阅读tensorflow documentation进行文本分类时，我在下面建立了一个脚本，用于训练文本分类模型(正/负)。有一件事我不确定。如何保存模型以便以后重用？另外，如何测试我拥有的输入测试集？在import tensorflow as tf

import tensorflow_hub as hub

import matplotlib.pyplot as plt

import numpy as np

import os

import pandas as pd

import re

import seaborn as sns

# Load all files from a directory in a DataFrame.

def load_directory_data(directory):

data = {}

data["sentence"] = []

data["sentiment"] = []

for file_path in os.listdir(directory):

with tf.gfile.GFile(os.path.join(directory, file_path), "r") as f:

data["sentence"].append(f.read())

data["sentiment"].append(re.match("\d+_(\d+)\.txt", file_path).group(1))

return pd.DataFrame.from_dict(data)

# Merge positive and negative examples, add a polarity column and shuffle.

def load_dataset(directory):

pos_df = load_directory_data(os.path.join(directory, "pos"))

neg_df = load_directory_data(os.path.join(directory, "neg"))

pos_df["polarity"] = 1

neg_df["polarity"] = 0

return pd.concat([pos_df, neg_df]).sample(frac=1).reset_index(drop=True)

# Download and process the dataset files.

def download_and_load_datasets(force_download=False):

dataset = tf.keras.utils.get_file(

fname="aclImdb.tar.gz",

origin="http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz",

extract=True)

train_df = load_dataset(os.path.join(os.path.dirname(dataset),

"aclImdb", "train"))

test_df = load_dataset(os.path.join(os.path.dirname(dataset),

"aclImdb", "test"))

return train_df, test_df

# Reduce logging output.

tf.logging.set_verbosity(tf.logging.ERROR)

train_df, test_df = download_and_load_datasets()

train_df.head()

# Training input on the whole training set with no limit on training epochs.

train_input_fn = tf.estimator.inputs.pandas_input_fn(

train_df, train_df["polarity"], num_epochs=None, shuffle=True)

# Prediction on the whole training set.

predict_train_input_fn = tf.estimator.inputs.pandas_input_fn(

train_df, train_df["polarity"], shuffle=False)

# Prediction on the test set.

predict_test_input_fn = tf.estimator.inputs.pandas_input_fn(

test_df, test_df["polarity"], shuffle=False)

embedded_text_feature_column = hub.text_embedding_column(

key="sentence",

module_spec="https://tfhub.dev/google/nnlm-en-dim128/1")

estimator = tf.estimator.DNNClassifier(

hidden_units=[500, 100],

feature_columns=[embedded_text_feature_column],

n_classes=2,

optimizer=tf.train.AdagradOptimizer(learning_rate=0.003))

# Training for 1,000 steps means 128,000 training examples with the default

# batch size. This is roughly equivalent to 5 epochs since the training dataset

# contains 25,000 examples.

estimator.train(input_fn=train_input_fn, steps=1000);

train_eval_result = estimator.evaluate(input_fn=predict_train_input_fn)

test_eval_result = estimator.evaluate(input_fn=predict_test_input_fn)

print "Training set accuracy: {accuracy}".format(**train_eval_result)

print "Test set accuracy: {accuracy}".format(**test_eval_result)

目前，如果我运行上面的脚本，它会重新训练完整的模型。我想重用这个模型，并将其输出为我所拥有的一些示例文本。我怎么能这么做？在

我尝试了以下方法来保存：

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但这会抛出一个错误，即Value Error: No variables to save