keras_conversion.py 文件源码

def convert_sequential_model(model,
                        num_dims=None,
                        nonlinear_mxts_mode=\
                         NonlinearMxtsMode.DeepLIFT_GenomicsDefault,
                        verbose=True,
                        dense_mxts_mode=DenseMxtsMode.Linear,
                        conv_mxts_mode=ConvMxtsMode.Linear,
                        maxpool_deeplift_mode=default_maxpool_deeplift_mode,
                        layer_overrides={}):
    if (verbose):
        print("nonlinear_mxts_mode is set to: "+str(nonlinear_mxts_mode))
    converted_layers = []
    if (model.layers[0].input_shape is not None):
        input_shape = model.layers[0].input_shape
        assert input_shape[0] is None #batch axis
        num_dims_input = len(input_shape)
        assert num_dims is None or num_dims_input==num_dims,\
        "num_dims argument of "+str(num_dims)+" is incompatible with"\
        +" the number of dims in layers[0].input_shape which is: "\
        +str(model.layers[0].input_shape)
        num_dims = num_dims_input
    else:
        input_shape = None
    converted_layers.append(
        blobs.Input(num_dims=num_dims, shape=input_shape, name="input"))
    #converted_layers is actually mutated to be extended with the
    #additional layers so the assignment is not strictly necessary,
    #but whatever
    converted_layers = sequential_container_conversion(
                layer=model, name="", verbose=verbose,
                nonlinear_mxts_mode=nonlinear_mxts_mode,
                dense_mxts_mode=dense_mxts_mode,
                conv_mxts_mode=conv_mxts_mode,
                maxpool_deeplift_mode=maxpool_deeplift_mode,
                converted_layers=converted_layers,
                layer_overrides=layer_overrides)
    deeplift.util.connect_list_of_layers(converted_layers)
    converted_layers[-1].build_fwd_pass_vars()
    return models.SequentialModel(converted_layers)