def get_zeros_dtype(t):
"""Return the dtype for the default gradient for a Tensor."""
if t.dtype == dtypes.resource:
handle_data = resource_variable_ops.get_eager_safe_handle_data(t)
if (handle_data is None or not handle_data.is_set or
len(handle_data.shape_and_type) != 1):
raise ValueError("Internal error: Tried to take gradients (or similar) "
"of a variable without handle data:\n%s" % str(t))
return handle_data.shape_and_type[0].dtype
return t.dtype
def shape_and_dtype(t):
"""Return the shape and dtype for the default gradient for a Tensor."""
if t.dtype == dtypes.resource:
handle_data = resource_variable_ops.get_eager_safe_handle_data(t)
if (handle_data is None or not handle_data.is_set
or len(handle_data.shape_and_type) != 1):
return tensor_shape.TensorShape(None), dtypes.float32
else:
shape_and_type = handle_data.shape_and_type[0]
return (tensor_shape.TensorShape(shape_and_type.shape),
dtypes.as_dtype(shape_and_type.dtype))
return t.shape, t.dtype
def get_zeros_dtype(t):
"""Return the dtype for the default gradient for a Tensor."""
if t.dtype == dtypes.resource:
handle_data = resource_variable_ops.get_eager_safe_handle_data(t)
if (handle_data is None or not handle_data.is_set
or len(handle_data.shape_and_type) != 1):
# TODO(srbs): Ideally we should raise an error here but returning float32
# for backwards compatibility.
return dtypes.float32
else:
return handle_data.shape_and_type[0].dtype
return t.dtype
def supports_default_grad(t):
"""Whether tensor `t` supports creating a default gradient.
This function assumes that `t` is of a trainable type.
Args:
t: Tensor
Returns:
Bool
"""
if t.dtype == dtypes.resource:
handle_data = resource_variable_ops.get_eager_safe_handle_data(t)
if (handle_data is None or not handle_data.is_set or
len(handle_data.shape_and_type) != 1):
return False
return True
def _get_handle_data(tensor):
return resource_variable_ops.get_eager_safe_handle_data(tensor)
