我正在尝试在python中运行一个脚本，用于我为学校开发的项目。基本上我使用贝叶斯RNN来训练我的数据集模型，我使用的是我教授建议的脚本，因为我没有使用或拥有GPU。当我运行脚本时，我在bash上出现此错误。我是python，RNNs和人工智能的新手，但我对这个领域非常感兴趣，但我需要一些有更多经验的人的指导。我试着理解错误的本质，但我不知道如何解决这个问题。我使用xubuntu与python版本2.7.14和tensorflow版本1.1。我发布的代码和错误如下：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
import numpy as np
import logging
from bayesian_rnn import BayesianRNN
from reader import ptb_raw_data, Batcher
from config import get_config
logger = logging.getLogger(__name__)
def get_running_avg_loss(name, metric, running_avg_metric, summary_writer, step, decay=0.999):
"""
Calculate the running average of losses.
"""
if running_avg_metric == 0:
running_avg_metric = metric
else:
running_avg_metric = running_avg_metric * decay + (1 - decay) * metric
loss_sum = tf.Summary()
loss_sum.value.add(tag="loss", simple_value=running_avg_metric)
summary_writer.add_summary(loss_sum, step)
logger.info("Metric Reported: {} : {}".format(name, running_avg_metric))
return running_avg_metric
def run_step(name, batcher, step_function, session, summary_writer, running_avg_metric, step, state, memory):
try:
inputs, targets = next(batcher)
except StopIteration:
batcher.refresh_generator()
inputs, targets = next(batcher)
summaries, loss, state, memory, step = step_function(session, inputs, targets,state, memory, step)
if summaries is not None:
summary_writer.add_summary(summaries, step)
running_avg_loss = get_running_avg_loss(name + "_loss", running_avg_metric, loss,
summary_writer, step)
return running_avg_loss, state, memory
def main(unused_args):
config = get_config(FLAGS.model_size)
log_dir = FLAGS.log_dir
if log_dir is not None:
if not os.path.exists(log_dir):
os.makedirs(log_dir, exist_ok=True)
logger.info("Preprocessing and loading data")
raw_data = ptb_raw_data(FLAGS.data_path, "ptb.train.txt", "ptb.valid.txt", "ptb.test.txt")
train_data, val_data, test_data, vocab, word_to_id = raw_data
if FLAGS.test:
config.batch_size = 1
config.num_steps = 1
logger.info("Building Model")
model = BayesianRNN(config, is_training=True)
model.build()
logger.info("Preparing Savers and Supervisors")
saver = tf.train.Saver()
train_summary_writer = tf.summary.FileWriter(os.path.join(log_dir, "train"))
val_summary_writer = tf.summary.FileWriter(os.path.join(log_dir, "validation"))
supervisor = tf.train.Supervisor(
logdir=log_dir,
is_chief=True,
saver=saver,
summary_op=None,
save_summaries_secs=60,
save_model_secs=600,
global_step=model.global_step)
sess = supervisor.prepare_or_wait_for_session(config=tf.ConfigProto(allow_soft_placement=True))
running_avg_loss = 0
val_running_avg_loss = 0
step = 0
state = np.zeros([model.batch_size, model.hidden_size]).astype(np.float32)
memory = np.zeros([model.batch_size, model.hidden_size]).astype(np.float32)
if FLAGS.test:
test_data_batcher = Batcher(test_data, model.batch_size, model.num_steps)
test_loss = 0.0
test_state = state
test_memory = memory
for i, (inputs, targets) in enumerate(test_data_batcher.iterator):
summaries, loss, test_state, test_memory, step = model.run_eval_step(sess, inputs, targets,
test_state, test_memory, i)
test_loss = ((test_loss * i) + loss) / (i + 1)
if supervisor.should_stop():
supervisor.stop()
logger.info("Final Test Loss: {}".format(test_loss))
supervisor.stop()
else:
train_data_batcher = Batcher(train_data, model.batch_size, model.num_steps)
val_data_batcher = Batcher(val_data, model.batch_size, model.num_steps)
reversed_val_data_batcher = Batcher(val_data, model.batch_size, model.num_steps, reverse=True)
train_state = state
val_state = state
train_memory = memory
val_memory = memory
while not supervisor.should_stop() and step < config.max_epoch:
running_avg_loss, train_state, train_memory = run_step("train",
train_data_batcher,
model.run_train_step,
sess,
train_summary_writer,
running_avg_loss,
step,
train_state,
train_memory)
val_running_avg_loss, val_state, val_memory = run_step("validation",
val_data_batcher,
model.run_eval_step,
sess,
val_summary_writer,
val_running_avg_loss,
step,
val_state,
val_memory)
if step % 1000 == 0:
try:
inputs, targets = next(train_data_batcher.iterator)
except StopIteration:
train_data_batcher.refresh_generator()
inputs, targets = next(train_data_batcher.iterator)
image_summary, global_step = model.run_image_summary(sess, inputs, targets, train_state, train_memory)
train_summary_writer.add_summary(image_summary, global_step)
if step > 30000 and step % 10000:
model.decay_learning_rate(sess)
step += 1
if step % 100 == 0:
train_summary_writer.flush()
val_summary_writer.flush()
supervisor.stop()
if __name__ == '__main__':
flags = tf.flags
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
level=logging.INFO)
flags.DEFINE_string("model_size", "small", "Size of model to train, either small, medium or large")
flags.DEFINE_string("data_path", os.path.expanduser("~")+'/Desktop/bayesian-rnn-master/ptb/simple-examples/data', "simple-examples/data")
flags.DEFINE_string("log_dir", "./log", "path to directory for saving tensorboard logs.")
flags.DEFINE_bool("test", False, "Evaluate model on test data.")
FLAGS = flags.FLAGS
from tensorflow.python.platform import flags
from sys import argv
flags.FLAGS._parse_flags()
main(argv)
错误：
$ python train.py
2018-05-23 19:10:08,332 - INFO - __main__ - Preprocessing and loading data
2018-05-23 19:10:08,967 - INFO - __main__ - Building Model
2018-05-23 19:10:08,967 - INFO - bayesian_rnn - Building model
2018-05-23 19:10:09,084 - INFO - bayesian_rnn - Building LSTM cell with weights drawn from q(phi) = N(phi | mu, sigma)
2018-05-23 19:10:09,129 - INFO - bayesian_rnn - Resampling weights using Posterior Sharpening
2018-05-23 19:10:19,430 - INFO - bayesian_rnn - Building LSTM cell with new weights sampled from posterior
2018-05-23 19:10:23,364 - INFO - bayesian_rnn - Adding training operations
[<tf.Tensor 'gradients_1/gradients/concat_grad/split_grad/concat:0' shape=(?, ?) dtype=float32>, None, None]
2018-05-23 19:11:19,185 - INFO - __main__ - Preparing Savers and Supervisors
2018-05-23 19:11:27.593976: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2018-05-23 19:11:27.595345: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2018-05-23 19:11:27.596740: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-05-23 19:11:27.598200: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-05-23 19:11:27.599350: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
INFO:tensorflow:global_step/sec: 0
2018-05-23 19:11:53,918 - INFO - tensorflow - global_step/sec: 0
Traceback (most recent call last):
File "train.py", line 170, in <module>
main(argv)
File "train.py", line 124, in main
train_memory)
File "train.py", line 36, in run_step
inputs, targets = next(batcher)
TypeError: Batcher object is not an iterator