mnist_swwae.py 文件源码

def convresblock(x, nfeats=8, ksize=3, nskipped=2, elu=True):
    """The proposed residual block from [4].

    Running with elu=True will use ELU nonlinearity and running with
    elu=False will use BatchNorm + RELU nonlinearity.  While ELU's are fast
    due to the fact they do not suffer from BatchNorm overhead, they may
    overfit because they do not offer the stochastic element of the batch
    formation process of BatchNorm, which acts as a good regularizer.

    # Arguments
        x: 4D tensor, the tensor to feed through the block
        nfeats: Integer, number of feature maps for conv layers.
        ksize: Integer, width and height of conv kernels in first convolution.
        nskipped: Integer, number of conv layers for the residual function.
        elu: Boolean, whether to use ELU or BN+RELU.

    # Input shape
        4D tensor with shape:
        `(batch, channels, rows, cols)`

    # Output shape
        4D tensor with shape:
        `(batch, filters, rows, cols)`
    """
    y0 = Conv2D(nfeats, ksize, padding='same')(x)
    y = y0
    for i in range(nskipped):
        if elu:
            y = ELU()(y)
        else:
            y = BatchNormalization(axis=1)(y)
            y = Activation('relu')(y)
        y = Conv2D(nfeats, 1, padding='same')(y)
    return layers.add([y0, y])