Python：在另一个

Question

我想创建一个名为NeuralNetworkClassifier的类，它设置并适合神经网络分类器，并返回其对某些测试数据的预测。

为了设置和拟合神经网络，我使用的是tensorflow的DNNClassifier类。

我的问题是我的fit方法因此返回类型为tensorflow.python.estimator.canned.dnn.DNNClassifier的对象，当我随后尝试在此DNNClassifier对象上运行predict方法时，没有任何反应。我想这是因为我试图在不属于这个类的对象上运行NeuralNetworkClassifier方法？

我该如何解决这个问题？有没有办法在我自己的类中继续使用DNNClassifier类？

以下是代码：

class NeuralNetworkClassifier(object):

    def __init__(self, batch_size, n_steps, n_hidden_units1, n_hidden_units2, 
                 n_classes):
        self.batch_size = batch_size
        self.n_steps = n_steps
        self.n_hidden_units1 = n_hidden_units1
        self.n_hidden_units2 = n_hidden_units2
        self.n_classes = n_classes

    def set_up_architecture(self, train_data_features, train_sentiments):

        # Convert the scarce scipy feature matrices to pandas dataframes
        train_df = pd.DataFrame(train_data_features.toarray())

        # Convert column names from numbers to strings
        train_df.columns = train_df.columns.astype(str)

        # Create feature columns which describe how to use the input
        feat_cols = []
        for key in train_df.keys(): 
            feat_cols.append(tf.feature_column.numeric_column(key=key))

        # Set up classifier with two hidden unit layers
        classifier = tf.estimator.DNNClassifier(
                                        feature_columns=feat_cols, 
                                        hidden_units=[self.n_hidden_units1, 
                                                      self.n_hidden_units2],
                                        n_classes=self.n_classes)

        return train_df, classifier

    def shape_train_input(self, features, labels, batch_size):
        """An input function for training"""

        # Convert the input to a dataset
        dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))

        # Shuffle, repeat, and batch the examples
        dataset = dataset.shuffle(1000).repeat().batch(self.batch_size)

        return dataset

    def fit(self, train_data_features, train_sentiments):

        train_df, classifier = self.set_up_architecture(train_data_features, 
                                                   train_sentiments)
        print(type(classifier))

        fitted_classifier = classifier.train(input_fn=
                                      lambda:self.shape_train_input(train_df,
                                                              train_sentiments,
                                                              self.batch_size),
                                                              steps=1000)

        return fitted_classifier

    def shape_pred_input(self, features, labels, batch_size):
        """An input function for evaluation or prediction"""

        features=dict(features)
        if labels is None:
            # No labels, use only features.
            inputs = features
        else:
            inputs = (features, labels)

        # Convert the inputs to a dataset
        dataset = tf.data.Dataset.from_tensor_slices(inputs)

        # Batch the examples
        assert batch_size is not None, "batch_size must not be None"
        dataset = dataset.batch(self.batch_size)

        return dataset

    def predict(self, test_data_features):

        # Convert the scarce scipy feature matrices to pandas dataframes
        test_df = pd.DataFrame(test_data_features.toarray())

        # Convert column names from numbers to strings
        test_df.columns = test_df.columns.astype(str)

        predictions = self.evaluate(input_fn=
                                    lambda:shape_pred_input(test_df, 
                                                            test_data['sentiment'], 
                                                            self.batch_size))

        return predictions

运行代码

neuralnet = _make_classifier(conf)    
# This creates an instance of the NeuralNetworkClassifier class through
# another function, which works fine. type(neuralnet) is 
# __main__.NeuralNetworkClassifier. So far so good.

neuralnet_fitted = neuralnet.fit(train_data_features, train_sentiments)
# This works, but neuralnet_fitted is a DNNClassifier, not a NeuralNetworkClassifier.

predictions = neuralnet_fitted.predict(test_data_features)
# I don't get an error message, but an empty generator is returned. 
# I presume this is the case because an object of class DNNClassifier, 
# and not NeuralNetworkClassifier, was passed to predict.