
import climate
import matplotlib.pyplot as plt
import theanets

from utils import load_cifar, plot_layers, plot_images

g = climate.add_arg_group('CIFAR Example')
g.add_argument('--features', type=int, default=32, metavar='N',
               help='train a model using N^2 hidden-layer features')


def main(args):
    train, valid, _ = load_cifar()

    e = theanets.Experiment(
        theanets.Autoencoder,
        layers=(3072, args.features ** 2, 3072))

    e.train(train, valid)

    plot_layers(e.network.weights, channels=3)
    plt.tight_layout()
    plt.show()

    valid = valid[:100]
    plot_images(valid, 121, 'Sample data', channels=3)
    plot_images(e.network.predict(valid), 122, 'Reconstructed data', channels=3)
    plt.tight_layout()
    plt.show()


if __name__ == '__main__':
    climate.call(main)


import climate
import matplotlib.pyplot as plt
import theanets

from utils import load_cifar, plot_layers, plot_images

g = climate.add_arg_group('CIFAR Example')
g.add_argument('--features', type=int, default=32, metavar='N',
               help='train a model using N^2 hidden-layer features')


def main(args):
    train, valid, _ = load_cifar()

    e = theanets.Experiment(
        theanets.Autoencoder,
        layers=(3072, args.features ** 2, 3072))

    e.train(train, valid)

    plot_layers([e.network.get_weights(1), e.network.get_weights('out')], channels=3)
    plt.tight_layout()
    plt.show()

    valid = valid[:100]
    plot_images(valid, 121, 'Sample data', channels=3)
    plot_images(e.network.predict(valid), 122, 'Reconstructed data', channels=3)
    plt.tight_layout()
    plt.show()


if __name__ == '__main__':
    climate.call(main)

Access weights using new interface.
Access weights using new interface.

  
  import climate
  import matplotlib.pyplot as plt
  import theanets
  
  from utils import load_cifar, plot_layers, plot_images
  
  g = climate.add_arg_group('CIFAR Example')
  g.add_argument('--features', type=int, default=32, metavar='N',
                 help='train a model using N^2 hidden-layer features')
  
  
  def main(args):
      train, valid, _ = load_cifar()
  
      e = theanets.Experiment(
          theanets.Autoencoder,
          layers=(3072, args.features ** 2, 3072))
  
      e.train(train, valid)
  
-     plot_layers(e.network.weights, channels=3)
+     plot_layers([e.network.get_weights(1), e.network.get_weights('out')], channels=3)
      plt.tight_layout()
      plt.show()
  
      valid = valid[:100]
      plot_images(valid, 121, 'Sample data', channels=3)
      plot_images(e.network.predict(valid), 122, 'Reconstructed data', channels=3)
      plt.tight_layout()
      plt.show()
  
  
  if __name__ == '__main__':
      climate.call(main)
  
Access weights using new interface.
## Code Before:

import climate
import matplotlib.pyplot as plt
import theanets

from utils import load_cifar, plot_layers, plot_images

g = climate.add_arg_group('CIFAR Example')
g.add_argument('--features', type=int, default=32, metavar='N',
               help='train a model using N^2 hidden-layer features')


def main(args):
    train, valid, _ = load_cifar()

    e = theanets.Experiment(
        theanets.Autoencoder,
        layers=(3072, args.features ** 2, 3072))

    e.train(train, valid)

    plot_layers(e.network.weights, channels=3)
    plt.tight_layout()
    plt.show()

    valid = valid[:100]
    plot_images(valid, 121, 'Sample data', channels=3)
    plot_images(e.network.predict(valid), 122, 'Reconstructed data', channels=3)
    plt.tight_layout()
    plt.show()


if __name__ == '__main__':
    climate.call(main)

## Instruction:
Access weights using new interface.
## Code After:

import climate
import matplotlib.pyplot as plt
import theanets

from utils import load_cifar, plot_layers, plot_images

g = climate.add_arg_group('CIFAR Example')
g.add_argument('--features', type=int, default=32, metavar='N',
               help='train a model using N^2 hidden-layer features')


def main(args):
    train, valid, _ = load_cifar()

    e = theanets.Experiment(
        theanets.Autoencoder,
        layers=(3072, args.features ** 2, 3072))

    e.train(train, valid)

    plot_layers([e.network.get_weights(1), e.network.get_weights('out')], channels=3)
    plt.tight_layout()
    plt.show()

    valid = valid[:100]
    plot_images(valid, 121, 'Sample data', channels=3)
    plot_images(e.network.predict(valid), 122, 'Reconstructed data', channels=3)
    plt.tight_layout()
    plt.show()


if __name__ == '__main__':
    climate.call(main)
