将代码中的训练模型改为vgg
时间: 2024-10-26 12:16:10 浏览: 8
FCN模型实现-Pytorch+预训练VGG16
要将代码中的训练模型从ResNet50更改为VGG16,可以按照以下步骤进行修改:
1. **导入VGG16模型**:首先需要导入VGG16模型。
2. **创建VGG16模型**:使用VGG16作为基础模型,并添加自定义的全连接层。
3. **编译模型**:保持编译部分不变。
4. **训练模型**:保持训练部分不变。
以下是修改后的代码:
```python
import os
import json
import numpy as np
import pandas as pd
from PIL import Image
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import f1_score, accuracy_score
# 定义路径常量
TRAIN_DATA_DIR = "C:\\Users\\24067\\Desktop\\peach_split\\train"
VAL_DATA_DIR = "C:\\Users\\24067\\Desktop\\peach_split\\val"
TEST_DATA_DIR = "C:\\Users\\24067\\Desktop\\peach_split\\test"
TRAIN_LABEL_PATH = "C:\\Users\\24067\\Desktop\\train_label.json"
VAL_LABEL_PATH = "C:\\Users\\24067\\Desktop\\val_label.json"
OUTPUT_PATH = "C:\\Users\\24067\\Desktop\\data\\submission.csv"
MODEL_SAVE_PATH = "C:\\Users\\24067\\Desktop\\data\\model.h5"
# 加载数据集
def load_data(data_dir, label_path):
with open(label_path, 'r') as f:
labels_list = json.load(f)
labels = {item['文件名']: item['标签'] for item in labels_list if '文件名' in item and '标签' in item}
images = []
targets = []
for file_name, label in labels.items():
img_path = os.path.join(data_dir, file_name)
if not os.path.exists(img_path):
continue
img = Image.open(img_path).resize((128, 128))
img_array = np.array(img) / 255.0
images.append(img_array)
targets.append(label)
if len(images) == 0:
raise ValueError("No valid images found.")
return np.array(images), np.array(targets)
# 加载训练集和验证集
X_train, y_train = load_data(TRAIN_DATA_DIR, TRAIN_LABEL_PATH)
X_val, y_val = load_data(VAL_DATA_DIR, VAL_LABEL_PATH)
# 标签映射
label_map = {'特级': 3, '一级': 2, '二级': 1, '三级': 0}
y_train = np.array([label_map[label] for label in y_train])
y_val = np.array([label_map[label] for label in y_val])
# 创建模型
def create_vgg_model(input_shape=(128, 128, 3)):
base_model = VGG16(weights=None, include_top=False, input_shape=input_shape)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(4, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
return model
# 实例化模型
model = create_vgg_model()
# 编译模型
model.compile(optimizer=Adam(learning_rate=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# 图像增强
datagen = ImageDataGenerator(
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
zoom_range=0.15,
horizontal_flip=True,
vertical_flip=True,
fill_mode='nearest'
)
# 训练模型
history = model.fit(datagen.flow(X_train, y_train, batch_size=32), epochs=50, validation_data=(X_val, y_val))
# 保存模型
model.save(MODEL_SAVE_PATH)
# 评估模型
def evaluate_model(model, X, y):
predictions = model.predict(X)
predicted_labels = np.argmax(predictions, axis=1)
accuracy = accuracy_score(y, predicted_labels)
f1 = f1_score(y, predicted_labels, average='weighted')
print(f'Accuracy: {accuracy:.4f}')
print(f'F1 Score: {f1:.4f}')
return accuracy, f1
evaluate_model(model, X_val, y_val)
# 预测测试集并保存结果
def predict_and_save(test_data_dir, output_path):
test_images = []
test_file_names = []
# 遍历测试集目录中的所有图像文件
for file_name in os.listdir(test_data_dir):
img_path = os.path.join(test_data_dir, file_name)
if not os.path.exists(img_path) or not file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
continue
img = Image.open(img_path).resize((128, 128))
img_array = np.array(img) / 255.0
test_images.append(img_array)
test_file_names.append(file_name)
test_images = np.array(test_images)
predictions = model.predict(test_images)
predicted_labels = np.argmax(predictions, axis=1)
label_map_inv = {v: k for k, v in label_map.items()}
predicted_labels = [label_map_inv[label] for label in predicted_labels]
submission_df = pd.DataFrame({'文件名': test_file_names, '标签': predicted_labels})
submission_df.to_csv(output_path, index=False)
# 进行预测并保存结果
predict_and_save(TEST_DATA_DIR, OUTPUT_PATH)
```
### 主要修改点:
1. **导入VGG16**:`from tensorflow.keras.applications.vgg16 import VGG16`
2. **创建VGG16模型**:在 `create_vgg_model` 函数中使用 `VGG16` 作为基础模型。
3. **其他部分保持不变**:包括数据加载、编译、训练、评估和预测等部分。
这样,代码就成功地将模型从ResNet50更改为VGG16了。
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