- 🍨本文为🔗365天深度学习训练营中的学习记录博客
- 🍖原作者：K同学啊
一、前置知识
1、CNN知识扫盲
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二、代码实现
1、准备工作
1.1.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") print(gpus)
2026-01-22 23:17:24.154547: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
1.2.导入数据
import os,PIL,pathlib import matplotlib.pyplot as plt import numpy as np from tensorflow import keras from tensorflow.keras import layers,models # 查看当前工作路径（确认路径是否正确） print("当前工作路径：", os.getcwd()) # 定义数据目录（建议用绝对路径更稳妥，相对路径依赖当前工作路径） data_dir = './data/day5_weather_photos/' data_dir = pathlib.Path(data_dir) # 获取数据目录下的所有子路径（文件夹或文件） data_paths = list(data_dir.glob('*')) # 提取每个子路径的名称（即类别名，自动适配系统分隔符） classeNames = [path.name for path in data_paths] classeNames
当前工作路径： /root/autodl-tmp/TensorFlow2 ['cloudy', 'rain', 'shine', 'sunrise']
1.3.查看数据
image_count = len(list(data_dir.glob('*/*.jpg'))) print("图片总数为：",image_count)
图片总数为： 1125
1.4.可视化图片
roses = list(data_dir.glob('sunrise/*.jpg')) PIL.Image.open(str(roses[0]))
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2、数据预处理
2.1.加载数据
- 使用image_dataset_from_directory方法将磁盘中的数据加载到tf.data.Dataset中
batch_size = 32 img_height = 180 img_width = 180 #训练集 train_ds = tf.keras.preprocessing.image_dataset_from_directory( data_dir, validation_split=0.2, subset="training", seed=123, image_size=(img_height, img_width), batch_size=batch_size)
Found 1125 files belonging to 4 classes. Using 900 files for training.
# 验证集 val_ds = tf.keras.preprocessing.image_dataset_from_directory( data_dir, validation_split=0.2, subset="validation", seed=123, image_size=(img_height, img_width), batch_size=batch_size)
Found 1125 files belonging to 4 classes. Using 225 files for validation.
class_names = train_ds.class_names print(class_names)
['cloudy', 'rain', 'shine', 'sunrise']
2.2.可视化数据
plt.figure(figsize=(20, 10)) for images, labels in train_ds.take(1): for i in range(20): ax = plt.subplot(5, 10, i + 1) plt.imshow(images[i].numpy().astype("uint8")) plt.title(class_names[labels[i]]) plt.axis("off")
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2.3.检查数据
- Image_batch是形状的张量（32,180,180,3）。这是一批形状180x180x3的32张图片（最后一维指的是彩色通道RGB）。
- Label_batch是形状（32，）的张量，这些标签对应32张图片
for image_batch, labels_batch in train_ds: print(image_batch.shape) print(labels_batch.shape) break
(32, 180, 180, 3) (32,)
2.4.配置数据集
AUTOTUNE = tf.data.AUTOTUNE train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE) val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
3、训练模型
3.1.构建CNN网络
num_classes = 4 model = models.Sequential([ layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)), layers.Conv2D(16, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)), # 卷积层1，卷积核3*3 layers.AveragePooling2D((2, 2)), # 池化层1，2*2采样 layers.Conv2D(32, (3, 3), activation='relu'), # 卷积层2，卷积核3*3 layers.AveragePooling2D((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.Dropout(0.3), # 让神经元以一定的概率停止工作，防止过拟合，提高模型的泛化能力。 layers.Flatten(), # Flatten层，连接卷积层与全连接层 layers.Dense(128, activation='relu'), # 全连接层，特征进一步提取 layers.Dense(num_classes) # 输出层，输出预期结果 ]) model.summary() # 打印网络结构
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= rescaling (Rescaling) (None, 180, 180, 3) 0 conv2d (Conv2D) (None, 178, 178, 16) 448 average_pooling2d (AverageP (None, 89, 89, 16) 0 ooling2D) conv2d_1 (Conv2D) (None, 87, 87, 32) 4640 average_pooling2d_1 (Averag (None, 43, 43, 32) 0 ePooling2D) conv2d_2 (Conv2D) (None, 41, 41, 64) 18496 dropout (Dropout) (None, 41, 41, 64) 0 flatten (Flatten) (None, 107584) 0 dense (Dense) (None, 128) 13770880 dense_1 (Dense) (None, 4) 516 ================================================================= Total params: 13,794,980 Trainable params: 13,794,980 Non-trainable params: 0 _________________________________________________________________
3.2.编译模型
# 设置优化器 opt = tf.keras.optimizers.Adam(learning_rate=0.001) model.compile(optimizer=opt, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
3.3.训练模型
epochs = 10 history = model.fit( train_ds, validation_data=val_ds, epochs=epochs )
Epoch 1/10 2026-01-22 23:33:53.565037: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8101 2026-01-22 23:33:57.017558: I tensorflow/stream_executor/cuda/cuda_blas.cc:1786] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once. 29/29 [==============================] - 8s 33ms/step - loss: 1.3037 - accuracy: 0.5789 - val_loss: 0.5884 - val_accuracy: 0.7689 Epoch 2/10 29/29 [==============================] - 0s 13ms/step - loss: 0.4996 - accuracy: 0.8111 - val_loss: 0.5592 - val_accuracy: 0.7778 Epoch 3/10 29/29 [==============================] - 0s 12ms/step - loss: 0.4080 - accuracy: 0.8500 - val_loss: 0.5595 - val_accuracy: 0.7911 Epoch 4/10 29/29 [==============================] - 0s 13ms/step - loss: 0.3297 - accuracy: 0.8711 - val_loss: 0.4933 - val_accuracy: 0.8178 Epoch 5/10 29/29 [==============================] - 0s 13ms/step - loss: 0.2496 - accuracy: 0.9067 - val_loss: 0.7107 - val_accuracy: 0.7556 Epoch 6/10 29/29 [==============================] - 0s 13ms/step - loss: 0.2371 - accuracy: 0.9044 - val_loss: 0.4809 - val_accuracy: 0.8178 Epoch 7/10 29/29 [==============================] - 0s 13ms/step - loss: 0.1588 - accuracy: 0.9433 - val_loss: 0.4546 - val_accuracy: 0.8533 Epoch 8/10 29/29 [==============================] - 0s 13ms/step - loss: 0.1706 - accuracy: 0.9367 - val_loss: 0.4848 - val_accuracy: 0.8133 Epoch 9/10 29/29 [==============================] - 0s 14ms/step - loss: 0.1061 - accuracy: 0.9600 - val_loss: 0.7095 - val_accuracy: 0.7689 Epoch 10/10 29/29 [==============================] - 0s 13ms/step - loss: 0.1328 - accuracy: 0.9578 - val_loss: 0.4769 - val_accuracy: 0.8622
4、模型评估
from datetime import datetime current_time = datetime.now() # 获取当前时间 acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs_range = range(epochs) plt.figure(figsize=(12, 4)) plt.subplot(1, 2, 1) plt.plot(epochs_range, acc, label='Training Accuracy') plt.plot(epochs_range, val_acc, label='Validation Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效 plt.subplot(1, 2, 2) plt.plot(epochs_range, loss, label='Training Loss') plt.plot(epochs_range, val_loss, label='Validation Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show()
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