layers_weightnormalization.ipynb

Kernel: Python 3

Copyright 2020 The TensorFlow Authors.

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TensorFlow Addons 层：WeightNormalization

在 TensorFlow.org 上查看

在 Google Colab 中运行

View source on GitHub

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概述

此笔记本将演示如何使用权重归一化层以及如何提升收敛。

WeightNormalization

用于加速深度神经网络训练的一个简单的重新参数化：

Tim Salimans、Diederik P. Kingma (2016)

通过以这种方式重新参数化权重，我们改善了优化问题的条件，并加快了随机梯度下降的收敛速度。我们的重新参数化受到批次归一化的启发，但没有在小批次中的样本之间引入任何依赖项。这意味着我们的方法也可以成功应用于递归模型（例如 LSTM）和对噪声敏感的应用（例如深度强化学习或生成模型），而批次归一化则不太适合这类模型和应用。尽管我们的方法要简单得多，但它仍可很大程度上为完整批次归一化提供加速。另外，我们方法的计算开销较低，从而允许在相同的时间内执行更多的优化步骤。

https://arxiv.org/abs/1602.07868

设置

!pip install -U tensorflow-addons

import tensorflow as tf
import tensorflow_addons as tfa

import numpy as np
from matplotlib import pyplot as plt

# Hyper Parameters
batch_size = 32
epochs = 10
num_classes=10

构建模型

# Standard ConvNet
reg_model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(6, 5, activation='relu'),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(16, 5, activation='relu'),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(120, activation='relu'),
    tf.keras.layers.Dense(84, activation='relu'),
    tf.keras.layers.Dense(num_classes, activation='softmax'),
])

# WeightNorm ConvNet
wn_model = tf.keras.Sequential([
    tfa.layers.WeightNormalization(tf.keras.layers.Conv2D(6, 5, activation='relu')),
    tf.keras.layers.MaxPooling2D(2, 2),
    tfa.layers.WeightNormalization(tf.keras.layers.Conv2D(16, 5, activation='relu')),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Flatten(),
    tfa.layers.WeightNormalization(tf.keras.layers.Dense(120, activation='relu')),
    tfa.layers.WeightNormalization(tf.keras.layers.Dense(84, activation='relu')),
    tfa.layers.WeightNormalization(tf.keras.layers.Dense(num_classes, activation='softmax')),
])

加载数据

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()

# Convert class vectors to binary class matrices.
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

训练模型

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

reg_history = reg_model.fit(x_train, y_train,
                            batch_size=batch_size,
                            epochs=epochs,
                            validation_data=(x_test, y_test),
                            shuffle=True)

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

wn_history = wn_model.fit(x_train, y_train,
                          batch_size=batch_size,
                          epochs=epochs,
                          validation_data=(x_test, y_test),
                          shuffle=True)

reg_accuracy = reg_history.history['accuracy']
wn_accuracy = wn_history.history['accuracy']

plt.plot(np.linspace(0, epochs,  epochs), reg_accuracy,
             color='red', label='Regular ConvNet')

plt.plot(np.linspace(0, epochs, epochs), wn_accuracy,
         color='blue', label='WeightNorm ConvNet')

plt.title('WeightNorm Accuracy Comparison')
plt.legend()
plt.grid(True)
plt.show()