多尺度CNN - Keras实现

Question

我想用python实现一个多尺度的CNN。我的目标是对三个不同的尺度使用三个不同的CNN，并将最终层的最终输出连接起来，并将它们提供给FC层以获得输出预测。

但我不明白该如何实现这一点。我知道如何实现单尺度CNN。

有人能在这方面帮我吗？

Answer 1

我不明白为什么要有3个CNN，因为你会得到与单个CNN相同的结果。也许你可以训练得更快些。也许你也可以做一些池化和一些resnet操作(我猜这可能会被证明类似于你想要的)。

然而，对于每个CNN，你需要一个成本函数来优化你使用的“启发式”(例如:提高认知度)。同样，你也可以像在NN风格转移中那样做一些事情，你可以在几个“目标”(内容和风格矩阵)之间比较结果；或者简单地训练3个CNN，然后切断最后一层(或冻结它们)，并使用已经训练好的权重再次训练，但现在使用目标FN层……

Answer 2

这是一个多输入CNN的例子。您只需引用提供每个网络输出的变量即可。然后使用连接，并将它们传递到密集网络或任何您喜欢的任务中。

def multires_CNN(filters, kernel_size, multires_data):
    '''uses Functional API for Keras 2.x support.
       multires data is output from load_standardized_multires()
    '''
    input_fullres = Input(multires_data[0].shape[1:], name = 'input_fullres')
    fullres_branch = Conv2D(filters, (kernel_size, kernel_size),
                     activation = LeakyReLU())(input_fullres)
    fullres_branch = MaxPooling2D(pool_size = (2,2))(fullres_branch)
    fullres_branch = BatchNormalization()(fullres_branch)
    fullres_branch = Flatten()(fullres_branch)

    input_medres = Input(multires_data[1].shape[1:], name = 'input_medres')
    medres_branch = Conv2D(filters, (kernel_size, kernel_size),
                     activation=LeakyReLU())(input_medres)
    medres_branch = MaxPooling2D(pool_size = (2,2))(medres_branch)
    medres_branch = BatchNormalization()(medres_branch)
    medres_branch = Flatten()(medres_branch)

    input_lowres = Input(multires_data[2].shape[1:], name = 'input_lowres')
    lowres_branch = Conv2D(filters, (kernel_size, kernel_size),
                     activation = LeakyReLU())(input_lowres)
    lowres_branch = MaxPooling2D(pool_size = (2,2))(lowres_branch)
    lowres_branch = BatchNormalization()(lowres_branch)
    lowres_branch = Flatten()(lowres_branch)

    merged_branches = concatenate([fullres_branch, medres_branch, lowres_branch])
    merged_branches = Dense(128, activation=LeakyReLU())(merged_branches)
    merged_branches = Dropout(0.5)(merged_branches)
    merged_branches = Dense(2,activation='linear')(merged_branches)

    model = Model(inputs=[input_fullres, input_medres ,input_lowres],
                  outputs=[merged_branches])
    model.compile(loss='mean_absolute_error', optimizer='adam')

    return model