utils.py 文件源码

def find_activation_layer(layer, node_index):
    """

    Args:
        layer(Layer):
        node_index:
    """
    output_shape = layer.get_output_shape_at(node_index)
    maybe_layer = layer
    node = maybe_layer.inbound_nodes[node_index]
    # Loop will be broken by an error if an output layer is encountered
    while True:
        # If maybe_layer has a nonlinear activation function return it and its index
        activation = getattr(maybe_layer, 'activation', linear)
        if activation.__name__ != 'linear':
            if maybe_layer.get_output_shape_at(node_index) != output_shape:
                ValueError('The activation layer ({0}), does not have the same'
                           ' output shape as {1]'.format(maybe_layer.name,
                                                         layer.name))
            return maybe_layer, node_index

        # If not, move to the next layer in the datastream
        next_nodes = get_shallower_nodes(node)
        # test if node is a list of nodes with more than one item
        if len(next_nodes) > 1:
            ValueError('The model must not branch between the chosen layer'
                       ' and the activation layer.')
        node = next_nodes[0]
        node_index = get_node_index(node)
        maybe_layer = node.outbound_layer

        # Check if maybe_layer has weights, no activation layer has been found
        if maybe_layer.weights and (
                not maybe_layer.__class__.__name__.startswith('Global')):
            AttributeError('There is no nonlinear activation layer between {0}'
                           ' and {1}'.format(layer.name, maybe_layer.name))