How to define Learning Rates and Early_STOP about deepctr HOT 2 CLOSED

import deepctr
from tensorflow.python.keras.optimizers import Adam,Adagrad
from tensorflow.python.keras.callbacks import EarlyStopping

model = deepctr.models.DeepFM({"sparse": sparse_feature_dict, "dense": dense_feature_list})
model.compile(Adagrad('0.0808'),'binary_crossentropy',metrics=['binary_crossentropy'])

es = EarlyStopping(monitor='val_binary_crossentropy')
history = model.fit(model_input, data[target].values,batch_size=256, epochs=10, verbose=2, validation_split=0.2,callbacks=[es] )