def testTensorArrayPackNotAllValuesAvailableFails(self):
with self.test_session():
h = gen_data_flow_ops._tensor_array(
dtype=tf.float32, tensor_array_name="foo", size=3)
with self.assertRaisesOpError(
"Could not read from TensorArray index 1 "
"because it has not yet been written to."):
with tf.control_dependencies([
gen_data_flow_ops._tensor_array_write(h, 0, [[4.0, 5.0]])]):
gen_data_flow_ops._tensor_array_pack(h, tf.float32).eval()
def pack(self, name=None):
"""Return the values in the TensorArray as a packed `Tensor`.
All of the values must have been written and their shapes must all match.
Args:
name: A name for the operation (optional).
Returns:
All the tensors in the TensorArray packed into one tensor.
"""
with ops.colocate_with(self._handle):
if self._elem_shape:
element_shape = self._elem_shape[0]
else:
element_shape = tensor_shape.TensorShape(None)
value = gen_data_flow_ops._tensor_array_pack(
handle=self._handle,
flow_in=self._flow,
dtype=self._dtype,
name=name,
element_shape=element_shape)
if self._elem_shape and self._elem_shape[0].dims is not None:
value.set_shape([None] + self._elem_shape[0].dims)
return value
def pack(self, name=None):
"""Return the values in the TensorArray as a packed `Tensor`."""
value = gen_data_flow_ops._tensor_array_pack(
handle=self._handle, flow_in=self._flow, dtype=self._dtype,
name=name)
return value
def _legacy_pack(self, name=None):
"""Return the values in the TensorArray as a packed `Tensor`.
This is the legacy version of pack, kept for testing
backwards compatibility.
All of the values must have been written and their shapes must all match.
Args:
name: A name for the operation (optional).
Returns:
All the tensors in the TensorArray packed into one tensor.
"""
with ops.colocate_with(self._handle):
if self._elem_shape:
element_shape = self._elem_shape[0]
else:
element_shape = tensor_shape.TensorShape(None)
value = gen_data_flow_ops._tensor_array_pack(handle=self._handle,
flow_in=self._flow,
dtype=self._dtype,
name=name,
element_shape=element_shape)
if self._elem_shape and self._elem_shape[0].dims is not None:
value.set_shape([None] + self._elem_shape[0].dims)
return value
def pack(self, name=None):
"""Return the values in the TensorArray as a packed `Tensor`."""
value = gen_data_flow_ops._tensor_array_pack(
handle=self._handle, flow_in=self._flow, dtype=self._dtype,
name=name)
return value
def pack(self, name=None):
"""Return the values in the TensorArray as a packed `Tensor`.
All of the values must have been written and their shapes must all match.
Args:
name: A name for the operation (optional).
Returns:
All the tensors in the TensorArray packed into one tensor.
"""
with ops.colocate_with(self._handle):
value = gen_data_flow_ops._tensor_array_pack(
handle=self._handle, flow_in=self._flow, dtype=self._dtype, name=name
)
return value
def pack(self, name=None):
"""Return the values in the TensorArray as a packed `Tensor`.
All of the values must have been written and their shapes must all match.
Args:
name: A name for the operation (optional).
Returns:
All the tensors in the TensorArray packed into one tensor.
"""
with ops.colocate_with(self._handle):
value = gen_data_flow_ops._tensor_array_pack(handle=self._handle,
flow_in=self._flow,
dtype=self._dtype,
name=name)
return value
def _testTensorArrayWritePack(self, tf_dtype, use_gpu):
dtype = tf_dtype.as_numpy_dtype()
with self.test_session(use_gpu=use_gpu):
h = gen_data_flow_ops._tensor_array(
dtype=tf_dtype, tensor_array_name="foo", size=3)
if tf_dtype == tf.string:
convert = lambda x: np.asarray(x).astype(np.str)
else:
convert = lambda x: np.asarray(x).astype(dtype)
writes = [
gen_data_flow_ops._tensor_array_write(h, 0, convert([[4.0, 5.0]])),
gen_data_flow_ops._tensor_array_write(h, 1, convert([[6.0, 7.0]])),
gen_data_flow_ops._tensor_array_write(h, 2, convert([[8.0, 9.0]]))]
with tf.control_dependencies(writes):
c0 = gen_data_flow_ops._tensor_array_pack(h, tf_dtype)
self.assertAllEqual(
convert([[[4.0, 5.0]], [[6.0, 7.0]], [[8.0, 9.0]]]), c0.eval())
