#!/usr/bin/env python
import climate
import matplotlib.pyplot as plt
import numpy as np
import theanets
from utils import load_cifar, plot_layers, plot_images
logging = climate.get_logger('cifar')
g = climate.add_group('CIFAR Example')
g.add_argument('--features',
type=int,
default=0,
metavar='N',
help='train a model using N^2 hidden-layer features')
K = 655 # this retains 99% of the variance in the cifar images.
def pca(dataset):
mean = dataset[:3000].mean(axis=0)
logging.info('computing whitening transform')
x = dataset[:3000] - mean
vals, vecs = np.linalg.eigh(np.dot(x.T, x) / len(x))
vals = vals[::-1]
vecs = vecs[:, ::-1]
vals = np.sqrt(vals[:K])
#!/usr/bin/env python
import climate
import matplotlib.pyplot as plt
import theanets
from utils import load_mnist, plot_layers, plot_images
g = climate.add_group('MNIST Example')
g.add_argument('--features',
type=int,
default=8,
metavar='N',
help='train a model using N^2 hidden-layer features')
def main(args):
train, valid, _ = load_mnist()
e = theanets.Experiment(theanets.Autoencoder,
layers=(784, args.features**2, 784))
e.train(train, valid)
plot_layers([e.network.find(1, 0), e.network.find(2, 0)])
plt.tight_layout()
plt.show()
v = valid[:100]
plot_images(v, 121, 'Sample data')
plot_images(e.network.predict(v), 122, 'Reconstructed data')
#!/usr/bin/env python
import climate
import matplotlib.pyplot as plt
import theanets
from utils import load_mnist, plot_layers, plot_images
g = climate.add_group('MNIST Example')
g.add_argument('--features', type=int, default=8, metavar='N',
help='train a model using N^2 hidden-layer features')
def main(args):
train, valid, _ = load_mnist()
e = theanets.Experiment(
theanets.Autoencoder,
layers=(784, args.features ** 2, 784))
e.train(train, valid, min_improvement=0.1)
plot_layers([e.network.find('hid1', 'w'), e.network.find('out', 'w')])
plt.tight_layout()
plt.show()
v = valid[:100]
plot_images(v, 121, 'Sample data')
plot_images(e.network.predict(v), 122, 'Reconstructed data')
plt.tight_layout()
plt.show()
import climate
import numpy as np
import pandas as pd
from .database import Experiment
logging = climate.get_logger(__name__)
g = climate.add_group('dropout-filling options')
g.add_argument('--root',
metavar='DIR',
help='load data files from tree at DIR')
g.add_argument('--output',
metavar='DIR',
help='save smoothed data files to tree at DIR')
g.add_argument('--pattern',
default='*',
metavar='SHPAT',
help='process only trials matching this pattern')
g.add_argument('--autoencoder-rank',
type=float,
metavar='K',
help='reconstruction rank')
g.add_argument('--svt-threshold',
type=float,
metavar='S',
help='truncate singular values at threshold S')
g.add_argument('--window',
type=int,
metavar='T',
help='process windows of T frames')
'''This module contains command line flags.'''
import climate
climate.add_arg('--help-activation', action='store_true',
help='show available activation functions')
climate.add_arg('--help-optimize', action='store_true',
help='show available optimization algorithms')
g = climate.add_group('Architecture')
g.add_argument('-n', '--layers', nargs='+', type=int, metavar='N',
help='construct a network with layers of size N1, N2, ...')
g.add_argument('-g', '--hidden-activation', default='logistic', metavar='FUNC',
help='function for hidden unit activations')
g.add_argument('--output-activation', default='linear', metavar='FUNC',
help='function for output unit activations')
g.add_argument('-t', '--tied-weights', action='store_true',
help='tie encoding and decoding weights')
g.add_argument('--decode-from', type=int, default=1, metavar='N',
help='decode from the final N layers of the net')
g = climate.add_group('Training')
g.add_argument('-O', '--optimize', default=(), nargs='+', metavar='ALGO',
help='train with the given optimization algorithm(s)')
g.add_argument('-p', '--patience', type=int, default=4, metavar='N',
help='stop training if less than --min-improvement for N validations')
g.add_argument('-v', '--validate-every', type=int, default=10, metavar='N',
help='validate the model every N updates')
g.add_argument('-b', '--batch-size', type=int, default=64, metavar='N',
help='train with mini-batches of size N')
g.add_argument('-B', '--train-batches', type=int, metavar='N',
"""Single-layer autoencoder example using MNIST digit data.
This example shows one way to train a single-layer autoencoder model using the
handwritten MNIST digits.
This example also shows the use of climate command-line arguments.
"""
import climate
import matplotlib.pyplot as plt
import theanets
from utils import load_mnist, plot_layers, plot_images
g = climate.add_group("MNIST Example")
g.add_argument("--features", type=int, default=8, metavar="N", help="train a model using N^2 hidden-layer features")
def main(args):
# load up the MNIST digit dataset.
train, valid, _ = load_mnist()
net = theanets.Autoencoder([784, args.features ** 2, 784], rng=42)
net.train(
train,
valid,
input_noise=0.1,
weight_l2=0.0001,
algo="rmsprop",
momentum=0.9,
#!/usr/bin/env python
import climate
import matplotlib.pyplot as plt
import numpy as np
import theanets
from utils import load_cifar, plot_layers, plot_images
logging = climate.get_logger('cifar')
g = climate.add_group('CIFAR Example')
g.add_argument('--features', type=int, default=0, metavar='N',
help='train a model using N^2 hidden-layer features')
K = 655 # this retains 99% of the variance in the cifar images.
def pca(dataset):
mean = dataset[:3000].mean(axis=0)
logging.info('computing whitening transform')
x = dataset[:3000] - mean
vals, vecs = np.linalg.eigh(np.dot(x.T, x) / len(x))
vals = vals[::-1]
vecs = vecs[:, ::-1]
vals = np.sqrt(vals[:K])
vecs = vecs[:, :K]
def whiten(x):
'''This module contains command line flags.'''
import climate
climate.add_arg('--help-activation',
action='store_true',
help='show available activation functions')
climate.add_arg('--help-optimize',
action='store_true',
help='show available optimization algorithms')
g = climate.add_group('Architecture')
g.add_argument('-n',
'--layers',
nargs='+',
type=int,
metavar='N',
help='construct a network with layers of size N1, N2, ...')
g.add_argument('-g',
'--hidden-activation',
default='logistic',
metavar='FUNC',
help='function for hidden unit activations')
g.add_argument('--output-activation',
default='linear',
metavar='FUNC',
help='function for output unit activations')
g.add_argument('-t',
'--tied-weights',
action='store_true',
help='tie encoding and decoding weights')
import climate
import numpy as np
import pandas as pd
from .database import Experiment
logging = climate.get_logger(__name__)
g = climate.add_group('dropout-filling options')
g.add_argument('--root', metavar='DIR',
help='load data files from tree at DIR')
g.add_argument('--output', metavar='DIR',
help='save smoothed data files to tree at DIR')
g.add_argument('--pattern', default='*', metavar='SHPAT',
help='process only trials matching this pattern')
g.add_argument('--autoencoder-rank', type=float, metavar='K',
help='reconstruction rank')
g.add_argument('--svt-threshold', type=float, metavar='S',
help='truncate singular values at threshold S')
g.add_argument('--window', type=int, metavar='T',
help='process windows of T frames')
CENTERS = [
'marker34-l-ilium',
'marker35-r-ilium',
'marker36-r-hip',
'marker43-l-hip',
]
PHASESPACE_TOLERANCE = 0.001 # error tolerance of phasespace system
