GnnForPrivacyScan/data/purposeSplit/visualize/visualize_2_4.py

def main(examples, args):
    print('Total Number of Examples:', len(examples))
    weights_file = os.path.expanduser(args.weights)
    print('Weights loading from:', weights_file)
    viz = Visualizer(padding=args.padding,
                     input_vocab=args.human_vocab,
                     output_vocab=args.machine_vocab)
    print('Loading models')
    pred_model = simpleNMT(trainable=False,
                           pad_length=args.padding,
                           n_chars=viz.input_vocab.size(),
                           n_labels=viz.output_vocab.size())

    pred_model.load_weights(weights_file, by_name=True)
    pred_model.compile(optimizer='adam', loss='categorical_crossentropy')

    proba_model = simpleNMT(trainable=False,
                            pad_length=args.padding,
                            n_chars=viz.input_vocab.size(),
                            n_labels=viz.output_vocab.size(),
                            return_probabilities=True)

    proba_model.load_weights(weights_file, by_name=True)
    proba_model.compile(optimizer='adam', loss='categorical_crossentropy')

    viz.set_models(pred_model, proba_model)

    print('Models loaded')

    for example in examples:
        viz.attention_map(example)

    print('Completed visualizations')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    named_args = parser.add_argument_group('named arguments')

    named_args.add_argument('-e', '--examples', metavar='|',
                            help="""Example string/file to visualize attention map for
                                    If file, it must end with '.txt'""",
                            required=True)
    named_args.add_argument('-w', '--weights', metavar='|',
                            help="""Location of weights""",
                            required=False,
                            default=SAMPLE_WEIGHTS)
    named_args.add_argument('-p', '--padding', metavar='|',
                            help="""Length of padding""",
                            required=False, default=50, type=int)
    named_args.add_argument('-hv', '--human-vocab', metavar='|',
                            help="""Path to the human vocabulary""",
                            required=False,
                            default=SAMPLE_HUMAN_VOCAB,
                            type=str)
    named_args.add_argument('-mv', '--machine-vocab', metavar='|',
                            help="""Path to the machine vocabulary""",
                            required=False,
                            default=SAMPLE_MACHINE_VOCAB,
                            type=str)
    args = parser.parse_args()

    if '.txt' in args.examples:
        examples = load_examples(args.examples)
    else:
        examples = [args.examples]

    main(examples, args)