import os
import argparse
import numpy as np
from capsnet_keras import capsulenet

if __name__ == "__main__":
    # setting hyper parameters
    parser = argparse.ArgumentParser(description="Capsule Network on MNIST.")
    parser.add_argument('--epochs', default=50, type=int)
    parser.add_argument('--batch_size', default=100, type=int)
    parser.add_argument('--lr', default=0.001, type=float,
                        help="Initial learning rate")
    parser.add_argument('--lr_decay', default=0.9, type=float,
                        help="The value multiplied by lr at each epoch. Set a larger value for larger epochs")
    parser.add_argument('--lam_recon', default=0.392, type=float,
                        help="The coefficient for the loss of decoder")
    parser.add_argument('-r', '--routings', default=3, type=int,
                        help="Number of iterations used in routing algorithm. should > 0")
    parser.add_argument('--shift_fraction', default=0.1, type=float,
                        help="Fraction of pixels to shift at most in each direction.")
    parser.add_argument('--debug', action='store_true',
                        help="Save weights by TensorBoard")
    parser.add_argument('--save_dir', default='./result')
    parser.add_argument('-t', '--testing', action='store_true',
                        help="Test the trained model on testing data set")
    parser.add_argument('--digit', default=5, type=int,
                        help="Digit to manipulate")
    parser.add_argument('-w', '--weights', default=None,
                        help="The path of the saved weights. Should be specified when testing")
    args = parser.parse_args()
    print(args)

    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    # load data
    (x_train, y_train), (x_test, y_test) = capsulenet.load_mnist()

    # define model
    model, eval_model, manipulate_model = capsulenet.build_caps_net(
        input_shape=x_train.shape[1:],
        n_class=len(np.unique(np.argmax(y_train, 1))),
        n_routings=args.routings
    )
    model.summary()

    # train or test
    if args.weights is not None:  # init the model weights with provided one
        model.load_weights(args.weights)
    if not args.testing:
        capsulenet.train(
            model=model,
            data=((x_train, y_train), (x_test, y_test)),
            save_dir=args.save_dir,
            batch_size=args.batch_size,
            epochs=args.epochs,
            lr=args.lr,
            lr_decay=args.lr_decay,
            lam_recon=args.lam_recon,
            shift_fraction=args.shift_fraction
        )
    else:  # as long as weights are given, will run testing
        if args.weights is None:
            print('No weights provided, using random initialized weights.')
        capsulenet.manipulate_latent(
            manipulate_model,
            (x_test, y_test),
            args.digit,
            args.save_dir
        )
        capsulenet.test(
            model=eval_model,
            data=(x_test, y_test),
            save_dir=args.save_dir
        )

dc3f3c6416c7efe60b78fc6fafe313db4323e473
<mask token>

<mask token>
if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Capsule Network on MNIST.')
    parser.add_argument('--epochs', default=50, type=int)
    parser.add_argument('--batch_size', default=100, type=int)
    parser.add_argument('--lr', default=0.001, type=float, help=
        'Initial learning rate')
    parser.add_argument('--lr_decay', default=0.9, type=float, help=
        'The value multiplied by lr at each epoch. Set a larger value for larger epochs'
        )
    parser.add_argument('--lam_recon', default=0.392, type=float, help=
        'The coefficient for the loss of decoder')
    parser.add_argument('-r', '--routings', default=3, type=int, help=
        'Number of iterations used in routing algorithm. should > 0')
    parser.add_argument('--shift_fraction', default=0.1, type=float, help=
        'Fraction of pixels to shift at most in each direction.')
    parser.add_argument('--debug', action='store_true', help=
        'Save weights by TensorBoard')
    parser.add_argument('--save_dir', default='./result')
    parser.add_argument('-t', '--testing', action='store_true', help=
        'Test the trained model on testing data set')
    parser.add_argument('--digit', default=5, type=int, help=
        'Digit to manipulate')
    parser.add_argument('-w', '--weights', default=None, help=
        'The path of the saved weights. Should be specified when testing')
    args = parser.parse_args()
    print(args)
    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)
    (x_train, y_train), (x_test, y_test) = capsulenet.load_mnist()
    model, eval_model, manipulate_model = capsulenet.build_caps_net(input_shape
        =x_train.shape[1:], n_class=len(np.unique(np.argmax(y_train, 1))),
        n_routings=args.routings)
    model.summary()
    if args.weights is not None:
        model.load_weights(args.weights)
    if not args.testing:
        capsulenet.train(model=model, data=((x_train, y_train), (x_test,
            y_test)), save_dir=args.save_dir, batch_size=args.batch_size,
            epochs=args.epochs, lr=args.lr, lr_decay=args.lr_decay,
            lam_recon=args.lam_recon, shift_fraction=args.shift_fraction)
    else:
        if args.weights is None:
            print('No weights provided, using random initialized weights.')
        capsulenet.manipulate_latent(manipulate_model, (x_test, y_test),
            args.digit, args.save_dir)
        capsulenet.test(model=eval_model, data=(x_test, y_test), save_dir=
            args.save_dir)

import os
import argparse
import numpy as np
from capsnet_keras import capsulenet
if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Capsule Network on MNIST.')
    parser.add_argument('--epochs', default=50, type=int)
    parser.add_argument('--batch_size', default=100, type=int)
    parser.add_argument('--lr', default=0.001, type=float, help=
        'Initial learning rate')
    parser.add_argument('--lr_decay', default=0.9, type=float, help=
        'The value multiplied by lr at each epoch. Set a larger value for larger epochs'
        )
    parser.add_argument('--lam_recon', default=0.392, type=float, help=
        'The coefficient for the loss of decoder')
    parser.add_argument('-r', '--routings', default=3, type=int, help=
        'Number of iterations used in routing algorithm. should > 0')
    parser.add_argument('--shift_fraction', default=0.1, type=float, help=
        'Fraction of pixels to shift at most in each direction.')
    parser.add_argument('--debug', action='store_true', help=
        'Save weights by TensorBoard')
    parser.add_argument('--save_dir', default='./result')
    parser.add_argument('-t', '--testing', action='store_true', help=
        'Test the trained model on testing data set')
    parser.add_argument('--digit', default=5, type=int, help=
        'Digit to manipulate')
    parser.add_argument('-w', '--weights', default=None, help=
        'The path of the saved weights. Should be specified when testing')
    args = parser.parse_args()
    print(args)
    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)
    (x_train, y_train), (x_test, y_test) = capsulenet.load_mnist()
    model, eval_model, manipulate_model = capsulenet.build_caps_net(input_shape
        =x_train.shape[1:], n_class=len(np.unique(np.argmax(y_train, 1))),
        n_routings=args.routings)
    model.summary()
    if args.weights is not None:
        model.load_weights(args.weights)
    if not args.testing:
        capsulenet.train(model=model, data=((x_train, y_train), (x_test,
            y_test)), save_dir=args.save_dir, batch_size=args.batch_size,
            epochs=args.epochs, lr=args.lr, lr_decay=args.lr_decay,
            lam_recon=args.lam_recon, shift_fraction=args.shift_fraction)
    else:
        if args.weights is None:
            print('No weights provided, using random initialized weights.')
        capsulenet.manipulate_latent(manipulate_model, (x_test, y_test),
            args.digit, args.save_dir)
        capsulenet.test(model=eval_model, data=(x_test, y_test), save_dir=
            args.save_dir)

import os
import argparse
import numpy as np
from capsnet_keras import capsulenet

if __name__ == "__main__":
    # setting hyper parameters
    parser = argparse.ArgumentParser(description="Capsule Network on MNIST.")
    parser.add_argument('--epochs', default=50, type=int)
    parser.add_argument('--batch_size', default=100, type=int)
    parser.add_argument('--lr', default=0.001, type=float,
                        help="Initial learning rate")
    parser.add_argument('--lr_decay', default=0.9, type=float,
                        help="The value multiplied by lr at each epoch. Set a larger value for larger epochs")
    parser.add_argument('--lam_recon', default=0.392, type=float,
                        help="The coefficient for the loss of decoder")
    parser.add_argument('-r', '--routings', default=3, type=int,
                        help="Number of iterations used in routing algorithm. should > 0")
    parser.add_argument('--shift_fraction', default=0.1, type=float,
                        help="Fraction of pixels to shift at most in each direction.")
    parser.add_argument('--debug', action='store_true',
                        help="Save weights by TensorBoard")
    parser.add_argument('--save_dir', default='./result')
    parser.add_argument('-t', '--testing', action='store_true',
                        help="Test the trained model on testing data set")
    parser.add_argument('--digit', default=5, type=int,
                        help="Digit to manipulate")
    parser.add_argument('-w', '--weights', default=None,
                        help="The path of the saved weights. Should be specified when testing")
    args = parser.parse_args()
    print(args)

    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    # load data
    (x_train, y_train), (x_test, y_test) = capsulenet.load_mnist()

    # define model
    model, eval_model, manipulate_model = capsulenet.build_caps_net(
        input_shape=x_train.shape[1:],
        n_class=len(np.unique(np.argmax(y_train, 1))),
        n_routings=args.routings
    )
    model.summary()

    # train or test
    if args.weights is not None:  # init the model weights with provided one
        model.load_weights(args.weights)
    if not args.testing:
        capsulenet.train(
            model=model,
            data=((x_train, y_train), (x_test, y_test)),
            save_dir=args.save_dir,
            batch_size=args.batch_size,
            epochs=args.epochs,
            lr=args.lr,
            lr_decay=args.lr_decay,
            lam_recon=args.lam_recon,
            shift_fraction=args.shift_fraction
        )
    else:  # as long as weights are given, will run testing
        if args.weights is None:
            print('No weights provided, using random initialized weights.')
        capsulenet.manipulate_latent(
            manipulate_model,
            (x_test, y_test),
            args.digit,
            args.save_dir
        )
        capsulenet.test(
            model=eval_model,
            data=(x_test, y_test),
            save_dir=args.save_dir
        )
