文章目录
- 前期工作
-
- [1. 设置GPU(如果使用的是CPU可以忽略这步)](#1. 设置GPU(如果使用的是CPU可以忽略这步))
- [2. 导入数据](#2. 导入数据)
- [3. 查看数据](#3. 查看数据)
- 二、数据预处理
-
- [1. 加载数据](#1. 加载数据)
- [2. 可视化数据](#2. 可视化数据)
- [3. 再次检查数据](#3. 再次检查数据)
- [4. 配置数据集](#4. 配置数据集)
- 三、构建CNN网络
- 四、编译
- 五、训练模型
- 六、模型评估
前期工作
1. 设置GPU(如果使用的是CPU可以忽略这步)
我的环境:
- 语言环境:Python3.6.5
- 编译器:jupyter notebook
- 深度学习环境:TensorFlow2.4.1
python
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
python
import matplotlib.pyplot as plt
import os,PIL
# 设置随机种子尽可能使结果可以重现
import numpy as np
np.random.seed(1)
# 设置随机种子尽可能使结果可以重现
import tensorflow as tf
tf.random.set_seed(1)
from tensorflow import keras
from tensorflow.keras import layers,models
import pathlib
data_dir = "weather_photos/"
data_dir = pathlib.Path(data_dir)
3. 查看数据
数据集一共分为cloudy
、rain
、shine
、sunrise
四类,分别存放于weather_photos
文件夹中以各自名字命名的子文件夹中。
python
image_count = len(list(data_dir.glob('*/*.jpg')))
print("图片总数为:",image_count)
python
roses = list(data_dir.glob('sunrise/*.jpg'))
PIL.Image.open(str(roses[0]))
二、数据预处理
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
python
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.
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.
我们可以通过class_names输出数据集的标签。标签将按字母顺序对应于目录名称。
python
class_names = train_ds.class_names
print(class_names)
['cloudy', 'rain', 'shine', 'sunrise']
2. 可视化数据
python
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")
3. 再次检查数据
python
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
(32, 180, 180, 3)
(32,)
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
4. 配置数据集
python
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
三、构建CNN网络
卷积神经网络(CNN)的输入是张量 (Tensor) 形式的 (image_height, image_width, color_channels)
,包含了图像高度、宽度及颜色信息。不需要输入batch size
。color_channels 为 (R,G,B) 分别对应 RGB 的三个颜色通道(color channel)。在此示例中,我们的 CNN 输入,fashion_mnist 数据集中的图片,形状是 (28, 28, 1)
即灰度图像。我们需要在声明第一层时将形状赋值给参数input_shape
。
python
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),
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 (AveragePo (None, 89, 89, 16) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 87, 87, 32) 4640
_________________________________________________________________
average_pooling2d_1 (Average (None, 43, 43, 32) 0
_________________________________________________________________
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, 5) 645
=================================================================
Total params: 13,795,109
Trainable params: 13,795,109
Non-trainable params: 0
_________________________________________________________________
四、编译
- 在准备对模型进行训练之前,还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的:
-
损失函数(loss):用于衡量模型在训练期间的准确率。
-
优化器(optimizer):决定模型如何根据其看到的数据和自身的损失函数进行更新。
-
指标(metrics):用于监控训练和测试步骤。以下示例使用了准确率,即被正确分类的图像的比率。
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'])
-
五、训练模型
epochs = 10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
python
Epoch 1/10
29/29 [==============================] - 6s 58ms/step - loss: 1.5865 - accuracy: 0.4463 - val_loss: 0.5837 - val_accuracy: 0.7689
Epoch 2/10
29/29 [==============================] - 0s 12ms/step - loss: 0.5289 - accuracy: 0.8295 - val_loss: 0.5405 - val_accuracy: 0.8133
Epoch 3/10
29/29 [==============================] - 0s 12ms/step - loss: 0.2930 - accuracy: 0.8967 - val_loss: 0.5364 - val_accuracy: 0.8000
Epoch 4/10
29/29 [==============================] - 0s 12ms/step - loss: 0.2742 - accuracy: 0.9074 - val_loss: 0.4034 - val_accuracy: 0.8267
Epoch 5/10
29/29 [==============================] - 0s 11ms/step - loss: 0.1952 - accuracy: 0.9383 - val_loss: 0.3874 - val_accuracy: 0.8844
Epoch 6/10
29/29 [==============================] - 0s 11ms/step - loss: 0.1592 - accuracy: 0.9468 - val_loss: 0.3680 - val_accuracy: 0.8756
Epoch 7/10
29/29 [==============================] - 0s 12ms/step - loss: 0.0836 - accuracy: 0.9755 - val_loss: 0.3429 - val_accuracy: 0.8756
Epoch 8/10
29/29 [==============================] - 0s 12ms/step - loss: 0.0943 - accuracy: 0.9692 - val_loss: 0.3836 - val_accuracy: 0.9067
Epoch 9/10
29/29 [==============================] - 0s 12ms/step - loss: 0.0344 - accuracy: 0.9909 - val_loss: 0.3578 - val_accuracy: 0.9067
Epoch 10/10
29/29 [==============================] - 0s 11ms/step - loss: 0.0950 - accuracy: 0.9708 - val_loss: 0.4710 - val_accuracy: 0.8356
六、模型评估
python
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.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()