python求解 val_loss不降反升该如何调整

Question

应该如何修改我的网络

def model_load(IMG_SHAPE=(150,150, 3), class_num=2):
    # 搭建模型
    model = tf.keras.models.Sequential([
        tf.keras.layers.experimental.preprocessing.Rescaling(1. / 255, input_shape=IMG_SHAPE),    
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2, 2),
        tf.keras.layers.Dropout(0.5),
        # Add another convolution
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2, 2),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Flatten(),
        # The same 128 dense layers, and 10 output layers as in the pre-convolution example:
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(class_num, activation='softmax')
    ])
    model.summary()
    model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

Answer 1

要不是过拟合了，要不就是验证集不对劲。很有可能验证集没处理好