def _make_args(self, X):
batch_size = getattr(self, 'batch_size', None)
if batch_size is None:
data = itertools.repeat([X])
elif batch_size < 1:
raise ValueError('need strictly positive batch size')
else:
data = iter_minibatches([X], self.batch_size, self.sample_dim)
args = ((i, {}) for i in data)
return args
def _make_args(self, X):
batch_size = getattr(self, 'batch_size', None)
if batch_size is None:
data = itertools.repeat([X])
elif batch_size < 1:
raise ValueError('need strictly positive batch size')
else:
data = iter_minibatches([X], self.batch_size, self.sample_dim)
args = ((i, {}) for i in data)
return args
def _make_args(self, X, Z):
X, Z = cast_array_to_local_type(X), cast_array_to_local_type(Z)
batch_size = getattr(self, 'batch_size', None)
if batch_size is None:
data = itertools.repeat([X, Z])
elif batch_size < 1:
raise ValueError('need strictly positive batch size')
else:
data = iter_minibatches([X, Z], self.batch_size, self.sample_dim)
args = ((i, {}) for i in data)
return args
def _make_args(self, X, Z):
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)
data = itertools.repeat([X, Z])
elif batch_size < 1:
raise ValueError('need strictly positive batch size')
else:
data = iter_minibatches([X, 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