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)