model_helpers.py 文件源码

def concatenate_end2end(net, in_layer, concat_h, layer_h, pos, nb_concat_features):
    """
    Auxiliary function that checks whether we should concatenate the output of
    a layer `in_layer` of a network `net` to some a tensor in `concat_vars`

    Parameters
    ----------
    net: dictionary containing layers of a network
    in_layer: name of a layer in net
    concat_h: list of layers to concatenate
    concat_vars: list of variables (tensors) to concatenate
    pos: position in lists `concat_h` and `concat_vars` we want to check
    nb_concat_features: number of features in the layer we want to concatenate
    """
    if pos < len(concat_h) and concat_h[pos] == 'input':
        concat_h[pos] = in_layer

    # if this is the layer we want to concatenate, create an InputLayer with the
    # tensor we want to concatenate and a ConcatLayer that does the job afterwards
    if in_layer in concat_h:
        net[in_layer + '_h'] = layer_h[pos]
        net[in_layer + '_concat'] = ConcatLayer((net[in_layer + '_h'],
                                            net[in_layer]), axis=1, cropping=None)
        pos += 1
        out = in_layer + '_concat'

        laySize = net[out].output_shape
        n_cl = laySize[1]
        print('Number of feature maps (concat):', n_cl)
    else:
        out = in_layer

    if concat_h and pos <= len(concat_h) and concat_h[pos-1] == 'noisy_input':
        concat_h[pos-1] = 'input'

    return pos, out