I am trying to draw the saliency maps with tensorflow 2.6.0. I defined the model with tensorflow.keras.models.Sequential and finish training. However, when I tried to compute gradient like this:
with GradientTape(persistent=True) as tape:
tape.watch(image)
result = model.predict(image)[:, 4]
gradient = tape.gradient(result, image)
where the image is the tensor and [:, 4] is for choosing the actual label for this image.
image.shape = (1, 48, 48, 1)
result.shape = (1, 10)
Then I got error message:
---------------------------------------------------------------------------
LookupError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/gradients_util.py in _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method, stop_gradients, unconnected_gradients, src_graph)
605 try:
--> 606 grad_fn = ops.get_gradient_function(op)
607 except LookupError:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in get_gradient_function(op)
2731 op_type = op.type
-> 2732 return gradient_registry.lookup(op_type)
2733
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/registry.py in lookup(self, name)
99 raise LookupError(
--> 100 "%s registry has no entry for: %s" % (self._name, name))
LookupError: gradient registry has no entry for: IteratorGetNext
During handling of the above exception, another exception occurred:
LookupError Traceback (most recent call last)
/tmp/ipykernel_36/2425374110.py in <module>
1 with GradientTape(persistent=True) as tape:
2 tape.watch(image)
----> 3 result = model.predict(image)[:, 4]
4 # result = tf.convert_to_tensor(result)
5 # probs = tf.nn.softmax(result, axis=-1)[:, 4]
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1749 for step in data_handler.steps():
1750 callbacks.on_predict_batch_begin(step)
-> 1751 tmp_batch_outputs = self.predict_function(iterator)
1752 if data_handler.should_sync:
1753 context.async_wait()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
922 # In this case we have not created variables on the first call. So we can
923 # run the first trace but we should fail if variables are created.
--> 924 results = self._stateful_fn(*args, **kwds)
925 if self._created_variables and not ALLOW_DYNAMIC_VARIABLE_CREATION:
926 raise ValueError("Creating variables on a non-first call to a function"
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
3038 filtered_flat_args) = self._maybe_define_function(args, kwargs)
3039 return graph_function._call_flat(
-> 3040 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
3041
3042 @property
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1967 possible_gradient_type,
1968 executing_eagerly)
-> 1969 forward_function, args_with_tangents = forward_backward.forward()
1970 if executing_eagerly:
1971 flat_outputs = forward_function.call(
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in forward(self)
1493 """Builds or retrieves a forward function for this call."""
1494 forward_function = self._functions.forward(
-> 1495 self._inference_args, self._input_tangents)
1496 return forward_function, self._inference_args self._input_tangents
1497
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in forward(self, inference_args, input_tangents)
1224 (self._forward, self._forward_graph, self._backward,
1225 self._forwardprop_output_indices, self._num_forwardprop_outputs) = (
-> 1226 self._forward_and_backward_functions(inference_args, input_tangents))
1227 return self._forward
1228
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _forward_and_backward_functions(self, inference_args, input_tangents)
1449 outputs = list(self._func_graph.outputs)
1450 self._build_functions_for_outputs(
-> 1451 outputs, inference_args, input_tangents)
1452
1453 (forward_function, forward_graph,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _build_functions_for_outputs(self, outputs, inference_args, input_tangents)
946 self._func_graph.inputs,
947 grad_ys=gradients_wrt_outputs,
--> 948 src_graph=self._func_graph)
949
950 if input_tangents:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/gradients_util.py in _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method, stop_gradients, unconnected_gradients, src_graph)
634 raise LookupError(
635 "No gradient defined for operation '%s' (op type: %s)" %
--> 636 (op.name, op.type))
637 if loop_state:
638 loop_state.EnterGradWhileContext(op, before=False)
LookupError: No gradient defined for operation 'IteratorGetNext' (op type: IteratorGetNext)
What should I do to solve this problem? Thanks for answering.
CodePudding user response：
Use model(image)[:, 4] instead of model.predict(image)[:, 4]. The last one transforms the output to numpy arrays (without any autograd functionalities) and then tensorflow can't compute the gradient. By the way in a persistent context, it is significantly less efficient to call tape.gradient inside the context because you will aslo record the gradient operations. The only situation where it is interesting is when you want higher degree derivation.
with tf.GradientTape(persistent=True) as tape:
tape.watch(image)
result = model(image)[:, 4]
gradient = tape.gradient(preds, A)