models.py 文件源码

def BiDi(input_shape,vocabSize,veclen,wordWeights,nLayers,nHidden,lr):
    assert len(nHidden) == nLayers, '#Neurons for each layer does not match #Layers'
    r_flag = True
    _Input = Input(shape = (input_shape,),dtype = 'int32')
    E = keras.layers.embeddings.Embedding(vocabSize,veclen,weights=(wordWeights,),mask_zero = True)(_Input)
    for ind in range(nLayers):
        if ind == (nLayers-1):
            r_flag = False
        fwd_layer = keras.layers.recurrent.GRU(nHidden[ind],init='glorot_uniform',inner_init='orthogonal',activation='tanh',inner_activation='hard_sigmoid',return_sequences = r_flag)(E)
        bkwd_layer = keras.layers.recurrent.GRU(nHidden[ind],init='glorot_uniform',inner_init='orthogonal',activation='tanh',inner_activation='hard_sigmoid',return_sequences = r_flag,go_backwards = True)(E)
        E = merge([fwd_layer,bkwd_layer],mode = 'ave')
        #nHidden/= 2

    Output = Dense(1,activation = 'sigmoid')(Dropout(0.5)(E))
    model = Model(input = _Input, output = Output)

    opt = keras.optimizers.Adam(lr)
    model.compile(loss='binary_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model