68f8bf4982f3bb27f10109af53554679613ef1f8,dlpy/applications/resnet.py,,ResNet50_Caffe,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,556

Before Change

    if not pre_trained_weights:
        model = Sequential(conn=conn, model_table=model_table)

        model.add(InputLayer(n_channels=n_channels, width=width, height=height, scale=scale, offsets=offsets,
                             random_flip=random_flip, random_crop=random_crop, random_mutation=random_mutation))
        // Top layers
        model.add(Conv2d(64, 7, act="identity", include_bias=False, stride=2))
        model.add(BN(act="relu"))

After Change

    conn.retrieve("loadactionset", _messagelevel="error", actionset="deeplearn")

    // get all the parms passed in
    parameters = locals()

    if not pre_trained_weights:
        model = Sequential(conn=conn, model_table=model_table)

        // get the input parameters
        input_parameters = get_layer_options(input_layer_options, parameters)
        model.add(InputLayer(**input_parameters))

        // Top layers
        model.add(Conv2d(64, 7, act="identity", include_bias=False, stride=2))

Instances