Meandering In Networks of Entities to Reach Verisimilar Answers

Last update: Dec 13, 2022

Overview

MINERVA

Meandering In Networks of Entities to Reach Verisimilar Answers

Code and models for the paper Go for a Walk and Arrive at the Answer - Reasoning over Paths in Knowledge Bases using Reinforcement Learning

MINERVA is a RL agent which answers queries in a knowledge graph of entities and relations. Starting from an entity node, MINERVA learns to navigate the graph conditioned on the input query till it reaches the answer entity. For example, give the query, (Colin Kaepernick, PLAYERHOMESTADIUM, ?), MINERVA takes the path in the knowledge graph below as highlighted. Note: Only the solid edges are observed in the graph, the dashed edges are unobsrved. gif courtesy of Bhuvi Gupta

Requirements

To install the various python dependencies (including tensorflow)

pip install -r requirements.txt

Training

Training MINERVA is easy!. The hyperparam configs for each experiments are in the configs directory. To start a particular experiment, just do

sh run.sh configs/${dataset}.sh

where the ${dataset}.sh is the name of the config file. For example,

sh run.sh configs/countries_s3.sh

Testing

We are also releasing pre-trained models so that you can directly use MINERVA for query answering. They are located in the saved_models directory. To load the model, set the load_model to 1 in the config file (default value 0) and model_load_dir to point to the saved_model. For example in configs/countries_s2.sh, make

load_model=1
model_load_dir="saved_models/countries_s2/model.ckpt"

Output

The code outputs the evaluation of MINERVA on the datasets provided. The metrics used for evaluation are Hits@{1,3,5,10,20} and MRR (which in the case of Countries is AUC-PR). Along with this, the code also outputs the answers MINERVA reached in a file.

Code Structure

The structure of the code is as follows

Code
├── Model
│    ├── Trainer
│    ├── Agent
│    ├── Environment
│    └── Baseline
├── Data
│    ├── Grapher
│    ├── Batcher
│    └── Data Preprocessing scripts
│            ├── create_vocab
│            ├── create_graph
│            ├── Trainer
│            └── Baseline

Data Format

To run MINERVA on a custom graph based dataset, you would need the graph and the queries as triples in the form of (e₁,r, e₂). Where e₁, and e₂ are nodes connected by the edge r. The vocab can of the dataset can be created using the create_vocab.py file found in data/data preprocessing scripts. The vocab needs to be stores in the json format {'entity/relation': ID}. The following shows the directory structure of the Kinship dataset.

kinship
    ├── graph.txt
    ├── train.txt
    ├── dev.txt
    ├── test.txt
    └── Vocab
            ├── entity_vocab.json
            └── relation_vocab.json

Citation

If you use this code, please cite our paper

@inproceedings{minerva,
  title = {Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning},
  author = {Das, Rajarshi and Dhuliawala, Shehzaad and Zaheer, Manzil and Vilnis, Luke and Durugkar, Ishan and Krishnamurthy, Akshay and Smola, Alex and McCallum, Andrew},
  booktitle = {ICLR},
  year = 2018
}

Comments

Reproducing NELL-995 MAP Results
Thanks very much for releasing the code in accompany with the paper. It definitely makes reproducing the experiments a lot easier. I've been playing with the code base and have some questions on reproducing the NELL-995 experiments.

The codebase does not contain the configuration file for NELL-995 experiments, nor does it contains the evaluation scripts for computing MAP. (Maybe you've missed them from the release?) I used the hyperparameters reported in "Experimental Details, section 2.3" and the appendix section 8.1 of the paper, which results in the following configuration file:

data_input_dir="datasets/data_preprocessed/nell-995/" vocab_dir="datasets/data_preprocessed/nell-995/vocab" total_iterations=1000 path_length=3 hidden_size=400 embedding_size=200 batch_size=64 beta=0.05 Lambda=0.02 use_entity_embeddings=1 train_entity_embeddings=1 train_relation_embeddings=1 base_output_dir="output/nell-995/" load_model=1 model_load_dir="saved_models/nell-995/model.ckpt"

I run train & test as specified in the README, and evaluate the decoding results using the MAP computation script produced by the DeepPath paper. (I assumed that the experiment setup is exactly the same as the DeepPath paper since you compared head-to-head with them.)

However, the MAP results I obtained this way is significantly lower compared to the reported results.

MINERVA concept_athleteplaysinleague MAP: 0.810746658312 (380 queries evaluated) MINERVA concept_athleteplaysforteam MAP: 0.649309434089 (386 queries evaluated) MINERVA concept_organizationheadquarteredincity MAP: 0.944878371403 (246 queries evaluated) MINERVA concept_athleteplayssport MAP: 0.919186046512 (602 queries evaluated) MINERVA concept_personborninlocation MAP: 0.775690686628 (192 queries evaluated) MINERVA concept_teamplayssport MAP: 0.762183612184 (111 queries evaluated) MINERVA concept_athletehomestadium MAP: 0.519108225108 (200 queries evaluated) MINERVA concept_worksfor MAP: 0.663530575465 (420 queries evaluated)

I did a few variation on embedding dimensions and also tried to freeze entity embeddings, yet none of the trials produced numbers close to the results tabulated in the MINERVA paper.

Would you please clarify the experiment setup for computing MAP? I want to make sure I did set the hyperparameters to the correct value. Besides, the DeepPath paper used a relation-dependent underlying graph per relation during inference. Did you also vary the graph per relation or used a base graph for all relations like you did for other datasets?

Many thanks.
opened by todpole3 14

Testing problem

Thanks for publishing the codebase.I have a question about testing.I set it up exactly as you mentioned in the README documentation.The rest arguments of testing are same as training.For example,'load_model=1' and 'model_load_dir="/output/worksfor/b2b4_3_0.05_100_0.05/model/model.ckpt"'.But I got the following error:

INFO:tensorflow:Restoring parameters from /output/worksfor/b2b4_3_0.05_100_0.05/model/model.ckpt
INFO:tensorflow:Restoring parameters from /output/worksfor/b2b4_3_0.05_100_0.05/model/model.ckpt
07/06/2019 03:42:44 PM: [ Restoring parameters from /output/worksfor/b2b4_3_0.05_100_0.05/model/model.ckpt ]
2019-07-06 15:42:44.906846: W tensorflow/core/framework/op_kernel.cc:1318] OP_REQUIRES failed at save_restore_tensor.cc:170 : Invalid argument: Unsuccessful TensorSliceReader constructor: Failed to get matching files on /output/worksfor/b2b4_3_0.05_100_0.05/model/model.ckpt: Not found: /output/worksfor/b2b4_3_0.05_100_0.05/model; No such file or directory
Traceback (most recent call last):
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1322, in _do_call
    return fn(*args)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1307, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1409, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Unsuccessful TensorSliceReader constructor: Failed to get matching files on /output/worksfor/b2b4_3_0.05_100_0.05/model/model.ckpt: Not found: /output/worksfor/b2b4_3_0.05_100_0.05/model; No such file or directory
	 [[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT, DT_INT32, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "code/model/trainer.py", line 574, in <module>
    trainer.initialize(restore=save_path, sess=sess)
  File "code/model/trainer.py", line 144, in initialize
    return  self.model_saver.restore(sess, restore)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 1802, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 900, in run
    run_metadata_ptr)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1135, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1316, in _do_run
    run_metadata)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1335, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Unsuccessful TensorSliceReader constructor: Failed to get matching files on /output/worksfor/b2b4_3_0.05_100_0.05/model/model.ckpt: Not found: /output/worksfor/b2b4_3_0.05_100_0.05/model; No such file or directory
	 [[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT, DT_INT32, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

Caused by op 'save/RestoreV2', defined at:
  File "code/model/trainer.py", line 574, in <module>
    trainer.initialize(restore=save_path, sess=sess)
  File "code/model/trainer.py", line 138, in initialize
    self.model_saver = tf.train.Saver(max_to_keep=2)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 1338, in __init__
    self.build()
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 1347, in build
    self._build(self._filename, build_save=True, build_restore=True)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 1384, in _build
    build_save=build_save, build_restore=build_restore)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 835, in _build_internal
    restore_sequentially, reshape)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 472, in _AddRestoreOps
    restore_sequentially)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 886, in bulk_restore
    return io_ops.restore_v2(filename_tensor, names, slices, dtypes)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/ops/gen_io_ops.py", line 1463, in restore_v2
    shape_and_slices=shape_and_slices, dtypes=dtypes, name=name)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
    op_def=op_def)
  File "/home/dr/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1718, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Unsuccessful TensorSliceReader constructor: Failed to get matching files on /output/worksfor/b2b4_3_0.05_100_0.05/model/model.ckpt: Not found: /output/worksfor/b2b4_3_0.05_100_0.05/model; No such file or directory
	 [[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT, DT_INT32, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

opened by DR73 7

Reproduce WN18RR results

I am trying to reproduce the results of WN18RR. I obtain much lower results despite using the predefined configs.

INFO:root:Hits@1: 0.3500 07/26/2018 02:40:17 PM: [ Hits@1: 0.3500 ] INFO:root:Hits@3: 0.4346 07/26/2018 02:40:17 PM: [ Hits@3: 0.4346 ]

In the paper Hits@1 is 0.413 and Hits@3 0.456

Moreover, in a new paper they are claiming a sota result compared to these lower results which makes this very confusing... https://arxiv.org/pdf/1802.04394.pdf

opened by FredericGodin 7
Table 4 experiments

Hi @rajarshd, @shehzaadzd,

For all the baseline methods, do you train on train.txt or on both train.txt and graph.txt? Do you have setting to reproduce the results? (e.g., Neural LP result is different from the ones in their paper for FB15k-237)

Thanks!

opened by posenhuang 7
is the config file you provide the optimal parameter??

Thanks for reading my question! is the config file you provide the optimal parameter?? I run a experiment on the dataset FB15K-237 with the default config file your provide . the results seems a lot different from the result in your paper?This is my result: INFO:root:Hits@1: 0.1069 04/29/2018 12:23:52 AM: [ Hits@1: 0.1069 ] INFO:root:Hits@3: 0.1904 04/29/2018 12:23:52 AM: [ Hits@3: 0.1904 ] INFO:root:Hits@5: 0.2314 04/29/2018 12:23:52 AM: [ Hits@5: 0.2314 ] INFO:root:Hits@10: 0.2864 04/29/2018 12:23:52 AM: [ Hits@10: 0.2864 ] INFO:root:Hits@20: 0.3413 04/29/2018 12:23:52 AM: [ Hits@20: 0.3413 ] INFO:root:auc: 0.1655 04/29/2018 12:23:52 AM: [ auc: 0.1655 ] Could you help me to reproduce the result of your paper??

opened by Lee-zix 7

grid-world dataset access

Hi @rajarshd, @shehzaadzd,

When I access the grid world data, I got the following error. I am using git-lfs version 1.2.

git lfs fetch --all
Scanning for all objects ever referenced...
* 26 objects found
Fetching objects...
Git LFS: (0 of 26 files, 26 skipped) 0 B / 6.34 MB, 6.34 MB skipped
[404] Object does not exist on the server
[e16e1d42fbd288e20d73337bbce88b29e1d889cfb409de87b87a7d3d3b3c06e5] Object does not exist on the server

Thanks!

opened by posenhuang 5

Encode the relation and entity ?

Hello, I read your paper and codes and there is a confusion.

About encoding relation and entity, here is my understanding. You don't encode them as vectors, instead using embedding matrices r and e and looking up in the embedding matrices according to their ids. And tf.nn.embedding_lookup() function could train the parameters in the embedding matrices.

Is that right?

opened by ProQianXiao 4
train/dev/test split

Why your dev triples are included in training data?

code/data/preprocessing_scripts/nell.py: out_file.write(e1+'\t'+r+'\t'+e2+'\n') if np.random.normal() > 0.2: ----dev.write(e1+'\t'+r+'\t'+e2+'\n')

Theoretically you are supposed to split it into 2 datasets (train/test) or 3 (train/dev/test) without overlaps. Please explain the reason behind this. Thank you.

opened by ghost 4
A whole solution for question-answering?

I have not found the natural language questions in the datasets. So I think I should turn the natural language questions to logic form like (e1,r,?) first. Am I right? Thank you very much!

opened by guotong1988 4
the accuracy can be better after fine tune the function `calc_cum_discounted_reward ` as follows
the code is at https://github.com/shehzaadzd/MINERVA/blob/d2f44ad7b48490fe73627cdd357e1465d67d9d75/code/model/trainer.py#L182-L183 I modified the code as a normal way to calculate returns(just change the time t), it becomes better. that is from :

for t in reversed(range(self.path_length)): running_add = self.gamma * running_add + cum_disc_reward[:, t] cum_disc_reward[:, t] = running_add

to :

for t in reversed(range(1, self.path_length)): running_add = self.gamma * running_add + cum_disc_reward[:, t] cum_disc_reward[:, t-1] = running_add

Thank you! I very appreciate you hard works, so I learned it line by line.
opened by suenpun 2
num_rollouts

Hi, I'm getting the following error:

File ".../grapher.py", line 65, in return_next_actions if entities[j] in all_correct_answers[i/rollouts] and entities[j] != correct_e2: TypeError: list indices must be integers or slices, not float

This function is called in environment.py:

next_actions = self.grapher.return_next_actions(self.current_entities, self.start_entities, self.query_relation,self.end_entities, self.all_answers, self.current_hop == self.path_len - 1,self.num_rollouts)

I wonder what the value of num_rollouts should be to prevent i/rollouts to become a float (as far as I see, i is already an integer.

opened by msaebi 2
Is this code an official implementation to the method in paper "Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks"?

On the "papers with code" website, this github repo is labeled as the official implementation of a paper named "Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks" (https://paperswithcode.com/paper/neuro-symbolic-inductive-logic-programming), which seems to be a mistake.

opened by yinjc8214 0
Export the mode using `tf.saved_model` and deploy it to production environment

Sorry to bother. When I'm trying to export the model using tf.saved_model to deploy it to a production environment (tf serving in docker), there are some problems. The exported graph format requires pure inputs and outputs signature, but in our model,the agent needs to interact with an environment. But all of the codes are written in numpy rather than tensorflow except the agent.py. So if I export the agent only(maybe failed), how the agent interacts with the environment? My idea: rewrite the environment in TF... but this is going to be a lot of work.

Thanks in advance.

opened by suenpun 1
How do you extract the general rules from the trained policy(like the (i) example in table 8)？

Thanks for your code! But I have some doubts about your displayed general rules. Is it just some similar result in test set or you come up with some guidelines for induction?

opened by xlk369293141 0

Unable to load the saved_model

I am running the model for countries_s1 model but getting the following error:

Executing python code/model/trainer.py --base_output_dir output/countries_s1/ --path_length 2 --hidden_size 25 --embedding_size 25     --batch_size 256 --beta 0.05 --Lambda 0.05 --use_entity_embeddings 1     --train_entity_embeddings 1 --train_relation_embeddings 1     --data_input_dir datasets/data_preprocessed/countries_s1/ --vocab_dir datasets/data_preprocessed/countries_s1/vocab --model_load_dir saved_models/countries_s1/model.ckpt --load_model 1 --total_iterations 1000 --nell_evaluation 0
HEY
Arguments:
                         LSTM_layers : 1
                              Lambda : 0.05
                     base_output_dir : output/countries_s1/
                          batch_size : 256
                                beta : 0.05
                        create_vocab : 0
                      data_input_dir : datasets/data_preprocessed/countries_s1/
                      embedding_size : 25
                          eval_every : 100
                               gamma : 1
                      grad_clip_norm : 5
                         hidden_size : 25
                          input_file : train.txt
                         input_files : ['datasets/data_preprocessed/countries_s1//train.txt']
                        l2_reg_const : 0.01
                       learning_rate : 0.001
                          load_model : True
                             log_dir : ./logs/
                       log_file_name : output/countries_s1//3f71_2_0.05_100_0.05/log.txt
                     max_num_actions : 200
                           model_dir : output/countries_s1//3f71_2_0.05_100_0.05/model/
                      model_load_dir : saved_models/countries_s1/model.ckpt
                     negative_reward : 0
                     nell_evaluation : 0
                        num_rollouts : 20
                          output_dir : output/countries_s1//3f71_2_0.05_100_0.05
                         output_file :
                         path_length : 2
                    path_logger_file : output/countries_s1//3f71_2_0.05_100_0.05
                                pool : max
                     positive_reward : 1.0
        pretrained_embeddings_action :
        pretrained_embeddings_entity :
                       test_rollouts : 100
                    total_iterations : 1000
             train_entity_embeddings : True
           train_relation_embeddings : True
               use_entity_embeddings : True
                           vocab_dir : datasets/data_preprocessed/countries_s1/vocab
INFO:root:reading vocab files...
01/28/2019 01:56:20 AM: [ reading vocab files... ]
INFO:root:Reading mid to name map
01/28/2019 01:56:20 AM: [ Reading mid to name map ]
INFO:root:Done..
01/28/2019 01:56:20 AM: [ Done.. ]
INFO:root:Total number of entities 273
01/28/2019 01:56:20 AM: [ Total number of entities 273 ]
INFO:root:Total number of relations 6
01/28/2019 01:56:20 AM: [ Total number of relations 6 ]
INFO:root:Skipping training
01/28/2019 01:56:20 AM: [ Skipping training ]
INFO:root:Loading model from saved_models/countries_s1/model.ckpt
01/28/2019 01:56:20 AM: [ Loading model from saved_models/countries_s1/model.ckpt ]
Reading vocab...
batcher loaded
KG constructed
Reading vocab...
Contains full graph
batcher loaded
KG constructed
Reading vocab...
Contains full graph
batcher loaded
KG constructed
2019-01-28 01:56:21.112771: 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.
2019-01-28 01:56:21.112794: 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.
2019-01-28 01:56:21.112801: 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.
2019-01-28 01:56:21.112806: 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.
2019-01-28 01:56:21.112812: 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.
2019-01-28 01:56:21.988648: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:06:00.0
Total memory: 10.91GiB
Free memory: 10.75GiB
2019-01-28 01:56:21.988740: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2019-01-28 01:56:21.988757: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0:   Y
2019-01-28 01:56:21.988799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:06:00.0)
INFO:root:Creating TF graph...
01/28/2019 01:56:22 AM: [ Creating TF graph... ]
INFO:root:TF Graph creation done..
01/28/2019 01:56:23 AM: [ TF Graph creation done.. ]
INFO:tensorflow:Restoring parameters from saved_models/countries_s1/model.ckpt
INFO:tensorflow:Restoring parameters from saved_models/countries_s1/model.ckpt
01/28/2019 01:56:23 AM: [ Restoring parameters from saved_models/countries_s1/model.ckpt ]
2019-01-28 01:56:23.830867: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
2019-01-28 01:56:23.846931: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847248: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847249: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847266: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847290: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847248: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847342: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847358: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847437: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847529: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847785: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847753: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847603: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847794: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847881: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.847816: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.848095: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.848317: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.848375: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.854893: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.854890: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.854966: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.854933: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.855353: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.855395: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam not found in checkpoint
2019-01-28 01:56:23.855675: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.855750: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.855791: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.855820: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.855785: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.856273: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.856468: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.856486: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.856592: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.856623: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
2019-01-28 01:56:23.856677: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
Traceback (most recent call last):
  File "code/model/trainer.py", line 573, in <module>
    trainer.initialize(restore=save_path, sess=sess)
  File "code/model/trainer.py", line 143, in initialize
    return  self.model_saver.restore(sess, restore)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1560, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 895, in run
    run_metadata_ptr)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1124, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
    options, run_metadata)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
         [[Node: save/RestoreV2_17/_3 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_80_save/RestoreV2_17", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]

Caused by op u'save/RestoreV2_9', defined at:
  File "code/model/trainer.py", line 573, in <module>
    trainer.initialize(restore=save_path, sess=sess)
  File "code/model/trainer.py", line 137, in initialize
    self.model_saver = tf.train.Saver(max_to_keep=2)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1140, in __init__
    self.build()
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1172, in build
    filename=self._filename)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 688, in build
    restore_sequentially, reshape)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 407, in _AddRestoreOps
    tensors = self.restore_op(filename_tensor, saveable, preferred_shard)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 247, in restore_op
    [spec.tensor.dtype])[0])
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/ops/gen_io_ops.py", line 663, in restore_v2
    dtypes=dtypes, name=name)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/home/ext_user2/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

NotFoundError (see above for traceback): Key entity_lookup_table/entity_lookup_table/Adam_1 not found in checkpoint
         [[Node: save/RestoreV2_9 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_9/tensor_names, save/RestoreV2_9/shape_and_slices)]]
         [[Node: save/RestoreV2_17/_3 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_80_save/RestoreV2_17", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]

opened by kunal017 4

parameter settings used in paper

Hello thank you for your code, it works smoothly without any problem. I found parameter settings in your code are different than ones in your paper and MAP results were much lower. Would you please post parameter settings used for experiments in your paper? I really appreciate it if you could provide config.sh file for paper experiments. For example, batch size, beta, lambda total_iterations, entity_embeddings, and train_entity_embeddings so on:

data_input_dir="datasets/data_preprocessed/athleteplaysinleague/" vocab_dir="datasets/data_preprocessed/athleteplaysinleague/vocab" total_iterations=120 path_length=3 hidden_size=400 embedding_size=200 batch_size=128 beta=0.05 Lambda=0.05 use_entity_embeddings=1 train_entity_embeddings=1 train_relation_embeddings=1 base_output_dir="output/athleteplaysinleague/" load_model=0 model_load_dir="/home/sdhuliawala/logs/RL-Path-RNN/wn18rrr/edb6_3_0.05_10_0.05/model/model.ckpt" nell_evaluation=1

opened by ghost 0