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How to define Learning Rates and Early_STOP #19
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Hi,@luoda888 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] ) |
OK! Thanks |
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