当我开始对我的tf.estimator.Estimator对象进行培训时，
Tensorflow在打印时自动创建CheckpointSaverHook
INFO:tensorflow:Create CheckpointSaverHook.
这个自动创建的SaverHook将在训练的开始和结束时保存我的模型。
我想要的是每n个训练步骤创建一个检查点。为此，我创建了自己的保存钩子并在训练时将其传递给我的估算器。
saver_hook = tf.train.CheckpointSaverHook(
checkpoint_dir = model_dir,
save_steps = 100
)
model.train(input_fn,steps=1500,hooks=[saver_hook])
这在理论上有效但我自己的CheckpointSaverHook只会保存*.meta个文件，而自动创建的文件会保存*.meta，*.index和*.data-XXXXX-of-XXXXX个文件。
我如何配置自己的SaverHook来做到这一点？
编辑：
添加了我的整个网络定义
network.py
import pickle
import random
import numpy as np
import tensorflow as tf
LEARNING_RATE = 0.002
class TFDotNet:
def __init__(self,model_dir):
# model def
self.model_dir = model_dir
self.model = tf.estimator.Estimator(model_fn=model_fn,model_dir=model_dir)
# hooks
self.summary_hook = tf.train.SummarySaverHook(
save_steps=50,
output_dir=model_dir,
scaffold=tf.train.Scaffold()
)
self.saver_hook = tf.train.CheckpointSaverHook(
checkpoint_dir=model_dir,
save_steps=100,
)
def train(self,x_train,y_train,steps=1500,batch_size=128):
""" train the neuralnetwork """
tf.logging.set_verbosity(tf.logging.INFO)
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'x': x_train}, y=y_train,batch_size=batch_size, num_epochs=None, shuffle=True
)
self.model.train(input_fn,steps=steps,hooks=[self.summary_hook,self.saver_hook])
def predict(self,x_predict):
""" predict some inputs """
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'x':x_predict}, y=None, batch_size=1, shuffle=False
)
return list(self.model.predict(input_fn))
def evaluate(self,x_test,y_test):
""" evaluate network on testset """
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'x': x_test}, y=y_test,batch_size=1, shuffle=False
)
return self.model.evaluate(input_fn)
def load_dataset(self,dataset_path):
""" loads a dataset from a serialized data file """
with open(dataset_path,'rb') as f:
return pickle.load(f)
def split_dataset(self,dataset,ratio,random_state=42):
""" splits a loaded dataset into training and testset """
random.seed(random_state)
random.shuffle(dataset)
length = int(ratio * len(dataset))
test_data = dataset[:length]
training_data = dataset[length:]
x_train = np.hstack([x for (x, y) in training_data]).transpose().astype('float32')
y_train = np.asarray([y for (x, y) in training_data]).reshape(-1, 1).astype('float32')
x_test = np.hstack([x for (x, y) in test_data]).transpose().astype('float32')
y_test = np.asarray([y for (x, y) in test_data]).reshape(-1, 1).astype('float32')
return x_train, y_train, x_test, y_test
def export(self):
""" exports the conv net """
def serving_input_receiver_fn():
# The outer dimension (None) allows us to batch up inputs for
# efficiency. However, it also means that if we want a prediction
# for a single instance, we'll need to wrap it in an outer list.
inputs = {"x": tf.placeholder(shape=[None, 900], dtype=tf.float32)}
return tf.estimator.export.ServingInputReceiver(inputs, inputs)
self.model.export_savedmodel(
export_dir_base=self.model_dir,
serving_input_receiver_fn=serving_input_receiver_fn)
def cnn_layout(features,reuse,is_training):
with tf.variable_scope('cnn',reuse=reuse):
# resize input to [batchsize,height,width,channel]
x = tf.reshape(features['x'], shape=[-1,30,30,1])
# conv1, 32 filter, 5 kernel
conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu, name='conv1')
# pool1, 2 stride, 2 kernel
pool1 = tf.layers.max_pooling2d(conv1, 2, 2, name='pool1')
# conv2, 64 filter, 3 kernel
conv2 = tf.layers.conv2d(pool1, 64, 3, activation=tf.nn.relu, name='conv2')
# pool2, 2 stride, 2 kernel
pool2 = tf.layers.max_pooling2d(conv2, 2, 2, name='pool2')
# flatten pool2
flatten = tf.contrib.layers.flatten(pool2)
# fc1 with 1024 neurons
fc1 = tf.layers.dense(flatten, 1024, name='fc1')
# 75% dropout
drop = tf.layers.dropout(fc1, rate=0.75, training=is_training, name='dropout')
# output logits
output = tf.layers.dense(drop, 1, name='output_logits')
return output
def model_fn(features, labels, mode):
# setup two networks one for training one for prediction while sharing weights
logits_train = cnn_layout(features=features,reuse=False,is_training=True)
logits_test = cnn_layout(features=features,reuse=True,is_training=False)
# predictions
probabilites = tf.sigmoid(logits_test, name='probabilities')
predictions = tf.round(probabilites,name='predictions')
export_outputs = tf.estimator.export.PredictOutput(outputs={'predictions':predictions,'probabilities':probabilites})
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode, predictions=predictions, export_outputs={'outputs':export_outputs})
# define loss and optimizer
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_train,labels=labels),name='loss')
optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE, name='optimizer')
train = optimizer.minimize(loss, global_step=tf.train.get_global_step(),name='train')
# accuracy for evaluation
accuracy = tf.metrics.accuracy(labels=labels,predictions=predictions,name='accuracy')
# summarys for tensorboard
tf.summary.scalar('loss',loss)
# return training and evalution spec
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train,
eval_metric_ops={'accuracy':accuracy}
)
training.py
from network import TFDotNet
from time import time
# settings
training_steps = 10000
mini_batch_size = 128
model_dir = 'neuralnet_data/02_networks/network01'
dataset_path = 'neuralnet_data/01_datasets/dataset.data'
# init dotnet
dotnet = TFDotNet(model_dir=model_dir)
# load dataset
print('loading dataset ...')
dataset = dotnet.load_dataset(dataset_path)
# split dataset
x_train, y_train, x_test, y_test = dotnet.split_dataset(dataset,0.1)
# train network
print('starting training ...')
t0 = time()
dotnet.train(x_train,y_train,steps=training_steps,batch_size=mini_batch_size)
print('Training took {}s'.format(time()-t0))
答案 0 :(得分：1)
这里的问题是，当没有指定Saver时（直接或通过脚手架），CheckpointSaverHook将在其构造函数中创建一个新的Saver。如果__init__未在与模型相同的图表中运行，则它将找不到任何变量，因此不会保存任何内容（https://github.com/tensorflow/tensorflow/issues/13265）。
假设您使用的是tf.estimator框架，那么您想要的图表在调用train之前就不存在了。
您应该能够通过在model_fn内创建保护程序来解决这个问题，并将其作为挂钩传递给EstimatorSpec。
答案 1 :(得分：0)
这是我的代码。它工作正常，完整的代码在mygithub
start_time = datetime.datetime.now()
saver_hook = tf.train.CheckpointSaverHook(
checkpoint_dir=FLAGS.train_dir,
save_steps=100,
)
config = tf.estimator.RunConfig()
config = config.replace(session_config=sess_config)
per_example_hook = ExamplesPerSecondHook(FLAGS.train_batch_size, every_n_steps=100)
hooks = [per_example_hook,saver_hook]
classifier = tf.estimator.Estimator(
model_fn=model_fn_cnn,
model_dir= FLAGS.train_dir,
config=config,
)
classifier.train(input_fn=functools.partial(input_fn,subset="training"),
steps=FLAGS.train_steps,
hooks=hooks
)
train_time = datetime.datetime.now() - start_time