def batch_init_fn(_):
indices_shape = array_ops.concat([[0], [array_ops.size(padded_shape) + 1]],
0)
return sparse_tensor.SparseTensor(
indices=gen_array_ops.empty(indices_shape, dtype=dtypes.int64),
values=constant_op.constant([], shape=[0], dtype=dataset.output_types),
dense_shape=array_ops.concat(
[np.array([0], dtype=np.int64), padded_shape], 0))
def batch_init_fn(_):
indices_shape = array_ops.concat([[0], [array_ops.size(padded_shape) + 1]],
0)
return sparse_tensor.SparseTensor(
indices=gen_array_ops.empty(indices_shape, dtype=dtypes.int64),
values=constant_op.constant([], shape=[0], dtype=dataset.output_types),
dense_shape=array_ops.concat(
[np.array([0], dtype=np.int64), padded_shape], 0))
def batch_init_fn(_):
indices_shape = array_ops.concat([[0], [array_ops.size(shape) + 1]], 0)
return sparse_tensor.SparseTensor(
indices=gen_array_ops.empty(indices_shape, dtype=dtypes.int64),
values=constant_op.constant(
[], shape=[0], dtype=dataset_ops.get_legacy_output_types(dataset)),
dense_shape=array_ops.concat(
[np.array([0], dtype=np.int64),
math_ops.cast(shape, dtypes.int64)], 0))
def batch_init_fn(_):
indices_shape = array_ops.concat([[0], [array_ops.size(shape) + 1]], 0)
return sparse_tensor.SparseTensor(
indices=gen_array_ops.empty(indices_shape, dtype=dtypes.int64),
values=constant_op.constant(
[], shape=[0], dtype=dataset_ops.get_legacy_output_types(dataset)),
dense_shape=array_ops.concat(
[np.array([0], dtype=np.int64),
math_ops.cast(shape, dtypes.int64)], 0))
def empty_like(x, init=None):
"""Returns a non-initialized tensor with the same shape and dtype as x.
Args:
x: A Tensor.
init: Initialize the returned tensor with the default value of
x.dtype(), if True. Otherwise, do not initialize. Defaults to
None.
Returns:
A tensor y, whose dtype and shape are the same as those of x.
y is guaranteed not to be an alias of x. Upon return, y may contain
arbitrary data.
"""
x = ops.convert_to_tensor(x)
return gen_array_ops.empty(array_ops.shape(x), x.dtype, init=init)
def empty_like(x, init=None):
"""Returns a non-initialized tensor with the same shape and dtype as x.
Args:
x: A Tensor.
init: Initialize the returned tensor with the default value of
x.dtype(), if True. Otherwise, do not initialize. Defaults to
None.
Returns:
A tensor y, whose dtype and shape are the same as those of x.
y is guaranteed not to be an alias of x. Upon return, y may contain
arbitrary data.
"""
x = ops.convert_to_tensor(x)
return gen_array_ops.empty(array_ops.shape(x), x.dtype, init=init)
def batch_init_fn(_): batch_shape = array_ops.concat([[0], shape], 0) return gen_array_ops.empty(batch_shape, dtype=dataset.output_types)
def batch_init_fn(_):
batch_shape = array_ops.concat([[0], shape], 0)
return gen_array_ops.empty(
batch_shape, dtype=dataset_ops.get_legacy_output_types(dataset))
def batch_init_fn(_):
batch_shape = array_ops.concat(
[np.array([0], dtype=np.int32), padded_shape], 0)
return gen_array_ops.empty(batch_shape, dtype=dataset_output_types)
def batch_init_fn(_):
batch_shape = array_ops.concat(
[np.array([0], dtype=np.int32), padded_shape], 0)
return gen_array_ops.empty(batch_shape, dtype=dataset.output_types)
