How to use Doc2Vec instead of default document preprocessing of skflow about skflow HOT 1 CLOSED

word_vectors = skflow.ops.categorical_variable(X, n_classes=n_words,
        embedding_size=EMBEDDING_SIZE, name='words')

def doc_vec_model(X, y):
    ... either nothing or skflow.ops.dnn for non linear transforms ...
    return skflow.models.logistic_regression(X, y)

classifier = skflow.TensorFlowEstimator(model_fn=doc_vec_model, n_classes=15,
    steps=1000, optimizer='Adam', learning_rate=0.01, continue_training=True)

# Continuesly train for 1000 steps & predict on test set.
while True:
    classifier.fit(X_train, y_train, logdir='/tmp/tf_examples/word_rnn')
    score = metrics.accuracy_score(classifier.predict(X_test), y_test)
    print('Accuracy: {0:f}'.format(score))