训练中的训练损失和验证损失峰值

Question

这是我的模型，以及我如何适应和训练它。我正在使用GRU模型生成NLP。它一开始运行良好，但在15世纪前后，一切都发生了变化。我现在还不知道该归咎于什么

 # Define a simple sequential model
  def create_model():
    model = Sequential()
    model.add(tf.keras.layers.GRU(256, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))
    model.add(Dropout(0.5))
    model.add(tf.keras.layers.GRU(512, return_sequences=True))
    model.add(Dropout(0.5))
    model.add(tf.keras.layers.GRU(700))
    model.add(Dropout(0.5))
    model.add(Dense(train_Y.shape[1], activation='softmax'))


 model.compile(optimizer='adam',
            loss='categorical_crossentropy',
            metrics=[tf.metrics.CategoricalCrossentropy(), 'accuracy'])

return model


# Create a basic model instance
model = create_model()

# Display the model's architecture
model.summary()

以下是模型代码的拟合和保存：

checkpoint_path = "/content/gdrive/My Drive/NLP/training_4/cp.ckpt"
#checkpoint_dir = os.path.dirname(checkpoint_path)

# Create a callback that saves the model's weights
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
                                             save_weights_only=True,
                                             save_best_only = True,
                                             verbose=1)

# Train the model with the new callback
history = model.fit(train_X, 
      train_Y,  
      epochs=50,
      batch_size = 50,
      validation_data=(X_valid, Y_valid),
      callbacks=[cp_callback])  # Pass callback to training

This is the plotted training and validation loss 显然，我还不能显示图像，因为我需要获得声誉积分

我能做些什么来纠正这个问题