def _make_args(self, X, Z, imp_weight=None):
from breze.learn.base import cast_array_to_local_type
batch_size = getattr(self, "batch_size", None)
if batch_size is None:
X, Z = cast_array_to_local_type(X), cast_array_to_local_type(Z)
if imp_weight is not None:
imp_weight = cast_array_to_local_type(imp_weight)
data = itertools.repeat([X, Z, imp_weight])
else:
data = itertools.repeat([X, Z])
elif batch_size < 1:
raise ValueError("need strictly positive batch size")
else:
if imp_weight is not None:
data = self.iter_minibatches(
[self.dim_max, self.Z, imp_weight], self.batch_size, list(self.sample_dim) + [self.sample_dim[0]]
)
data = (
(cast_array_to_local_type(x), cast_array_to_local_type(z), cast_array_to_local_type(w))
for x, z, w in data
)
else:
data = self.iter_minibatches([self.X, self.Z], self.batch_size, self.sample_dim)
# data = self.iter_minibatches([self.dim_max, self.Z], self.batch_size,self.sample_dim)
data = ((cast_array_to_local_type(x), cast_array_to_local_type(z)) for x, z in data)
args = ((i, {}) for i in data)
return args
def _make_args(self, X, Z, imp_weight=None):
from breze.learn.base import cast_array_to_local_type
batch_size = getattr(self, 'batch_size', None)
if batch_size is None:
X, Z = cast_array_to_local_type(X), cast_array_to_local_type(Z)
if imp_weight is not None:
imp_weight = cast_array_to_local_type(imp_weight)
data = itertools.repeat([X, Z, imp_weight])
else:
data = itertools.repeat([X, Z])
elif batch_size < 1:
raise ValueError('need strictly positive batch size')
else:
if imp_weight is not None:
data = self.iter_minibatches(
[self.dim_max, self.Z, imp_weight], self.batch_size,
list(self.sample_dim) + [self.sample_dim[0]])
data = ((cast_array_to_local_type(x),
cast_array_to_local_type(z),
cast_array_to_local_type(w)) for x, z, w in data)
else:
data = self.iter_minibatches([self.X, self.Z], self.batch_size,
self.sample_dim)
#data = self.iter_minibatches([self.dim_max, self.Z], self.batch_size,self.sample_dim)
data = ((cast_array_to_local_type(x),
cast_array_to_local_type(z)) for x, z in data)
args = ((i, {}) for i in data)
return args
# 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
X, Z, VX, VZ, TX, TZ = [cast_array_to_local_type(i) for i in (X, Z, VX,VZ, TX, TZ)]
batch_size = 100
#optimizer = 'rmsprop', {'step_rate': 1e-4, 'momentum': 0.95, 'decay': .95, 'offset': 1e-6}
#optimizer = 'adam', {'step_rate': .5, 'momentum': 0.9, 'decay': .95, 'offset': 1e-6}
optimizer = 'gd'
fast_dropout = True
if fast_dropout:
class MyVAE(mlp.FastDropoutVariationalAutoEncoder,
mlp.FastDropoutMlpGaussLatentVAEMixin,
mlp.FastDropoutMlpBernoulliVisibleVAEMixin):
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
X, Z, VX, VZ, TX, TZ = [
cast_array_to_local_type(i) for i in (X, Z, VX, VZ, TX, TZ)
]
batch_size = 100
#optimizer = 'rmsprop', {'step_rate': 1e-4, 'momentum': 0.95, 'decay': .95, 'offset': 1e-6}
#optimizer = 'adam', {'step_rate': .5, 'momentum': 0.9, 'decay': .95, 'offset': 1e-6}
optimizer = 'gd'
fast_dropout = True
if fast_dropout:
class MyVAE(mlp.FastDropoutVariationalAutoEncoder,
mlp.FastDropoutMlpGaussLatentVAEMixin,
mlp.FastDropoutMlpBernoulliVisibleVAEMixin):
pass
