# draw_network(net.graph, filename = 'autoencoder.png')
net.pretty_print()
net.train(epochs=(10, 10),
validate_after_epochs=1,
training_accuracy=True,
show_progress=True,
early_terminate=True,
verbose=verbose)
if __name__ == '__main__':
import sys
dataset = None
if len(sys.argv) > 1:
if sys.argv[1] == 'create_dataset':
from yann.special.datasets import cook_mnist_normalized_zero_mean as cook_mnist
data = cook_mnist(verbose=2)
dataset = data.dataset_location()
else:
dataset = sys.argv[1]
else:
print "provide dataset"
if dataset is None:
print " creating a new dataset to run through"
from yann.special.datasets import cook_mnist_normalized_zero_mean as cook_mnist
data = cook_mnist(verbose=2)
dataset = data.dataset_location()
autoencoder(dataset, verbose=2)
convolutional_autoencoder(dataset, verbose=2)
# See how the network looks like.
net.pretty_print()
# Train the network.
net.train()
# Test for acccuracy.
net.test()
## Boiler Plate ##
if __name__ == '__main__':
dataset = None
import sys
if len(sys.argv) > 1:
if sys.argv[1] == 'create_dataset':
from yann.special.datasets import cook_mnist
data = cook_mnist(verbose=3)
dataset = data.dataset_location()
else:
dataset = sys.argv[1]
else:
print "provide dataset"
if dataset is None:
print " creating a new dataset to run through"
from yann.special.datasets import cook_mnist
data = cook_mnist(verbose=3)
dataset = data.dataset_location()
log_reg(dataset)
early_terminate=True,
verbose=verbose)
net.test(verbose=verbose)
## Boiler Plate ##
if __name__ == '__main__':
import sys
dataset = None
if len(sys.argv) > 1:
if sys.argv[1] == 'create_dataset':
from yann.special.datasets import cook_cifar10
data = cook_cifar10(verbose=2)
dataset = data.dataset_location()
else:
dataset = sys.argv[1]
else:
print "provide dataset"
if dataset is None:
print " creating a new dataset to run through"
from yann.special.datasets import cook_cifar10
from yann.special.datasets import cook_mnist
# data = cook_cifar10 (verbose = 2)
data = cook_mnist()
dataset = data.dataset_location()
lenet5(dataset, verbose=2)
# lenet_maxout (dataset, verbose = 3)
