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Finetuning ResNet50 with keras - val_loss keeps increasing

I am trying to customize resnet50 using keras with a tensorflow backend. However, upon tranining my val_loss keeps increasing. Trying different learning rates and batch sizes does not resolve the problem.

Using different preprocessing methods such as rescaling or using the preprocess_input function for resnet50 inside the ImageDataGenerator did not not solve the problem either.

This is the code I am using

Importing and preprocessing data:

from keras.preprocessing.image import ImageDataGenerator
from keras.applications.resnet50 import preprocess_input, decode_predictions

IMAGE_SIZE = 224
BATCH_SIZE = 32

num_classes = 27

main_path = "C:/Users/aaron/Desktop/DATEN/data"

gesamt_path = os.path.join(main_path, "ML_DATA")
labels = listdir(gesamt_path)

data_generator = ImageDataGenerator(#rescale=1./255,
validation_split=0.20,
preprocessing_function=preprocess_input)

train_generator = data_generator.flow_from_directory(gesamt_path, target_size=(IMAGE_SIZE, IMAGE_SIZE), shuffle=True, seed=13,
class_mode='categorical', batch_size=BATCH_SIZE, subset="training")

validation_generator = data_generator.flow_from_directory(gesamt_path, target_size=(IMAGE_SIZE, IMAGE_SIZE), shuffle=False, seed=13,
class_mode='categorical', batch_size=BATCH_SIZE, subset="validation")


Defining and training the model

img_width = 224
img_height = 224

model = keras.applications.resnet50.ResNet50()

classes = list(iter(train_generator.class_indices))
model.layers.pop()
for layer in model.layers:
layer.trainable=False
last = model.layers[-1].output
x = Dense(len(classes), activation="softmax")(last)
finetuned_model = Model(model.input, x)
finetuned_model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
for c in train_generator.class_indices:
classes[train_generator.class_indices[c]] = c
finetuned_model.classes = classes



earlystopCallback = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=8, verbose=1, mode='auto')
tbCallBack = keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)

history = finetuned_model.fit_generator(train_generator,
validation_data=validation_generator,
epochs=85, verbose=1,callbacks=[tbCallBack,earlystopCallback])


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