我正在尝试实现服务功能，以便能够对保存的文本分类模型进行预测。据我所知，目标是创建一个与train_input_fn / eval_input_fn几乎完全相同的函数？我有以下实现这些功能：
def generate_training_input_fn(filename):
train_raw = pd.read_csv(filename[0], header=None)
x_train = train_raw.iloc[:, 1]
y_train = train_raw.iloc[:, 0]
vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
n_words = len(vocab_processor.vocabulary_)
#print('Total words: %d' % n_words)
# Save a vocabulary list to file. Needed by the serving_input_fn for exporting the model.
with open('vocab_processor.pickle', 'wb') as f:
pickle.dump(vocab_processor, f)
features = tf.contrib.layers.bow_encoder(
x_train, vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
return features, y_train
def generate_eval_input_fn(filename):
eval_raw = pd.read_csv(filename[0], header=None)
x_eval = eval_raw.iloc[:, 1]
y_eval = eval_raw.iloc[:, 0]
with open('vocab_processor.pickle', 'rb') as f:
vocab_processor = pickle.load(f)
n_words = len(vocab_processor.vocabulary_)
x_eval = np.array(list(vocab_processor.transform(x_eval)))
features = tf.contrib.layers.bow_encoder(
x_eval, vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
#labels = tf.one_hot(y_eval, 15, 1, 0)
return features, y_eval
有评论“将词汇表保存到文件。需要由serving_input_fn导出模型”但是没有实现这样的功能，并且使用export_strategies = None创建了实验（从另一个编码器获得此代码）。我试图在人口普查教程中实现serve_input_fn
def csv_serving_input_fn():
csv_row = tf.placeholder(shape=[None],dtype=tf.string)
features = parse_csv(csv_row)
return tf.contrib.learn.InputFnOps(features, None, {'csv_row': csv_row})
但不知道如何实现parse_csv，因为我的generate_eval_input_fn将整个csv作为pandas DataFrame