from blocks.graph import ComputationGraph
from blocks.model import Model
from blocks.monitoring import aggregation
from blocks.extensions import FinishAfter, Timing, Printing
from blocks.extensions.monitoring import (DataStreamMonitoring,
TrainingDataMonitoring)
from blocks.extensions.plot import Plot
from blocks.main_loop import MainLoop
from blocks_contrib.bricks.filtering import TemporalVarComp, VarianceComponent, SparseFilter
from blocks_contrib.extensions import DataStreamMonitoringAndSaving
floatX = theano.config.floatX
mnist = MNIST('train', sources=['features'])
data, _ = mnist._load_mnist()
means = data.mean(axis=0)
def _add_enumerator(n_steps, batch_size):
def func(data):
enum = np.zeros((n_steps, batch_size)).astype(floatX)
return (enum,)
return func
def allrotations(image, N):
angles = np.linspace(0, 350, N)
R = np.zeros((N, 784))
for i in xrange(N):
img = rotate(image, angles[i])
