I'm trying to add image data to a Kaggle notebook so I can run a convolutional neural network but I'm having trouble doing this via ImageDataGenerator. This is the link to my Kaggle notebook

These are my imports:

import numpy as np # linear algebra#
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from random import randint
from sklearn.utils import shuffle
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf#
from tensorflow import keras#
from tensorflow.keras.models import Sequential#
from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D#
from tensorflow.keras.optimizers import Adam #
from tensorflow.keras.metrics import categorical_crossentropy #
from tensorflow.keras.preprocessing.image import ImageDataGenerator #
from sklearn.metrics import confusion_matrix #
import itertools #
import matplotlib.pyplot as plt #
import os
import shutil
import random
import glob
import warnings

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

Here is my code where I attempt to import the data using the ImageDataGenerator:

#Define datagen:
datagen = ImageDataGenerator(rescale=1./255)

#Train Data:
cat_train_datagen = datagen.flow_from_directory('../input/dog-vs-cat-images-data/dogcat/train/cats',
                                            batch_size=500, shuffle=True)
dog_train_datagen = datagen.flow_from_directory('../input/dog-vs-cat-images-data/dogcat/train/dogs',
                                               batch_size=500, shuffle=True)

#Valid Data:
cat_valid_datagen = datagen.flow_from_directory('../input/dog-vs-cat-images-data/dogcat/validation/cats',
                                               batch_size=100, shuffle=True)
dog_valid_datagen = datagen.flow_from_directory('../input/dog-vs-cat-images-data/dogcat/validation/dogs',
                                               batch_size=100, shuffle=True)

#Test Data:
test_datagen = datagen.flow_from_directory('../input/dog-vs-cat-images-data/dogcat/test1/test1',
                                          batch_size=100, shuffle=True)

This is my terminal output:

Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.

Any input would be greatly appreciated, as I'm fairly new to Keras and am unsure whether I am using ImageDataGenerator correctly.


The path you are providing to the flow_from_directory method is one level to deep. The data generator expects a path to a directory which contains one subdirectory for each class in your dataset, see tensorflow documentation. This github gist shows how to apply the ImageDataGenerator to a dataset (coincidentally also using 'cat' and 'dog classes') together with the correct folder structure to use. Changing the provided path to ../input/dog-vs-cat-images-data/dogcat/train should solve the issue.


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