def load_or_dataset():
if "or_O" not in loaded_datasets:
trn_data = np.load(os.path.join(get_base_dir(), "datasets", "ordata.npz"))
tst_data = np.load(os.path.join(get_base_dir(), "datasets", "ordata_test.npz"))
loaded_datasets["or_OX"] = gp.as_garray(trn_data["O"])
loaded_datasets["or_OZ"] = gp.as_garray(trn_data["OZ"])
loaded_datasets["or_TOX"] = gp.as_garray(tst_data["O"])
loaded_datasets["or_TOZ"] = gp.as_garray(tst_data["OZ"])
return (loaded_datasets["or_OX"], loaded_datasets["or_TOX"], loaded_datasets["or_OZ"], loaded_datasets["or_TOZ"])
def load_mnist(with_verification_set):
with gzip.open(os.path.join(get_base_dir(), "datasets", "mnist.pkl.gz"), "rb") as f:
(X, Z), (VX, VZ), (TX, TZ) = cPickle.load(f)
# X = np.asarray(X, dtype='float32')
# VX = np.asarray(VX, dtype='float32')
# TX = np.asarray(TX, dtype='float32')
if with_verification_set:
if "mnistv_X" not in loaded_datasets:
loaded_datasets["mnistv_X"] = gp.as_garray(X)
loaded_datasets["mnistv_Z"] = Z
loaded_datasets["mnistv_VX"] = gp.as_garray(VX)
loaded_datasets["mnistv_VZ"] = VZ
loaded_datasets["mnistv_TX"] = gp.as_garray(TX)
loaded_datasets["mnistv_TZ"] = TZ
return (
loaded_datasets["mnistv_X"],
loaded_datasets["mnistv_VX"],
loaded_datasets["mnistv_TX"],
loaded_datasets["mnistv_Z"],
loaded_datasets["mnistv_VZ"],
loaded_datasets["mnistv_TZ"],
)
else:
if "mnist_X" not in loaded_datasets:
loaded_datasets["mnist_X"] = gp.as_garray(np.concatenate((X, VX), axis=0))
loaded_datasets["mnist_Z"] = np.concatenate((Z, VZ), axis=0)
loaded_datasets["mnist_TX"] = gp.as_garray(TX)
loaded_datasets["mnist_TZ"] = TZ
return (
loaded_datasets["mnist_X"],
loaded_datasets["mnist_TX"],
loaded_datasets["mnist_Z"],
loaded_datasets["mnist_TZ"],
)
import gzip
import time
import numpy as np
import theano.tensor as T
import climin.stops
import climin.initialize
from brummlearn.mlp import Mlp, DropoutMlp
from brummlearn.data import one_hot
from ml.common.util import get_base_dir
savepath = "../mnist_dropout_model.npz"
datafile = get_base_dir() + "/datasets/mnist.pkl.gz"
# Load data.
with gzip.open(datafile,'rb') as f:
train_set, val_set, test_set = cPickle.load(f)
X, Z = train_set
VX, VZ = val_set
TX, TZ = test_set
Z = one_hot(Z, 10)
VZ = one_hot(VZ, 10)
TZ = one_hot(TZ, 10)
image_dims = 28, 28
def load_ruslan_mnist():
mdata = scipy.io.loadmat(os.path.join(get_base_dir(), "datasets", "mnist.mat"))
return (gp.as_garray(mdata["fbatchdata"]), gp.as_garray(mdata["test_fbatchdata"]))
