TensorFlow 实战卷积神经网络之 LeNet

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LeNet

项目简介

1994 年深度学习三巨头之一的 Yan LeCun 提出了 LeNet 神经网络，这是最早的卷积神经网络。1998 年 Yan LeCun 在论文 “Gradient-Based Learning Applied to Document Recognition” 中将这种卷积神经网络命名为 “LeNet-5”。LeNet 已经包含了现在卷积神经网络中的卷积层，池化层，全连接层，已经具备了卷积神经网络必须的基本组件。

Gradient-Based Learning Applied to Document Recognition

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=726791

Architecture of LeNet-5 (Convolutional Neural Networks) for digit recognition

数据处理

同卷积神经网络中的 MNIST 数据集处理方法。

TensorFlow 卷积神经网络手写数字识别数据集介绍

http://www.tensorflownews.com/2018/03/26/tensorflow-mnist/

模型实现

经典的卷积神经网络，TensorFlow 官方已经实现，并且封装在了 tensorflow 库中，以下内容截取自 TensorFlow 官方 Github。

models/research/slim/nets/lenet.py

https://github.com/tensorflow/models/blob/master/research/slim/nets/lenet.py

import tensorflow as tfslim = tf.contrib.slimdef lenet(images, num_classes=10, is_training=False,
dropout_keep_prob=0.5,
prediction_fn=slim.softmax,
scope='LeNet'):
end_points = {}
with tf.variable_scope(scope, 'LeNet', [images]):
net = end_points['conv1'] = slim.conv2d(images, 32, [5, 5], scope='conv1')
net = end_points['pool1'] = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
net = end_points['conv2'] = slim.conv2d(net, 64, [5, 5], scope='conv2')
net = end_points['pool2'] = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
net = slim.flatten(net)
end_points['Flatten'] = net net = end_points['fc3'] = slim.fully_connected(net, 1024, scope='fc3')
if not num_classes:
return net, end_points
net = end_points['dropout3'] = slim.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout3')
logits = end_points['Logits'] = slim.fully_connected(
net, num_classes, activation_fn=None, scope='fc4') end_points['Predictions'] = prediction_fn(logits, scope='Predictions') return logits, end_points
lenet.default_image_size = 28def lenet_arg_scope(weight_decay=0.0):
"""Defines the default lenet argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the inception v3 model.
"""
with slim.arg_scope(
[slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay),
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
activation_fn=tf.nn.relu) as sc:
return sc


模型优化

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