我的测试损失在增加,但神经网络的训练损失在减少。我该怎么办?

My Neural Network

def buildModel(optimizer):
    model = tf.keras.models.Sequential([
    Dense(100, activation = 'relu'),
    Dense(82, activation = 'relu'),
    Dense(20, activation = 'relu'),
    Dense(6, activation = 'relu'),
    Dense(20, activation = 'softmax')
    ])
    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    return model

tf.keras.optimizers.legacy.Adam()

model = buildModel('adam')
history = model.fit(train_x,train_y_lst, validation_data=(test_x, test_y_lst),epochs = 50,batch_size = 32,verbose = 0)

绘图

plt.figure()
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training and Validation Loss Curves')
plt.legend()

# Plot training and validation accuracy
plt.figure()
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.title('Training and Validation Accuracy Curves')
plt.legend()

plt.show()

Loss vs Epochs

测试的准确性也很差

Accuracy vs Epoch

我是新来的,有什么建议吗?

我原以为考试损失会像火车损失一样减少。

我的test_x看起来像这样

  -0.84335711]
 [-0.1898388  -1.4177287   0.24718753 ... -0.33010045  0.77921928
  -1.56813293]
 [ 0.51887204 -1.34965479  0.19069737 ...  0.56236361 -0.03741466
  -0.24596578]
 ...
 [-0.11631875  0.46366703 -1.04400684 ...  0.23282911 -2.10649511
  -0.41883463]
 [-1.03632829  0.05419996 -2.22371652 ...  0.47133847 -1.70391277
  -1.42387687]
 [-0.12011524 -0.72294703 -0.74587529 ...  0.11331488 -1.81362912
  -0.11828704]]

测试_y_lst

array([[1, 0, 0, ..., 0, 0, 0],
       [1, 0, 0, ..., 0, 0, 0],
       [1, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]])

多分类问题。