我有这个片段,为我的模型
import pandas as pd
import tensorflow as tf
from tensorflow.contrib import learn
from tensorflow.contrib.learn.python import SKCompat
#Assume my dataset is using X['train'] as input and y['train'] as output
regressor = SKCompat(learn.Estimator(model_fn=lstm_model(TIMESTEPS, RNN_LAYERS, DENSE_LAYERS),model_dir=LOG_DIR))
validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'], every_n_steps=PRINT_STEPS, early_stopping_rounds=1000)
regressor.fit(X['train'], y['train'],
monitors=[validation_monitor],
batch_size=BATCH_SIZE,
steps=TRAINING_STEPS)
#After training this model I want to save it in a folder, so I can use the trained model for implementing in my algorithm to predict the output
#What is the correct format to use here to save my model in a folder called 'saved_model'
regressor.export_savedmodel('/saved_model/')
#I want to import it later in some other code, How can I import it?
#is there any function like import model from file?
如何保存这个估计器?我试着为tf.contrib.学习.Estimator.export_savedmodel,我没有成功吗?感谢帮助。在
函数export_savedmodel需要服务于_input_receiver_fn的参数,这是一个没有参数的函数,它定义来自模型和预测器的输入。因此,您必须创建自己的服务“输入接收器”,其中模型输入类型与训练脚本中的模型输入相匹配,预测值输入类型与测试脚本中的预测值输入匹配。 另一方面,如果创建自定义模型,则必须定义由函数tf.estimator.出口.PredictOutput,它的输入是一个字典,它定义的名称必须与测试脚本中的预测器输出名称相匹配。在
例如:
训练脚本
测试脚本
^{pr2}$(在python3.6.3、Tensorflow 1.4.0中测试代码)
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