Keras搭建LSTM：调用fit()时明明传入了两个array却报错称只传了一个？

猪仔很忙

求助。如代码所示，模型有两个输入，在fit时validation_data分别传入了val_data、val_labels，shape与两个模型的输入相同，分别为(834,10,7)、(834,1)：

1. def get_loss(y_true, y_pred):
   
2.     pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
   
3.     pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
   
4.     return -K.mean(0.75 * K.pow(1. - pt_1, 0) * K.log(pt_1)) - K.mean((1 - 0.75) * K.pow(pt_0, 0) * K.log(1. - pt_0))
   
5. 
   
6. 
   
7. def get_adversary(args):
   
8.     y_true = args[0]
   
9.     y_pred = args[1]
   
10.     v_final = args[2]
    
11.     loss = get_loss(y_true, y_pred)
    
12.     perturb = tf.gradients(loss, [v_final])[0]
    
13.     v_final_adv = tf.add(v_final, perturb)
    
14.     return v_final_adv
    
15. 
    
16. 
    
17. def my_loss(args):
    
18.     y_true = args[0]
    
19.     pred = args[1]
    
20.     adv_pred = args[2]
    
21.     loss1 = get_loss(y_true, pred)
    
22.     loss2 = get_loss(y_true, adv_pred)
    
23.     return loss1 + 0.1 * loss2
    
24. 
    
25. 
    
26. def _build_model(time_step, feature_dim, modelName):
    
27.     trace_input = Input(shape=(time_step, feature_dim), name='trace')
    
28.     label_input = Input(shape=(1,), name='label')
    
29. 
    
30.     if modelName == 'FEMT_LSTM':
    
31.         v_final = RNN_SEPARATE_2(time_step, feature_dim)(trace_input)
    
32.     else:
    
33.         v_final = RNN(time_step, feature_dim)(trace_input)
    
34. 
    
35.     pred = Dense(1, activation='sigmoid')(v_final)
    
36.     v_final_adv = Lambda(get_adversary, output_shape=(256,))([label_input, pred, v_final])
    
37.     adv_pred = Dense(1, activation='sigmoid')(v_final_adv)
    
38. 
    
39.     loss = Lambda(my_loss, name='loss')([label_input, pred, adv_pred])
    
40.     model = Model(inputs=[trace_input, label_input], outputs=[loss])
    
41. 
    
42.     loss_layer = model.get_layer('loss').output
    
43.     model.add_loss(loss_layer)
    
44. 
    
45.     model.compile(optimizer=optimizers.Adam(lr=0.001, clipvalue=15))
    
46.     return model
    
47. 
    
48. 
    
49. def build_train(data, machineID, modelName):
    
50.     train_data, train_labels, val_data, val_labels, test_data, test_labels = data
    
51. 
    
52.     num_samples = train_data.shape[0]
    
53.     time_step = train_data.shape[1]
    
54.     feature_dim = train_data.shape[2]
    
55. 
    
56.     model = _build_model(time_step, feature_dim, modelName)
    
57. 
    
58.     print('Train...')
    
59.     model_save_path = './lib/model_cp_adv_rnn_{}'.format(machineID)
    
60. 
    
61.     call_backs = [Call_back_0(valid_data=[val_data, val_labels, test_data, test_labels],  # test_data, test_labels
    
62.                               model_save_path=model_save_path),
    
63.                   ReduceLROnPlateau(monitor='val_loss', factor=0.8, patience=4, mode='min'),  # 4
    
64.                   ModelCheckpoint(filepath=model_save_path, monitor='val_loss', save_best_only=True,
    
65.                                   save_weights_only=True, mode='min')]
    
66. 
    
67.     model.fit({'trace': train_data, 'label': train_labels},
    
68.               batch_size=64,
    
69.               epochs=30,  # 30 60
    
70.               callbacks=call_backs,
    
71.               validation_data=(val_data, val_labels))

复制代码

却出现报错：

1. Traceback (most recent call last):
   
2.   File "<input>", line 1, in <module>
   
3.   File "D:\JetBrains\PyCharm 2020.1.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
   
4.     pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
   
5.   File "D:\JetBrains\PyCharm 2020.1.2\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
   
6.     exec(compile(contents+"\n", file, 'exec'), glob, loc)
   
7.   File "D:/PycharmProjects/turningpoint-master/main.py", line 67, in <module>
   
8.     modelName=modelName)
   
9.   File "D:\PycharmProjects\turningpoint-master\baseline_main_rnn.py", line 37, in train_rnn_turning_point
   
10.     clf = rnn_turning_point.build_train(data, machineID, modelName)
    
11.   File "D:\PycharmProjects\turningpoint-master\model\rnn_turning_point.py", line 86, in build_train
    
12.     validation_data=[val_data, val_labels])
    
13.   File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\keras\engine\training.py", line 972, in fit
    
14.     batch_size=batch_size)
    
15.   File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\keras\engine\training.py", line 751, in _standardize_user_data
    
16.     exception_prefix='input')
    
17.   File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\keras\engine\training_utils.py", line 102, in standardize_input_data
    
18.     str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
    
19. 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), but instead got the following list of 1 arrays: [array([[[0.23777019, 0.30065992, 0.37679494, ..., 0.32888769,
    
20.          0.69490097, 0.37461882],
    
21.         [0.39814235, 0.25375877, 0.20461036, ..., 0.18472258,
    
22.          0.62059676, 0.65594323],
    
23.        ...

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明明传入了两个输入集，却报错称少了一个，无法理解，希望有人能指点迷津。

陈尚涵

没看懂你的问题

猪仔很忙

71行validation_data那儿需要两个array，报错显示我少传入了一个，但是我明明已经传了两个，分别是val_data, val_labels