>- ** 本文为[365天深度学习训练营](小团体~第六波) 中的学习记录博客** >- ** 原作者：[K同学啊 | 接辅导、项目定制](https://mtyjkh.blog.csdn.net/)**

 我的环境：

语言环境：Python3.7

编译器：jupyter lab

深度学习环境：Pytorch

一、前期工作

1. 设置GPU

如果使用的是CPU可以忽略这步

import tensorflow as tf

gpus = tf.config.list_physical_devices("GPU")

if gpus:

gpu0 = gpus[0]

tf.config.experimental.set_memory_growth(gpu0, True)

tf.config.set_visible_devices([gpu0],"GPU")

2. 导入数据

import tensorflow as tf

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. 归一化

# 将像素的值标准化至0到1的区间内。

train_images, test_images = train_images / 255.0, test_images / 255.0

train_images.shape,test_images.shape,train_labels.shape,test_labels.shape

((50000, 32, 32, 3), (10000, 32, 32, 3), (50000, 1), (10000, 1))

4. 可视化

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer','dog', 'frog', 'horse', 'ship', 'truck']

plt.figure(figsize=(20,10))

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_names[train_labels[i][0]])

plt.show()

​

二、构建CNN网络

model = models.Sequential([

layers.Conv2D(32,(3,3),activation='relu',input_shape=(32,32,3)),

layers.MaxPooling2D((2,2)),

layers.Conv2D(64,(3,3),activation='relu'),

layers.MaxPooling2D((2,2)),

layers.Conv2D(64,(3,3),activation='relu'),

layers.Dropout(0.3),

layers.Flatten(),

layers.Dense(64,activation='relu'),

layers.Dense(10)

])

三、编译

model.compile(optimizer='adam',

loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

metrics=['accuracy'])

四、训练模型

history = model.fit(train_images, train_labels, epochs=10,

validation_data=(test_images, test_labels))

Train on 50000 samples, validate on 10000 samples

Epoch 1/12

50000/50000 [==============================] - 91s 2ms/sample - loss: 1.5793 - accuracy: 0.4201 - val_loss: 1.2517 - val_accuracy: 0.5556

Epoch 2/12

50000/50000 [==============================] - 74s 1ms/sample - loss: 1.2183 - accuracy: 0.5663 - val_loss: 1.0955 - val_accuracy: 0.6137

Epoch 3/12

50000/50000 [==============================] - 82s 2ms/sample - loss: 1.0833 - accuracy: 0.6190 - val_loss: 1.0281 - val_accuracy: 0.6345

Epoch 4/12

50000/50000 [==============================] - 70s 1ms/sample - loss: 0.9939 - accuracy: 0.6496 - val_loss: 0.9479 - val_accuracy: 0.6675

Epoch 5/12

50000/50000 [==============================] - 71s 1ms/sample - loss: 0.9087 - accuracy: 0.6801 - val_loss: 0.8683 - val_accuracy: 0.6989

Epoch 6/12

50000/50000 [==============================] - 46s 928us/sample - loss: 0.8534 - accuracy: 0.6996 - val_loss: 0.8908 - val_accuracy: 0.6935

Epoch 7/12

50000/50000 [==============================] - 41s 823us/sample - loss: 0.8180 - accuracy: 0.7115 - val_loss: 0.8270 - val_accuracy: 0.7138

Epoch 8/12

50000/50000 [==============================] - 43s 858us/sample - loss: 0.7790 - accuracy: 0.7270 - val_loss: 0.8048 - val_accuracy: 0.7198

Epoch 9/12

50000/50000 [==============================] - 42s 842us/sample - loss: 0.7536 - accuracy: 0.7346 - val_loss: 0.8498 - val_accuracy: 0.7085

Epoch 10/12

50000/50000 [==============================] - 41s 827us/sample - loss: 0.7250 - accuracy: 0.7439 - val_loss: 0.7828 - val_accuracy: 0.7333

Epoch 11/12

50000/50000 [==============================] - 42s 835us/sample - loss: 0.7000 - accuracy: 0.7543 - val_loss: 0.8128 - val_accuracy: 0.7200

Epoch 12/12

50000/50000 [==============================] - 41s 829us/sample - loss: 0.6784 - accuracy: 0.7610 - val_loss: 0.7695 - val_accuracy: 0.7377

五、预测

通过模型进行预测得到的是每一个类别的概率，数字越大该图片为该类别的可能性越大

plt.imshow(test_images[1])

import numpy as np

pre = model.predict(test_images)

print(class_names[np.argmax(pre[1])])

ship

六、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)

print(test_loss)

print(test_acc)

0.7694846038818359

0.7377

参考链接

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