- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍦 参考文章地址： 🔗深度学习100例-卷积神经网络（CNN）彩色图片分类 | 第2天
- 🍖 作者：K同学啊
一、环境配置
python3.6.13，TensorFlow2.4.0-gpu，cuda 11.0，cudnn8.0.5
二、前期准备
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
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
# 导入cifar10数据，依次分别为训练集图片、训练集标签、测试集图片、测试集标签
(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
print(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.可视化图片
# 10个类别
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer','dog', 'frog', 'horse', 'ship', 'truck']
# 进行图像大小为20宽、10长的绘图(单位为英寸inch)
plt.figure(figsize=(20,10))
for i in range(20):
# 将整个figure分成2行10列，绘制第i+1个子图。
plt.subplot(2,10,i+1)
# 设置不显示x轴刻度
plt.xticks([])
# 设置不显示y轴刻度
plt.yticks([])
# 设置不显示子图网格线
plt.grid(False)
# 将数组的值以图片的形式展示出来，cmap参数用于设置颜色图谱，"plt.cm.binary"为matplotlib.cm中的色表(具体内容可网上搜索)
plt.imshow(train_images[i], cmap=plt.cm.binary)
# 设置x轴标签显示为图片对应的标签
plt.xlabel(train_labels[i])
# 显示图片
plt.show()
myplot.png
二、构建CNN网络模型
#构建3层卷积2层池化1层全连接层
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)), # 卷积层1，卷积核3*3 输入宽高32，通道3为彩色图片
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
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 30, 30, 32) 896
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 13, 13, 64) 18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 4, 4, 64) 36928
_________________________________________________________________
flatten (Flatten) (None, 1024) 0
_________________________________________________________________
dense (Dense) (None, 64) 65600
_________________________________________________________________
dense_1 (Dense) (None, 10) 650
=================================================================
Total params: 122,570
Trainable params: 122,570
Non-trainable params: 0
_________________________________________________________________
三、编译模型
"""
这里设置优化器、损失函数以及metrics
"""
# model.compile()方法用于在配置训练方法时，告知训练时用的优化器、损失函数和准确率评测标准
model.compile(
# 设置优化器为Adam优化器
optimizer='adam',
# 设置损失函数为交叉熵损失函数（tf.keras.losses.SparseCategoricalCrossentropy()）
# from_logits为True时，会将y_pred转化为概率（用softmax），否则不进行转换，通常情况下用True结果更稳定
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
# 设置性能指标列表，将在模型训练时监控列表中的指标
metrics=['accuracy'])
四、训练模型
"""
这里设置输入训练数据集（图片及标签）、验证数据集（图片及标签）以及迭代次数epochs
"""
history = model.fit(
# 输入训练集图片
train_images,
# 输入训练集标签
train_labels,
# 设置10个epoch，每一个epoch都将会把所有的数据输入模型完成一次训练。
epochs=10,
# 设置验证集
validation_data=(test_images, test_labels))
"""
训练了10个epoch 最后精度达到0.7103
"""
Epoch 1/10
1563/1563 [==============================] - 8s 4ms/step - loss: 1.7340 - accuracy: 0.3617 - val_loss: 1.2603 - val_accuracy: 0.5387
Epoch 2/10
1563/1563 [==============================] - 5s 3ms/step - loss: 1.1849 - accuracy: 0.5786 - val_loss: 1.1559 - val_accuracy: 0.6003
Epoch 3/10
1563/1563 [==============================] - 5s 3ms/step - loss: 0.9992 - accuracy: 0.6472 - val_loss: 0.9702 - val_accuracy: 0.6639
Epoch 4/10
1563/1563 [==============================] - 6s 4ms/step - loss: 0.8702 - accuracy: 0.6899 - val_loss: 0.9143 - val_accuracy: 0.6812
Epoch 5/10
1563/1563 [==============================] - 6s 4ms/step - loss: 0.7830 - accuracy: 0.7253 - val_loss: 0.9222 - val_accuracy: 0.6837
Epoch 6/10
1563/1563 [==============================] - 6s 4ms/step - loss: 0.7226 - accuracy: 0.7430 - val_loss: 0.8695 - val_accuracy: 0.7051
Epoch 7/10
1563/1563 [==============================] - 5s 3ms/step - loss: 0.6677 - accuracy: 0.7649 - val_loss: 0.8878 - val_accuracy: 0.7009
Epoch 8/10
1563/1563 [==============================] - 5s 3ms/step - loss: 0.6139 - accuracy: 0.7830 - val_loss: 0.8839 - val_accuracy: 0.7098
Epoch 9/10
1563/1563 [==============================] - 6s 4ms/step - loss: 0.5656 - accuracy: 0.7981 - val_loss: 0.8782 - val_accuracy: 0.7149
Epoch 10/10
1563/1563 [==============================] - 5s 4ms/step - loss: 0.5224 - accuracy: 0.8131 - val_loss: 0.9054 - val_accuracy: 0.7103
五、预测
通过模型进行预测得到的每一个类别的概率，数字越大该图片为该类别的可能性越大
plt.imshow(test_images[1])
plt.show() #显示要预测的图片
import numpy as np
#预测图片
pre = model.predict(test_images)
print(class_names[np.argmax(pre[1])])
ship.png
"""
预测结果 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.xlim([1, 10]) #这里x轴和y轴的范围都需要声明，不然图像会有一个默认的范围，会导致图片变形
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)
"""
313/313 - 0s - loss: 0.9054 - accuracy: 0.7103
"""
print(test_acc)
"""
0.7103000283241272
"""
Accuracy.png
