models.py 文件源码

def bi_lstm(input_shape, n_classes,layers=2, neurons=80, dropout=0.3):
    """
    just replace ANN by RNNs
    """
    model = Sequential(name='pure_rnn')
    model.add(Bidirectional(LSTM(neurons, return_sequences=False if layers==1 else True, dropout=dropout, recurrent_dropout=dropout), input_shape=input_shape))

    model.add(LSTM(neurons, return_sequences=False if layers==1 else True, input_shape=input_shape,dropout=dropout, recurrent_dropout=dropout))
    for i in range(layers-1):
        model.add(Bidirectional(LSTM(neurons, return_sequences=False if i==layers-2 else True,dropout=dropout, recurrent_dropout=dropout)))
    model.add(Dense(n_classes, activation = 'softmax'))
    model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001), metrics=[keras.metrics.categorical_accuracy])
    return model