一、前期准备
1. 导入数据
# Import the required libraries
import numpy as np
import PIL, pathlib
from PIL import Image
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers
import random
# Load the data
data_dir = './data/48-data/'
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[2] for path in data_paths]
image_count = len(list(data_dir.glob('*/*')))
print("Total number of images:", image_count)
二、数据预处理
1. 加载数据
# Data loading and preprocessing
batch_size = 16
img_height = 336
img_width = 336
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=12,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=12,
image_size=(img_height, img_width),
batch_size=batch_size)
class_names = train_ds.class_names
print(class_names)
Nicole Kidman', 'Robert Downey Jr', 'Sandra Bullock', 'Scarlett Johansson', 'Tom Cruise', 'Tom Hanks', 'Will Smith']
2. 检查数据
# Check the shape of the data
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
3. 配置数据集
AUTOTUNE = tf.data.AUTOTUNE
def train_preprocessing(image,label):
return (image/255.0,label)
train_ds = (
train_ds.cache()
.shuffle(1000)
.map(train_preprocessing) # 这里可以设置预处理函数
# .batch(batch_size) # 在image_dataset_from_directory处已经设置了batch_size
.prefetch(buffer_size=AUTOTUNE)
)
val_ds = (
val_ds.cache()
.shuffle(1000)
.map(train_preprocessing) # 这里可以设置预处理函数
# .batch(batch_size) # 在image_dataset_from_directory处已经设置了batch_size
.prefetch(buffer_size=AUTOTUNE)
)
4. 数据可视化
plt.rcParams['font.family'] = 'SimHei' # 设置字体为黑体（支持中文）
plt.rcParams['axes.unicode_minus'] = False # 正常显示负号
plt.figure(figsize=(10, 8)) # 图形的宽为10高为5
plt.suptitle("数据展示")
for images, labels in train_ds.take(1):
for i in range(15):
plt.subplot(4, 5, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
# 显示图片
plt.imshow(images[i])
# 显示标签
plt.xlabel(class_names[labels[i]-1])
plt.show()
三、训练模型
1. 构建模型
def create_model(optimizer='adam'):
# 加载预训练模型
vgg16_base_model = tf.keras.applications.vgg16.VGG16(weights='imagenet',
include_top=False,
input_shape=(img_width, img_height, 3),
pooling='avg')
for layer in vgg16_base_model.layers:
layer.trainable = False
X = vgg16_base_model.output
X = Dense(170, activation='relu')(X)
X = BatchNormalization()(X)
X = Dropout(0.5)(X)
output = Dense(len(class_names), activation='softmax')(X)
vgg16_model = Model(inputs=vgg16_base_model.input, outputs=output)
vgg16_model.compile(optimizer=optimizer,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return vgg16_model
model1 = create_model(optimizer=tf.keras.optimizers.Adam())
model2 = create_model(optimizer=tf.keras.optimizers.SGD())
model2.summary()
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
58889256/58889256 [==============================] - 5s 0us/step
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 336, 336, 3)] 0
block1_conv1 (Conv2D) (None, 336, 336, 64) 1792
block1_conv2 (Conv2D) (None, 336, 336, 64) 36928
block1_pool (MaxPooling2D) (None, 168, 168, 64) 0
block2_conv1 (Conv2D) (None, 168, 168, 128) 73856
block2_conv2 (Conv2D) (None, 168, 168, 128) 147584
block2_pool (MaxPooling2D) (None, 84, 84, 128) 0
block3_conv1 (Conv2D) (None, 84, 84, 256) 295168
block3_conv2 (Conv2D) (None, 84, 84, 256) 590080
block3_conv3 (Conv2D) (None, 84, 84, 256) 590080
block3_pool (MaxPooling2D) (None, 42, 42, 256) 0
block4_conv1 (Conv2D) (None, 42, 42, 512) 1180160
block4_conv2 (Conv2D) (None, 42, 42, 512) 2359808
block4_conv3 (Conv2D) (None, 42, 42, 512) 2359808
block4_pool (MaxPooling2D) (None, 21, 21, 512) 0
block5_conv1 (Conv2D) (None, 21, 21, 512) 2359808
block5_conv2 (Conv2D) (None, 21, 21, 512) 2359808
block5_conv3 (Conv2D) (None, 21, 21, 512) 2359808
block5_pool (MaxPooling2D) (None, 10, 10, 512) 0
global_average_pooling2d_1 (None, 512) 0
(GlobalAveragePooling2D)
dense_2 (Dense) (None, 170) 87210
batch_normalization_1 (Bat (None, 170) 680
chNormalization)
dropout_1 (Dropout) (None, 170) 0
dense_3 (Dense) (None, 17) 2907
=================================================================
Total params: 14805485 (56.48 MB)
Trainable params: 90457 (353.35 KB)
Non-trainable params: 14715028 (56.13 MB)
_________________________________________________________________
3. 训练模型
# Train the model
NO_EPOCHS = 50
history_model1 = model1.fit(train_ds, epochs=NO_EPOCHS, verbose=1, validation_data=val_ds)
history_model2 = model2.fit(train_ds, epochs=NO_EPOCHS, verbose=1, validation_data=val_ds)
四、模型评估
1. Loss与Accuracy图
plt.rcParams['savefig.dpi'] = 300 #图片像素
plt.rcParams['figure.dpi'] = 300 #分辨率
current_time = datetime.now() # 获取当前时间
acc1 = history_model1.history['accuracy']
acc2 = history_model2.history['accuracy']
val_acc1 = history_model1.history['val_accuracy']
val_acc2 = history_model2.history['val_accuracy']
loss1 = history_model1.history['loss']
loss2 = history_model2.history['loss']
val_loss1 = history_model1.history['val_loss']
val_loss2 = history_model2.history['val_loss']
epochs_range = range(len(acc1))
plt.figure(figsize=(16, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc1, label='Training Accuracy-Adam')
plt.plot(epochs_range, acc2, label='Training Accuracy-SGD')
plt.plot(epochs_range, val_acc1, label='Validation Accuracy-Adam')
plt.plot(epochs_range, val_acc2, label='Validation Accuracy-SGD')
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
# 设置刻度间隔，x轴每1一个刻度
ax = plt.gca()
ax.xaxis.set_major_locator(MultipleLocator(1))
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss1, label='Training Loss-Adam')
plt.plot(epochs_range, loss2, label='Training Loss-SGD')
plt.plot(epochs_range, val_loss1, label='Validation Loss-Adam')
plt.plot(epochs_range, val_loss2, label='Validation Loss-SGD')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
# 设置刻度间隔，x轴每1一个刻度
ax = plt.gca()
ax.xaxis.set_major_locator(MultipleLocator(1))
plt.show()
2. 评估模型
def test_accuracy_report(model):
score = model.evaluate(val_ds, verbose=0)
print('Loss function: %s, accuracy:' % score[0], score[1])
test_accuracy_report(model2)
