import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
os.environ["TF_CPP_MIN_LOG_LEVEL"]='3' # 忽略 Error
#隱藏警告
import warnings
warnings.filterwarnings('ignore')
# 1.1 設置GPU
import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")
if gpus:tf.config.experimental.set_memory_growth(gpus[0], True)  #設置GPU顯存用量按需使用tf.config.set_visible_devices([gpus[0]],"GPU")
# 打印顯卡信息，確認GPU可用
print(gpus)

# 1.2 導入數據
import matplotlib.pyplot as plt
# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用來正常顯示中文標簽
plt.rcParams['axes.unicode_minus'] = False  # 用來正常顯示負號
import os,PIL,pathlib
data_dir = "./data"
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*')))
print("圖片總數為：",image_count)

# 二、數據預處理
# 2.1 加載數據
batch_size = 8
img_height = 224
img_width = 224
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)

# 2.2 再次檢查數據
for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break

# 2.3 配置數據集
AUTOTUNE = tf.data.AUTOTUNEdef preprocess_image(image,label):return (image/255.0,label)
# 歸一化處理
train_ds = train_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE)
val_ds = val_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE)
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

plt.figure(figsize=(15, 10))  # 圖形的寬為15高為10
for images, labels in train_ds.take(1):for i in range(8):ax = plt.subplot(5, 8, i + 1)plt.imshow(images[i])plt.title(class_names[labels[i]])plt.axis("off")

# 三、構建VGG-16網絡
from tensorflow.keras import layers, models, Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropoutdef VGG16(nb_classes, input_shape):input_tensor = Input(shape=input_shape)# 1st blockx = 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 blockx = 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 blockx = 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 blockx = 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 blockx = 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 connectionx = 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 modelmodel=VGG16(1000, (img_width, img_height, 3))
model.summary()

model.compile(optimizer="adam",loss='sparse_categorical_crossentropy',metrics=['accuracy'])

# 五、訓練模型
from tqdm import tqdm
import tensorflow.keras.backend as Kepochs = 10
lr = 1e-4# 記錄訓練數據，方便后面的分析
history_train_loss = []
history_train_accuracy = []
history_val_loss = []
history_val_accuracy = []
for epoch in range(epochs):train_total = len(train_ds)val_total = len(val_ds)with tqdm(total=train_total, desc=f'Epoch {epoch + 1}/{epochs}', mininterval=1, ncols=100) as pbar:lr = lr * 0.92K.set_value(model.optimizer.lr, lr)for image, label in train_ds:history = model.train_on_batch(image, label)train_loss = history[0]train_accuracy = history[1]pbar.set_postfix({"loss": "%.4f" % train_loss,"accuracy": "%.4f" % train_accuracy,"lr": K.get_value(model.optimizer.lr)})pbar.update(1)history_train_loss.append(train_loss)history_train_accuracy.append(train_accuracy)print('開始驗證！')with tqdm(total=val_total, desc=f'Epoch {epoch + 1}/{epochs}', mininterval=0.3, ncols=100) as pbar:for image, label in val_ds:history = model.test_on_batch(image, label)val_loss = history[0]val_accuracy = history[1]pbar.set_postfix({"loss": "%.4f" % val_loss,"accuracy": "%.4f" % val_accuracy})pbar.update(1)history_val_loss.append(val_loss)history_val_accuracy.append(val_accuracy)print('結束驗證！')print("驗證loss為：%.4f" % val_loss)print("驗證準確率為：%.4f" % val_accuracy)

epochs_range = range(epochs)
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)plt.plot(epochs_range, history_train_accuracy, label='Training Accuracy')
plt.plot(epochs_range, history_val_accuracy, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')plt.subplot(1, 2, 2)
plt.plot(epochs_range, history_train_loss, label='Training Loss')
plt.plot(epochs_range, history_val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

# 七、預測
import numpy as np
# 采用加載的模型（new_model）來看預測結果
plt.figure(figsize=(18, 3))  # 圖形的寬為18高為5
plt.suptitle("預測結果展示")
for images, labels in val_ds.take(1):for i in range(8):ax = plt.subplot(1, 8, i + 1)# 顯示圖片plt.imshow(images[i].numpy())# 需要給圖片增加一個維度img_array = tf.expand_dims(images[i], 0)# 使用模型預測圖片中的人物predictions = model.predict(img_array)plt.title(class_names[np.argmax(predictions)])plt.axis("off")