[BUG] using NextItNet in main/examples/00_quick_start/sequential_recsys_amazondataset.ipynb

[OhitsaSteve]

Hi :)

Description

We want to use the NextItNet Recommender. For that we are using the quickstart example, friendly provided by you.
(main/examples/00_quick_start/sequential_recsys_amazondataset.ipynb)
Additionally we have modify some lines in the ipynb like by your comments described.
Unfortunately it asserts by line "model = SeqModel(hparams, input_creator, seed=RANDOM_SEED)" at part "2. Create model" with

ValueError                                Traceback (most recent call last)
Cell In[34], line 1
----> 1 model = SeqModel(hparams, input_creator, seed=RANDOM_SEED)
      3 ## sometimes we don't want to train a model from scratch
      4 ## then we can load a pre-trained model like this: 
      5 #model.load_model(r'your_model_path')

File /opt/conda/lib/python3.11/site-packages/recommenders/models/deeprec/models/sequential/sequential_base_model.py:51, in SequentialBaseModel.__init__(self, hparams, iterator_creator, graph, seed)
     46 with self.graph.as_default():
     47     self.sequence_length = tf.compat.v1.placeholder(
     48         tf.int32, [None], name="sequence_length"
     49     )
---> 51 super().__init__(hparams, iterator_creator, graph=self.graph, seed=seed)

File /opt/conda/lib/python3.11/site-packages/recommenders/models/deeprec/models/base_model.py:56, in BaseModel.__init__(self, hparams, iterator_creator, graph, seed)
     52 self.group = tf.compat.v1.placeholder(tf.int32, shape=(), name="group")
     54 self.initializer = self._get_initializer()
---> 56 self.logit = self._build_graph()
     57 self.pred = self._get_pred(self.logit, self.hparams.method)
     59 self.loss = self._get_loss()

File /opt/conda/lib/python3.11/site-packages/recommenders/models/deeprec/models/sequential/sequential_base_model.py:71, in SequentialBaseModel._build_graph(self)
     69 self._build_embedding()
     70 self._lookup_from_embedding()
---> 71 model_output = self._build_seq_graph()
     72 logit = self._fcn_net(model_output, hparams.layer_sizes, scope="logit_fcn")
     73 self._add_norm()

File /opt/conda/lib/python3.11/site-packages/recommenders/models/deeprec/models/sequential/sli_rec.py:71, in SLI_RECModel._build_seq_graph(self)
     68     tf.compat.v1.summary.histogram("LSTM_outputs", rnn_outputs)
     70 with tf.compat.v1.variable_scope("attention_fcn"):
---> 71     att_outputs2 = self._attention_fcn(
     72         self.target_item_embedding, rnn_outputs
     73     )
     74     att_fea2 = tf.reduce_sum(input_tensor=att_outputs2, axis=1)
     75     tf.compat.v1.summary.histogram("att_fea2", att_fea2)

File /opt/conda/lib/python3.11/site-packages/recommenders/models/deeprec/models/sequential/sli_rec.py:113, in SLI_RECModel._attention_fcn(self, query, user_embedding)
    110 query_size = query.shape[1]
    111 boolean_mask = tf.equal(self.mask, tf.ones_like(self.mask))
--> 113 attention_mat = tf.compat.v1.get_variable(
    114     name="attention_mat",
    115     shape=[user_embedding.shape.as_list()[-1], query_size],
    116     initializer=self.initializer,
    117 )
    118 att_inputs = tf.tensordot(user_embedding, attention_mat, [[2], [0]])
    120 queries = tf.reshape(
    121     tf.tile(query, [1, att_inputs.shape[1]]), tf.shape(input=att_inputs)
    122 )

File /opt/conda/lib/python3.11/site-packages/tensorflow/python/ops/variable_scope.py:1630, in get_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)
   1614 @tf_export(v1=["get_variable"])
   1615 def get_variable(name,
   1616                  shape=None,
   (...)
   1628                  synchronization=VariableSynchronization.AUTO,
   1629                  aggregation=VariableAggregation.NONE):
-> 1630   return get_variable_scope().get_variable(
   1631       _get_default_variable_store(),
   1632       name,
   1633       shape=shape,
   1634       dtype=dtype,
   1635       initializer=initializer,
   1636       regularizer=regularizer,
   1637       trainable=trainable,
   1638       collections=collections,
   1639       caching_device=caching_device,
   1640       partitioner=partitioner,
   1641       validate_shape=validate_shape,
   1642       use_resource=use_resource,
   1643       custom_getter=custom_getter,
   1644       constraint=constraint,
   1645       synchronization=synchronization,
   1646       aggregation=aggregation)

File /opt/conda/lib/python3.11/site-packages/tensorflow/python/ops/variable_scope.py:1340, in VariableScope.get_variable(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)
   1338 if dtype is None:
   1339   dtype = self._dtype
-> 1340 return var_store.get_variable(
   1341     full_name,
   1342     shape=shape,
   1343     dtype=dtype,
   1344     initializer=initializer,
   1345     regularizer=regularizer,
   1346     reuse=reuse,
   1347     trainable=trainable,
   1348     collections=collections,
   1349     caching_device=caching_device,
   1350     partitioner=partitioner,
   1351     validate_shape=validate_shape,
   1352     use_resource=use_resource,
   1353     custom_getter=custom_getter,
   1354     constraint=constraint,
   1355     synchronization=synchronization,
   1356     aggregation=aggregation)

File /opt/conda/lib/python3.11/site-packages/tensorflow/python/ops/variable_scope.py:585, in _VariableStore.get_variable(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)
    583   return custom_getter(**custom_getter_kwargs)
    584 else:
--> 585   return _true_getter(
    586       name,
    587       shape=shape,
    588       dtype=dtype,
    589       initializer=initializer,
    590       regularizer=regularizer,
    591       reuse=reuse,
    592       trainable=trainable,
    593       collections=collections,
    594       caching_device=caching_device,
    595       partitioner=partitioner,
    596       validate_shape=validate_shape,
    597       use_resource=use_resource,
    598       constraint=constraint,
    599       synchronization=synchronization,
    600       aggregation=aggregation)

File /opt/conda/lib/python3.11/site-packages/tensorflow/python/ops/variable_scope.py:538, in _VariableStore.get_variable.<locals>._true_getter(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, constraint, synchronization, aggregation)
    532 if "%s/part_0" % name in self._vars:
    533   raise ValueError(
    534       "No partitioner was provided, but a partitioned version of the "
    535       "variable was found: %s/part_0. Perhaps a variable of the same "
    536       "name was already created with partitioning?" % name)
--> 538 return self._get_single_variable(
    539     name=name,
    540     shape=shape,
    541     dtype=dtype,
    542     initializer=initializer,
    543     regularizer=regularizer,
    544     reuse=reuse,
    545     trainable=trainable,
    546     collections=collections,
    547     caching_device=caching_device,
    548     validate_shape=validate_shape,
    549     use_resource=use_resource,
    550     constraint=constraint,
    551     synchronization=synchronization,
    552     aggregation=aggregation)

File /opt/conda/lib/python3.11/site-packages/tensorflow/python/ops/variable_scope.py:958, in _VariableStore._get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint, synchronization, aggregation)
    956       variable_dtype = None
    957     else:
--> 958       raise ValueError("The initializer passed is not valid. It should "
    959                        "be a callable with no arguments and the "
    960                        "shape should not be provided or an instance of "
    961                        "`tf.keras.initializers.*' and `shape` should be "
    962                        "fully defined.")
    964 # Create the variable.
    965 if use_resource is None:
    966   # Set the default value if unspecified.

ValueError: The initializer passed is not valid. It should be a callable with no arguments and the shape should not be provided or an instance of `tf.keras.initializers.*' and `shape` should be fully defined. 

How do we replicate the issue?

take original "main/examples/00_quick_start/sequential_recsys_amazondataset.ipynb" and use NextItNet as through your comments described.

Expected behavior (i.e. solution)

run NextItNet successful with amazondata set.
We was apply to run all codeblocks with the other GRU [2], Caser [3], A2SVD [1], SLi_Rec [1], and SUM [5] recommender.

Other Comments

Full code until error :

import sys
import tensorflow.compat.v1 as tf
tf.get_logger().setLevel('ERROR') # only show error messages

from recommenders.utils.timer import Timer
from recommenders.utils.constants import SEED
from recommenders.models.deeprec.deeprec_utils import (
    prepare_hparams
)
from recommenders.datasets.amazon_reviews import download_and_extract, data_preprocessing
from recommenders.models.deeprec.models.sequential.sli_rec import SLI_RECModel as SeqModel
####  to use the other model, use one of the following lines:
# from recommenders.models.deeprec.models.sequential.asvd import A2SVDModel as SeqModel
# from recommenders.models.deeprec.models.sequential.caser import CaserModel as SeqModel
# from recommenders.models.deeprec.models.sequential.gru import GRUModel as SeqModel
# from recommenders.models.deeprec.models.sequential.sum import SUMModel as SeqModel
from recommenders.models.deeprec.models.sequential.nextitnet import NextItNetModel
#from recommenders.models.deeprec.io.sequential_iterator import SequentialIterator
from recommenders.models.deeprec.io.nextitnet_iterator import NextItNetIterator
#from recommenders.utils.notebook_utils import store_metadata

print(f"System version: {sys.version}")
print(f"Tensorflow version: {tf.__version__}")

EPOCHS = 10
BATCH_SIZE = 400
RANDOM_SEED = SEED  # Set None for non-deterministic result

data_path = os.path.join("..", "..", "tests", "resources", "deeprec", "slirec")


##  ATTENTION: change to the corresponding config file, e.g., caser.yaml for CaserModel, sum.yaml for SUMModel
yaml_file = '../../recommenders/models/deeprec/config/sli_rec.yaml'  


ta_path, r'train_data')
valid_file = os.path.join(data_path, r'valid_data')
test_file = os.path.join(data_path, r'test_data')
user_vocab = os.path.join(data_path, r'user_vocab.pkl')
item_vocab = os.path.join(data_path, r'item_vocab.pkl')
cate_vocab = os.path.join(data_path, r'category_vocab.pkl')
output_file = os.path.join(data_path, r'output.txt')

reviews_name = 'reviews_Movies_and_TV_5.json'
meta_name = 'meta_Movies_and_TV.json'
reviews_file = os.path.join(data_path, reviews_name)
meta_file = os.path.join(data_path, meta_name)
train_num_ngs = 4 # number of negative instances with a positive instance for training
valid_num_ngs = 4 # number of negative instances with a positive instance for validation
test_num_ngs = 9 # number of negative instances with a positive instance for testing
sample_rate = 0.01 # sample a small item set for training and testing here for fast example

input_files = [reviews_file, meta_file, train_file, valid_file, test_file, user_vocab, item_vocab, cate_vocab]

if not os.path.exists(train_file):
    download_and_extract(reviews_name, reviews_file)
    download_and_extract(meta_name, meta_file)
    #data_preprocessing(*input_files, sample_rate=sample_rate, valid_num_ngs=valid_num_ngs, test_num_ngs=test_num_ngs)
    #### uncomment this for the NextItNet model, because it does not need to unfold the user history
    data_preprocessing(*input_files, sample_rate=sample_rate, valid_num_ngs=valid_num_ngs, test_num_ngs=test_num_ngs, is_history_expanding=False)

### NOTE:  
### remember to use `_create_vocab(train_file, user_vocab, item_vocab, cate_vocab)` to generate the user_vocab, item_vocab and cate_vocab files, if you are using your own dataset rather than using our demo Amazon dataset.
hparams = prepare_hparams(yaml_file, 
                          embed_l2=0., 
                          layer_l2=0., 
                          learning_rate=0.001,  # set to 0.01 if batch normalization is disable
                          epochs=EPOCHS,
                          batch_size=BATCH_SIZE,
                          show_step=20,
                          MODEL_DIR=os.path.join(data_path, "model/"),
                          SUMMARIES_DIR=os.path.join(data_path, "summary/"),
                          user_vocab=user_vocab,
                          item_vocab=item_vocab,
                          cate_vocab=cate_vocab,
                          need_sample=True,
                          train_num_ngs=train_num_ngs, # provides the number of negative instances for each positive instance for loss computation.
            )

#input_creator = SequentialIterator
#### uncomment this for the NextItNet model, because it needs a special data iterator for training
input_creator = NextItNetIterator

model = SeqModel(hparams, input_creator, seed=RANDOM_SEED)

## sometimes we don't want to train a model from scratch
## then we can load a pre-trained model like this: 
#model.load_model(r'your_model_path')

=> ERROR

[miguelgfierro]

This might be related to the issues we've been having with the newest Keras. @SimonYansenZhao do you think this could be related to the issues with TF?

@OhitsaSteve can you check that you have TF < 2.16 #2071

[OhitsaSteve]

Thanks for your support :)

Yes, we are using TF < 2.16 . its currently 2.15.0

Btw in the upper code block its still the "sli_req.yaml" , we tried it with the modified "nextitnet" , sorry

/lectures/194.035-2024S/Group_35$ pip list
Package                      Version
---------------------------- --------------------
absl-py                      1.4.0
aiohttp                      3.9.3
aiosignal                    1.3.1
alembic                      1.13.1
altair                       5.2.0
annotated-types              0.6.0
anyio                        4.3.0
appdirs                      1.4.4
archspec                     0.2.3
argon2-cffi                  23.1.0
argon2-cffi-bindings         21.2.0
array-record                 0.5.0
arrow                        1.3.0
asttokens                    2.4.1
astunparse                   1.6.3
async-generator              1.10
async-lru                    2.0.4
attrs                        23.2.0
Babel                        2.14.0
bash_kernel                  0.9.3
beautifulsoup4               4.12.3
binaryornot                  0.4.4
black                        24.3.0
bleach                       6.1.0
blingfire                    0.1.8
blinker                      1.7.0
blis                         0.7.11
bokeh                        3.3.4
boltons                      23.1.1
Bottleneck                   1.3.8
bottombar                    2.1
branca                       0.7.1
Brotli                       1.1.0
cached-property              1.5.2
cachetools                   5.3.3
catalogue                    2.0.10
category-encoders            2.6.3
certifi                      2024.2.2
certipy                      0.1.3
cffi                         1.16.0
cftime                       1.6.3
chardet                      5.2.0
charset-normalizer           3.3.2
click                        8.1.7
click-plugins                1.1.1
cligj                        0.7.2
cloudpathlib                 0.16.0
cloudpickle                  3.0.0
colorama                     0.4.4
colorcet                     3.1.0
colorlog                     4.7.2
comm                         0.2.1
conda                        24.1.2
conda-libmamba-solver        24.1.0
conda-package-handling       2.2.0
conda_package_streaming      0.9.0
confection                   0.1.4
ConfigArgParse               1.7
configobj                    5.0.8
contourpy                    1.2.0
cookiecutter                 2.6.0
cornac                       1.18.0
cryptography                 42.0.5
cycler                       0.12.1
cymem                        2.0.8
Cython                       3.0.9
cytoolz                      0.12.3
dask                         2024.2.1
debugpy                      1.8.1
decorator                    5.1.1
defusedxml                   0.7.1
descartes                    1.1.0
dill                         0.3.8
distlib                      0.3.8
distributed                  2024.2.1
distro                       1.9.0
dm-tree                      0.1.8
ebrec                        0.0.1
editables                    0.5
entrypoints                  0.4
et-xmlfile                   1.1.0
etils                        1.8.0
exceptiongroup               1.2.0
executing                    2.0.1
Farama-Notifications         0.0.4
fastai                       2.7.14
fastcore                     1.5.29
fastdownload                 0.0.7
fastjsonschema               2.19.1
fastprogress                 1.0.3
filelock                     3.13.3
fiona                        1.9.6
flake8                       7.0.0
Flask                        3.0.3
Flask-Cors                   4.0.1
Flask-Login                  0.6.3
flatbuffers                  24.3.25
fonttools                    4.49.0
fqdn                         1.5.1
frozenlist                   1.4.1
fsspec                       2024.2.0
future                       1.0.0
gast                         0.5.4
gensim                       4.3.2
geopandas                    0.14.3
gevent                       24.2.1
geventhttpclient             2.3.1
gitdb                        4.0.11
GitPython                    3.1.42
gmpy2                        2.1.2
google-auth                  2.29.0
google-auth-oauthlib         1.2.0
google-pasta                 0.2.0
googleapis-common-protos     1.63.0
grader-labextension          0.3.1
grader-service               0.4.1
graphviz                     0.20.3
greenlet                     3.0.3
grpcio                       1.62.1
gymnasium                    0.29.1
h11                          0.14.0
h2                           4.1.0
h5netcdf                     1.3.0
h5py                         3.10.0
hatch                        1.9.4
hatch-jupyter-builder        0.9.1
hatch-nodejs-version         0.3.2
hatchling                    1.21.1
holoviews                    1.18.3
hpack                        4.0.0
httpcore                     1.0.4
httpx                        0.27.0
huggingface-hub              0.22.1
hvplot                       0.9.2
hyperframe                   6.0.1
hyperlink                    21.0.0
hyperopt                     0.2.7
hypothesis                   6.103.2
idna                         3.6
imagecodecs                  2024.1.1
imageio                      2.34.0
importlib_metadata           7.0.2
importlib_resources          6.1.3
iniconfig                    2.0.0
ipyflex                      0.2.6
ipykernel                    6.29.3
ipyleaflet                   0.18.2
ipympl                       0.9.3
ipysheet                     0.7.0
ipytablewidgets              0.3.1
ipython                      8.22.2
ipython-genutils             0.2.0
ipyvue                       1.10.2
ipyvuetify                   1.9.3
ipywidgets                   8.1.2
isodate                      0.6.1
isoduration                  20.11.0
itsdangerous                 2.2.0
jaraco.classes               3.3.1
jaraco.context               4.3.0
jaraco.functools             4.0.0
jax                          0.4.25
jedi                         0.19.1
jeepney                      0.8.0
Jinja2                       3.1.3
joblib                       1.3.2
json5                        0.9.22
jsonpatch                    1.33
jsonpointer                  2.4
jsonschema                   4.21.1
jsonschema-specifications    2023.12.1
jupyter                      1.0.0
jupyter_client               8.6.0
jupyter-code-server          0.0.0
jupyter-console              6.6.3
jupyter_core                 5.7.1
jupyter-events               0.9.0
jupyter-lsp                  2.2.4
jupyter-pluto-proxy          0.1.2
jupyter-resource-usage       1.0.2
jupyter_server               2.13.0
jupyter-server-mathjax       0.2.6
jupyter_server_proxy         4.1.0
jupyter_server_terminals     0.5.2
jupyter-telemetry            0.1.0
jupyterhub                   4.0.2
jupyterlab                   4.1.4
jupyterlab_git               0.50.0
jupyterlab-mathjax3          4.3.0
jupyterlab_pygments          0.3.0
jupyterlab_server            2.25.3
jupyterlab_widgets           3.0.10
kaggle                       1.6.8
keras                        2.15.0
Keras-Preprocessing          1.1.2
keyring                      25.0.0
kiwisolver                   1.4.5
kmeans-pytorch               0.3
kubernetes                   29.0.0
langcodes                    3.3.0
lazy_loader                  0.3
lets-plot                    4.3.0
libclang                     18.1.1
libmambapy                   1.5.7
lightfm                      1.17
lightgbm                     4.3.0
lightning-utilities          0.11.1
linkify-it-py                2.0.3
llvmlite                     0.42.0
locket                       1.0.0
locust                       2.29.0
lxml                         5.1.0
lz4                          4.3.3
madmom                       0.17.dev0
Mako                         1.3.2
mamba                        1.5.7
Markdown                     3.6
markdown-it-py               3.0.0
MarkupSafe                   2.1.5
matplotlib                   3.8.3
matplotlib-inline            0.1.6
mccabe                       0.7.0
mdit-py-plugins              0.4.0
mdurl                        0.1.2
memory-profiler              0.61.0
menuinst                     2.0.2
mido                         1.3.2
mir_eval                     0.7
mistune                      3.0.2
ml-dtypes                    0.2.0
more-itertools               10.2.0
mpltools                     0.2.0
mpmath                       1.3.0
msgpack                      1.0.7
multidict                    6.0.5
multimethod                  1.10
munkres                      1.1.4
murmurhash                   1.0.10
mypy-extensions              1.0.0
namex                        0.0.7
nbclassic                    1.0.0
nbclient                     0.7.4
nbconvert                    7.16.2
nbdime                       4.0.1
nbformat                     5.9.2
nest_asyncio                 1.6.0
netCDF4                      1.6.5
networkx                     3.2.1
nltk                         3.8.1
notebook                     7.1.1
notebook_shim                0.2.4
numba                        0.59.0
numexpr                      2.9.0
numpy                        1.26.0
nutils                       8.6
nutils-poly                  1.0.0
nvidia-cublas-cu12           12.3.4.1
nvidia-cuda-cupti-cu12       12.3.101
nvidia-cuda-nvcc-cu12        12.3.107
nvidia-cuda-nvrtc-cu12       12.3.107
nvidia-cuda-runtime-cu12     12.3.101
nvidia-cudnn-cu12            8.9.7.29
nvidia-cufft-cu12            11.0.12.1
nvidia-curand-cu12           10.3.4.107
nvidia-cusolver-cu12         11.5.4.101
nvidia-cusparse-cu12         12.2.0.103
nvidia-nccl-cu12             2.19.3
nvidia-nvjitlink-cu12        12.3.101
oauthlib                     3.2.2
opencv-python-headless       4.9.0.80
openpyxl                     3.1.2
opt-einsum                   3.3.0
optree                       0.11.0
overrides                    7.7.0
packaging                    23.2
palettable                   3.3.3
pamela                       1.1.0
pandas                       2.2.2
pandas-datareader            0.10.0
pandera                      0.19.3
pandocfilters                1.5.0
panel                        1.3.8
param                        2.1.0
parso                        0.8.3
partd                        1.4.1
pathspec                     0.12.1
patsy                        0.5.6
pep8                         1.7.1
pexpect                      4.9.0
pickleshare                  0.7.5
pillow                       10.2.0
pip                          24.0
pkgutil_resolve_name         1.3.10
platformdirs                 4.2.0
plotly                       5.20.0
pluggy                       1.4.0
polars                       0.20.8
powerlaw                     1.5
preshed                      3.0.9
prometheus_client            0.20.0
promise                      2.3
prompt-toolkit               3.0.42
protobuf                     3.20.3
psutil                       5.9.8
psycopg2-binary              2.9.9
ptyprocess                   0.7.0
pure-eval                    0.2.2
py-cpuinfo                   9.0.0
py3Dmol                      2.1.0
py4j                         0.10.9.7
pyarrow                      15.0.0
pyarrow-hotfix               0.6
pyasn1                       0.6.0
pyasn1_modules               0.4.0
pybaselines                  1.1.0
pycodestyle                  2.11.1
pycosat                      0.6.6
pycparser                    2.21
pycurl                       7.45.3
pydantic                     2.6.4
pydantic_core                2.16.3
pyflakes                     3.2.0
pygame                       2.5.2
Pygments                     2.17.2
PyJWT                        2.8.0
PyMySQL                      1.1.0
pyOpenSSL                    24.0.0
pyparsing                    3.1.2
pypng                        0.20220715.0
pyproj                       3.6.1
PySocks                      1.7.1
pyspark                      3.5.1
pytest                       8.1.1
python-dateutil              2.9.0
python-json-logger           2.0.7
python-slugify               8.0.4
pytorch-lightning            2.2.1
pytz                         2024.1
pyviz_comms                  3.0.2
PyWavelets                   1.4.1
PyYAML                       6.0.1
pyzmq                        25.1.2
qtconsole                    5.5.1
QtPy                         2.4.1
ray                          2.24.0
rdkit                        2023.9.5
recbole                      1.2.0
recommenders                 1.2.0
referencing                  0.33.0
regex                        2023.12.25
requests                     2.31.0
requests-oauthlib            2.0.0
retrying                     1.3.4
rfc3339-validator            0.1.4
rfc3986-validator            0.1.1
rich                         13.7.1
rpds-py                      0.18.0
rpy2                         3.5.11
rsa                          4.9
ruamel.yaml                  0.18.6
ruamel.yaml.clib             0.2.8
safetensors                  0.4.2
scikit-image                 0.22.0
scikit-learn                 1.4.0
scikit-plot                  0.3.7
scikit-surprise              1.1.4
scipy                        1.12.0
seaborn                      0.13.2
SecretStorage                3.3.3
Send2Trash                   1.8.2
setuptools                   69.1.1
shapely                      2.0.3
shellingham                  1.5.4
simpervisor                  1.0.0
simplegeneric                0.8.1
six                          1.16.0
smart-open                   6.4.0
smmap                        5.0.0
sniffio                      1.3.1
sortedcontainers             2.4.0
soupsieve                    2.5
spacy                        3.7.4
spacy-legacy                 3.0.12
spacy-loggers                1.0.5
SQLAlchemy                   1.4.46
srsly                        2.4.8
stack-data                   0.6.2
statsmodels                  0.14.1
stringly                     1.0b3
sympy                        1.12
tables                       3.9.2
tabulate                     0.9.0
taskipy                      1.12.2
tblib                        3.0.0
tenacity                     8.2.3
tensorboard                  2.15.2
tensorboard-data-server      0.7.2
tensorflow                   2.15.0
tensorflow-addons            0.23.0
tensorflow-datasets          4.9.4
tensorflow-estimator         2.15.0
tensorflow-io-gcs-filesystem 0.36.0
tensorflow-metadata          1.14.0
tensorflow-probability       0.24.0
termcolor                    2.4.0
terminado                    0.18.0
text-unidecode               1.3
texttable                    1.7.0
tf-slim                      1.1.0
thinc                        8.2.3
thop                         0.1.1.post2209072238
threadpoolctl                3.3.0
tifffile                     2024.2.12
tinycss2                     1.2.1
tokenizers                   0.15.2
toml                         0.10.2
tomli                        2.0.1
tomli_w                      1.0.0
tomlkit                      0.12.4
toolz                        0.12.1
torch                        2.1.2+cu121
torchaudio                   2.1.2+cu121
torchdata                    0.7.1
torchinfo                    1.8.0
torchmetrics                 1.3.2
torchtext                    0.16.2+cpu
torchvision                  0.16.2+cu121
tornado                      6.4
tornado-sqlalchemy           0.8.0
tqdm                         4.66.2
traitlets                    5.14.1
traittypes                   0.2.1
transformers                 4.37.2
treelog                      1.0
triton                       2.1.0
trove-classifiers            2024.3.25
truststore                   0.8.0
typeguard                    2.13.3
typer                        0.9.4
types-python-dateutil        2.8.19.20240106
typing_extensions            4.10.0
typing-inspect               0.9.0
typing-utils                 0.1.0
tzdata                       2024.1
tzlocal                      5.2
uc-micro-py                  1.0.3
uri-template                 1.3.0
urllib3                      2.2.1
userpath                     1.9.2
vega                         4.0.0
virtualenv                   20.25.1
voila                        0.5.5
wasabi                       1.1.2
wcwidth                      0.2.13
weasel                       0.3.4
webcolors                    1.13
webencodings                 0.5.1
websocket-client             1.7.0
websockets                   12.0
Werkzeug                     3.0.1
wheel                        0.42.0
widgetsnbextension           4.0.10
wrapt                        1.14.1
xarray                       2024.2.0
xgboost                      2.0.3
xlrd                         2.0.1
xyzservices                  2023.10.1
yarl                         1.9.4
zict                         3.0.0
zipp                         3.17.0
zope.event                   5.0
zope.interface               6.4.post2
zstandard                    0.22.0
``

[OhitsaSteve]

However, we are using the quickstart example as a startpoint for our project. Is there an other example for "nextitnet" recommender?

[OhitsaSteve]

Hi @miguelgfierro :)

After some time we found the mistake, now it is working ! :)

The problem isnt Tensorflow. The Problem is the setup of the quickstart example.
To use the nextitnet recommender it is necessary to un/comment some line of codes.
But someone missed to add a line.

At Code block 7 you find this :

model = SeqModel(hparams, input_creator, seed=RANDOM_SEED)

To use NextItNet you have use the model :

 model = NextItNetModel(hparams, input_creator, seed=RANDOM_SEED)

Sounds easy, but without any clue about this setup and with those good guidelines, how to use NextItnet, and this strange Tensorflow error, really hard to find.

**### =>

Therefore , please add the "uncomment line" to code block 7 to use NextItNetModel.**

or you give me the permission to commit and create a pullrequest :)

[miguelgfierro]

@OhitsaSteve yes feel free