TensorFlow:恢复RNN网络后损失急剧上升 [英] TensorFlow: loss jumps up after restoring RNN net
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问题描述
- 操作系统:Windows 7 64位
- 从预置点安装的Tensorflow(无CUDA):1.0.1
- Python 3.5.2 64位
我在恢复网络(RNN字符库语言模型)时遇到问题.以下是存在相同问题的简化版本.
I have problems with restoring my net (RNN character base language model). Below is a simplified version with the same problem.
例如，当我第一次运行它时，会得到这个.
When I run it the first time, I get, for example, this.
...
step 160: loss = 1.956 (perplexity = 7.069016620211226)
step 180: loss = 1.837 (perplexity = 6.274748642468816)
step 200: loss = 1.825 (perplexity = 6.202084762557817)
但是在第二次运行中，恢复参数后，我得到了.
But on the second run, after restoring parameters, I get this.
step 220: loss = 2.346 (perplexity = 10.446611983898903)
step 240: loss = 2.346 (perplexity = 10.446709120339545)
...
所有tf变量似乎都已正确还原，包括状态，该状态将被馈送到RNN. 数据位置也被恢复(从步骤"开始).
All the tf variables seem to be correctly restored, including the state, which will be fed to RNN. Data position is also restored (from 'step').
我还为MNIST识别模型制作了一个类似的程序，该程序运行良好:恢复前后的损失是连续的.
I also made a similar program for MNIST recognition model, and this one works fine: the losses before and after the restoring are continuous.
还有其他应保存和恢复的参数或状态吗?
Are there any other parameters or states that should be saved and restored?
import argparse
import os
import tensorflow as tf
import numpy as np
import math
B = 20 # batch size
H = 200 # size of hidden layer of neurons
T = 25 # number of time steps to unroll the RNN for
data_file = 'ptb.train.txt' # any plain text file will do
checkpoint_dir = "tmp"
#----------------
# prepare data
#----------------
data = open(data_file, 'r').read()
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
print('data has {0} characters, {1} unique.'.format(data_size, vocab_size))
char_to_ix = { ch:i for i,ch in enumerate(chars) }
ix_to_char = { i:ch for i,ch in enumerate(chars) }
input_index_raw = np.array([char_to_ix[ch] for ch in data])
input_index_raw = input_index_raw[0:len(input_index_raw) // T * T]
input_index_raw_shift = np.append(input_index_raw[1:], input_index_raw[0])
input_all = input_index_raw.reshape([-1, T])
target_all = input_index_raw_shift.reshape([-1, T])
num_packed_data = len(input_all)
#----------------
# build model
#----------------
class Model(object):
def __init__(self):
self.input_ph = tf.placeholder(tf.int32, [None, T], name="input_ph")
self.target_ph = tf.placeholder(tf.int32, [None, T], name="target_ph")
embedding = tf.get_variable("embedding", [vocab_size, H], initializer=tf.random_normal_initializer(), dtype=tf.float32)
# input_ph is B x T.
# input_embedded is B x T x H.
input_embedded = tf.nn.embedding_lookup(embedding, self.input_ph)
cell = tf.contrib.rnn.BasicRNNCell(H)
self.state_ph = tf.placeholder(tf.float32, (None, cell.state_size), name="state_ph")
# Make state variable so that it will be saved by the saver.
self.state = tf.get_variable("state", (B, cell.state_size), initializer=tf.zeros_initializer(), trainable=False, dtype=tf.float32)
# Construct initial_state according to whether restoring or not.
self.isRestore = tf.placeholder(tf.bool, shape=(), name="isRestore")
zero_state = cell.zero_state(B, dtype=tf.float32)
self.initial_state = tf.cond(self.isRestore, lambda: self.state, lambda: zero_state)
# input_embedded : B x T x H
# output: B x T x H
# state : B x cell.state_size
output, state_ = tf.nn.dynamic_rnn(cell, input_embedded, initial_state=self.state_ph)
self.final_state = tf.assign(self.state, state_)
# reshape to (B * T) x H.
output_flat = tf.reshape(output, [-1, H])
# Convert hidden layer's output to vector of logits for each vocabulary.
softmax_w = tf.get_variable("softmax_w", [H, vocab_size], dtype=tf.float32)
softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=tf.float32)
logits = tf.matmul(output_flat, softmax_w) + softmax_b
# cross_entropy is a vector of length B * T
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.reshape(self.target_ph, [-1]), logits=logits)
self.loss = tf.reduce_mean(cross_entropy)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
self.global_step = tf.get_variable("global_step", (), initializer=tf.zeros_initializer(), trainable=False, dtype=tf.int32)
self.training_op = optimizer.minimize(cross_entropy, global_step=self.global_step)
def train_batch(self, sess, input_batch, target_batch, initial_state):
final_state_, _, final_loss = sess.run([self.final_state, self.training_op, self.loss], feed_dict={self.input_ph: input_batch, self.target_ph: target_batch, self.state_ph: initial_state})
return final_state_, final_loss
# main
with tf.Session() as sess:
if not tf.gfile.Exists(checkpoint_dir):
tf.gfile.MakeDirs(checkpoint_dir)
batch_stride = num_packed_data // B
# make model
model = Model()
saver = tf.train.Saver()
# always initialize
init = tf.global_variables_initializer()
init.run()
# restore if necessary
isRestore = False
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
isRestore = True
last_model = ckpt.model_checkpoint_path
print("Loading " + last_model)
saver.restore(sess, last_model)
# set initial step
step = tf.train.global_step(sess, model.global_step) + 1
print("start step = {0}".format(step))
# fetch initial state
state = sess.run(model.initial_state, feed_dict={model.isRestore: isRestore})
print("Initial state: {0}".format(state))
while True:
# prepare batch data
idx = [(step + x * batch_stride) % num_packed_data for x in range(0, B)]
input_batch = input_all[idx]
target_batch = target_all[idx]
state, last_loss = model.train_batch(sess, input_batch, target_batch, state)
if step % 20 == 0:
print('step {0}: loss = {1:.3f} (perplexity = {2})'.format(step, last_loss, math.exp(last_loss)))
if step % 200 == 0:
saved_file = saver.save(sess, os.path.join(checkpoint_dir, "model.ckpt"), global_step=step)
print("Saved to " + saved_file)
print("Last state: {0}".format(model.state.eval()))
break;
step = step + 1
推荐答案
问题已解决.它与RNN或TensorFlow无关.
The problem is solved. It had nothing to do with RNN nor TensorFlow.
我改变了
chars = list(set(data))
到
chars = sorted(set(data))
现在可以使用了.
这是因为 Python使用随机哈希函数建立集合，每次python重新启动时，'chars'都有不同的顺序.
This is because python uses a random hash function to build the set, and every time python restarted, 'chars' had a different ordering.
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