testSigmoidSmaller.py 文件源码

def modelC256P3C256P3C256P3f128_conc_f128(inputLength, inputDim):
    inputA = Input(shape=(inputLength,), dtype='int32')
    inputB = Input(shape=(inputLength,), dtype='int32')
    # One hot encoding
    oheInputA = Lambda(K.one_hot, arguments={
                       'num_classes': inputDim}, output_shape=(inputLength, inputDim))(inputA)
    oheInputB = Lambda(K.one_hot, arguments={
                       'num_classes': inputDim}, output_shape=(inputLength, inputDim))(inputB)
    net = netC256P3C256P3C256P3f128(inputLength, inputDim)
    processedA = net(oheInputA)
    processedB = net(oheInputB)
    # Concatenate
    conc = Concatenate()([processedA, processedB])
    x = BatchNormalization()(conc)
    # Dense
    x = Dense(128, activation='relu')(x)
    x = Dropout(0.5)(x)
    x = BatchNormalization()(x)
    predictions = Dense(1, activation='sigmoid')(x)
    model = Model([inputA, inputB], predictions)
    return model