference">reference to `_set_invalid_parameter_handler\'\r\ncollect2.exe: error: ld returned 1 exit status\r\n'
Mapped name None to device cuda: GeForce GTX 960M (0000:01:00.0)
Epoch 1/10
Traceback (most recent call last):
  File "C:/Users/Def/PycharmProjects/KerasUkExpenditure/TweetParsing.py", line 136, in <module>
    epochs=10)
  File "C:\Users\Def\Anaconda3\envs\Final\lib\site-packages\keras\legacy\interfaces.py", line 88, in wrapper
    return func(*args, **kwargs)
  File "C:\Users\Def\Anaconda3\envs\Final\lib\site-packages\keras\models.py", line 1097, in fit_generator
    initial_epoch=initial_epoch)
  File "C:\Users\Def\Anaconda3\envs\Final\lib\site-packages\keras\legacy\interfaces.py", line 88, in wrapper
    return func(*args, **kwargs)
  File "C:\Users\Def\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 1876, in fit_generator
    class_weight=class_weight)
  File "C:\Users\Def\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 1614, in train_on_batch
    check_batch_axis=True)
  File "C:\Users\Def\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 1307, in _standardize_user_data
    _check_array_lengths(x, y, sample_weights)
  File "C:\Users\Def\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 229, in _check_array_lengths
    'and ' + str(list(set_y)[0]) + ' target samples.')
ValueError: Input arrays should have the same number of samples as target arrays. Found 1000 input samples and 1 target samples.

I have a matrix that is 1000 elements long since I only have a com/tag/maximum">maximum corpus of 1000 com/tag/words">words which is specified in the Tokenizer().

I then have the sentiment which is either a 0 for negative or a 1 for positive.

My question is why do I receive the error? I have tried to use the transform on both the data and labels and I still receive the same error. here is my code.

from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.preprocessing.text import Tokenizer
import numpy as np
import pandas as pd
import pickle
import matplotlib.pyplot as plt
import re

"""
the amount of samples out to the 1 million to use, my 960m 2GB can only handle
about 30,000ish at the moment depending on a number of neurons in the
deep layer and a number of layers.
"""
maxSamples = 3000

#Load the CSV and get the correct columns
data =
                                                

                                                                                                        
                                                    
                                                
                                                


                                                    

                                                        
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