深度学习| 通过蒸馏收敛一个更优模型部署

阅读目录(Content)

• 蒸馏收敛 
• 导入基础包
• 构造Distiller类
• 创建学生和教师模型

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蒸馏收敛 

基于keras的知识蒸馏(Knowledge Distillation)-分类与回归

如果通过蒸馏收敛到一个更优的的部署模型

Knowledge Distillation  Introduction to Knowledge Distillation

知识提取是一种模型压缩过程，其中对小（学生）模型进行训练，以匹配预先训练的大（教师）模型。通过最小化损失函数，将知识从教师模型转移到学生身上，目的是匹配软化的教师逻辑和基本事实

标签。通过在softmax中应用“温度”标度函数来软化logits，有效地平滑了概率分布，并揭示了教师学习到的课堂间关系。

Hinton et al. (2015)  

导入基础包

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np

构造Distiller类

自定义Distiller()类覆盖Model方法train_step、test_step和compile()。为使用蒸馏器，我们需要：

训练有素的教师模型
要训练的学生模型
关于学生预测和基本事实之间差异的学生损失函数
关于学生软预测和教师软标签之间差异的蒸馏损失函数以及温度
衡量学生体重和蒸馏损失的阿尔法因素
针对学生的优化器和（可选）评估绩效的指标
在train_step方法中，我们执行教师和学生的前向传递，分别通过α和1-alpha对student_loss和distraction_loss进行加权来计算损失，并执行后向传递。注意：只有学生权重会更新，因此我们只计算学生权重的梯度。

在test_step方法中，我们在提供的数据集上评估学生模型。

class Distiller(keras.Model):
    def __init__(self, student, teacher):
        super().__init__()
        self.teacher = teacher
        self.student = student
 
    def compile(
        self,
        optimizer,
        metrics,
        student_loss_fn,
        distillation_loss_fn,
        alpha=0.1,
        temperature=3,
    ):
        """ Configure the distiller.
        Args:
            optimizer: Keras optimizer for the student weights
            metrics: Keras metrics for evaluation
            student_loss_fn: Loss function of difference between student
                predictions and ground-truth
            distillation_loss_fn: Loss function of difference between soft
                student predictions and soft teacher predictions
            alpha: weight to student_loss_fn and 1-alpha to distillation_loss_fn
            temperature: Temperature for softening probability distributions.
                Larger temperature gives softer distributions.
        """
        super().compile(optimizer=optimizer, metrics=metrics)
        self.student_loss_fn = student_loss_fn
        self.distillation_loss_fn = distillation_loss_fn
        self.alpha = alpha
        self.temperature = temperature
 
    def train_step(self, data):
        # Unpack data 解析数据
        x, y = data
 
        # Forward pass of teacher 前向传递 
        teacher_predictions = self.teacher(x, training=False)
 
        with tf.GradientTape() as tape:
            # Forward pass of student 前向传递 
            student_predictions = self.student(x, training=True)
 
            # Compute losses
            student_loss = self.student_loss_fn(y, student_predictions)
 
            # Compute scaled distillation loss from https://arxiv.org/abs/1503.02531
            # The magnitudes of the gradients produced by the soft targets scale
            # as 1/T^2, multiply them by T^2 when using both hard and soft targets.
            distillation_loss = (
                self.distillation_loss_fn(
                    tf.nn.softmax(teacher_predictions / self.temperature, axis=1),
                    tf.nn.softmax(student_predictions / self.temperature, axis=1),
                )
                * self.temperature**2
            )
            # Total loss: alpha*hard loss + (1-alpha)*soft loss
            loss = self.alpha * student_loss + (1 - self.alpha) * distillation_loss
 
        # Compute gradients
        trainable_vars = self.student.trainable_variables
        gradients = tape.gradient(loss, trainable_vars)
 
        # Update weights
        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
 
        # Update the metrics configured in `compile()`.
        self.compiled_metrics.update_state(y, student_predictions)
 
        # Return a dict of performance
        results = {m.name: m.result() for m in self.metrics}
        results.update(
            {"student_loss": student_loss, "distillation_loss": distillation_loss}
        )
        return results
 
    def test_step(self, data):
        # Unpack the data
        x, y = data
 
        # Compute predictions
        y_prediction = self.student(x, training=False)
 
        # Calculate the loss
        student_loss = self.student_loss_fn(y, y_prediction)
 
        # Update the metrics.
        self.compiled_metrics.update_state(y, y_prediction)
 
        # Return a dict of performance
        results = {m.name: m.result() for m in self.metrics}
        results.update({"student_loss": student_loss})
        return results

创建学生和教师模型

首先，创建一个教师模型和一个较小的学生模型。这两个模型都是卷积神经网络，使用Sequential()创建，也可以是其他Keras模型。

# Create the teacher
teacher = keras.Sequential(
    [
        keras.Input(shape=(28, 28, 1)),
        layers.Conv2D(256, (3, 3), strides=(2, 2), padding="same"),
        layers.LeakyReLU(alpha=0.2),
        layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding="same"),
        layers.Conv2D(512, (3, 3), strides=(2, 2), padding="same"),
        layers.Flatten(),
        layers.Dense(10),
    ],
    name="teacher",
)
 
# Create the student
student = keras.Sequential(
    [
        keras.Input(shape=(28, 28, 1)),
        layers.Conv2D(16, (3, 3), strides=(2, 2), padding="same"),
        layers.LeakyReLU(alpha=0.2),
        layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding="same"),
        layers.Conv2D(32, (3, 3), strides=(2, 2), padding="same"),
        layers.Flatten(),
        layers.Dense(10),
    ],
    name="student",
)
 
# Clone student for later comparison
student_scratch = keras.models.clone_model(student)

准备数据集

用于训练教师和提取教师的数据集是MNIST，并且该过程对于任何其他数据集都是等效的，例如CIFAR-10，只要选择合适的模型。学生和老师都在训练集上接受训练，并在测试集上进行评估

# Prepare the train and test dataset.
batch_size = 64
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
 
# Normalize data
x_train = x_train.astype("float32") / 255.0
x_train = np.reshape(x_train, (-1, 28, 28, 1))
 
x_test = x_test.astype("float32") / 255.0
x_test = np.reshape(x_test, (-1, 28, 28, 1))

训练教师模型

在知识提炼中，我们假设老师是经过训练和固定的。因此，我们从以通常的方式在训练集上训练教师模型开始。

# Train teacher as usual
teacher.compile(
    optimizer=keras.optimizers.Adam(),
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
 
# Train and evaluate teacher on data.
teacher.fit(x_train, y_train, epochs=5)
teacher.evaluate(x_test, y_test)

 

Epoch 1/5
1875/1875 [==============================] - 162s 86ms/step - loss: 0.1438 - sparse_categorical_accuracy: 0.9553
Epoch 2/5
1875/1875 [==============================] - 172s 92ms/step - loss: 0.0905 - sparse_categorical_accuracy: 0.9732
Epoch 3/5
1875/1875 [==============================] - 172s 92ms/step - loss: 0.0798 - sparse_categorical_accuracy: 0.9768
Epoch 4/5
1875/1875 [==============================] - 171s 91ms/step - loss: 0.0767 - sparse_categorical_accuracy: 0.9785
Epoch 5/5
1875/1875 [==============================] - 179s 95ms/step - loss: 0.0699 - sparse_categorical_accuracy: 0.9808
313/313 [==============================] - 6s 20ms/step - loss: 0.0894 - sparse_categorical_accuracy: 0.9763
[0.08935610204935074, 0.9763000011444092]

从老师蒸馏到学生

已经训练了教师模型，只需要初始化Distiller（学生，教师）实例，用所需的损失、超参数和优化器对其进行compile（），并将教师提取给学生。从头开始训练学生进行比较； 

# Initialize and compile distiller
distiller = Distiller(student=student, teacher=teacher)
distiller.compile(
    optimizer=keras.optimizers.Adam(),
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
    student_loss_fn=keras.losses.SparseCategoricalCrossentropy(from_logits=True),#需要进行回归的时候可相应替换损失函数
    distillation_loss_fn=keras.losses.KLDivergence(),
    alpha=0.1,
    temperature=10,
)
 
# Distill teacher to student
distiller.fit(x_train, y_train, epochs=3)
 
# Evaluate student on test dataset
distiller.evaluate(x_test, y_test)

结果数据如下

Epoch 1/3
1875/1875 [==============================] - 37s 19ms/step - sparse_categorical_accuracy: 0.8863 - student_loss: 0.5352 - distillation_loss: 8.6172
Epoch 2/3
1875/1875 [==============================] - 37s 20ms/step - sparse_categorical_accuracy: 0.9647 - student_loss: 0.1374 - distillation_loss: 1.8981
Epoch 3/3
1875/1875 [==============================] - 38s 20ms/step - sparse_categorical_accuracy: 0.9718 - student_loss: 0.1047 - distillation_loss: 1.2105
313/313 [==============================] - 1s 2ms/step - sparse_categorical_accuracy: 0.9732 - student_loss: 0.1035

[0.9732000231742859, 0.0381324402987957]

从头开始训练学生进行比较 

#Train student model from scratch for comparison
student_scratch.compile(
    optimizer=keras.optimizers.Adam(),
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
)

# Train and evaluate student trained from scratch.
student_scratch.fit(x_train, y_train, epochs=3)
student_scratch.evaluate(x_test, y_test)

#student(train from scratch) accuracy: 0.9778
#0.9896 VS. 0.9778

 

Epoch 1/3
1875/1875 [==============================] - 7s 4ms/step - loss: 0.0680 - sparse_categorical_accuracy: 0.9791
Epoch 2/3
1875/1875 [==============================] - 7s 4ms/step - loss: 0.0597 - sparse_categorical_accuracy: 0.9819
Epoch 3/3
1875/1875 [==============================] - 7s 4ms/step - loss: 0.0545 - sparse_categorical_accuracy: 0.9829
313/313 [==============================] - 1s 2ms/step - loss: 0.0640 - sparse_categorical_accuracy: 0.9797
[0.06404071301221848, 0.9797000288963318]

如果老师接受了5个epoch的训练，而学生在这个老师身上被提炼了3个epoch，那么在这个例子中，与从头开始训练相同的学生模型相比，甚至与老师本身相比，应该会体验到一种成绩提升。

应该期望老师的准确率在97.6%左右，从头开始训练的学生的准确率应该在97.6%附近，蒸馏的学生应该在98.1%左右。