一、模型训练Code
importmatplotlib.pyplotaspltimporttimefromnumpyimportasarrayfromnumpyimportuniquefromnumpyimportargmaxfromtensorflow.keras.datasets.mnistimportload_datafromtensorflow.kerasimportSequentialfromtensorflow.keras.layersimportDensefromtensorflow.keras.layersimportConv2Dfromtensorflow.keras.layersimportMaxPool2Dfromtensorflow.keras.layersimportFlattenfromtensorflow.keras.layersimportDropout#load MNIST dataset(x_train, y_train), (x_test, y_test) =load_data() print(f'Train: X={x_train.shape}, y={y_train.shape}') print(f'Test: X={x_test.shape}, y={y_test.shape}') # reshape data to have a single channelx_train=x_train.reshape((x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)) x_test=x_test.reshape((x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)) # normalize pixel valuesx_train=x_train.astype('float32') /255.0x_test=x_test.astype('float32') /255.0# set input image shapeinput_shape=x_train.shape[1:] # set number of classesn_classes=len(unique(y_train)) # define modelmodel=Sequential() model.add(Conv2D(64, (3,3), activation='relu', input_shape=input_shape)) model.add(MaxPool2D((2, 2))) model.add(Conv2D(32, (3,3), activation='relu')) model.add(MaxPool2D((2, 2))) model.add(Flatten()) model.add(Dense(50, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(n_classes, activation='softmax')) # define loss and optimizermodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # fit the modelmodel.fit(x_train, y_train, epochs=10, batch_size=128, verbose=1) # evaluate the modelloss, acc=model.evaluate(x_test, y_test, verbose=0) print('Accuracy: %.3f'%acc) #save modelts=int(time.time()) file_path=f"./img_classifier/{ts}/"model.save(filepath=file_path, save_format='tf')
- 直接运行代码训练获取的模型：
二、部署模型
- 2.1 安装docker
- 2.2 Installing Tensorflow Serving
dockerpulltensorflow/serving
- 2.3 部署模型
dockerrun-p8501:8501--nametfserving_classifier--mounttype=bind,source=/root/demo/img_classifier/,target=/models/img_classifier-eMODEL_NAME=img_classifier-ttensorflow/serving
三、模型调用
- 3.1 服务地址
http://《部署服务的机器IP》:8501/v1/models/img_classifier:predict
注意：如果机器有内外地址，测试机器和服务部署机器内外可达，建议使用内外地址，如果测试机器与服务机器内外不可达，建议可以使用部署服务机器的公网IP地址。
- 3.2 Python测试Code
importrequestsimportjsonfromtensorflow.keras.datasets.mnistimportload_data#load MNIST dataset(_, _), (x_test, y_test) =load_data() # reshape data to have a single channelx_test=x_test.reshape((x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)) # normalize pixel valuesx_test=x_test.astype('float32') /255.0#server URLurl='http://192.168.0.***:8501/v1/models/img_classifier:predict'defmake_prediction(instances): data=json.dumps({"signature_name": "serving_default", "instances": instances.tolist()}) headers= {"content-type": "application/json"} json_response=requests.post(url, data=data, headers=headers) predictions=json.loads(json_response.text)['predictions'] returnpredictionsxx=x_test[0:1] predictions=make_prediction(xx) print(predictions)
- 3.3 Post Man测试
- 测试Body Json文本
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更多参考
How to Serve Machine Learning Models With TensorFlow Serving and Docker
