深度学习实战第二周--彩色图片的识别

>- 🍨 本文为[🔗365天深度学习训练营](https://mp.weixin.qq.com/s/xLjALoOD8HPZcH563En8bQ) 中的学习记录博客
>- 参考文章：365天深度学习训练营-第5周：运动鞋品牌识别（训练营内部成员可读）
>- 🍖 原作者：[K同学啊](https://mp.weixin.qq.com/s/xLjALoOD8HPZcH563En8bQ)

本周识别彩色图片，采取卷积神经网络搭建网络，数据集数量适中，但评价指标的数值值得提高

1.设置设备，本文采用自己电脑自带的GPU1080Ti

#设置GPU
import tensorflow as tf
gpus=tf.config.list_physical_devices("GPU")
if gpus:gpus0=gpus[0]#如果有多个GPU，则仅仅使用第0个GPUtf.config.experimental.set_memory_growth(gpu0,True)#设置GPUS显存按需使用tf.config.set_visible_devices([gpus0],"GPUS")

2.数据集导入

from tensorflow.keras import datasets,layers,models
import matplotlib.pyplot as plt
(train_images,train_labels),(test_images,test_labels)=datasets.cifar10.load_data()

3.数据标准化

由于像素点最大值为155，最小值为0，这里直接让他们除以255.0即可完成数据标准化

#像素标准化
train_images,test_images=train_images/255.0,test_images/255.0
train_images.shape,test_images.shape,train_labels.shape,test_labels.shape

4.数据可视化

class_name=['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']
plt.figure(figsize=(20,20))
for i in range(20):plt.subplot(5,10,i+1)plt.xticks([])plt.yticks([])plt.grid(False)plt.imshow(train_images[i],cmap=plt.cm.binary)plt.xlabel(class_name[train_labels[i][0]])

5.模型搭建

#搭建模型
model=models.Sequential([layers.Conv2D(32,(3,3),activation='relu',input_shape=(32,32,3)),layers.MaxPool2D((2,2)),layers.Conv2D(64,(3,3),activation='relu'),layers.MaxPooling2D((2,2)),layers.Conv2D(64,(3,3),activation='relu'),layers.Flatten(),layers.Dense(64,activation='relu'),layers.Dense(10)
]
)

6.模型编译

model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])

7.模型训练

history=model.fit(train_images,train_labels,epochs=20,validation_data=(test_images,test_labels))

Epoch 1/20
1563/1563 [==============================] - 29s 17ms/step - loss: 1.5689 - accuracy: 0.4253 - val_loss: 1.2995 - val_accuracy: 0.5338
Epoch 2/20
1563/1563 [==============================] - 28s 18ms/step - loss: 1.2116 - accuracy: 0.5707 - val_loss: 1.1985 - val_accuracy: 0.5798
Epoch 3/20
1563/1563 [==============================] - 27s 17ms/step - loss: 1.0499 - accuracy: 0.6286 - val_loss: 1.0068 - val_accuracy: 0.6500
Epoch 4/20
1563/1563 [==============================] - 26s 17ms/step - loss: 0.9501 - accuracy: 0.6669 - val_loss: 0.9589 - val_accuracy: 0.6664
Epoch 5/20
1563/1563 [==============================] - 27s 17ms/step - loss: 0.8733 - accuracy: 0.6961 - val_loss: 0.9424 - val_accuracy: 0.6728
Epoch 6/20
1563/1563 [==============================] - 26s 17ms/step - loss: 0.8169 - accuracy: 0.7130 - val_loss: 0.9840 - val_accuracy: 0.6608
Epoch 7/20
1563/1563 [==============================] - 27s 18ms/step - loss: 0.7566 - accuracy: 0.7348 - val_loss: 0.8962 - val_accuracy: 0.6925
Epoch 8/20
1563/1563 [==============================] - 28s 18ms/step - loss: 0.7127 - accuracy: 0.7510 - val_loss: 0.9304 - val_accuracy: 0.6812
Epoch 9/20
1563/1563 [==============================] - 27s 17ms/step - loss: 0.6721 - accuracy: 0.7640 - val_loss: 0.8733 - val_accuracy: 0.7022
Epoch 10/20
1563/1563 [==============================] - 27s 17ms/step - loss: 0.6339 - accuracy: 0.7771 - val_loss: 0.9032 - val_accuracy: 0.6993
Epoch 11/20
1563/1563 [==============================] - 27s 17ms/step - loss: 0.6008 - accuracy: 0.7880 - val_loss: 0.9038 - val_accuracy: 0.7067
Epoch 12/20
1563/1563 [==============================] - 27s 17ms/step - loss: 0.5655 - accuracy: 0.7982 - val_loss: 0.8896 - val_accuracy: 0.7067
Epoch 13/20
1563/1563 [==============================] - 27s 17ms/step - loss: 0.5337 - accuracy: 0.8115 - val_loss: 0.9292 - val_accuracy: 0.6994
Epoch 14/20
1563/1563 [==============================] - 26s 17ms/step - loss: 0.5067 - accuracy: 0.8173 - val_loss: 0.9405 - val_accuracy: 0.7077
Epoch 15/20
1563/1563 [==============================] - 28s 18ms/step - loss: 0.4762 - accuracy: 0.8319 - val_loss: 0.9913 - val_accuracy: 0.6900
Epoch 16/20
1563/1563 [==============================] - 27s 17ms/step - loss: 0.4546 - accuracy: 0.8382 - val_loss: 1.0190 - val_accuracy: 0.7094
Epoch 17/20
1563/1563 [==============================] - 26s 17ms/step - loss: 0.4279 - accuracy: 0.8476 - val_loss: 1.0170 - val_accuracy: 0.6988
Epoch 18/20
1563/1563 [==============================] - 27s 17ms/step - loss: 0.4046 - accuracy: 0.8553 - val_loss: 1.0816 - val_accuracy: 0.6956
Epoch 19/20
1563/1563 [==============================] - 26s 17ms/step - loss: 0.3849 - accuracy: 0.8621 - val_loss: 1.1430 - val_accuracy: 0.6888
Epoch 20/20
1563/1563 [==============================] - 25s 16ms/step - loss: 0.3599 - accuracy: 0.8723 - val_loss: 1.1654 - val_accuracy: 0.6961

8.可视化测试集

plt.imshow(test_images[1])

模型预测:

import numpy as np
pre=model.predict(test_images)
print(class_name[np.argmax(pre[1])])

模型评估:

#模型评估
import matplotlib.pyplot as plt
plt.plot(history.history['accuracy'],label='accuracy')
plt.plot(history.history['val_accuracy'],label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5,1])
plt.legend(loc="lower right")
plt.show()
test_loss,test_acc=model.evaluate(test_images,test_labels,verbose=2)