在Keras模型中为model.predict（）输入正确的问题

我有一个DQN代理，它接收由指示其位置的数值和表示来自多个用户的请求的2D数组组成的状态。

我的架构神经网络的尝试描述在这里。

型号汇总

现在的问题在model.predict（）方法中，它是这样写的：

target_f = self.model.predict(state)

在方法中：

def replay(self, batch_size): # method that trains NN with experiences sampled from memory
        minibatch = sample(self.memory, batch_size) 
        for state, action, reward, next_state, done in minibatch: 
            target = reward 
            if not done: 
                target = (reward + self.gamma * np.amax(self.model.predict(next_state)[0]))
            target_f = self.model.predict(state) 
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0) 
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

状态可以在哪里（agentPosition，[[1、0、0]，[0、0、0]，[0、1、0]，...]）

例如，如果我有11个用户（等于2D请求数组中的行数），则状态的形状为（2，（11，3））。

错误提示：

ValueError: Failed to find data adapter that can handle input: (<class 'list'> containing values of types {"<class 'numpy.int32'>", '(<class \'list\'> containing values of types {"<class \'numpy.ndarray\'>"})'}), <class 'NoneType'>

相反，如果我这样写：

target_f = target_f = self.model.predict(np.array([state[0], state[1]]))

该错误则不同：

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), for inputs ['dense_63_input', 'input_20'] but instead got the following list of 1 arrays: [array([[54],
       [((1, 0, 0), (0, 0, 0), (0, 0, 1), (0, 0, 1), (0, 0, 0), (0, 1, 0), (0, 0, 1), (0, 0, 1), (1, 0, 0), (1, 0, 0), (0, 0, 1))]],
      dtype=object)]...

编辑：我按照接受的解决方案中的指示进行操作，但出现此错误：

IndexError                                Traceback (most recent call last)
<ipython-input-25-e99a46675915> in <module>
     28 
     29     if len(agent.memory) > batch_size:
---> 30         agent.replay(batch_size) # train the agent by replaying the experiences of the episode
     31 
     32     if e % 100 == 0:

<ipython-input-23-91f0ef2e2650> in replay(self, batch_size)
    129                                             np.array(next_state[1]) ])[0])) # (maximum target Q based on future action a')
    130             target_f = self.model.predict( [ np.array(state[0]), \
--> 131                                             np.array(state[1]) ] ) # approximately map current state to future discounted reward
    132             target_f[0][action] = target
    133             self.model.fit(state, target_f, epochs=1, verbose=0)

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1011         max_queue_size=max_queue_size,
   1012         workers=workers,
-> 1013         use_multiprocessing=use_multiprocessing)
   1014 
   1015   def reset_metrics(self):

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in predict(self, model, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    496         model, ModeKeys.PREDICT, x=x, batch_size=batch_size, verbose=verbose,
    497         steps=steps, callbacks=callbacks, max_queue_size=max_queue_size,
--> 498         workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
    499 
    500 

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    408     strategy = _get_distribution_strategy(model)
    409     batch_size, steps = dist_utils.process_batch_and_step_size(
--> 410         strategy, x, batch_size, steps, mode)
    411     dist_utils.validate_callbacks(input_callbacks=callbacks,
    412                                   optimizer=model.optimizer)

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py in process_batch_and_step_size(strategy, inputs, batch_size, steps_per_epoch, mode, validation_split)
    460   first_x_value = nest.flatten(inputs)[0]
    461   if isinstance(first_x_value, np.ndarray):
--> 462     num_samples = first_x_value.shape[0]
    463     if validation_split and 0. < validation_split < 1.:
    464       num_samples = int(num_samples * (1 - validation_split))

IndexError: tuple index out of range

的state[1]是这样的一个元组（（1，0，0），（0，1，0），...）

的形状np.array(state[0])为（）。的形状np.array(state[1])是（11，3）。

如果我写：

self.model.predict( [ np.array(state[0]).reshape(-1,1), np.array(state[1]) ] ) 

它给出一个错误：

ValueError                                Traceback (most recent call last)
<ipython-input-28-e99a46675915> in <module>
     28 
     29     if len(agent.memory) > batch_size:
---> 30         agent.replay(batch_size) # train the agent by replaying the experiences of the episode
     31 
     32     if e % 100 == 0:

<ipython-input-26-5df3ba3feb8f> in replay(self, batch_size)
    129                                             np.array(next_state[1]) ])[0])) # (maximum target Q based on future action a')
    130             target_f = self.model.predict( [ np.array(state[0]).reshape(-1, 1), \
--> 131                                             np.array(state[1]) ] ) # approximately map current state to future discounted reward
    132             target_f[0][action] = target
    133             self.model.fit(state, target_f, epochs=1, verbose=0)

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1011         max_queue_size=max_queue_size,
   1012         workers=workers,
-> 1013         use_multiprocessing=use_multiprocessing)
   1014 
   1015   def reset_metrics(self):

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in predict(self, model, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    496         model, ModeKeys.PREDICT, x=x, batch_size=batch_size, verbose=verbose,
    497         steps=steps, callbacks=callbacks, max_queue_size=max_queue_size,
--> 498         workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
    499 
    500 

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    424           max_queue_size=max_queue_size,
    425           workers=workers,
--> 426           use_multiprocessing=use_multiprocessing)
    427       total_samples = _get_total_number_of_samples(adapter)
    428       use_sample = total_samples is not None

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    644     standardize_function = None
    645     x, y, sample_weights = standardize(
--> 646         x, y, sample_weight=sample_weights)
    647   elif adapter_cls is data_adapter.ListsOfScalarsDataAdapter:
    648     standardize_function = standardize

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
   2381         is_dataset=is_dataset,
   2382         class_weight=class_weight,
-> 2383         batch_size=batch_size)
   2384 
   2385   def _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs,

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs, is_dataset, class_weight, batch_size)
   2408           feed_input_shapes,
   2409           check_batch_axis=False,  # Don't enforce the batch size.
-> 2410           exception_prefix='input')
   2411 
   2412     # Get typespecs for the input data and sanitize it if necessary.

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    571                            ': expected ' + names[i] + ' to have ' +
    572                            str(len(shape)) + ' dimensions, but got array '
--> 573                            'with shape ' + str(data_shape))
    574         if not check_batch_axis:
    575           data_shape = data_shape[1:]

ValueError: Error when checking input: expected dense_42_input to have 3 dimensions, but got array with shape (1, 1)