Keras fit_generator(), is this the correct usage?

manpreet Best Answer 2 years ago

So far I have come up with this hacky code here, this code runs and outputs

Epoch com/tag/1">10/com/tag/1">10
       com/tag/1">1/3000 [..............................] - ETA: 27s - loss: 0.3075 - acc: 0.7270
       6/3000 [..............................] - ETA: 54s - loss: 0.3075 - acc: 0.7355
.....
    2996/3000 [============================>.] - ETA: 0s - loss: 0.3076 - acc: 0.7337
    2998/3000 [============================>.] - ETA: 0s - loss: 0.3076 - acc: 0.7337
    3000/3000 [==============================] - 59s - loss: 0.3076 - acc: 0.7337    
    Traceback (most recent call last):
      File "C:/Users/Def/PycharmProjects/KerasUkExpenditure/TweetParsing.py", com/tag/line">line com/tag/1">140, in <module>
        (loss, acc) = model.fit_generator(generator(tokenizer=t, startIndex=startIndex,batchSize=amountOfData),
    TypeError: 'History' object is not iterable

    Process finished with exit code com/tag/1">1

I'm confused by "'History' object is not iterable", what does this mean?

This is the first time I've tried to do batch training and testing and I'm not sure i've implemented it correctly as most the examples I've seen oncom/tag/line">line are for images. Here is the 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

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

#Load the CSV and get the correct columns
data = pd.read_csv("C:\\Users\\Def\\Desktop\\Sentiment Analysis Datasetcom/tag/1">1.csv")
dataX = pd.DataFrame()
dataY = pd.DataFrame()
dataY[['Sentiment']] = data[['Sentiment']]
dataX[['SentimentText']] = data[['SentimentText']]

dataY = dataY.iloc[0:maxSamples
                                                

                                                                                                        
                                                    
                                                
                                                


                                                    

                                                        
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