一、前期工作
1、设置GPU
import tensorflow as tf gpus = tf.config.list_physical_devices("GPU") if gpus: gpu0 = gpus[0] #如果有多个GPU，仅使用第0个GPU tf.config.experimental.set_memory_growth(gpu0, True) #设置GPU显存用量按需使用 tf.config.set_visible_devices([gpu0],"GPU")
2、导入数据
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
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)), #卷积层1，卷积核3*3 layers.MaxPooling2D((2, 2)), #池化层1，2*2采样 layers.Conv2D(64, (3, 3), activation='relu'), #卷积层2，卷积核3*3 layers.MaxPooling2D((2, 2)), #池化层2，2*2采样 layers.Conv2D(64, (3, 3), activation='relu'), #卷积层3，卷积核3*3 layers.Flatten(), #Flatten层，连接卷积层与全连接层 layers.Dense(64, activation='relu'), #全连接层，特征进一步提取 layers.Dense(10) #输出层，输出预期结果 ]) model.summary() # 打印网络结构
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))
五、预测
plt.imshow(test_images[1234])
import numpy as np pre = model.predict(test_images) print(class_names[np.argmax(pre[1234])])
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)
313/313 - 1s - loss: 0.8676 - accuracy: 0.7040 - 1s/epoch - 4ms/step
print(test_acc)
0.7039999961853027
