我正在尝试使用talos库调整CNN模型的超参数，但是却遇到了确保函数返回模型错误的错误。但是在我返回两个变量的函数中。
我尝试了许多文章，但是它们相同的命令运行良好。我正在kaggle笔记本上编写代码
def Talos_Model(X_train, y_train, X_test, y_test, params):
#parameters defined
lr = params['lr']
epochs=params['epochs']
dropout_rate=params['dropout']
optimizer=params['optimizer']
loss=params['loss']
last_activation=params['last_activation']
activation=params['activation']
clipnorm=params['clipnorm']
decay=params['decay']
momentum=params['momentum']
l1=params['l1']
l2=params['l2']
No_of_CONV_and_Maxpool_layers=params['No_of_CONV_and_Maxpool_layers']
No_of_Dense_Layers =params['No_of_Dense_Layers']
No_of_Units_in_dense_layers=params['No_of_Units_in_dense_layers']
Kernal_Size=params['Kernal_Size']
Conv2d_filters=params['Conv2d_filters']
pool_size_p=params['pool_size']
padding_p=params['padding']
#model sequential
model=Sequential()
for i in range(0,No_of_CONV_and_Maxpool_layers):
model.add(Conv2D(Conv2d_filters, Kernal_Size ,padding=padding_p))
model.add(Activation(activation))
model.add(MaxPooling2D(pool_size=pool_size_p,strides=(2,2)))
model.add(Flatten())
for i in range (0,No_of_Dense_Layers):
model.add(Dense(units=No_of_Units_in_dense_layers,activation=activation, kernel_regularizer=regularizers.l2(l2),
activity_regularizer=regularizers.l1(l1)))
model.add(Dense(units=20,activation=activation))
model.add(Dense(units=2,activation=activation))
model.compile(loss=loss,optimizer=params['optimizer'](lr=lr, decay=decay, momentum=momentum),
metrics=['accuracy'])
out = model.fit(X_train, y_train, epochs=params['epochs'])
return out,model
import talos as ta
params = {'lr': (0.1, 0.01,1 ),
'epochs': [10,5,15],
'dropout': (0, 0.40, 0.8),
'optimizer': ["Adam","Adagrad","sgd"],
'loss': ["binary_crossentropy","mean_squared_error","mean_absolute_error","squared_hinge"],
'last_activation': ["softmax","sigmoid"],
'activation' :["relu","selu","linear"],
'clipnorm':(0.0,0.5,1),
'decay':(1e-6,1e-4,1e-2),
'momentum':(0.9,0.5,0.2),
'l1': (0.01,0.001,0.0001),
'l2': (0.01,0.001,0.0001),
'No_of_CONV_and_Maxpool_layers':[2,3,4],
'No_of_Dense_Layers': [2,3,4],
'No_of_Units_in_dense_layers':[128,64,32,256],
'Kernal_Size':[(2,2),(4,4),(6,6)],
'Conv2d_filters':[60,40,80,120],
'pool_size':[(2,2),(4,4),(6,6)],
'padding':["valid","same"]
}
h = ta.Scan(X_train, y_train, params=params,
model=Talos_Model,
dataset_name='DR',
experiment_no='1',
grid_downsample=.01)
Thanking for taking this under considration
错误引用：
0%| | 0/5598 [00:00<?, ?it/s]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/talos/scan/scan_round.py in scan_round(self)
31 try:
---> 32 _hr_out, self.keras_model = ingest_model(self)
33 except TypeError as err:
/opt/conda/lib/python3.6/site-packages/talos/model/ingest_model.py in ingest_model(self)
9 self.y_val,
---> 10 self.round_params)
<ipython-input-93-d0c3779dc659> in Talos_Model(X_train, y_train, X_test, y_test, params)
43
---> 44 model.compile(loss=loss,optimizer=params['optimizer'](lr=lr, decay=decay, momentum=momentum),
45 metrics=['accuracy'])
TypeError: 'str' object is not callable
During handling of the above exception, another exception occurred:
TalosReturnError Traceback (most recent call last)
<ipython-input-95-5853eb1b121e> in <module>()
3 dataset_name='DR',
4 experiment_no='1',
----> 5 grid_downsample=.01)
/opt/conda/lib/python3.6/site-packages/talos/scan/Scan.py in __init__(self, x, y, params, model, dataset_name, experiment_no, x_val, y_val, val_split, shuffle, round_limit, grid_downsample, random_method, seed, search_method, reduction_method, reduction_interval, reduction_window, reduction_threshold, reduction_metric, reduce_loss, last_epoch_value, clear_tf_session, disable_progress_bar, print_params, debug)
161 # input parameters section ends
162
--> 163 self._null = self.runtime()
164
165 def runtime(self):
/opt/conda/lib/python3.6/site-packages/talos/scan/Scan.py in runtime(self)
166
167 self = scan_prepare(self)
--> 168 self = scan_run(self)
/opt/conda/lib/python3.6/site-packages/talos/scan/scan_run.py in scan_run(self)
18 disable=self.disable_progress_bar)
19 while len(self.param_log) != 0:
---> 20 self = scan_round(self)
21 self.pbar.update(1)
22 self.pbar.close()
/opt/conda/lib/python3.6/site-packages/talos/scan/scan_round.py in scan_round(self)
35 raise TalosTypeError("Activation should be as object and not string in params")
36 else:
---> 37 raise TalosReturnError("Make sure that input model returns 'out, model' where out is history object from model.fit()")
38
39 # set end time and log
TalosReturnError: Make sure that input model returns 'out, model' where out is history object from model.fit()
答案 0 :(得分：2)
抱歉，我正在将无效参数传递给优化器
model.compile(loss=loss,optimizer=params['optimizer'](lr=lr, decay=decay, momentum=momentum),
metrics=['accuracy'])
在此 optimizer = params ['optimizer']（lr = lr，衰减=衰减，动量=动量） params ['optimizer']具有字符串值，例如“ adam”，我们不能在括号（（lr = lr，衰减=衰减，动量=动量））中传递字符串参数，因此我们必须在进入编译功能之前，先准备好我们的优化程序，我们可以像这样
optimizer=params["optimizer"]
if optimizer=="Adam":
opt=keras.optimizers.Adam(lr=lr, decay=decay, beta_1=0.9, beta_2=0.999)
if optimizer=="Adagrad":
opt=keras.optimizers.Adagrad(lr=lr, epsilon=None, decay=decay)
if optimizer=="sgd":
opt=keras.optimizers.SGD(lr=lr, momentum=momentum, decay=decay, nesterov=False)