{"id":185494,"date":"2024-05-09T14:33:48","date_gmt":"2024-05-09T06:33:48","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/185494.html"},"modified":"2024-05-09T14:33:53","modified_gmt":"2024-05-09T06:33:53","slug":"%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e4%b8%ad%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8%e4%bf%9d%e5%ad%98%e7%9a%84%e6%a8%a1%e5%9e%8b%e8%bf%9b%e8%a1%8c%e9%a2%84%e6%b5%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/185494.html","title":{"rendered":"\u673a\u5668\u5b66\u4e60\u4e2d\u5982\u4f55\u4f7f\u7528\u4fdd\u5b58\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b"},"content":{"rendered":"<p style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/26083337\/c3a7b1be-9c2e-468f-970b-c4a5900f2ee7.webp\" alt=\"\u673a\u5668\u5b66\u4e60\u4e2d\u5982\u4f55\u4f7f\u7528\u4fdd\u5b58\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\" \/><\/p>\n<p><p><strong>\u4f7f\u7528\u4fdd\u5b58\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u9700\u8981\u4ee5\u4e0b\u91cd\u8981\u6b65\u9aa4\uff1a\u52a0\u8f7d\u6a21\u578b\u3001\u51c6\u5907\u6570\u636e\u3001\u6267\u884c\u9884\u6d4b\u3002<\/strong> \u5176\u4e2d\uff0c<strong>\u52a0\u8f7d\u6a21\u578b<\/strong>\u662f\u81f3\u5173\u91cd\u8981\u7684\u4e00\u6b65\uff0c\u56e0\u4e3a\u53ea\u6709\u6210\u529f\u6062\u590d\u4e86\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u53ca\u5176\u53c2\u6570\uff0c\u624d\u80fd\u4fdd\u8bc1\u9884\u6d4b\u7684\u51c6\u786e\u6027\u548c\u6709\u6548\u6027\u3002\u6a21\u578b\u7684\u52a0\u8f7d\u65b9\u5f0f\u53d6\u51b3\u4e8e\u5176\u4fdd\u5b58\u65f6\u4f7f\u7528\u7684\u683c\u5f0f\uff0c\u5982Python\u7684pickle\u683c\u5f0f\u3001TensorFlow\u7684SavedModel\u6216\u8005\u5176\u4ed6\u7684\u4e13\u4e1a\u683c\u5f0f\u5982HDF5\u3002\u4e00\u65e6\u6a21\u578b\u52a0\u8f7d\u6210\u529f\uff0c\u901a\u8fc7\u6a21\u578b\u6240\u8981\u6c42\u7684\u6570\u636e\u683c\u5f0f\u5bf9\u5f85\u9884\u6d4b\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u7136\u540e\u5c31\u53ef\u4ee5\u4f7f\u7528\u6a21\u578b\u6267\u884c\u9884\u6d4b\u4e86\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001<strong>\u52a0\u8f7d\u6a21\u578b<\/strong><\/p>\n<\/p>\n<p><p>\u5728<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u9879\u76ee\u4e2d\uff0c<strong>\u6a21\u578b\u4fdd\u5b58<\/strong> \u901a\u5e38\u5728\u8bad\u7ec3\u9636\u6bb5\u5b8c\u6210\u3002\u4e00\u65e6\u6a21\u578b\u88ab\u8bad\u7ec3\u5e76\u9a8c\u8bc1\u8868\u73b0\u826f\u597d\uff0c\u5b83\u5c31\u53ef\u4ee5\u88ab\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u4ee5\u4f9b\u540e\u7eed\u4f7f\u7528\u3002\u52a0\u8f7d\u6a21\u578b\u65f6\uff0c\u4f60\u9700\u8981\u786e\u4fdd\u52a0\u8f7d\u65b9\u6cd5\u4e0e\u4fdd\u5b58\u65b9\u6cd5\u517c\u5bb9\u3002<\/p>\n<\/p>\n<p><p>\u5bf9\u4e8e\u7528Python\u5f00\u53d1\u7684\u9879\u76ee\uff0c\u5982\u679c\u4f7f\u7528\u4e86 <strong>pickle\u5e93<\/strong> \u6765\u4fdd\u5b58\u6a21\u578b\uff0c\u4f60\u53ef\u4ee5\u7528\u4ee5\u4e0b\u4ee3\u7801\u52a0\u8f7d\u5b83\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pickle<\/p>\n<h2><strong>\u52a0\u8f7d\u6a21\u578b<\/strong><\/h2>\n<p>with open(&#039;model.pkl&#039;, &#039;rb&#039;) as file:<\/p>\n<p>    model = pickle.load(file)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5bf9\u4e8e\u4f7f\u7528<strong>TensorFlow\u6216Keras<\/strong>\u5f00\u53d1\u7684\u9879\u76ee\uff0c\u6a21\u578b\u53ef\u80fd\u88ab\u4fdd\u5b58\u4e3aSavedModel\u683c\u5f0f\u6216HDF5\u683c\u5f0f\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528\u76f8\u5e94\u7684\u5e93\u51fd\u6570\u6765\u52a0\u8f7d\u5b83\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from tensorflow.keras.models import load_model<\/p>\n<h2><strong>\u52a0\u8f7d\u6a21\u578b<\/strong><\/h2>\n<p>model = load_model(&#039;model.h5&#039;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e8c\u3001<strong>\u51c6\u5907\u6570\u636e<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u52a0\u8f7d\u6a21\u578b\u540e\uff0c\u4e0b\u4e00\u6b65\u662f\u51c6\u5907\u5f85\u9884\u6d4b\u7684<strong>\u6570\u636e\u96c6<\/strong>\u3002\u901a\u5e38\u6765\u8bf4\uff0c\u6570\u636e\u9700\u8981\u4e0e\u8bad\u7ec3\u6a21\u578b\u65f6\u4f7f\u7528\u7684\u6570\u636e\u5177\u6709\u76f8\u540c\u7684\u683c\u5f0f\u548c\u5904\u7406\u6d41\u7a0b\uff0c\u8fd9\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u5f52\u4e00\u5316\u3001\u7279\u5f81\u9009\u62e9\u548c\u7ef4\u5ea6\u53d8\u6362\u7b49\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><p>\u4f8b\u5982\uff0c\u5982\u679c\u6a21\u578b\u8bad\u7ec3\u65f6\u4f7f\u7528\u7684\u6570\u636e\u88ab\u6807\u51c6\u5316\u4e3a\u5747\u503c\u4e3a0\uff0c\u6807\u51c6\u5dee\u4e3a1\uff0c\u90a3\u4e48\u9884\u6d4b\u6570\u636e\u4e5f\u9700\u8981\u8fdb\u884c\u76f8\u540c\u7684\u53d8\u6362\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.preprocessing import StandardScaler<\/p>\n<p>scaler = StandardScaler()<\/p>\n<h2><strong>\u5047\u8bbeX_test\u662f\u5f85\u9884\u6d4b\u7684\u6570\u636e\u96c6<\/strong><\/h2>\n<p>X_test_scaled = scaler.fit_transform(X_test)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e09\u3001<strong>\u6267\u884c\u9884\u6d4b<\/strong><\/p>\n<\/p>\n<p><p>\u4e00\u65e6\u6570\u636e\u51c6\u5907\u5c31\u7eea\uff0c\u5c31\u53ef\u4ee5\u4f7f\u7528\u52a0\u8f7d\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u4e86\u3002\u8fd9\u901a\u5e38\u6d89\u53ca\u5230\u8c03\u7528\u6a21\u578b\u7684\u9884\u6d4b\u65b9\u6cd5\uff0c\u5e76\u5c06\u5904\u7406\u597d\u7684\u6570\u636e\u4f20\u9012\u7ed9\u5b83\u3002<\/p>\n<\/p>\n<p><p>\u5bf9\u4e8e\u5927\u591a\u6570\u673a\u5668\u5b66\u4e60\u5e93\u6765\u8bf4\uff0c\u8fd9\u4e00\u6b65\u9aa4\u53ef\u4ee5\u7b80\u5316\u4e3a\u4f7f\u7528\u6a21\u578b\u5bf9\u8c61\u7684<code>.predict()<\/code>\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u6267\u884c\u9884\u6d4b<\/p>\n<p>predictions = model.predict(X_test_scaled)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u9884\u6d4b\u4e4b\u540e\uff0c\u4f60\u901a\u5e38\u4f1a\u83b7\u53d6\u4e00\u4e2a\u9884\u6d4b\u7ed3\u679c\u6570\u7ec4\uff0c\u4f60\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5206\u6790\u8fd9\u4e9b\u9884\u6d4b\u6570\u636e\uff0c\u6bd4\u5982\u8ba1\u7b97\u9884\u6d4b\u51c6\u786e\u7387\u3001\u751f\u6210\u62a5\u544a\u6216\u5c06\u8fd9\u4e9b\u9884\u6d4b\u7528\u4e8e\u5e94\u7528\u7a0b\u5e8f\u3002 <\/p>\n<\/p>\n<p><p>\u56db\u3001<strong>\u8bc4\u4f30\u9884\u6d4b<\/strong><\/p>\n<\/p>\n<p><p><strong>\u9884\u6d4b\u8bc4\u4f30<\/strong> \u5bf9\u4e8e\u9a8c\u8bc1\u6a21\u578b\u5728\u5b9e\u9645\u60c5\u51b5\u4e2d\u7684\u8868\u73b0\u81f3\u5173\u91cd\u8981\u3002\u5982\u679c\u5728\u6a21\u578b\u5f00\u53d1\u9636\u6bb5\u4fdd\u7559\u4e86\u4e00\u4e2a\u6d4b\u8bd5\u96c6\uff0c\u90a3\u4e48\u73b0\u5728\u5c31\u53ef\u4ee5\u7528\u5b83\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002\u53ef\u4ee5\u4f7f\u7528\u5404\u79cd\u5ea6\u91cf\u6807\u51c6\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\uff0c\u5982\u51c6\u786e\u7387\u3001\u6df7\u6dc6\u77e9\u9635\u3001\u53ec\u56de\u7387\u3001\u7cbe\u786e\u5ea6\u548cF1\u5f97\u5206\u7b49\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.metrics import accuracy_score<\/p>\n<h2><strong>\u8ba1\u7b97\u51c6\u786e\u7387<\/strong><\/h2>\n<p>acc = accuracy_score(y_true, predictions)<\/p>\n<p>print(f&#039;Model accuracy: {acc}&#039;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>(eval):app.flush_caches()\u4e94\u3001<strong>\u6301\u7eed\u4f18\u5316<\/strong><\/p>\n<\/p>\n<p><p>\u6a21\u578b\u5728\u88ab\u90e8\u7f72\u540e\uff0c\u53ef\u80fd\u4f1a\u56e0\u4e3a\u6570\u636e\u5206\u5e03\u7684\u53d8\u5316\u9700\u8981\u6301\u7eed\u7684\u4f18\u5316\u548c\u66f4\u65b0\u3002<strong>\u6a21\u578b\u4f18\u5316<\/strong> \u5305\u62ec\u91cd\u65b0\u8bad\u7ec3\u3001\u7d27\u5f20\u548c\u8c03\u6574\u3002\u4e3a\u6b64\uff0c\u6570\u636e\u79d1\u5b66\u5bb6\u9700\u7ecf\u5e38\u56de\u987e\u6a21\u578b\u7684\u9884\u6d4b\u8868\u73b0\uff0c\u5e76\u4e0e\u5b9e\u9645\u60c5\u51b5\u5bf9\u6bd4\uff0c\u8fdb\u884c\u5fc5\u8981\u7684\u8c03\u6574\u3002\u8fd9\u53ef\u80fd\u6d89\u53ca\u5230\u66f4\u6362\u6a21\u578b\u7ed3\u6784\u3001\u8c03\u6574\u8d85\u53c2\u6570\u6216\u91c7\u7528\u4e0d\u540c\u7684\u7279\u5f81\u7ec4\u5408\u7b49\u7b56\u7565\u3002\u8fdb\u884c\u8fd9\u4e9b\u8c03\u6574\u540e\uff0c\u6a21\u578b\u9700\u8981\u518d\u6b21\u88ab\u9a8c\u8bc1\u5176\u6548\u7387\u5e76\u4fdd\u5b58\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u5047\u8bbenew_data\u662f\u65b0\u6536\u96c6\u7684\u6570\u636e\uff0c\u9700\u8981\u8fdb\u884c\u6a21\u578b\u4f18\u5316<\/p>\n<h2><strong>\u6b64\u5904\u7701\u7565\u6570\u636e\u51c6\u5907\u548c\u9884\u5904\u7406\u4ee3\u7801<\/strong><\/h2>\n<h2><strong>\u91cd\u65b0\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n<p>model.fit(new_data, new_labels)<\/p>\n<h2><strong>\u8bc4\u4f30\u6a21\u578b\u5e76\u8fdb\u884c\u4f18\u5316<\/strong><\/h2>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u516d\u3001<strong>\u81ea\u52a8\u5316\u4e0e\u96c6\u6210<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u771f\u5b9e\u4e16\u754c\u7684\u573a\u666f\u4e2d\uff0c\u901a\u5e38\u9700\u8981\u5c06\u6a21\u578b\u9884\u6d4b\u96c6\u6210\u5230\u81ea\u52a8\u5316\u7684\u7cfb\u7edf\u4e2d\uff0c\u4f8b\u5982\u4f9b\u5e94\u94fe\u7cfb\u7edf\u3001\u63a8\u8350\u5f15\u64ce\u6216\u8005\u667a\u80fd\u6cbb\u7406\u7cfb\u7edf\u3002\u4e3a\u5b9e\u73b0<strong>\u81ea\u52a8\u5316\u4e0e\u96c6\u6210<\/strong>\uff0c\u9664\u4e86\u52a0\u8f7d\u5df2\u4fdd\u5b58\u7684\u6a21\u578b\u548c\u6267\u884c\u9884\u6d4b\u4e4b\u5916\uff0c\u8fd8\u9700\u8981\u5f00\u53d1\u5fc5\u8981\u7684API\u63a5\u53e3\uff0c<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from flask import Flask, request, jsonify<\/p>\n<p>app = Flask(__name__)<\/p>\n<p>@app.route(&#039;\/predict&#039;, methods=[&#039;POST&#039;])<\/p>\n<p>def predict_api():<\/p>\n<p>    data = request.get_json()<\/p>\n<p>    # \u6570\u636e\u5904\u7406\u903b\u8f91\u7701\u7565...<\/p>\n<p>    prediction = model.predict(processed_data)<\/p>\n<p>    return jsonify(prediction.tolist())<\/p>\n<p>if __name__ == &#039;__m<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>n__&#039;:<\/p>\n<p>    app.run(debug=True)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u8fd9\u6837\u7684\u63a5\u53e3\uff0c\u5176\u4ed6\u7cfb\u7edf\u53ef\u4ee5\u76f4\u63a5\u53d1\u9001\u6570\u636e\u5230\u9884\u6d4bAPI\u5e76\u83b7\u53d6\u9884\u6d4b\u7ed3\u679c\u3002 \u8fd9\u79cd\u96c6\u6210\u65b9\u5f0f\u5927\u5927\u63d0\u9ad8\u4e86\u6a21\u578b\u7684\u5b9e\u7528\u6027\u5e76\u53ef\u80fd\u6269\u5c55\u5176\u5f71\u54cd\u529b\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p><strong>1. \u5982\u4f55\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u4f7f\u7528\u4fdd\u5b58\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\uff1f<\/strong><\/p>\n<p>\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4fdd\u5b58\u7684\u6a21\u578b\u6765\u8fdb\u884c\u9884\u6d4b\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u52a0\u8f7d\u5df2\u4fdd\u5b58\u7684\u6a21\u578b\u3002\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u52a0\u8f7d\u7684\u6a21\u578b\u6765\u5bf9\u65b0\u7684\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528\u6a21\u578b\u7684\u9884\u6d4b\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002\u5c06\u65b0\u7684\u6570\u636e\u8f93\u5165\u5230\u9884\u6d4b\u65b9\u6cd5\u4e2d\uff0c\u6a21\u578b\u5c06\u8fd4\u56de\u9884\u6d4b\u7ed3\u679c\u3002\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528\u4fdd\u5b58\u7684\u6a21\u578b\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u4e86\u3002<\/p>\n<p><strong>2. \u6a21\u578b\u4fdd\u5b58\u540e\u5982\u4f55\u4f7f\u7528\u5b83\u8fdb\u884c\u9884\u6d4b\uff1f<\/strong><\/p>\n<p>\u6a21\u578b\u4fdd\u5b58\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5b83\u6765\u8fdb\u884c\u9884\u6d4b\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u9002\u5f53\u7684\u5e93\u6216\u6846\u67b6\u52a0\u8f7d\u6a21\u578b\u3002\u52a0\u8f7d\u6a21\u578b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u52a0\u8f7d\u7684\u6a21\u578b\u6765\u5bf9\u65b0\u7684\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u3002\u901a\u5e38\uff0c\u6211\u4eec\u53ef\u4ee5\u8c03\u7528\u6a21\u578b\u7684\u9884\u6d4b\u65b9\u6cd5\uff0c\u5e76\u5c06\u65b0\u6570\u636e\u4f5c\u4e3a\u8f93\u5165\u4f20\u9012\u7ed9\u8be5\u65b9\u6cd5\u3002\u6a21\u578b\u5c06\u4f7f\u7528\u5176\u5b66\u4e60\u5230\u7684\u53c2\u6570\u548c\u7279\u5f81\u6765\u751f\u6210\u9884\u6d4b\u7ed3\u679c\u3002\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5229\u7528\u4fdd\u5b58\u7684\u6a21\u578b\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u4e86\u3002<\/p>\n<p><strong>3. 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[&hellip;]","protected":false},"author":3,"featured_media":185496,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/185494"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=185494"}],"version-history":[{"count":0,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/185494\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/185496"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=185494"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=185494"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=185494"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}