python - Tensorflow Session.Run() 张量对象不可调用

Question

我有一个为 ptb example 训练的 rnn 模型使用 tensorflow 的 ptb_word .下面我有一个代码，我试图在其中打印一些示例来测试经过训练的模型。当我在 probs, state = sess.run([mtest.output_probs(), mtest ._final_state], feed_dict=feed_dict)

究竟是什么导致了这个错误？

代码如下:

import numpy as np
import os
import tensorflow as tf
from ptb_word_lm import *
from tensorflow.models.rnn.ptb import reader
from tensorflow.python.platform import gfile

data_path = "/home/usr/simple-examples/data/"
raw_data = reader.ptb_raw_data(data_path)
train_data, valid_data, test_data, vocabulary = raw_data

test_path = os.path.join(data_path, "ptb.test.txt")
word_to_id = reader._build_vocab(test_path)


eval_config = get_config()
eval_config.batch_size = 1
eval_config.num_steps = 1

sess = tf.Session()

initializer = tf.random_uniform_initializer(-eval_config.init_scale,
                                            eval_config.init_scale)
test_input = PTBInput(config=eval_config, data=test_data, name="TestInput")
with tf.variable_scope("model", reuse=None, initializer=initializer):
    mtest = PTBModel(is_training=False, config=eval_config, input_=test_input)

sess.run(tf.initialize_all_variables())

saver = tf.train.import_meta_graph('/home/usr/models/medium/model.ckpt-50979.meta')

ckpt = tf.train.get_checkpoint_state('/home/usr/models/medium/')
if ckpt and gfile.Exists(ckpt.model_checkpoint_path):
    msg = 'Reading model parameters from %s' % ckpt.model_checkpoint_path
    print(msg)
    saver.restore(sess, ckpt.model_checkpoint_path)

def pick_from_weight(weight, pows=1.0):
    weight = weight**pows
    t = np.cumsum(weight)
    s = np.sum(weight)
    return int(np.searchsorted(t, np.random.rand(1) * s))

while True:
    number_of_sentences = 10
    sentence_cnt = 0
    text = '\n'
    end_of_sentence_char = word_to_id['<eos>']
    input_char = np.array([[end_of_sentence_char]])
    state = sess.run(mtest.initial_state)
    for attr in  mtest.__dict__:
        print attr
    print 'all attributes above'
    while sentence_cnt < number_of_sentences:
        feed_dict = {mtest._input: input_char,
                     mtest.initial_state: state}

        probs, state = sess.run([mtest.output_probs(), mtest._final_state], feed_dict=feed_dict)

        print 'after state'
        sampled_char = pick_from_weight(probs[0])
        print sampled_char
        if sampled_char == end_of_sentence_char:
            text += '.\n'
            sentence_cnt += 1
        else:
            text += ' ' + id_to_word[sampled_char]
        input_char = np.array([[sampled_char]])
    print(text)
    raw_input('press any key to continue ...')

Answer 1

查看 GitHub 上的引用代码我找不到 output_props，所以可能版本不同。但是，由于 mtest.initial_state 是一个 @property，我假设 mtest.output_props 也是一个。即尝试

probs, state = sess.run([mtest.output_probs, mtest.final_state], feed_dict=feed_dict)

相反，即不使用括号。

此外，mtest._final_state 是一个内部变量，不应直接使用。您可能想改用 mtest.final_state。