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visual_plot.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.rcParams["font.sans-serif"] = ["SimHei"]
+plt.rcParams["axes.unicode_minus"] = False
+
+# ---------------------- 图1：Loss曲线（训练loss） ----------------------
+loss_df = pd.read_csv("loss.csv")
+plt.figure(figsize=(10,4))
+plt.plot(loss_df["epoch"], loss_df["loss"], color="#e74c3c", linewidth=2, label="训练Loss")
+plt.xlabel("Epoch 训练轮次")
+plt.ylabel("Loss 损失值")
+plt.title("MLP训练Loss变化曲线")
+plt.legend()
+plt.grid(alpha=0.3)
+plt.tight_layout()
+
+# 创建images文件夹存放图片
+import os
+if not os.path.exists("images"):
+    os.mkdir("images")
+plt.savefig("images/loss_curve.png", dpi=300)
+plt.close()
+
+# ---------------------- 图2：10类别预测分布柱状图 ----------------------
+label_df = pd.read_csv("my_labels.csv")
+cate_count = label_df["label"].value_counts().sort_index()
+cate_names = ["剧情","喜剧","科幻","悬疑","动作","爱情","动画","犯罪","奇幻","记录"]
+
+plt.figure(figsize=(10,4))
+bars = plt.bar([str(i) for i in range(10)], cate_count.values, color="#3498db")
+plt.xlabel("类别编号")
+plt.ylabel("样本数量")
+plt.title("10个电影类别样本分布柱状图")
+plt.xticks(range(10), cate_names, rotation=30)
+plt.grid(axis="y", alpha=0.3)
+plt.tight_layout()
+plt.savefig("images/category_bar.png", dpi=300)
+plt.close()
+
+print("可视化绘图完成，图片保存在 images/ 文件夹")