Equivalent of 'class_indices' attribute of 'flow_from_directory' object in case of 'ImageDataGenerator' object

End to End example which uses ImageDataGenerator.flow_from_dataframe and which answers your question of How can I get the indices of the classes

from tensorflow.keras.models import Sequential
#Import from keras_preprocessing not from keras.preprocessing
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import regularizers, optimizers
import pandas as pd
import numpy as np


def append_ext(fn):
    return fn+".png"
traindf=pd.read_csv("trainLabels.csv",dtype=str)
testdf=pd.read_csv("sampleSubmission.csv",dtype=str)
traindf["id"]=traindf["id"].apply(append_ext)
testdf["id"]=testdf["id"].apply(append_ext)
datagen=ImageDataGenerator(rescale=1./255.,validation_split=0.25)

train_generator=datagen.flow_from_dataframe(
dataframe=traindf,
directory="train/",
x_col="id",
y_col="label",
subset="training",
batch_size=32,
seed=42,
shuffle=True,
class_mode="categorical",
target_size=(32,32))

valid_generator=datagen.flow_from_dataframe(
dataframe=traindf,
directory="train/",
x_col="id",
y_col="label",
subset="validation",
batch_size=32,
seed=42,
shuffle=True,
class_mode="categorical",
target_size=(32,32))

test_datagen=ImageDataGenerator(rescale=1./255.)
test_generator=test_datagen.flow_from_dataframe(
dataframe=testdf,
directory="test/",
x_col="id",
y_col=None,
batch_size=32,
seed=42,
shuffle=False,
class_mode=None,
target_size=(32,32))

model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
                 input_shape=(32,32,3)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizers.RMSprop(lr=0.0001, decay=1e-6),loss="categorical_crossentropy",metrics=["accuracy"])

STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size
STEP_SIZE_VALID=valid_generator.n//valid_generator.batch_size
STEP_SIZE_TEST=test_generator.n//test_generator.batch_size
model.fit_generator(generator=train_generator,
                    steps_per_epoch=STEP_SIZE_TRAIN,
                    validation_data=valid_generator,
                    validation_steps=STEP_SIZE_VALID,
                    epochs=10
)

model.evaluate_generator(generator=valid_generator,
steps=STEP_SIZE_TEST)

test_generator.reset()
pred=model.predict_generator(test_generator,
steps=STEP_SIZE_TEST,
verbose=1)

predicted_class_indices=np.argmax(pred,axis=1)


labels = (train_generator.class_indices)
labels = dict((v,k) for k,v in labels.items())
predictions = [labels[k] for k in predicted_class_indices]

Finally, we print the Classes as shown below:

print(predictions)

Output of above print statement is:

['bird',
 'dog',
 'bird',
 'cat',
 'horse',
 'deer',
 'deer',
 'airplane',
 'cat',
 'cat',
 'ship',
 'bird',
 'automobile',..........]

For more information, please refer this Article written by Vijaya Bhaskar.

Hope this helps. Happy Learning!