# Keras Model Predict is not predicting all images flowing from directory?

I have the following code where I have done all the training and passed the testing set as a flow from directory. After that when I pass that object into the model.predict option, the array received is not of the same length as the test set length. Code:

PATH = '/content/testing'
testGen.reset()
testGen = valAug.flow_from_directory(
PATH,
class_mode="categorical",
target_size=(75, 75),
color_mode="rgb",
shuffle=False,
batch_size=BS)

predIdxs = model1.predict_generator(testGen,
steps=(totalTest // 32))
print(len(predIdxs))
# for each image in the testing set we need to find the index of the
# label with corresponding largest predicted probability
predIdxs = np.argmax(predIdxs, axis=1)
print(len(predIdxs))
import pandas as pd
from glob import glob
test_df = pd.DataFrame()
id = []
for x in glob('/content/testing/0/*'):
id.append(x)
for x in glob('/content/testing/1/*'):
id.append(x)
test_df['id'] = id
test_df['category'] = predIdxs
print(test_df)
test_df.to_csv('submission.csv', index=False)


After that the output I got is as follows:

Found 55505 images belonging to 2 classes.
55488
55488
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-81-09c1488e5ac6> in <module>()
25     id.append(x)
26 test_df['id'] = id
---> 27 test_df['category'] = predIdxs
28 print(test_df)
29 test_df.to_csv('submission.csv', index=False)

3 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/construction.py in sanitize_index(data, index, copy)
609
610     if len(data) != len(index):
--> 611         raise ValueError("Length of values does not match length of index")
612
613     if isinstance(data, ABCIndexClass) and not copy:

ValueError: Length of values does not match length of index


Lenght of test set(totalTest = 55505) but only 55488 data is predicted. Why is data lost here? P.S: The model I have used is a pretrained Inception V3 model where I have downloaded the weights beforehand and run the model. I got about 85% accuracy. And I have tried the same method using Resnet block also and I have received the results without error. Why am I getting an error here? Any help would be appreciated.

predIdxs = model1.predict_generator(testGen,