基于Pytorch的CNN网络识别花朵类型

1. 导入库

• 主要导入了用于深度学习、数据处理、可视化等的库：

  import torch
  import torch.nn as nn
  import torch.optim as optim
  from torch.utils.data import DataLoader, random_split
  from torchvision import datasets, transforms
  import os
  import matplotlib.pyplot as plt
  from PIL import Image
  import pandas as pd
  import numpy as np
  from tqdm import tqdm
  

2. 数据预处理和可视化

• 设置数据集路径：定义数据集的根目录和类别列表。

  data_dir = './flower_images'
  categories = ['Lilly', 'Lotus', 'Orchid', 'Sunflower', 'Tulip']
  

• 展示样本图片：展示每个类别的第一张图片，统一调整为150x150像素。

  fig, axes = plt.subplots(1, 5, figsize=(12, 6))
  for i, category in enumerate(categories):
      category_path = os.path.join(data_dir, category)
      image_files = os.listdir(category_path)
      image_path = os.path.join(category_path, image_files[0])
      img = Image.open(image_path)
      img = img.resize((150, 150))
      axes[i].imshow(img)
      axes[i].set_title(category)
      axes[i].axis('off')
  plt.tight_layout()
  plt.show()
  

3. 数据统计

• 统计每个类别的图像数量，并绘制条形图。

  df = pd.DataFrame()
  name = []
  counts = []
  for i in os.listdir(data_dir):
      name.append(i)
      counts.append(len(os.listdir(data_dir+'/'+i)))
  df['Name'] = name
  df['Counts'] = counts
  df.head()
  plt.bar(df['Name'], df['Counts'],