这也太强了吧，我用它识别交通标志，准确率竟然高达97.9%
Posted K同学啊
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篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了这也太强了吧，我用它识别交通标志，准确率竟然高达97.9%相关的知识，希望对你有一定的参考价值。
🔱 大家好，我是 👉 K同学啊，本文选自：《深度学习100例》， 该系列将持续更新 欢迎 点赞👍、收藏⭐、关注👀
文章目录
一、前期工作
我的环境：
- 语言环境：Python3.6.5
- 编译器：jupyter notebook
- 深度学习环境：TensorFlow2.4.1
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🚀 来自专栏：《深度学习100例》
1. 设置GPU
如果使用的是CPU可以注释掉这部分的代码。
import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")
if gpus:
tf.config.experimental.set_memory_growth(gpus[0], True) #设置GPU显存用量按需使用
tf.config.set_visible_devices([gpus[0]],"GPU")
2. 导入数据
import matplotlib.pyplot as plt
# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
import os,PIL,pathlib
# 设置随机种子尽可能使结果可以重现
import pandas as pd
import numpy as np
np.random.seed(1)
# 设置随机种子尽可能使结果可以重现
import tensorflow as tf
tf.random.set_seed(1)
from tensorflow import keras
from tensorflow.keras import layers,models
# 导入图片数据
pictures_dir = "D:/jupyter notebook/DL-100-days/datasets/14_traffic_sign/images"
pictures_dir = pathlib.Path(pictures_dir)
# 导入训练数据的图片路径名及标签
train = pd.read_csv("D:/jupyter notebook/DL-100-days/datasets/14_traffic_sign/annotations.csv")
3. 查看数据
image_count = len(list(pictures_dir.glob('*.png')))
print("图片总数为：",image_count)
图片总数为： 5998
train.head()
| file_name | category | |
|---|---|---|
| 0 | 000_0001.png | 0 |
| 1 | 000_0002.png | 0 |
| 2 | 000_0003.png | 0 |
| 3 | 000_0010.png | 0 |
| 4 | 000_0011.png | 0 |
二、构建一个tf.data.Dataset
1.加载数据
数据集中已经划分好了测试集与训练集，这次只需要进行分别加载就好了。
def preprocess_image(image):
image = tf.image.decode_jpeg(image, channels=3) # 编码解码处理
image = tf.image.resize(image, [299,299]) # 图片调整
return image/255.0 # 归一化处理
def load_and_preprocess_image(path):
image = tf.io.read_file(path)
return preprocess_image(image)
AUTOTUNE = tf.data.experimental.AUTOTUNE
common_paths = "D:/jupyter notebook/DL-100-days/datasets/14_traffic_sign/images/"
# 训练数据的标签
train_image_label = [i for i in train["category"]]
train_label_ds = tf.data.Dataset.from_tensor_slices(train_image_label)
# 训练数据的路径
train_image_paths = [ common_paths+i for i in train["file_name"]]
# 加载图片路径
train_path_ds = tf.data.Dataset.from_tensor_slices(train_image_paths)
# 加载图片数据
train_image_ds = train_path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)
# 将图片与标签进行对应打包
image_label_ds = tf.data.Dataset.zip((train_image_ds, train_label_ds))
image_label_ds
<ZipDataset shapes: ((299, 299, 3), ()), types: (tf.float32, tf.int32)>
plt.figure(figsize=(20,4))
for i in range(20):
plt.subplot(2,10,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
# 显示图片
images = plt.imread(train_image_paths[i])
plt.imshow(images)
# 显示标签
plt.xlabel(train_image_label[i])
plt.show()
2. 配置数据集
- shuffle() ： 打乱数据，关于此函数的详细介绍可以参考：https://zhuanlan.zhihu.com/p/42417456
- prefetch() ：预取数据，加速运行，其详细介绍可以参考我前两篇文章，里面都有讲解。
- cache() ：将数据集缓存到内存当中，加速运行
我的电脑GPU配置是 NVIDIA GeForce RTX 3080 ,BATCH_SIZE = 8显存就会报OOM显存不足的错误，在BATCH_SIZE = 6时运行正常，每一个epoch时间约为130秒，大家请根据自己的电脑配置动态调整 BATCH_SIZE。采用CPU训练的同学，我建议将 BATCH_SIZE 调整为2或者1。
BATCH_SIZE = 6
# 将训练数据集拆分成训练集与验证集
train_ds = image_label_ds.take(5000).shuffle(1000) # 前1500个batch
val_ds = image_label_ds.skip(5000).shuffle(1000) # 跳过前1500，选取后面的
train_ds = train_ds.batch(BATCH_SIZE)
train_ds = train_ds.prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.batch(BATCH_SIZE)
val_ds = val_ds.prefetch(buffer_size=AUTOTUNE)
val_ds
<PrefetchDataset shapes: ((None, 299, 299, 3), (None,)), types: (tf.float32, tf.int32)>
# 查看数据 shape 进行检查
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
(6, 299, 299, 3)
(6,)
# 再次查看数据，确认是否被打乱
plt.figure(figsize=(8,8))
for images, labels in train_ds.take(1):
for i in range(6):
ax = plt.subplot(4, 3, i + 1)
plt.imshow(images[i])
plt.title(labels[i].numpy()) #使用.numpy()将张量转换为 NumPy 数组
plt.axis("off")
三、Inception-ResNet-v2介绍
关于Inception系列的介绍可以见：https://baike.baidu.com/item/Inception%E7%BB%93%E6%9E%84 ，个人认为这些在现阶段只需要将模型走一遍（学会搭建），后期如果需要的话，可以再回头来进行详细研究。
这个模型相比之前写过的一些模型可能较为复杂一些，先放一张图整体感受一下它
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-FIUSdpHG-1625128184125)(pictures/14-1.png)]
关于上面卷积的计算还比较蒙的同学可以参考我这篇文章哈：卷积的计算
四、构建Inception-ResNet-v2网络
1.自己搭建
下面是本文的重点 InceptionResNetV2 网络模型的构建，可以试着按照上面的图自己构建一下 InceptionResNetV2，这部分我主要是参考官网的构建过程，将其单独拎了出来。
from tensorflow.keras import layers, models, Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, Dense, Flatten, Dropout,BatchNormalization,Activation
from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D, Concatenate, Lambda,GlobalAveragePooling2D
from tensorflow.keras import backend as K
def conv2d_bn(x,filters,kernel_size,strides=1,padding='same',activation='relu',use_bias=False,name=None):
x = Conv2D(filters,kernel_size,strides=strides,padding=padding,use_bias=use_bias,name=name)(x)
if not use_bias:
bn_axis = 1 if K.image_data_format() == 'channels_first' else 3
bn_name = None if name is None else name + '_bn'
x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
if activation is not None:
ac_name = None if name is None else name + '_ac'
x = Activation(activation, name=ac_name)(x)
return x
def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):
if block_type == 'block35':
branch_0 = conv2d_bn(x, 32, 1)
branch_1 = conv2d_bn(x, 32, 1)
branch_1 = conv2d_bn(branch_1, 32, 3)
branch_2 = conv2d_bn(x, 32, 1)
branch_2 = conv2d_bn(branch_2, 48, 3)
branch_2 = conv2d_bn(branch_2, 64, 3)
branches = [branch_0, branch_1, branch_2]
elif block_type == 'block17':
branch_0 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(x, 128, 1)
branch_1 = conv2d_bn(branch_1, 160, [1, 7])
branch_1 = conv2d_bn(branch_1, 192, [7, 1])
branches = [branch_0, branch_1]
elif block_type == 'block8':
branch_0 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(branch_1, 224, [1, 3])
branch_1 = conv2d_bn(branch_1, 256, [3, 1])
branches = [branch_0, branch_1]
else:
raise ValueError('Unknown Inception-ResNet block type. '
'Expects "block35", "block17" or "block8", '
'but got: ' + str(block_type))
block_name = block_type + '_' + str(block_idx)
mixed = Concatenate(name=block_name + '_mixed')(branches)
up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,use_bias=True,name=block_name + '_conv')
x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
output_shape=K.int_shape(x)[1:],
arguments={'scale': scale},
name=block_name)([x, up])
if activation is not None:
x = Activation(activation, name=block_name + '_ac')(x)
return x
def InceptionResNetV2(input_shape=[299,299,3],classes=1000):
inputs = Input(shape=input_shape)
# Stem block
x = conv2d_bn(inputs, 32, 3, strides=2, padding='valid')
x = conv2d_bn(x, 32, 3, padding='valid')
x = conv2d_bn(x, 64, 3)
x = MaxPooling2D(3, strides=2)(x)
x = conv2d_bn(x, 80, 1, padding='valid')
x = conv2d_bn(x, 192, 3, padding='valid')
x = MaxPooling2D(3, strides=2)(x)
# Mixed 5b (Inception-A block)
branch_0 = conv2d_bn(x, 96, 1)
branch_1 = conv2d_bn(x, 48, 1)
branch_1 = conv2d_bn(branch_1, 64, 5)
branch_2 = conv2d_bn(x, 64, 1)
branch_2 = conv2d_bn(branch_2, 96, 3)
branch_2 = conv2d_bn(branch_2, 96, 3)
branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 64, 1)
branches = [branch_0, branch_1, branch_2, branch_pool]
x = Concatenate(name='mixed_5b')(branches)
# 10次 Inception-ResNet-A block
for block_idx in range(1, 11):
x = inception_resnet_block(x, scale=0.17, block_type='block35', block_idx=block_idx)
# Reduction-A block
branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid')
branch_1 = conv2d_bn(x, 256, 1)
branch_1 = conv2d_bn(branch_1, 256, 3)
branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid')
branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
branches = [branch_0, branch_1, branch_pool]
x = Concatenate(name='mixed_6a')(branches)
# 20次 Inception-ResNet-B block
for block_idx in range(1, 21):
x = inception_resnet_block(x, scale=0.1, block_type='block17', block_idx=block_idx)
# Reduction-B block
branch_0 = conv2d_bn(x, 256, 1)
branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid')
branch_1 = conv2d_bn(x, 256, 1)
branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid')
branch_2 = conv2d_bn(x, 256, 1)
branch_2 = conv2d_bn(branch_2, 288, 3)
branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid')
branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
branches = [branch_0, branch_1, branch_2, branch_pool]
x = Concatenate(name='mixed_7a')(branches)
# 10次 Inception-ResNet-C block
for block_idx in range(1, 10):
x = inception_resnet_block(x, scale=0.2, block_type='block8', block_idx=block_idx)
x = inception_resnet_block(x, scale=1., activation=None, block_type='block8', block_idx=10)
x = conv2d_bn(x, 1536, 1, name='conv_7b')
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dense(classes, activation='softmax', name='predictions')(x)
# 创建模型
model = Model(inputs, x, name='inception_resnet_v2')
return model
model = InceptionResNetV2([299,299,3],58)
model.summary()
Model: "inception_resnet_v2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 299, 299, 3) 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 149, 149, 32) 864 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 149, 149, 32) 96 conv2d[0][0]
__________________________________________________________________________________________________
....................
__________________________________________________________________________________________________
conv_7b (Conv2D) (None, 8, 8, 1536) 3194880 block8_10[0][0]
__________________________________________________________________________________________________
conv_7b_bn (BatchNormalization) (None, 8, 8, 1536) 4608 conv_7b[0][0]
__________________________________________________________________________________________________
conv_7b_ac (Activation) (None, 8, 8, 1536) 0 conv_7b_bn[0][0]
__________________________________________________________________________________________________
avg_pool (GlobalAveragePooling2 (None, 1536) 0 conv_7b_ac[0][0]
__________________________________________________________________________________________________
predictions (Dense) (None, 58) 89146 avg_pool[0][0]
==================================================================================================
Total params: 54,425,882
Trainable params: 54,365,338
Non-trainable params: 60,544
__________________________________________________________________________________________________
2.官方模型
# import tensorflow as tf
# # 如果使用官方模型需要将图片shape调整为 [299,299,3]，目前图片的shape是 [150,150,3]
# model = tf.keras.applications.inception_resnet_v2.InceptionResNetV2()
# model.summary()
五、设置动态学习率
这里先罗列一下学习率大与学习率小的优缺点。
-
学习率大
- 优点：
1、加快学习速率。
2、有助于跳出局部最优值。 - 缺点：
1、导致模型训练不收敛。
2、单单使用大学习率容易导致模型不精确。
- 优点：
-
900万！！！！！！！！这也太强了吧！！！我的老天！！！！！！！！！！
Python深度学习自然语言处理工具Stanza试用！这也太强大了吧！
哇塞！GitHub 上这些 Java repo 太强了吧，看完哪个都变大佬！