一、前期工作
1.设置GPU¶
import tensorflow as tf gpus = tf.config.list_physical_devices("GPU") if gpus: tf.config.experimental.set_memory_growth(gpus[0], True) #设置GPU显存用量按需使用 tf.config.set_visible_devices([gpus[0]],"GPU")
2. 导入数据
from tensorflow import keras from tensorflow.keras import layers,models import numpy as np import matplotlib.pyplot as plt import os,PIL,pathlib data_dir = "./49-data/" data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*.png'))) print("图片总数为：",image_count)
图片总数为： 1200
二、数据预处理
1. 加载数据
batch_size = 32 img_height = 224 img_width = 224
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 1200 files belonging to 4 classes.
Using 960 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 1200 files belonging to 4 classes.
Using 240 files for validation.
class_names = train_ds.class_names print(class_names)
['Dark', 'Green', 'Light', 'Medium']
2. 可视化数据
plt.figure(figsize=(10, 4)) # 图形的宽为10高为5 for images, labels in train_ds.take(1): for i in range(10): ax = plt.subplot(2, 5, i + 1) plt.imshow(images[i].numpy().astype("uint8")) plt.title(class_names[labels[i]]) plt.axis("off")
for image_batch, labels_batch in train_ds: print(image_batch.shape) print(labels_batch.shape) break
(32, 224, 224, 3)
(32,)
3. 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)
normalization_layer = layers.experimental.preprocessing.Rescaling(1./255) train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y)) val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(val_ds)) first_image = image_batch[0] # 查看归一化后的数据 print(np.min(first_image), np.max(first_image))
0.0 1.0
三、构建VGG-16网络
VGG优缺点分析：
VGG优点
VGG的结构非常简洁，整个网络都使用了同样大小的卷积核尺寸（3x3）和最大池化尺寸（2x2）。
VGG缺点
1)训练时间过长，调参难度大。2)需要的存储容量大，不利于部署。例如存储VGG-16权重值文件的大小为500多MB，不利于安装到嵌入式系统中。
1. 官方模型
# model = tf.keras.applications.VGG16(weights='imagenet') # model.summary()
2. 自建模型
from tensorflow.keras import layers, models, Input from tensorflow.keras.models import Model from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout def VGG16(nb_classes, input_shape): input_tensor = Input(shape=input_shape) # 1st block x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv1')(input_tensor) x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv2')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block1_pool')(x) # 2nd block x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv1')(x) x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv2')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block2_pool')(x) # 3rd block x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv1')(x) x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv2')(x) x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv3')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block3_pool')(x) # 4th block x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv1')(x) x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv2')(x) x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv3')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block4_pool')(x) # 5th block x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv1')(x) x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv2')(x) x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv3')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block5_pool')(x) # full connection x = Flatten()(x) x = Dense(4096, activation='relu', name='fc1')(x) x = Dense(4096, activation='relu', name='fc2')(x) output_tensor = Dense(nb_classes, activation='softmax', name='predictions')(x) model = Model(input_tensor, output_tensor) return model model=VGG16(len(class_names), (img_width, img_height, 3)) model.summary()
Model: "model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 224, 224, 3)] 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 _________________________________________________________________ flatten (Flatten) (None, 25088) 0 _________________________________________________________________ fc1 (Dense) (None, 4096) 102764544 _________________________________________________________________ fc2 (Dense) (None, 4096) 16781312 _________________________________________________________________ predictions (Dense) (None, 4) 16388 ================================================================= Total params: 134,276,932 Trainable params: 134,276,932 Non-trainable params: 0 _________________________________________________________________
3. 网络结构图
结构说明：
- 13个卷积层（Convolutional Layer），分别用blockX_convX表示
- 3个全连接层（Fully connected Layer），分别用fcX与predictions表示
- 5个池化层（Pool layer），分别用blockX_pool表示
VGG-16包含了16个隐藏层（13个卷积层和3个全连接层），故称为VGG-16。
四、编译
# 设置初始学习率 initial_learning_rate = 1e-4 lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay( initial_learning_rate, decay_steps=30, # 敲黑板！！！这里是指 steps，不是指epochs decay_rate=0.92, # lr经过一次衰减就会变成 decay_rate*lr staircase=True) # 设置优化器 opt = tf.keras.optimizers.Adam(learning_rate=initial_learning_rate) model.compile(optimizer=opt, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
五、训练模型
epochs = 20 history = model.fit( train_ds, validation_data=val_ds, epochs=epochs )
30/30 [==============================] - 7s 236ms/step - loss: 0.1117 - accuracy: 0.9638 - val_loss: 0.0425 - val_accuracy: 0.9875
六、可视化结果
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()
from PIL import Image import numpy as np img = np.array(Image.open("./49-data/Green/green (102).png")) #这里选择你需要预测的图片 image = tf.image.resize(img, [img_height, img_width]) img_array = tf.expand_dims(image, 0) predictions = model.predict(img_array) # 这里选用你已经训练好的模型 print("预测结果为：",class_names[np.argmax(predictions)])
预测结果为： Green
