{"id":27083,"date":"2024-05-29T12:45:29","date_gmt":"2024-05-29T04:45:29","guid":{"rendered":"https:\/\/www.zhidianwl.net\/zhidianwl\/?p=27083"},"modified":"2024-05-29T12:45:29","modified_gmt":"2024-05-29T04:45:29","slug":"tf%e8%8b%b9%e6%9e%9c%e4%b8%8a%e6%9e%b6%e5%ba%94%e7%94%a8%e5%95%86%e5%ba%97%e6%93%8d%e4%bd%9c%e4%bb%8b%e7%bb%8d","status":"publish","type":"post","link":"https:\/\/www.zhidianwl.net\/zhidianwl\/2024\/05\/29\/tf%e8%8b%b9%e6%9e%9c%e4%b8%8a%e6%9e%b6%e5%ba%94%e7%94%a8%e5%95%86%e5%ba%97%e6%93%8d%e4%bd%9c%e4%bb%8b%e7%bb%8d\/","title":{"rendered":"tf\u82f9\u679c\u4e0a\u67b6\u5e94\u7528\u5546\u5e97\u64cd\u4f5c\u4ecb\u7ecd"},"content":{"rendered":"
TensorFlow\uff08TF\uff09\u662f\u7531Google Brain\u56e2\u961f\u5f00\u53d1\u7684\u5f00\u6e90\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u548c\u5176\u4ed6\u673a\u5668\u5b66\u4e60\u6a21\u578b\u3002\u7531\u4e8e\u5176\u5f3a\u5927\u7684\u529f\u80fd\u548c\u6613\u7528\u6027\uff0cTF\u5df2\u6210\u4e3a\u8bb8\u591a\u5e94\u7528\u7a0b\u5e8f\u7684\u9996\u9009\u673a\u5668\u5b66\u4e60\u6846\u67b6\u4e4b\u4e00\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u4ecb\u7ecdTF\u5728\u82f9\u679c\u4e0a\u67b6\u7684\u539f\u7406\u548c\u8be6\u7ec6\u6b65\u9aa4\u3002<\/p>\n
TF\u5728\u82f9\u679c\u4e0a\u67b6\u7684\u539f\u7406<\/p>\n
\u5728\u82f9\u679c\u4e0a\u67b6TF\u9700\u8981\u4f7f\u7528\u82f9\u679c\u7684Core ML\u6846\u67b6\uff0c\u8be5\u6846\u67b6\u5141\u8bb8\u5f00\u53d1\u4eba\u5458\u5728iOS\u548cmacOS\u8bbe\u5907\u4e0a\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b\u3002Core ML\u53ef\u4ee5\u5c06\u73b0\u6709\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b\u8f6c\u6362\u4e3aCore ML\u6a21\u578b\uff0c\u4ee5\u4fbf\u5728\u82f9\u679c\u8bbe\u5907\u4e0a\u8fd0\u884c\u3002\u56e0\u6b64\uff0c\u8981\u5c06TF\u6a21\u578b\u90e8\u7f72\u5230\u82f9\u679c\u8bbe\u5907\u4e0a\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528Core ML\u5c06\u5176\u8f6c\u6362\u4e3aCore ML\u6a21\u578b\u3002<\/p>\n
\u8f6c\u6362TF\u6a21\u578b\u4e3aCore ML\u6a21\u578b\u7684\u5de5\u5177\u662fTF-CoreML\uff0c\u5b83\u662f\u4e00\u4e2aPython\u5e93\uff0c\u53ef\u4ee5\u5c06TF\u6a21\u578b\u8f6c\u6362\u4e3aCore ML\u6a21\u578b\u3002TF-CoreML\u5e93\u5c06TF\u6a21\u578b\u8f6c\u6362\u4e3aCore ML\u6a21\u578b\u7684\u8fc7\u7a0b\u5305\u62ec\u4ee5\u4e0b\u6b65\u9aa4\uff1a<\/p>\n
1. \u5c06TF\u6a21\u578b\u8f6c\u6362\u4e3aCore ML\u6a21\u578b\u6240\u9700\u7684\u8f93\u5165\u683c\u5f0f\u3002Core ML\u6a21\u578b\u9700\u8981\u5c06\u8f93\u5165\u6570\u636e\u8f6c\u6362\u4e3aCore ML\u8f93\u5165\u683c\u5f0f\uff0c\u8fd9\u662f\u7531Core ML\u6846\u67b6\u5b9a\u4e49\u7684\u3002TF-CoreML\u5e93\u5c06TF\u6a21\u578b\u7684\u8f93\u5165\u8f6c\u6362\u4e3aCore ML\u8f93\u5165\u683c\u5f0f\u3002<\/p>\n
2. \u5c06TF\u6a21\u578b\u8f6c\u6362\u4e3aCore ML\u6a21\u578b\u6240\u9700\u7684\u8f93\u51fa\u683c\u5f0f\u3002Core ML\u6a21\u578b\u9700\u8981\u5c06\u8f93\u51fa\u6570\u636e\u8f6c\u6362\u4e3aCore ML\u8f93\u51fa\u683c\u5f0f\uff0c\u8fd9\u662f\u7531Core ML\u6846\u67b6\u5b9a\u4e49\u7684\u3002TF-CoreML\u5e93\u5c06TF\u6a21\u578b\u7684\u8f93\u51fa\u8f6c\u6362\u4e3aCore ML\u8f93\u51fa\u683c\u5f0f\u3002<\/p>\n
3. \u5c06TF\u6a21\u578b\u8f6c\u6362\u4e3aCore ML\u6a21\u578b\u6240\u9700\u7684\u6743\u91cd\u548c\u504f\u5dee\u3002Core ML\u6a21\u578b\u9700\u8981\u5c06\u6743\u91cd\u548c\u504f\u5dee\u8f6c\u6362\u4e3aCore ML\u6743\u91cd\u548c\u504f\u5dee\u683c\u5f0f\uff0c\u8fd9\u662f\u7531Core ML\u6846\u67b6\u5b9a\u4e49\u7684\u3002TF-CoreML\u5e93\u5c06TF\u6a21\u578b\u7684\u6743\u91cd\u548c\u504f\u5dee\u8f6c\u6362\u4e3aCore ML\u6743\u91cd\u548c\u504f\u5dee\u683c\u5f0f\u3002<\/p>\n
4. \u5c06\u8f6c\u6362\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u4e3aCore ML\u6a21\u578b\u6587\u4ef6\u3002\u5c06\u8f6c\u6362\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u4e3aCore ML\u6a21\u578b\u6587\u4ef6\uff0c\u4ee5\u4fbf\u5728iOS\u548cmacOS\u8bbe\u5907\u4e0a\u4f7f\u7528\u3002<\/p>\n
TF\u5728\u82f9\u679c\u4e0a\u67b6\u7684\u8be6\u7ec6\u6b65\u9aa4<\/p>\n
\u5728\u5c06TF\u6a21\u578b\u90e8\u7f72\u5230\u82f9\u679c\u8bbe\u5907\u4e0a\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5b8c\u6210\u4ee5\u4e0b\u6b65\u9aa4\uff1a<\/p>\n
1. \u5b89\u88c5TF-CoreML\u5e93\u3002TF-CoreML\u5e93\u662f\u4e00\u4e2aPython\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u5b89\u88c5\u3002\u5b89\u88c5\u547d\u4ee4\u5982\u4e0b\uff1a<\/p>\n
“`<\/p>\n
pip install tfcoreml<\/p>\n
“`<\/p>\n
2. \u51c6\u5907TF\u6a21\u578b\u3002\u5728\u5c06TF\u6a21\u578b\u8f6c<\/p>\n
\u6362\u4e3aCore ML\u6a21\u578b\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u597dTF\u6a21\u578b\u3002TF\u6a21\u578b\u53ef\u4ee5\u4f7f\u7528TensorFlow\u6216Keras\u521b\u5efa\u3002<\/p>\n 3. \u5c06TF\u6a21\u578b\u8f6c\u6362\u4e3aCore ML\u6a21\u578b\u3002\u4f7f\u7528TF-CoreML\u5e93\u5c06TF\u6a21\u578b\u8f6c\u6362\u4e3aCore ML\u6a21\u578b\u3002\u8f6c\u6362\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n “`python<\/p>\n import tfcoreml<\/p>\n # Convert the TensorFlow model to Core ML<\/p>\n coreml_model = tfcoreml.convert(<\/p>\n tf_model_path=’path\/to\/tf\/model’,<\/p>\n mlmodel_path=’path\/to\/coreml\/model’,<\/p>\n output_feature_names=[‘output_node_name’],<\/p>\n<\/figure>\n<\/p>\n