在 Keras 模型中为 model.predict() 输入正确的问题
Problem having the right input for model.predict() in Keras model
我有一个 DQN 代理，它接收一个状态，该状态由表示其位置的数值和表示来自多个用户的请求的二维数组组成。
我构建神经网络的尝试如所述here。
模型总结
现在问题出在model.predict()方法上，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
其中state可以是(agentPosition, [[1, 0, 0], [0, 0, 0], [0, 1, 0], ...])
例如，如果我有 11 个用户（等于请求的二维数组中的行数），状态的形状是 (2, (11, 3))。
错误说：
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'>
如果我这样写：
target_f = target_f = self.model.predict(np.array([state[0], state[1]]))
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说明进行操作，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
state[1] 是这样的元组 ((1, 0, 0), (0, 1, 0), ...)
np.array(state[0])的形状是()。
np.array(state[1]) 的形状是 (11, 3).
如果我写：
self.model.predict( [ np.array(state[0]).reshape(-1,1), np.array(state[1]) ] )
报错：
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)
这样写
target_f = self.model.predict( [ np.array(state[0]), np.array(state[1]) ] )
我有一个 DQN 代理，它接收一个状态，该状态由表示其位置的数值和表示来自多个用户的请求的二维数组组成。
我构建神经网络的尝试如所述here。
模型总结
现在问题出在model.predict()方法上，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
其中state可以是(agentPosition, [[1, 0, 0], [0, 0, 0], [0, 1, 0], ...])
例如，如果我有 11 个用户（等于请求的二维数组中的行数），状态的形状是 (2, (11, 3))。
错误说：
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'>
如果我这样写：
target_f = target_f = self.model.predict(np.array([state[0], state[1]]))
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说明进行操作，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
state[1] 是这样的元组 ((1, 0, 0), (0, 1, 0), ...)
np.array(state[0])的形状是()。
np.array(state[1]) 的形状是 (11, 3).
如果我写：
self.model.predict( [ np.array(state[0]).reshape(-1,1), np.array(state[1]) ] )
报错：
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)
这样写
target_f = self.model.predict( [ np.array(state[0]), np.array(state[1]) ] )