我有一些使用 keras 的旧工作代码。我最近把它掸掉并尝试使用它，但使用的是当前版本的 keras/tensorflow。调用 Predict_on_batch 时收到警告/错误：
W tensorflow/core/data/root_dataset.cc:167] 优化循环失败：已取消：操作已取消
我尝试用谷歌搜索这个问题，令我惊讶的是，网络上似乎没有很好的解释导致此问题的原因或如何解决它。这是我发现的：
https://github.com/tensorflow/tensorflow/issues/48689 https://github.com/tensorflow/tensorflow/issues/48689
https://discuss.tensorflow.org/t/optimization-loop-failed-cancelled-operation-was-cancelled/1524 https://discuss.tensorflow.org/t/optimization-loop-failed-cancelled-operation-was-cancelled/1524
它列出的一个答案是确保批量大小不大于整个集合。这里的情况并非如此。
代码有点长，所以我不能轻易地展示全部内容。这是一个深度强化学习应用程序，因此深度学习代码分为两个主要功能，我将在此处展示：
class DQN(QContract):
def __init__(self, states, actions, lr, DDQN=False):
self.history = []
act_relu = activations.relu
act_linear = activations.linear
top_layer = 150
middle_layer = 120
# Create Network: Default Parameters from https://towardsdatascience.com/solving-lunar-lander-openaigym-reinforcement-learning-785675066197
model = Sequential()
layer = layers.Dense(top_layer, input_dim=states, activation=act_relu)
model.add(layer)
layer = layers.Dense(middle_layer, activation=act_relu)
model.add(layer)
layer = layers.Dense(actions, activation=act_linear)
model.add(layer)
opt = optimizers.Adam(learning_rate=lr)
model.compile(loss='mse', optimizer=opt)
# Create DDQN-like networks
self.modelA = model
#self.modelB = copy.deepcopy(model)
self.batch_size = 100
self.current = "A"
self.count = 0
def Update(self, state, action, reward, new_state, gamma, alpha=None):
# Preform Replay
row_count = self.batch_size
if len(self.history) < row_count: return
# Column names
state = 0
action = 1
reward = 2
next_state = 3
done = 4
# Get samples in mini-batches
samples = random.sample(self.history, row_count)
# Separate into separate arrays
states_array = [sample[state] for sample in samples]
actions_array = [sample[action] for sample in samples]
rewards_array = [sample[reward] for sample in samples]
next_states_array = [sample[next_state] for sample in samples]
done_array = [sample[done] for sample in samples]
# Turn into arrays
states_array = np.array(states_array)
actions_array = np.array(actions_array)
rewards_array = np.array(rewards_array)
next_states_array = np.array(next_states_array)
done_array = (1.0 - np.array(done_array))
# train on states_array
X = states_array
# Create y (i.e. labels for supervised learning)
if self.current == "A":
model1 = self.modelA
model2 = self.modelA
else:
model1 = self.modelA
model2 = self.modelA
predicted_values = self.modelA.predict_on_batch(states_array)
next_predicted_values = self.modelA.predict_on_batch(next_states_array)
actual_values = rewards_array + gamma * np.amax(next_predicted_values, axis=1) * done_array
predicted_values[list(range(row_count)), actions_array] = actual_values
y = predicted_values
# Update network
self.current = "A"
if self.current == "A":
print('Do fit'+str(self.count))
self.count += 1
self.modelA.fit(X, y, epochs=1, verbose=0)
self.current = "B"
else:
self.modelA.fit(X, y, epochs=1, verbose=0)
self.current = "A"
有一次我试图做一个 DQN，但现在还不行，所以请忽略拥有两个模型的尝试。目前已禁用。
这似乎是一个相当简单的问题，但我似乎无法弄清楚。我什至尝试单步执行代码，发现单步执行调试器时并没有发生这种情况。
这个线程 https://discuss.tensorflow.org/t/optimization-loop-failed-cancelled-operation-was-cancelled/1524/29（OP提到）现在有几个回复表明添加以下行可以删除错误消息：
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpu, True)
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