如何使用 tensorflow/keras 计算梯度？
How can I compute gradient with tensorflow/keras?
我正在尝试使用 tensorflow 2.6.0 绘制 显着图 。我用 tensorflow.keras.models.Sequential 定义了模型并完成了训练。但是，当我尝试像这样计算梯度时：
with GradientTape(persistent=True) as tape:
tape.watch(image)
result = model.predict(image)[:, 4]
gradient = tape.gradient(result, image)
其中 image 是张量，[:, 4] 用于为该图像选择实际标签。
image.shape = (1, 48, 48, 1)
result.shape = (1, 10)
然后我收到错误信息：
---------------------------------------------------------------------------
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)
我应该怎么做才能解决这个问题？感谢您的回答。
使用 model(image)[:, 4] 而不是 model.predict(image)[:, 4]。最后一个将输出转换为 numpy 数组（没有任何 autograd 功能），然后 tensorflow 无法计算梯度。顺便说一句，在持久上下文中，在上下文中调用 tape.gradient 的效率要低得多，因为您还会记录梯度操作。唯一有趣的情况是当你想要更高程度的推导时。
with tf.GradientTape(persistent=True) as tape:
tape.watch(image)
result = model(image)[:, 4]
gradient = tape.gradient(preds, A)
我正在尝试使用 tensorflow 2.6.0 绘制 显着图 。我用 tensorflow.keras.models.Sequential 定义了模型并完成了训练。但是，当我尝试像这样计算梯度时：
with GradientTape(persistent=True) as tape:
tape.watch(image)
result = model.predict(image)[:, 4]
gradient = tape.gradient(result, image)
其中 image 是张量，[:, 4] 用于为该图像选择实际标签。
image.shape = (1, 48, 48, 1)
result.shape = (1, 10)
然后我收到错误信息：
---------------------------------------------------------------------------
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)
我应该怎么做才能解决这个问题？感谢您的回答。
使用 model(image)[:, 4] 而不是 model.predict(image)[:, 4]。最后一个将输出转换为 numpy 数组（没有任何 autograd 功能），然后 tensorflow 无法计算梯度。顺便说一句，在持久上下文中，在上下文中调用 tape.gradient 的效率要低得多，因为您还会记录梯度操作。唯一有趣的情况是当你想要更高程度的推导时。
with tf.GradientTape(persistent=True) as tape:
tape.watch(image)
result = model(image)[:, 4]
gradient = tape.gradient(preds, A)