PROSAGA码农传奇-数据防泄漏-张量流/ keras神经网络中的过度拟合和数据泄漏

早上好，我是机器学习和神经网络领域的新手。我正在尝试建立一个完全连接的神经网络来解决回归问题。数据集由18个要素和1个标签组成，所有这些都是物理量。您可以在下面找到代码。我沿着历时上传损失函数演化图（您可以在下面找到它）。我不确定是否过度拟合。有人可以向我解释为什么存在过度拟合吗？
<pre><code>import pandas as pd
import numpy as np

from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectFromModel
from sklearn import preprocessing

from sklearn.model_selection import train_test_split

from matplotlib import pyplot as plt

import keras
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
from tensorflow.keras.callbacks import EarlyStopping
from keras import optimizers
from sklearn.metrics import r2_score
from keras import regularizers
from keras import backend
from tensorflow.keras import regularizers
from keras.regularizers import l2

# =============================================================================
# Scelgo il test size
# =============================================================================
test_size = 0.2

dataset = pd.read_csv('DataSet.csv', decimal=',', delimiter = ";")

label = dataset.iloc[:,-1]
features = dataset.drop(columns = ['Label'])

y_max_pre_normalize = max(label)
y_min_pre_normalize = min(label)

def denormalize(y):
    final_value = y*(y_max_pre_normalize-y_min_pre_normalize)+y_min_pre_normalize
    return final_value

# =============================================================================
# Split
# =============================================================================

X_train1, X_test1, y_train1, y_test1 = train_test_split(features, label, test_size = test_size, shuffle = True)

y_test2 = y_test1.to_frame()
y_train2 = y_train1.to_frame()

# =============================================================================
# Normalizzo
# =============================================================================
scaler1 = preprocessing.MinMaxScaler()
scaler2 = preprocessing.MinMaxScaler()
X_train = scaler1.fit_transform(X_train1)
X_test = scaler2.fit_transform(X_test1)

scaler3 = preprocessing.MinMaxScaler()
scaler4 = preprocessing.MinMaxScaler()
y_train = scaler3.fit_transform(y_train2)
y_test = scaler4.fit_transform(y_test2)

# =============================================================================
# Creo la rete
# =============================================================================
optimizer = tf.keras.optimizers.Adam(lr=0.001)
model = Sequential()

model.add(Dense(60, input_shape = (X_train.shape[1],), activation = 'relu',kernel_initializer='glorot_uniform'))
model.add(Dropout(0.2))
model.add(Dense(60, activation = 'relu',kernel_initializer='glorot_uniform'))
model.add(Dropout(0.2))
model.add(Dense(60, activation = 'relu',kernel_initializer='glorot_uniform'))

model.add(Dense(1,activation = 'linear',kernel_initializer='glorot_uniform'))

model.compile(loss = 'mse', optimizer = optimizer, metrics = ['mse'])

history = model.fit(X_train, y_train, epochs = 100,
                    validation_split = 0.1, shuffle=True, batch_size=250
                    )

history_dict = history.history

loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']

y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)

y_train_pred = denormalize(y_train_pred)
y_test_pred = denormalize(y_test_pred)

plt.figure()
plt.plot((y_test1),(y_test_pred),'.', color='darkviolet', alpha=1, marker='o', markersize = 2, markeredgecolor = 'black', markeredgewidth = 0.1)
plt.plot((np.array((-0.1,7))),(np.array((-0.1,7))),'-', color='magenta')
plt.xlabel('True')
plt.ylabel('Predicted')
plt.title('Test')

plt.figure()
plt.plot((y_train1),(y_train_pred),'.', color='darkviolet', alpha=1, marker='o', markersize = 2, markeredgecolor = 'black', markeredgewidth = 0.1)
plt.plot((np.array((-0.1,7))),(np.array((-0.1,7))),'-', color='magenta')
plt.xlabel('True')
plt.ylabel('Predicted')
plt.title('Train')

plt.figure()
plt.plot(loss_values,'b',label = 'training loss')
plt.plot(val_loss_values,'r',label = 'val training loss')
plt.xlabel('Epochs')
plt.ylabel('Loss Function')
plt.legend()

print("\n\nThe R2 score on the test set is:\t{:0.3f}".format(r2_score(y_test_pred, y_test1)))

print("The R2 score on the train set is:\t{:0.3f}".format(r2_score(y_train_pred, y_train1)))
from sklearn import metrics

# Measure MSE error. 
score = metrics.mean_squared_error(y_test_pred,y_test1)
print("\n\nFinal score test (MSE): %0.4f" %(score))
score1 = metrics.mean_squared_error(y_train_pred,y_train1)
print("Final score train (MSE): %0.4f" %(score1))
score2 = np.sqrt(metrics.mean_squared_error(y_test_pred,y_test1))
print(f"Final score test (RMSE): %0.4f" %(score2))
score3 = np.sqrt(metrics.mean_squared_error(y_train_pred,y_train1))
print(f"Final score train (RMSE): %0.4f" %(score3))
</code></pre>在此处输入图片说明https://i.stack.imgur.com/1LgL9.png编辑：我还尝试了其他功能，以提高功能重要性并提高n_epoch，这些是结果：功能重要性：在此处输入图片说明https://i.stack.imgur.com/RoD1w.png无功能重要性：在此处输入图片说明https://i.stack.imgur.com/oBGBl.png