Python与CNN的碰撞详解
作者:石奥博123
AlexNet介绍
AlexNet是2012年ISLVRC 2012(ImageNet Large Scale Visual RecognitionChallenge)竞赛的冠军网络,分类准确率由传统的 70%+提升到 80%+。它是由Hinton和他的学生Alex Krizhevsky设计的。也是在那年之后,深度学习开始迅速发展。
idea
(1)首次利用 GPU 进行网络加速训练。
(2)使用了 ReLU 激活函数,而不是传统的 Sigmoid 激活函数以及 Tanh 激活函数。
(3)使用了 LRN 局部响应归一化。
(4)在全连接层的前两层中使用了 Dropout 随机失活神经元操作,以减少过拟合。
过拟合
根本原因是特征维度过多,模型假设过于复杂,参数过多,训练数据过少,噪声过多,导致拟合的函数完美的预测训练集,但对新数据的测试集预测结果差。 过度的拟合了训练数据,而没有考虑到泛化能力。
解决方案
使用 Dropout 的方式在网络正向传播过程中随机失活一部分神经元。
卷积后矩阵尺寸计算公式
经卷积后的矩阵尺寸大小计算公式为: N = (W − F + 2P ) / S + 1
① 输入图片大小 W×W
② Filter大小 F×F
③ 步长 S
④ padding的像素数 P
AlexNet网络结构
layer_name | kernel_size | kernel_num | padding | stride |
Conv1 | 11 | 96 | [1, 2] | 4 |
Maxpool1 | 3 | None | 0 | 2 |
Conv2 | 5 | 256 | [2, 2] | 1 |
Maxpool2 | 3 | None | 0 | 2 |
Conv3 | 3 | 384 | [1, 1] | 1 |
Conv4 | 3 | 384 | [1, 1] | 1 |
Conv5 | 3 | 256 | [1, 1] | 1 |
Maxpool3 | 3 | None | 0 | 2 |
FC1 | 2048 | None | None | None |
FC2 | 2048 | None | None | None |
FC3 | 1000 | None | None | None |
model代码
from tensorflow.keras import layers, models, Model, Sequential def AlexNet_v1(im_height=224, im_width=224, num_classes=1000): # tensorflow中的tensor通道排序是NHWC input_image = layers.Input(shape=(im_height, im_width, 3), dtype="float32") # output(None, 224, 224, 3) x = layers.ZeroPadding2D(((1, 2), (1, 2)))(input_image) # output(None, 227, 227, 3) x = layers.Conv2D(48, kernel_size=11, strides=4, activation="relu")(x) # output(None, 55, 55, 48) x = layers.MaxPool2D(pool_size=3, strides=2)(x) # output(None, 27, 27, 48) x = layers.Conv2D(128, kernel_size=5, padding="same", activation="relu")(x) # output(None, 27, 27, 128) x = layers.MaxPool2D(pool_size=3, strides=2)(x) # output(None, 13, 13, 128) x = layers.Conv2D(192, kernel_size=3, padding="same", activation="relu")(x) # output(None, 13, 13, 192) x = layers.Conv2D(192, kernel_size=3, padding="same", activation="relu")(x) # output(None, 13, 13, 192) x = layers.Conv2D(128, kernel_size=3, padding="same", activation="relu")(x) # output(None, 13, 13, 128) x = layers.MaxPool2D(pool_size=3, strides=2)(x) # output(None, 6, 6, 128) x = layers.Flatten()(x) # output(None, 6*6*128) x = layers.Dropout(0.2)(x) x = layers.Dense(2048, activation="relu")(x) # output(None, 2048) x = layers.Dropout(0.2)(x) x = layers.Dense(2048, activation="relu")(x) # output(None, 2048) x = layers.Dense(num_classes)(x) # output(None, 5) predict = layers.Softmax()(x) model = models.Model(inputs=input_image, outputs=predict) return model class AlexNet_v2(Model): def __init__(self, num_classes=1000): super(AlexNet_v2, self).__init__() self.features = Sequential([ layers.ZeroPadding2D(((1, 2), (1, 2))), # output(None, 227, 227, 3) layers.Conv2D(48, kernel_size=11, strides=4, activation="relu"), # output(None, 55, 55, 48) layers.MaxPool2D(pool_size=3, strides=2), # output(None, 27, 27, 48) layers.Conv2D(128, kernel_size=5, padding="same", activation="relu"), # output(None, 27, 27, 128) layers.MaxPool2D(pool_size=3, strides=2), # output(None, 13, 13, 128) layers.Conv2D(192, kernel_size=3, padding="same", activation="relu"), # output(None, 13, 13, 192) layers.Conv2D(192, kernel_size=3, padding="same", activation="relu"), # output(None, 13, 13, 192) layers.Conv2D(128, kernel_size=3, padding="same", activation="relu"), # output(None, 13, 13, 128) layers.MaxPool2D(pool_size=3, strides=2)]) # output(None, 6, 6, 128) self.flatten = layers.Flatten() self.classifier = Sequential([ layers.Dropout(0.2), layers.Dense(1024, activation="relu"), # output(None, 2048) layers.Dropout(0.2), layers.Dense(128, activation="relu"), # output(None, 2048) layers.Dense(num_classes), # output(None, 5) layers.Softmax() ]) def call(self, inputs, **kwargs): x = self.features(inputs) x = self.flatten(x) x = self.classifier(x) return x
VGGNet介绍
VGG在2014年由牛津大学著名研究组VGG (Visual Geometry Group) 提出,斩获该年ImageNet竞赛中 Localization Task (定位 任务) 第一名 和 Classification Task (分类任务) 第二名。
idea
通过堆叠多个3x3的卷积核来替代大尺度卷积核 (减少所需参数) 论文中提到,可以通过堆叠两个3x3的卷 积核替代5x5的卷积核,堆叠三个3x3的 卷积核替代7x7的卷积核。
假设输入输出channel为C
7x7卷积核所需参数:7 x 7 x C x C = 49C^2
3x3卷积核所需参数:3 x 3 x C x C + 3 x 3 x C x C + 3 x 3 x C x C = 27C^2
感受野
在卷积神经网络中,决定某一层输出 结果中一个元素所对应的输入层的区域大 小,被称作感受野(receptive field)。通俗 的解释是,输出feature map上的一个单元 对应输入层上的区域大小。
感受野计算公式
F ( i ) =(F ( i + 1) -1) x Stride + Ksize
F(i)为第i层感受野, Stride为第i层的步距, Ksize为卷积核或采样核尺寸
VGGNet网络结构
model代码
from tensorflow.keras import layers, Model, Sequential #import sort_pool2d import tensorflow as tf CONV_KERNEL_INITIALIZER = { 'class_name': 'VarianceScaling', 'config': { 'scale': 2.0, 'mode': 'fan_out', 'distribution': 'truncated_normal' } } DENSE_KERNEL_INITIALIZER = { 'class_name': 'VarianceScaling', 'config': { 'scale': 1. / 3., 'mode': 'fan_out', 'distribution': 'uniform' } } def VGG(feature, im_height=224, im_width=224, num_classes=1000): # tensorflow中的tensor通道排序是NHWC input_image = layers.Input(shape=(im_height, im_width, 3), dtype="float32") x = feature(input_image) x = layers.Flatten()(x) x = layers.Dropout(rate=0.5)(x) x = layers.Dense(2048, activation='relu', kernel_initializer=DENSE_KERNEL_INITIALIZER)(x) x = layers.Dropout(rate=0.5)(x) x = layers.Dense(2048, activation='relu', kernel_initializer=DENSE_KERNEL_INITIALIZER)(x) x = layers.Dense(num_classes, kernel_initializer=DENSE_KERNEL_INITIALIZER)(x) output = layers.Softmax()(x) model = Model(inputs=input_image, outputs=output) return model def make_feature(cfg): feature_layers = [] for v in cfg: if v == "M": feature_layers.append(layers.MaxPool2D(pool_size=2, strides=2)) # elif v == "S": # feature_layers.append(layers.sort_pool2d(x)) else: conv2d = layers.Conv2D(v, kernel_size=3, padding="SAME", activation="relu", kernel_initializer=CONV_KERNEL_INITIALIZER) feature_layers.append(conv2d) return Sequential(feature_layers, name="feature") cfgs = { 'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], 'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], } def vgg(model_name="vgg16", im_height=224, im_width=224, num_classes=1000): assert model_name in cfgs.keys(), "not support model {}".format(model_name) cfg = cfgs[model_name] model = VGG(make_feature(cfg), im_height=im_height, im_width=im_width, num_classes=num_classes) return model
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