再写一个行人属性识别训练没有验证集路径的图片代码

好的，以下是一个简单的行人属性识别训练代码，可以处理没有验证集路径的图片数据集：

import os
import random
import numpy as np
from PIL import Image
import tensorflow as tf

# 定义数据集路径和标签
data_dir = "path/to/dataset"
labels = ["male", "female", "young", "old", "adult", "teenager"]

# 读取数据集并预处理
def preprocess_data():
    data = []
    for label in labels:
        label_folder = os.path.join(data_dir, label)
        for filename in os.listdir(label_folder):
            img_path = os.path.join(label_folder, filename)
            img = Image.open(img_path).resize((224, 224))
            img = np.array(img) / 255.0
            data.append((img, label))
    random.shuffle(data)
    return data

# 划分训练集和验证集
def split_data(data, split_ratio):
    split_index = int(split_ratio * len(data))
    train_data = data[:split_index]
    val_data = data[split_index:]
    return train_data, val_data

# 定义模型
def create_model():
    base_model = tf.keras.applications.ResNet50(include_top=False, input_shape=(224, 224, 3))
    for layer in base_model.layers:
        layer.trainable = False
    global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
    prediction_layer = tf.keras.layers.Dense(len(labels), activation="softmax")
    model = tf.keras.Sequential([
        base_model,
        global_average_layer,
        prediction_layer
    ])
    model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
    return model

# 训练模型
def train_model():
    data = preprocess_data()
    train_data, val_data = split_data(data, 0.8)
    train_images, train_labels = zip(*train_data)
    val_images, val_labels = zip(*val_data)
    train_images = np.array(train_images)
    train_labels = np.array([labels.index(label) for label in train_labels])
    val_images = np.array(val_images)
    val_labels = np.array([labels.index(label) for label in val_labels])
    model = create_model()
    model.fit(train_images, train_labels, epochs=10, validation_data=(val_images, val_labels))
    return model

# 测试模型
def test_model(model):
    test_images = []
    for filename in os.listdir("path/to/test/images"):
        img_path = os.path.join("path/to/test/images", filename)
        img = Image.open(img_path).resize((224, 224))
        img = np.array(img) / 255.0
        test_images.append(img)
    test_images = np.array(test_images)
    predictions = model.predict(test_images)
    predicted_labels = [labels[np.argmax(prediction)] for prediction in predictions]
    return predicted_labels

# 训练并测试模型
model = train_model()
predicted_labels = test_model(model)
print(predicted_labels)

这个代码中，我们首先定义了数据集路径和标签，然后使用 `preprocess_data()` 函数读取数据集并预处理，接着使用 `split_data()` 函数划分训练集和验证集，然后使用 `create_model()` 函数定义模型，使用 `train_model()` 函数训练模型，使用 `test_model()` 函数测试模型，最后输出预测标签。需要注意的是，由于没有验证集路径，我们在训练模型时直接使用了划分后的训练集和验证集，而在测试模型时使用了预定义的测试图片路径。