TensorFlow：SKCompat折舊警告

Question

注意：我的第一個問題在這裏。赦免缺乏細節或信息。如果需要，我們很樂意澄清。

我在Mac上運行TensorFlow 1.0.0，並使用learn.Estimator類

WARNING:tensorflow:From :25: calling fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

我試圖尋找這個類，當我不斷收到這樣的警告並沒有零信息的問候它。完整的代碼張貼在這裏

https://github.com/austinmwhaley/DeepFarm/blob/master/prototype_1.ipynb

請讓我知道，如果有任何其他的信息，任何人都需要

Answer 1

您可以從tensorflow.contrib.learn.python導入SKCompat：

from tensorflow.contrib.learn.python import SKCompat 

然後用SKCompat（）例如包裝您的估算器像這樣：

classifier = SKCompat(tf.contrib.learn.LinearClassifier(args)) 

Answer 2

，或者你只是使用模型定義的updated Estimator API of TensorFlow r1.1

的API與參數，只返回類型或函數名的一些小變化頗爲相似。下面是我用一個例子：

def model_fn(): 
    def _build_model(features, labels, mode, params): 
     # 1. Configure the model via TensorFlow operations 
     # Connect the first hidden layer to input layer (features) with relu activation 
     y = tf.contrib.layers.fully_connected(features, num_outputs=64, activation_fn=tf.nn.relu, 
               weights_initializer=tf.contrib.layers.xavier_initializer()) 
     y = tf.contrib.layers.fully_connected(y, num_outputs=64, activation_fn=tf.nn.relu, 
               weights_initializer=tf.contrib.layers.xavier_initializer()) 
     y = tf.contrib.layers.fully_connected(y, num_outputs=1, activation_fn=tf.nn.sigmoid, 
               weights_initializer=tf.contrib.layers.xavier_initializer()) 

     predictions = y 

     # 2. Define the loss function for training/evaluation 
     if mode == tf.estimator.ModeKeys.TRAIN or mode == tf.estimator.ModeKeys.EVAL: 
      loss = tf.reduce_mean((predictions - labels) ** 2) 
     else: 
      loss = None 

     if mode != tf.estimator.ModeKeys.PREDICT: 
      eval_metric_ops = { 
       "rmse": tf.metrics.root_mean_squared_error(tf.cast(labels, tf.float32), predictions), 
       "accuracy": tf.metrics.accuracy(tf.cast(labels, tf.float32), predictions), 
       "precision": tf.metrics.precision(tf.cast(labels, tf.float32), predictions) 
      } 
     else: 
      eval_metric_ops = None 

     # 3. Define the training operation/optimizer 
     if mode == tf.estimator.ModeKeys.TRAIN: 
      train_op = tf.contrib.layers.optimize_loss(
       loss=loss, 
       global_step=tf.contrib.framework.get_global_step(), 
       learning_rate=0.001, 
       optimizer="Adam") 
     else: 
      train_op = None 

     if mode == tf.estimator.ModeKeys.PREDICT: 
      predictions_dict = {"pred": predictions} 
     else: 
      predictions_dict = None 

     # 5. Return predictions/loss/train_op/eval_metric_ops in ModelFnOps object 
     return tf.estimator.EstimatorSpec(mode=mode, 
              predictions=predictions_dict, 
              loss=loss, 
              train_op=train_op, 
              eval_metric_ops=eval_metric_ops) 
    return _build_model 

而且你可以使用這個模型則是這樣的：

e = tf.estimator.Estimator(model_fn=model_fn(), params=None) 
e.train(input_fn=input_fn(), steps=1000) 

的輸入功能TensorFlow R1.1的例子中可以找到我回答here。