LeNet、AlexNet、VGG、ZF

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LeNet5

LeNet模型理解

CIFAR10

CIFAR10模型理解简述

AlexNet

Caffe深度学习之图像分类模型AlexNet解读

在imagenet上的图像分类challenge上Alex提出的alexnet网络结构模型赢得了2012届的冠军。要研究CNN类型DL网络模型在图像分类上的应用，就逃不开研究alexnet，这是CNN在图像分类上的经典模型（DL火起来之后）。

在DL开源实现caffe的model样例中，它也给出了alexnet的复现，具体网络配置文件如下 train_val.prototxt

接下来本文将一步步对该网络配置结构中各个层进行详细的解读（训练阶段）：

各种layer的operation更多解释可以参考 Caffe Layer Catalogue

从计算该模型的数据流过程中，该模型参数大概5kw+。

conv1阶段DFD（data flow diagram）：
conv2阶段DFD（data flow diagram）：
conv3阶段DFD（data flow diagram）：
conv4阶段DFD（data flow diagram）：
conv5阶段DFD（data flow diagram）：
fc6阶段DFD（data flow diagram）：
fc7阶段DFD（data flow diagram）：
fc8阶段DFD（data flow diagram）：

AlexNet 之结构篇

AlexNet 之算法篇

AlexNet&Imagenet学习笔记

CVPR 2015 之深度学习篇 Part 1 － AlexNet 和 VGG-Net

Alex / OverFeat / VGG 中的卷积参数

import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)import tensorflow as tf# 定义网络超参数learning_rate = 0.001training_iters = 200000batch_size = 64display_step = 20# 定义网络参数n_input = 784 # 输入的维度n_classes = 10 # 标签的维度dropout = 0.8 # Dropout 的概率# 占位符输入x = tf.placeholder(tf.types.float32, [None, n_input])y = tf.placeholder(tf.types.float32, [None, n_classes])keep_prob = tf.placeholder(tf.types.float32)# 卷积操作def conv2d(name, l_input, w, b):    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'),b), name=name)# 最大下采样操作def max_pool(name, l_input, k):    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)# 归一化操作def norm(name, l_input, lsize=4):    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)# 定义整个网络 def alex_net(_X, _weights, _biases, _dropout):    # 向量转为矩阵    _X = tf.reshape(_X, shape=[-1, 28, 28, 1])    # 卷积层    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])    # 下采样层    pool1 = max_pool('pool1', conv1, k=2)    # 归一化层    norm1 = norm('norm1', pool1, lsize=4)    # Dropout    norm1 = tf.nn.dropout(norm1, _dropout)    # 卷积    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])    # 下采样    pool2 = max_pool('pool2', conv2, k=2)    # 归一化    norm2 = norm('norm2', pool2, lsize=4)    # Dropout    norm2 = tf.nn.dropout(norm2, _dropout)    # 卷积    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])    # 下采样    pool3 = max_pool('pool3', conv3, k=2)    # 归一化    norm3 = norm('norm3', pool3, lsize=4)    # Dropout    norm3 = tf.nn.dropout(norm3, _dropout)    # 全连接层，先把特征图转为向量    dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]])     dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')     # 全连接层    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation    # 网络输出层    out = tf.matmul(dense2, _weights['out']) + _biases['out']    return out# 存储所有的网络参数weights = {    'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),    'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),    'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),    'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),    'wd2': tf.Variable(tf.random_normal([1024, 1024])),    'out': tf.Variable(tf.random_normal([1024, 10]))}biases = {    'bc1': tf.Variable(tf.random_normal([64])),    'bc2': tf.Variable(tf.random_normal([128])),    'bc3': tf.Variable(tf.random_normal([256])),    'bd1': tf.Variable(tf.random_normal([1024])),    'bd2': tf.Variable(tf.random_normal([1024])),    'out': tf.Variable(tf.random_normal([n_classes]))}# 构建模型pred = alex_net(x, weights, biases, keep_prob)# 定义损失函数和学习步骤cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# 测试网络correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# 初始化所有的共享变量init = tf.initialize_all_variables()# 开启一个训练with tf.Session() as sess:    sess.run(init)    step = 1    # Keep training until reach max iterations    while step * batch_size < training_iters:        batch_xs, batch_ys = mnist.train.next_batch(batch_size)        # 获取批数据        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})        if step % display_step == 0:            # 计算精度            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})            # 计算损失值            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})            print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)        step += 1    print "Optimization Finished!"    # 计算测试精度    print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})

VGG

Very Deep Convolutional Networks for Large-Scale Image Recognition

Very Deep Convolutional Networks for Large-Scale Image Recognition(VGG模型)

VGG-16 prototxt

网络结构

# -*- coding: utf-8 -*-import chainerfrom chainer import Variableimport chainer.links as Limport chainer.functions as Fclass VGGNet(chainer.Chain):    """    VGGNet    - It takes (224, 224, 3) sized image as imput    """    def __init__(self):        super(VGGNet, self).__init__(            conv1_1=L.Convolution2D(3, 64, 3, stride=1, pad=1),            conv1_2=L.Convolution2D(64, 64, 3, stride=1, pad=1),            conv2_1=L.Convolution2D(64, 128, 3, stride=1, pad=1),            conv2_2=L.Convolution2D(128, 128, 3, stride=1, pad=1),            conv3_1=L.Convolution2D(128, 256, 3, stride=1, pad=1),            conv3_2=L.Convolution2D(256, 256, 3, stride=1, pad=1),            conv3_3=L.Convolution2D(256, 256, 3, stride=1, pad=1),            conv4_1=L.Convolution2D(256, 512, 3, stride=1, pad=1),            conv4_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),            conv4_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),            conv5_1=L.Convolution2D(512, 512, 3, stride=1, pad=1),            conv5_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),            conv5_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),            fc6=L.Linear(25088, 4096),            fc7=L.Linear(4096, 4096),            fc8=L.Linear(4096, 1000)        )        self.train = False    def __call__(self, x, t):        h = F.relu(self.conv1_1(x))        h = F.relu(self.conv1_2(h))        h = F.max_pooling_2d(h, 2, stride=2)        h = F.relu(self.conv2_1(h))        h = F.relu(self.conv2_2(h))        h = F.max_pooling_2d(h, 2, stride=2)        h = F.relu(self.conv3_1(h))        h = F.relu(self.conv3_2(h))        h = F.relu(self.conv3_3(h))        h = F.max_pooling_2d(h, 2, stride=2)        h = F.relu(self.conv4_1(h))        h = F.relu(self.conv4_2(h))        h = F.relu(self.conv4_3(h))        h = F.max_pooling_2d(h, 2, stride=2)        h = F.relu(self.conv5_1(h))        h = F.relu(self.conv5_2(h))        h = F.relu(self.conv5_3(h))        h = F.max_pooling_2d(h, 2, stride=2)        h = F.dropout(F.relu(self.fc6(h)), train=self.train, ratio=0.5)        h = F.dropout(F.relu(self.fc7(h)), train=self.train, ratio=0.5)        h = self.fc8(h)        if self.train:            self.loss = F.softmax_cross_entropy(h, t)            self.acc = F.accuracy(h, t)            return self.loss        else:            self.pred = F.softmax(h)            return self.pred

深度学习常用的Data Set数据集和CNN Model总结

zf net

ZF-net

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