我在尝试多元输入时遇到了一个值错误。
这是我得到的错误:
ValueError: Inputs have incompatible shapes. Received shapes (5,) and (24,)
这是我输入的形状:
X shape (2100, 84, 5)
y shape (2100, 24)
这是我的模型摘要:
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 80, 5)] 0 []
lstm_1 (LSTM) (None, 80, 24) 2880 ['input_2[0][0]']
lstm_2 (LSTM) (None, 80, 24) 4704 ['lstm_1[0][0]']
add (Add) (None, 80, 24) 0 ['lstm_1[0][0]',
'lstm_2[0][0]']
lambda (Lambda) (None, 24) 0 ['add[0][0]']
lstm_3 (LSTM) (None, 24) 4704 ['add[0][0]']
add_1 (Add) (None, 24) 0 ['lambda[0][0]',
'lstm_3[0][0]']
==================================================================================================
Total params: 12,288
Trainable params: 12,288
Non-trainable params: 0
这是我的模型代码:
def make_residual_lstm_layers(input, rnn_width, rnn_depth, rnn_dropout):
x = input
for i in range(rnn_depth):
return_sequences = i < rnn_depth - 1
x_rnn = LSTM(rnn_width, recurrent_dropout=rnn_dropout, dropout=rnn_dropout, return_sequences=return_sequences)(x)
if return_sequences:
if i > 0 or input.shape[-1] == rnn_width:
x = add([x, x_rnn])
else:
x = x_rnn
else:
def slice_last(x):
return x[..., -1, :]
x = add([Lambda(slice_last)(x), x_rnn])
return x
我是这样定义输入的:
input = Input(shape=(X_train.shape[1], X_train.shape[2]))
output = make_residual_lstm_layers(input, rnn_width=24, rnn_depth=d, rnn_dropout=e)
model = Model(inputs=input, outputs=output)
这是我收到的全部错误:
ValueError Traceback (most recent call last)
<ipython-input-16-973dead00adb> in <module>
1 hyper = Hyperactive()
2 hyper.add_search(rlstm, search_space, optimizer=optimizer, n_iter=100)
----> 3 hyper.run()
~\anaconda3\lib\site-packages\hyperactive\hyperactive.py in run(self, max_time)
136 opt.max_time = max_time
137
--> 138 self.results_list = run_search(
139 self.opt_pros, self.distribution, self.n_processes
140 )
~\anaconda3\lib\site-packages\hyperactive\run_search.py in run_search(opt_pros, distribution, n_processes)
49
50 if n_processes == 1:
---> 51 results_list = single_process(_process_, process_infos)
52 else:
53 (distribution, process_func), dist_paras = _get_distribution(distribution)
~\anaconda3\lib\site-packages\hyperactive\distribution.py in single_process(process_func, process_infos)
9
10 def single_process(process_func, process_infos):
---> 11 results = [process_func(*info) for info in process_infos]
12
13 return results
~\anaconda3\lib\site-packages\hyperactive\distribution.py in <listcomp>(.0)
9
10 def single_process(process_func, process_infos):
---> 11 results = [process_func(*info) for info in process_infos]
12
13 return results
~\anaconda3\lib\site-packages\hyperactive\process.py in _process_(nth_process, optimizer)
5
6 def _process_(nth_process, optimizer):
----> 7 optimizer.search(nth_process)
8
9 return {
~\anaconda3\lib\site-packages\hyperactive\optimizers\gfo_wrapper.py in search(self, nth_process)
137 gfo_objective_function = gfo_wrapper_model(self.s_space())
138
--> 139 self._optimizer.search(
140 objective_function=gfo_objective_function,
141 n_iter=self.n_iter,
~\anaconda3\lib\site-packages\gradient_free_optimizers\search.py in search(self, objective_function, n_iter, max_time, max_score, early_stopping, memory, memory_warm_start, verbosity)
133 if self.stop.check(self.start_time, self.p_bar.score_best, self.score_l):
134 break
--> 135 self._initialization(init_pos, nth_iter)
136
137 self.finish_initialization()
~\anaconda3\lib\site-packages\gradient_free_optimizers\times_tracker.py in wrapper(self, *args, **kwargs)
25 def wrapper(self, *args, **kwargs):
26 t = time.time()
---> 27 res = func(self, *args, **kwargs)
28 self.iter_times.append(time.time() - t)
29 return res
~\anaconda3\lib\site-packages\gradient_free_optimizers\search.py in _initialization(self, init_pos, nth_iter)
55 self.init_pos(init_pos)
56
---> 57 score_new = self._score(init_pos)
58 self.evaluate(score_new)
59
~\anaconda3\lib\site-packages\gradient_free_optimizers\times_tracker.py in wrapper(self, *args, **kwargs)
16 def wrapper(self, *args, **kwargs):
17 t = time.time()
---> 18 res = func(self, *args, **kwargs)
19 self.eval_times.append(time.time() - t)
20 return res
~\anaconda3\lib\site-packages\gradient_free_optimizers\search.py in _score(self, pos)
46 @TimesTracker.eval_time
47 def _score(self, pos):
---> 48 return self.score(pos)
49
50 @TimesTracker.iter_time
~\anaconda3\lib\site-packages\gradient_free_optimizers\results_manager.py in _wrapper(pos)
29 value = self.conv.position2value(pos)
30 para = self.conv.value2para(value)
---> 31 results_dict = self._obj_func_results(objective_function, para)
32
33 self.results_list.append({**results_dict, **para})
~\anaconda3\lib\site-packages\gradient_free_optimizers\results_manager.py in _obj_func_results(self, objective_function, para)
12
13 def _obj_func_results(self, objective_function, para):
---> 14 results = objective_function(para)
15
16 if isinstance(results, tuple):
~\anaconda3\lib\site-packages\gradient_free_optimizers\memory.py in wrapper(para)
43 return self.memory_dict[pos_tuple]
44 else:
---> 45 score = objective_function(para)
46
47 self.memory_dict[pos_tuple] = score
~\anaconda3\lib\site-packages\hyperactive\optimizers\objective_function.py in _model(para)
35 para = gfo2hyper(search_space, para)
36 self.para_dict = para
---> 37 results = self.objective_function(self)
38
39 return results
<ipython-input-13-5b4490c109de> in rlstm(opt)
50 print("q shape",q.shape)
51 input = Input(shape=(X_train.shape[1], X_train.shape[2]))
---> 52 output = make_residual_lstm_layers(input, rnn_width=24, rnn_depth=d, rnn_dropout=e)
53 model = Model(inputs=input, outputs=output)
54 model.summary()
<ipython-input-4-8c85b2bfe824> in make_residual_lstm_layers(input, rnn_width, rnn_depth, rnn_dropout)
22 def slice_last(x):
23 return x[..., -1, :]
---> 24 x = add([Lambda(slice_last)(x), x_rnn])
25 return x
~\anaconda3\lib\site-packages\keras\layers\merge.py in add(inputs, **kwargs)
786
787 """
--> 788 return Add(**kwargs)(inputs)
789
790
~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
~\anaconda3\lib\site-packages\keras\layers\merge.py in _compute_elemwise_op_output_shape(self, shape1, shape2)
76 else:
77 if i != j:
---> 78 raise ValueError(
79 'Inputs have incompatible shapes. '
80 f'Received shapes {shape1} and {shape2}')
ValueError: Inputs have incompatible shapes. Received shapes (5,) and (24,)
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