# Python library
# -----------------------------------------------------------------

import pandas as pd
import numpy as np
import seaborn as sns
from tensorflow import keras
import matplotlib.pyplot as plt
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split

# -----------------------------------------------------------------

# 1) created from the data 
#-----------------------------------------------------------------

np.random.seed(0) 
m = 100 

X = np.linspace(0, 10, m).reshape(m,1) 
y = X + np.random.randn(m, 1)  

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

scaler = MinMaxScaler()
X_train= scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

#-----------------------------------------------------------------


# 2) Data visualization
#-----------------------------------------------------------------

print('dimensions de X:', X.shape)
print('dimensions de y:', y.shape)
plt.scatter(X,y)
plt.show()

#-----------------------------------------------------------------


# 3) Configuration of the Neural Network Layers
#-----------------------------------------------------------------

model = keras.Sequential()
model.add(keras.layers.Dense(100, activation='relu', input_dim=1))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(100, activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(1, activation='relu'))

#-----------------------------------------------------------------


# 4) Use the validation stick to train the model and display the learning curve   
#-----------------------------------------------------------------
    
Model = keras.Sequential([
keras.layers.Dense(4, activation='relu', input_dim=2),
keras.layers.Dense(2, activation='relu'),
keras.layers.Dense(1, activation='relu')])
   
opt = keras.optimizers.Adam()
Model.compile(opt, loss= 'mse')

Model = KerasRegressor(Model,batch_size=10,verbose=1, epochs=1000)   
   
val_score = cross_val_score(Model, X_train, y_train, cv=10)    
#plt.plot(val_score)    

#-----------------------------------------------------------------

当我运行附加代码正常它应该工作，但由于某种原因，它显示这个错误：
：14：弃用警告：KerasRegressor已弃用，请改用Sci-Keras（https://github.com/adriangb/scikeras）。有关迁移的帮助，请参阅https://www.adriangb.com/scikeras/stable/migration.html。模型= KerasRegressor（模型，批处理大小= 10，详细信息= 1，epochs = 1000）/usr/local/lib/python3.8/dist-packages/sklearn/模型选择/验证。py：372：拟合失败警告：总共10次拟合中有10次失败。这些参数在这些训练测试分区上的分数将设置为nan。如果这些失败不是预期的，您可以尝试通过设置error_score ='raise '来调试它们。

以下是有关故障的更多详细信息：

10 fits failed with the following error: Traceback (most recent call last): File "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py", line 680, in _fit_and_score estimator.fit(X_train, y_train,**fit_params) File "/usr/local/lib/python3.8/dist-packages/keras/wrappers/scikit_learn.py", line 152, in fit self.model = self.build_fn( File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler raise e.with_traceback(filtered_tb) from None File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 3100, in _split_out_first_arg raise ValueError( ValueError: The first argument to Layer.call must always be passed.
警告. warn（一些拟合失败消息，拟合失败警告）