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？
本帖最后由 猪仔很忙 于 2020-8-8 20:36 编辑求助。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)：
def get_loss(y_true, y_pred):
pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
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))
def get_adversary(args):
y_true = args
y_pred = args
v_final = args
loss = get_loss(y_true, y_pred)
perturb = tf.gradients(loss, )
v_final_adv = tf.add(v_final, perturb)
return v_final_adv
def my_loss(args):
y_true = args
pred = args
adv_pred = args
loss1 = get_loss(y_true, pred)
loss2 = get_loss(y_true, adv_pred)
return loss1 + 0.1 * loss2
def _build_model(time_step, feature_dim, modelName):
trace_input = Input(shape=(time_step, feature_dim), name='trace')
label_input = Input(shape=(1,), name='label')
if modelName == 'FEMT_LSTM':
v_final = RNN_SEPARATE_2(time_step, feature_dim)(trace_input)
else:
v_final = RNN(time_step, feature_dim)(trace_input)
pred = Dense(1, activation='sigmoid')(v_final)
v_final_adv = Lambda(get_adversary, output_shape=(256,))()
adv_pred = Dense(1, activation='sigmoid')(v_final_adv)
loss = Lambda(my_loss, name='loss')()
model = Model(inputs=, outputs=)
loss_layer = model.get_layer('loss').output
model.add_loss(loss_layer)
model.compile(optimizer=optimizers.Adam(lr=0.001, clipvalue=15))
return model
def build_train(data, machineID, modelName):
train_data, train_labels, val_data, val_labels, test_data, test_labels = data
num_samples = train_data.shape
time_step = train_data.shape
feature_dim = train_data.shape
model = _build_model(time_step, feature_dim, modelName)
print('Train...')
model_save_path = './lib/model_cp_adv_rnn_{}'.format(machineID)
call_backs = ,# test_data, test_labels
model_save_path=model_save_path),
ReduceLROnPlateau(monitor='val_loss', factor=0.8, patience=4, mode='min'),# 4
ModelCheckpoint(filepath=model_save_path, monitor='val_loss', save_best_only=True,
save_weights_only=True, mode='min')]
model.fit({'trace': train_data, 'label': train_labels},
batch_size=64,
epochs=30,# 30 60
callbacks=call_backs,
validation_data=(val_data, val_labels))
却出现报错：
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "D:\JetBrains\PyCharm 2020.1.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars)# execute the script
File "D:\JetBrains\PyCharm 2020.1.2\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "D:/PycharmProjects/turningpoint-master/main.py", line 67, in <module>
modelName=modelName)
File "D:\PycharmProjects\turningpoint-master\baseline_main_rnn.py", line 37, in train_rnn_turning_point
clf = rnn_turning_point.build_train(data, machineID, modelName)
File "D:\PycharmProjects\turningpoint-master\model\rnn_turning_point.py", line 86, in build_train
validation_data=)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\keras\engine\training.py", line 972, in fit
batch_size=batch_size)
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\keras\engine\training.py", line 751, in _standardize_user_data
exception_prefix='input')
File "D:\Anaconda3\envs\edogawaAi\lib\site-packages\keras\engine\training_utils.py", line 102, in standardize_input_data
str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
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,
0.69490097, 0.37461882],
[0.39814235, 0.25375877, 0.20461036, ..., 0.18472258,
0.62059676, 0.65594323],
...
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。 没看懂你的问题{:5_94:} {:10_257:}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
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