處理不平衡的數據集的時候，可以使用對數據加權來提高數量較小類的被選中的概率，具體方式如下
fit(self, x, y, batch_size=32, nb_epoch=10, verbose=1, callbacks=[], validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None)
class_weight：字典，將不同的類別映射為不同的權值，該參數用來在訓練過程中調整損失函數（只能用於訓練）
sample_weight：權值的numpy array，用於在訓練時調整損失函數（僅用於訓練）。可以傳遞一個1D的與樣本等長的向量用於對樣本進行1對1的加權，或者在面對時序數據時，傳遞一個的形式為（samples，sequence_length）的矩陣來為每個時間步上的樣本賦不同的權。這種情況下請確定在編譯模型時添加了sample_weight_mode=’temporal’。
具體使用可以如下：
設置不同累的權值，如下：類0，權值1；類1，權值50
cw = {0: 1, 1: 50}
訓練模型
model.fit(x_train, y_train,batch_size=batch_size,epochs=epochs,verbose=1,callbacks=cbks,validation_data=(x_test, y_test), shuffle=True,class_weight=cw)
如果僅僅是類不平衡，則使用class_weight，sample_weights則是類內樣本之間還不平衡的時候使用。
class_weight affects the relative weight of each class in the calculation of the objective function.
sample_weights, as the name suggests, allows further control of the relative weight of samples that belong to the same class.
Class weights are useful when training on highly skewed data sets; for example, a classifier to detect fraudulent transactions.
Sample weights are useful when you don't have equal confidence in the samples in your batch. A common example is performing regression on measurements with variable uncertainty.
https://datascience.stackexchange.com/questions/13490/how-to-set-class-weights-for-imbalanced-classes-in-keras
http://blog.csdn.net/lk7688535/article/details/52875046
https://stackoverflow.com/questions/38891390/keras-lstm-with-class-weights
https://stackoverflow.com/questions/43459317/keras-class-weight-vs-sample-weights-in-the-fit-generator
https://stackoverflow.com/questions/41648129/balancing-an-imbalanced-dataset-with-keras-image-generator
https://stackoverflow.com/questions/41815354/keras-flow-from-directory-over-or-undersample-a-class
http://www.ijetae.com/files/Volume2Issue4/IJETAE_0412_07.pdf
http://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
https://stackoverflow.com/questions/44666910/keras-image-preprocessing-unbalanced-data
http://blog.csdn.net/u011401509/article/details/52625014
https://www.analyticsvidhya.com/blog/2016/09/this-machine-learning-project-on-imbalanced-data-can-add-value-to-your-resume/