Python 验证损失和验证精度曲线随预训练模型波动

我目前正在学习神经网络，当我试图学习CNN时遇到了问题，我试图训练包含音乐类型谱图的数据。我的数据由27000个光谱图组成，分为3类（类型）。我的数据按9:1的比例分割，用于培训和验证

有人能帮我吗？为什么我的验证损失/准确性会波动？我正在使用Keras的MobileNet V2，并将其与3个致密层连接。以下是我的代码片段：

train_datagen = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    validation_split=0.1)

train_generator = train_datagen.flow_from_dataframe(
    dataframe=traindf,
    directory="...",
    color_mode='rgb',
    x_col="ID",
    y_col="Class",
    subset="training",
    batch_size=32,
    seed=42,
    shuffle=True,
    class_mode="categorical",
    target_size=(64, 64))

valid_generator = train_datagen.flow_from_dataframe(
    dataframe=traindf,
    directory="...",
    color_mode='rgb',
    x_col="ID",
    y_col="Class",
    subset="validation",
    batch_size=32,
    seed=42,
    shuffle=True,
    class_mode="categorical",
    target_size=(64, 64))

base_model = MobileNetV2(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1025, activation='relu')(x)
x = Dense(1025, activation='relu')(x)
x = Dense(512, activation='relu')(x)
preds = Dense(3, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=preds)

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

step_size_train = train_generator.n//train_generator.batch_size
step_size_valid = valid_generator.n//valid_generator.batch_size
history = model.fit_generator(
    generator=train_generator,
    steps_per_epoch=step_size_train,
    validation_data=valid_generator,
    validation_steps=step_size_valid,
    epochs=75)

这是我的验证损失和验证精度曲线波动太大的图片

有没有办法减少波动或使其更好？我在这里是否有过盈或过盈的问题？我尝试过使用Dropout（），但它只会让情况变得更糟。我需要做什么来解决这个问题

谢谢你，

---

Aquilla Setiawan Kanadi.

首先，验证损失和验证准确性的图片缺失

为了回答您的问题，下面可能是您的验证丢失和验证精度波动的原因-

您已向基础模型添加了大约1.25倍的权重以构建模型<代码>（型号可培训参数5115398-基本型\型号可培训参数2223872=2891526）

节目统计：

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, Dense
from keras.utils.layer_utils import count_params

class color:
   PURPLE = '\033[95m'
   CYAN = '\033[96m'
   DARKCYAN = '\033[36m'
   BLUE = '\033[94m'
   GREEN = '\033[92m'
   YELLOW = '\033[93m'
   RED = '\033[91m'
   BOLD = '\033[1m'
   UNDERLINE = '\033[4m'
   END = '\033[0m'

base_model = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=False)

#base_model.summary()
trainable_count = count_params(base_model.trainable_weights)
non_trainable_count = count_params(base_model.non_trainable_weights)
print("\n",color.BOLD + '  base_model Statistics !' + color.END)
print("Trainable Parameters :", color.BOLD + str(trainable_count) + color.END)
print("Non Trainable Parameters :", non_trainable_count,"\n")

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1025, activation='relu')(x)
x = Dense(1025, activation='relu')(x)
x = Dense(512, activation='relu')(x)
preds = Dense(3, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=preds)

#model.summary()
trainable_count = count_params(model.trainable_weights)
non_trainable_count = count_params(model.non_trainable_weights)
print(color.BOLD + '    model Statistics !' + color.END)
print("Trainable Parameters :", color.BOLD + str(trainable_count) + color.END)
print("Non Trainable Parameters :", non_trainable_count,"\n")

new_weights_added = count_params(model.trainable_weights) - count_params(base_model.trainable_weights)
print("Additional trainable weights added to the model excluding basel model trainable weights :", color.BOLD + str(new_weights_added) + color.END)

输出-

WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.

   base_model Statistics !
Trainable Parameters : 2223872
Non Trainable Parameters : 34112 

    model Statistics !
Trainable Parameters : 5115398
Non Trainable Parameters : 34112 

Additional trainable weights added to the model excluding basel model trainable weights : 2891526

您正在训练完整的模型权重（MobileNet V2权重和附加层权重）

您的问题的解决方案是-

自定义附加层，使新的可训练参数与基础模型可训练参数相比最小。可以添加最大池层和较少的密集层

通过

base\u model.trainable=False

冻结基本模型，只需训练添加到MobileNetV2层顶部的新层

或

解冻基础模型的顶层（MobileNet V2层），并将底层设置为不可训练。你可以按照下面的方法来做，我们将模型冻结到第100层，剩下的层是可以训练的-

# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))

# Fine-tune from this layer onwards
fine_tune_at = 100

# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
  layer.trainable =  False

输出-

Number of layers in the base model:  155

通过超参数调整训练模型。您可以找到有关超参数调整的更多信息

---

我认为损失的画面不见了。此外，在模型训练期间共享日志也是一个好主意，无论有无辍学。如果数据是非机密的，您可以共享这些数据，这样我们就可以尝试在我们这边重现这个问题。谢谢