I want to know the effect of Add and Multiply in keras by functionality. The dumb way of thinking is that they are meant to add and multiply keras tensors. I want to know when are they to be used. For example, look at the code below from https://github.com/titu1994/keras-squeeze-excite-network/blob/master/se.py. Why use Multiply in spatial_squeeze_excite_block and why use Add in channel_spatial_squeeze_excite? Can we switch Add and Multiply in these functions? Why not?

def spatial_squeeze_excite_block(input):
    ''' Create a spatial squeeze-excite block
    Args:
        input: input tensor
    Returns: a keras tensor
    References
    -   [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/abs/1803.02579)
    '''

    se = Conv2D(1, (1, 1), activation='sigmoid', use_bias=False,
                kernel_initializer='he_normal')(input)

    x = Multiply([input, se])
    return x


def channel_spatial_squeeze_excite(input, ratio=16):
    ''' Create a spatial squeeze-excite block
    Args:
        input: input tensor
        filters: number of output filters
    Returns: a keras tensor
    References
    -   [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
    -   [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/abs/1803.02579)
    '''

    cse = squeeze_excite_block(input, ratio)
    sse = spatial_squeeze_excite_block(input)

    x = Add([cse, sse])
    return x

I want to know the effect of Add and Multiply in keras by functionality. The dumb way of thinking is that they are meant to add and multiply keras tensors. I want to know when are they to be used. For example, look at the code below from here. Why use Multiply in spatial_squeeze_excite_block and why use Add in channel_spatial_squeeze_excite? Can we switch Add and Multiply in these functions? Why not?

def spatial_squeeze_excite_block(input):
    ''' Create a spatial squeeze-excite block
    Args:
        input: input tensor
    Returns: a keras tensor
    References
    -   [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/abs/1803.02579)
    '''

    se = Conv2D(1, (1, 1), activation='sigmoid', use_bias=False,
                kernel_initializer='he_normal')(input)

    x = Multiply([input, se])
    return x


def channel_spatial_squeeze_excite(input, ratio=16):
    ''' Create a spatial squeeze-excite block
    Args:
        input: input tensor
        filters: number of output filters
    Returns: a keras tensor
    References
    -   [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
    -   [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/abs/1803.02579)
    '''

    cse = squeeze_excite_block(input, ratio)
    sse = spatial_squeeze_excite_block(input)

    x = Add([cse, sse])
    return x