我建立了一个专门的类来构建、训练、保存然后加载我的模型。保存是通过 tf.saved_model.simple_save 完成的,然后通过 tf.saved_model.loader.load 恢复的。
训练和推理是使用数据集 API 完成的。使用经过训练的模型时一切正常。
但是,如果我恢复保存的模型,则推理会中断并引发此错误:
FailedPreconditionError (see above for traceback): GetNext() failed because the iterator has not been initialized. Ensure that you have run the initializer operation for this iterator before getting the next element.
[[Node: datasets/cond/IteratorGetNext_1 = IteratorGetNextoutput_shapes=[[?,?,30], [?,5]], output_types=[DT_INT32, DT_INT32], _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
我确定迭代器已初始化(print
按预期显示,请参阅下面的代码)。这可能与图表变量所属有关吗?还有其他想法吗?我有点被困在这里
(简化)代码:
class Model():
def __init__(self):
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph)
with self.graph.as_default():
model.features_data_ph = tf.Placeholder(...)
model.labels_data_ph = tf.Placeholder(...)
def build(self):
with self.graph.as_default():
self.logits = my_model(self.input_tensor)
self.loss = my_loss(self.logits, self.labels_tensor)
def train(self):
my_training_procedure()
def set_datasets(self):
with self.graph.as_default():
with tf.variable_scope('datasets'):
self.dataset = tf.data.Dataset.from_tensor_slices((self.features_data_ph, self.labels_data_ph))
self.iter = self.dataset.make_initializable_iterator()
self.input_tensor, self.labels_tensor = self.iter.get_next
def initialize_iterators(self, inference_data):
with self.graph.as_default():
feats = inference_data
labs = np.zeros((len(feats), self.hp.num_classes))
self.sess.run(self.iter.initializer,
feed_dict={self.features_data_ph: feats,
self.labels_data_ph: labs})
print('Iterator ready to infer')
def infer(self, inference_data):
self.initialize_iterators(inference_data)
return sess.run(self.logits)
def save(self, path):
inputs = {"features_data_ph": self.features_data_ph,
"labels_data_ph": self.labels_data_ph}
outputs = {"logits": self.model.logits}
tf.saved_model.simple_save(self.sess, path)
@staticmethod
def restore(path):
model = Model()
tf.saved_model.loader.load(model.sess, [tag_constants.SERVING], path)
model.features_data_ph = model.graph.get_tensor_by_name("features_data_ph:0")
model.labels_data_ph = model.graph.get_tensor_by_name("labels_data_ph:0")
model.logits = model.graph.get_tensor_by_name("model/classifier/dense/BiasAdd:0")
model.set_datasets()
return model
失败例程:
model1 = Model()
model1.build()
model1.train()
model1.save(model1_path)
...
model2 = Model.restore(model1_path)
model2.infer(some_numpy_array) # Error here, after print, at sess.run()
(恢复模型有效,原始模型和恢复模型之间的张量值匹配)
最佳答案
我遇到了同样的问题,我相信问题是您正在初始化一个新的数据集对象,而不是初始化与模型一起保存的迭代器。
尝试:
make_iter = model.get_operation_by_name("YOURPREFIX/MakeIterator")
sess.run(make_iter, feed_dict)
model.infer(some_numpy_array)
关于python - 失败前提条件错误: GetNext() failed after loading a Tensorflow Saved_Model,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/50354306/