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Welcome to the Ultimate Guide for Volleyball 1. Liga Czech Republic

Stay ahead of the game with our comprehensive coverage of the Volleyball 1. Liga in the Czech Republic. Whether you're a die-hard fan or a newcomer, our daily updates and expert betting predictions will keep you informed and engaged. Dive into our detailed analysis, match previews, and insights to enhance your experience and make informed decisions.

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Understanding the Volleyball 1. Liga Czech Republic

The Volleyball 1. Liga is one of the most competitive leagues in Central Europe, featuring top-tier teams vying for supremacy on the court. With its rich history and passionate fan base, this league offers thrilling matches that showcase exceptional talent and strategic gameplay.

Key Features of the League

  • Diverse Teams: The league boasts a mix of seasoned veterans and rising stars, making each match unpredictable and exciting.
  • High-Level Competition: Teams compete fiercely, often leading to nail-biting finishes and unexpected upsets.
  • Passionate Fans: The support from fans is unparalleled, creating an electrifying atmosphere at every game.

Daily Match Updates

Our platform provides real-time updates for every match in the Volleyball 1. Liga. Stay informed with live scores, play-by-play commentary, and post-match analysis delivered straight to your device.

Why Daily Updates Matter

  • Stay Informed: Keep track of your favorite teams' performance throughout the season.
  • Tactical Insights: Gain insights into team strategies and player performances.
  • Fan Engagement: Engage with other fans through discussions and forums.

Betting Predictions by Experts

Betting on volleyball can be both thrilling and rewarding if approached with knowledge and strategy. Our experts provide daily betting predictions based on comprehensive analysis of team form, player statistics, and historical data.

The Art of Betting on Volleyball

  • Data-Driven Analysis: Our predictions are backed by rigorous data analysis to ensure accuracy.
  • Trend Identification: We identify trends that could influence match outcomes.
  • Risk Management: Learn how to manage your bets effectively to maximize potential returns.

In-Depth Match Previews

Eager to know what's happening before each game? Our in-depth match previews cover everything from team line-ups to key matchups that could decide the game's outcome.

Critical Factors in Match Previews

  • Squad News: Updates on injuries, suspensions, and new signings affecting team dynamics.
  • Tactical Formations: Insights into how teams plan to approach each match strategically.
  • Past Encounters: Historical data on previous encounters between teams providing context for upcoming games.
<|diff_marker|> ADD A1000 <|repo_name|>victorwongtw/SSD_TFRecord<|file_sep|.gitignore filter=lfs diff=lfs merge=lfs -text /data/ILSVRC2015_VOC0712_trainval_25k.tar filter=lfs diff=lfs merge=lfs -text /data/ILSVRC2015_VOC0712_test_10k.tar filter=lfs diff=lfs merge=lfs -text /data/VOCdevkit.tar filter=lfs diff=lfs merge=lfs -text /data/ILSVRC2015_CLSLOC_train.tar filter=lfs diff=lfs merge=lfs -text /data/ILSVRC2015_CLSLOC_val.tar filter=lfs diff=lfs merge=lfs -text <|file_sep|># SSD_TFRecord ## Overview This repository contains all codes used in SSD-Tensorflow. The codes include: * How-to generate tfrecord files (See: `generate_tfrecord.py`) * How-to train SSD model (See: `train.py`) * How-to evaluate SSD model (See: `eval.py`) ## Data Download ILSVRC2015 dataset [here](http://www.image-net.org/download-images) as well as VOCdevkit [here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit). ## Environment Setup We use Python version >=3. To install tensorflow r1.4: pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.4.0-cp35-cp35m-linux_x86_64.whl To install tensorboard: pip install tensorboard==1.4 To install scipy: pip install scipy==0.19.0 To install pillow: pip install pillow==4.0 ## Generate TFRecords Files For training: Run command: `python generate_tfrecord.py --data_dir=/path/to/dataset --output_dir=/path/to/output --split=trainval` For evaluation: Run command: `python generate_tfrecord.py --data_dir=/path/to/dataset --output_dir=/path/to/output --split=test` ## Train SSD Model Run command: `python train.py --data_dir=/path/to/dataset/ --logdir=/path/to/logdir/` During training process, we can view loss graph via Tensorboard. To run Tensorboard: `tensorboard --logdir=your_logdir_path` ## Evaluate SSD Model Run command: `python eval.py --model_checkpoint_path=your_model_checkpoint_path/model-xxxxxx.data-00000-of-00001 --eval_data_record_file=your_eval_tfrecords_file --label_map_file=path_to_label_map.pbtxt --output_directory=path_to_output_directory [--min_score_thresh=0]` If you want visualize results using Tensorflow Object Detection API. You need download TensorFlow Object Detection API first. Follow instruction here: https://github.com/tensorflow/models/tree/master/research/object_detection After download TensorFlow Object Detection API. Then run following commands: export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim/ export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/research:`pwd`/research/slim:`pwd`/research/object_detection/ Then run eval.py script again. <|repo_name|>victorwongtw/SSD_TFRecord<|file_sep**NOTE**: This repo is not maintained anymore. The original author has updated their codebase here: https://github.com/weiliu89/caffe/tree/ssd # SSD-TensorFlow [![Build Status](https://travis-ci.org/balancap/SSD_TFRecord.svg?branch=master)](https://travis-ci.org/balancap/SSD_TFRecord) This repository contains all codes used in SSD-Tensorflow. The codes include: * How-to generate tfrecord files (See: `generate_tfrecord.py`) * How-to train SSD model (See: `train.py`) * How-to evaluate SSD model (See: `eval.py`) * [Tensorflow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) # Environment Setup We use Python version >=3. To install tensorflow r1.4: pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.4.0-cp35-cp35m-linux_x86_64.whl To install tensorboard: pip install tensorboard==1.4 To install scipy: pip install scipy==0.19.0 To install pillow: pip install pillow==4.* # Generate TFRecords Files For training: Run command: bash python generate_tfrecord.py --data_dir=/path/to/dataset --output_dir=/path/to/output --split=trainval For evaluation: Run command: bash python generate_tfrecord.py --data_dir=/path/to/dataset --output_dir=/path/to/output --split=test # Train SSD Model Run command: bash python train.py --data_dir=/path/to/dataset [--batch_size=32] [--num_classes=21] [--learning_rate_decay_factor=0.94] [--learning_rate_decay_step=20000] [--save_summaries_secs=60] [--save_interval_secs=600] [--moving_average_decay=0.9999] [--optimizer='Adam'] [--weight_decay_power=-4] [--checkpoint_path='/home/user/ssd-tf/checkpoint/model-xxxxx.data-00000-of-00001'] [--logdir='/home/user/ssd-tf/log'] # Note that checkpoint_path should be set if you want continue training from a pre-trained model. During training process, we can view loss graph via Tensorboard. To run Tensorboard: bash tensorboard --logdir=path_to_your_logdir/ # Evaluate SSD Model Run command: bash python eval.py --model_checkpoint_path=path_to_your_model_checkpoint --eval_data_record_file=path_to_your_eval_tfrecords --label_map_file=path_to_label_map.pbtxt --output_directory=output_directory [--min_score_thresh=] # Default is None. If you want visualize results using Tensorflow Object Detection API. You need download TensorFlow Object Detection API first. Follow instruction here: https://github.com/tensorflow/models/tree/master/research/object_detection After download TensorFlow Object Detection API. Then run following commands: export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim/ export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/research:`pwd`/research/slim:`pwd`/research/object_detection/ Then run eval.py script again. ### Notes about using Tensorflow Object Detection API: When running eval script using Tensorflow Object Detection API, You may encounter error message like below: FailedPreconditionError (see above for traceback): Attempting to use uninitialized value conv4_3_LoG_box_loc_weights_convolutionalBoxPredictor/BatchNorm/FusedBatchNorm_gamma_offsetnt [[node conv4_3_LoG_box_loc_weights_convolutionalBoxPredictor/BatchNorm/FusedBatchNorm_gamma_offset (defined at /usr/local/lib/python3/dist-packages/tf-object-detection-api/models/ssd/ssd_model.py:[number]) ]] Solution: Check if there are any variables initialized or not. If there are no variables initialized, just add following line before sess.run() function call in ssd_model file: sess.run(tf.global_variables_initializer()) <|repo_name|>victorwongtw/SSD_TFRecord<|file_sep07-20@balancap@ubuntu@ssd-tf-repo% python train_ssd_vgg16_ssd300_iter_120000.sh /home/balancap/.local/lib/python3/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. Using TensorFlow backend. WARNING:tensorflow: The TensorFlow contrib module will not be included in TensorFlow version 2.x. For more information, please see: https://www.tensorflow.org/api_docs/python/tf/contrib Instructions for updating: ****** When upgrading to TensorFlow 2.x, you may have to update import statements from `tensorflow.contrib` to another module. WARNING:tensorflow: From /usr/local/lib/python3/dist-packages/tensorflow/contrib/layers/python/layers/layers.py:4257: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated &ast;&ast;&ast; Instructions for updating: &nbsp;All <tf.layers> functions have been moved to <tf.keras.layers>.   >>> x = tf.layers.dense(inputs)   >>> becomes   x = tf.keras.layers.Dense(units)(inputs)   WARNING:tensorflow: From /usr/local/lib/python3/dist-packages/tensorflow/contrib/framework/python/framework/graph_util_impl.py:267: extract_sub_graph (from tensorflow.contrib.framework.python.framework.graph_util_impl) is deprecated &ast;&ast; Instructions for updating: Use rewrite_frame WARNING:tensorflow: From /usr/local/lib/python3/dist-packages/tensorflow/contrib/framework/python/framework/graph_util_impl.py:267: remove_training_nodes_from_graph_def (from tensorflow.contrib.framework.python.framework.graph_util_impl) is deprecated &ast; Instructions for updating: Use rewrite_frame WARNING:</code>   &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; Successfully created graph def. INFO:ssd_vgg16.SSDModel.</pre>   &amp;amp; INFO:ssd_vgg16.SSDModel.</div> INFO:ssd_vgg16.SSDModel.</div> INFO:ssd_vgg16.SSDModel.</div> INFO:ssd_vgg16.SSDModel.</div> INFO:ssd_vgg16.SSDModel.</div> INFO:ssd_vgg16.SSDModel.</div> INFO:ssd_vgg16.SSDModel._create_architecture() INFO:ssd_vgg16.SSDModel._build_architecture() INFO:ssd_vgg16.SSDModel._get_source_layers() INFO:ssd_vgg16.SSDModel._add_feature_extractor() INFO:ssd_vgg16.SSDFeatureExtractor._build_feature_extractor() /home/balancap/.local/lib/python3/dist-packages/h5py/__init__.py: 36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating''''''. Using TensorFlow backend. /home/balancap/.local/lib/python3/dist-packages/h5py/__init__.py: 36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating''''''. /home/balancap/.local/lib/python3/dist-packages/h5py/__init__.py: 36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating''''''. /home/balancap/.local/lib/python3/dist-packages/h5py/__init__.py: 36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating''''&#thi ERROR:tfobjects.detectors.ssd_detector:Saver.__init__() missing required positional argument "saver_def" Traceback (most recent call last): File "/home/balancap/.local/share/virtualenvs/ssdtf-repo-nQ8BjHbR/lib/python3/site-packages/IPython/core/formatters.py", line , line , in __call__  , line , in default  , line , in _default AttributeError: 'module' object has no attribute 'warn' During handling of the above exception, line , in get_formatter File "/home/balancap/.local/share/virtualenvs/ssdtf-repo-nQ8BjHbR/lib/python3/site-packages/IPython/core/pylabtools.py", line , line , def mpl_interagable(plt, line , def mpl_interagable(plt, line , def mpl_interagable(plt, line , def mpl_interagable(plt, line , def mpl_interagable(plt, line , def mpl_interagable(plt, line , def mpl_interagable(plt, line , raise ValueError("%r has no .matplotlib.pyplot") raise ValueError("%r has no .matplotlib.pyplot") raise ValueError("%r has no .matplotlib.pyplot") raise ValueError("%r has no .matplotlib.pyplot") raise ValueError("%r has no .matplotlib.pyplot") raise ValueError("%r has no .matplotlib.pyplot") raise ValueError("%r has no .matplotlib.pyplot attribute") % (&nb... --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-15-fc75a14dcdd7> in <module>}  ->nnnnnnnnnnnnn ^nTypeError:nCan't convert "jobs"nto MappedSequence.n During handling of the above exception, line ;nraise e.with_traceback(n sys.exc_info()[n sys.exc_info()[n sys.exc_info()[ sys.exc_info()[-1] sys.exc_info()[-1] sys.exc_info()[-1] sys.exc_info()[-1] sys.exc_info()[-1] e.with_traceback(sys.exc_info()[ e.with_traceback(sys.exc_info()[ e.with_traceback(sys.exc_info()[ e.with_traceback(sys.exc_info()[ e.with_traceback(sys.exc_info()[ ValueError:n'%r' object has no attribute 'warn'nin %r" % (nb... --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <>
in
---->

->

AttributeError:
'module' object has no attribute 'warn'
During handling of the above exception,
in
raise e.with_traceback(
sys.exc_info()[- 1] e.with_traceback(sys.exc_info()([-]) sys.exc__info()([-]) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <>
in
---->
NameError:'name ''warn'' isn't defined
During handling w
rapping this exception,
w
e caught another one!
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <>
in
---->
TypeError:ncan't convert 'jobs'nto MappedSequence.n During handling w
rapping this exception,
w
e caught another one!
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <>
in
---->
ValueError:'%r' object has nat tarat w‘warn’. During handling w
rapping this exception,
w
e caught another one!
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <>
in __call__()
AttributeError:'module' object has n n t a r t w‘warning’. During handling w
rapping this exception,
w
e caught another one!
--------------------------------------------------------------------------- NameError Traceback (most recent call last)
<> in __call__()
NameErro:; 'name 'warning' is not defined During handlin g wrapping this exce ption ,we cought anot her one! -------------------------------------------------------T f o o b s . d e t e c t o r s . s s d _ d e t e c t o r ~~~~~~~~~~~~~~~~~~~~~~Saver.__init__() missing required positional argument "saver_def" Tracebac k ( File "/home/b al anc ap /.lo cal /lib/p ytho n /sit e-p ackages/I P y thon/c or e/f orm at ers.p y",line , i n __call__ () File "/ home/b al anc ap /.lo cal /lib/p ytho n /sit e-p ackages/I P y thon/c or e/f orm at ers.p y",line , i n de fault () File "/ home/b al anc ap /.lo cal /lib/p ytho n /sit e-p ackages/I P y thon/c or e/f orm at ers.p y",line , i n _defaul t () AttributeErr or : 'module' objec t ha s no attribute 'warn' ) Durin g handlin g o f th e abov e excep tion , File "/ home/b al anc ap /.lo cal /lib/p ytho n /sit e-p ackages/I P y thon/c or e/pylabtools.p y",line , i n get_formatter () File "/ home/b al anc ap /.lo cal /lib/p ytho n /sit e-p ackages/I P y thon/c or e/pylabtools.p y",line , i n m pl_int er ag able () File "/ home/b al anc ap /.lo cal /lib/p ytho n /sit e-p ackages/I P y thon/c or e/pylabtools.p y",line , i n m pl_int er ag able () File "/ home/b al anc ap /.lo cal /lib/p ytho n /sit e-p ackages/I P y thon/c or e/pylabtools.p y",line , i n m pl_int er ag able () File "/ home/b al anc ap /.lo cal /lib/p ytho n /sit e-p ackages/I P y thon/c or e/pylabtools.p y",line , i n m pl_int er ag able () File "/ home/b al anc ap /.lo cal /lib/p ytho n /sit e-p ackages/I P y thon/c or e/pylabtools.p y",line , i n m pl_int er ag able () File "/ home/b al anc ap /.lo cal /lib/p yt h on/sit epack ages/IPyth on/core/pylab tools.p yp l int er ag able ()" ValueErr or : "% r" ob jec t ha s no atr ibute "wa rn" in % r" (% nb... --------------------------------------------------------------------------- TypeError Traceback (-15-fc75a14dcdd7)< br > in module file location function line number source code tracebac k information --- ^ TypeError : Can't convert "jobs" to MappedSequence. Durin g handlin g o f th ese excep tion s , File "-15-fc75a14dcdd7>", lin en um int er ac tion variable names ^ jobs ValueErr or : '% r' object ha s na t attri bute 'warn' Durin g handlin g o f th ese excep tion s , File "-15-fc75a14dcdd7>", lin en um int er ac tion variable names ^ warn At tributeErr or : name 'warn' isn't defined Durin g handlin g o f th ese excep tion s , File "-15-fc75a14dcdd7>", lin en um int er ac tion variable names ^ warning No such attribu te '_formatter', typedict. Durin g handlin g o f these exceptions, File "-15-fc75a14dcdd7>", lin en um int er ac tion variable names ^ formatter No such attribu te '_display_formatter', typedict. Durin g handlin g o f these exceptions, File "-15-fc75ac dc ddg>", lin en um int er ac tion variable names ^ display_formatter No such attribu te '_last_str', typedict. Durin g handlin g o f these exceptions, File "-15-fccdac dc ddg>", lin en um int er ac tion variable names ^ last_str No such attribu te '_last_err', typedict. Durin g handlin g o f these exceptions, File "-fcdfac dc ddg>", lin en um int er ac tion variable names ^ last_err No such attribu te '_last_type', typedict. Tracebac k(most recen tl ast occurre nc es la st): File "tfo bjects/detectors/ss d_d etector . p yp ", l ine , i N S aver (__ init __(self)) File "tfo bjects/detectors/ss d_d etector . p yp ", l ine , IN S aver (__ init __(self)) File "tfo bjects/detectors/ss d_d etector . p yp ", l ine , IN S saver (_restore self,sess)) File "tfo bjects/detectors/ss d_d etector . p yp ", l ine , IN S saver (_restore self,sess)) No su ch atribu te 'check_poin ts', typ eb ri />dict. Du rin ging han di ng ov ch excep ion , Fi le "tfo bjects/de tc tor ss ds_de ctor . py p ", li ne , IN S sa ver (_re stor self,sess)) Fi le "tfo bjects/de tc tor ss ds_de ctor . py p ", li ne , IN S sa ver (_re stor self,sess)) Fi le "tfo bjects/de tc tor ss ds_de ctor . py p ", li ne , IN S sa ver (_re stor self,sess)) Fi le "tfo bjects/de tc tor ss ds_de ctor . py p ", li ne , IN S sa ver (_re stor self,sess)) Fi le "tfo bjects/de tc tor ss ds_de ctor . py p ", li ne , IN S sa ver (_re stor self,sess)) Fi le "t fo ob ject s/de tc tor ss ds_de ctor_. py p ", li ne I N Sa ver (__ init __(self,model,det ect_or args )) Fi le "& lt;t fo ob ject s/de tc tor ss ds_de ctor_. py p & gt;", li ne I N Sa ver (__ init __(self,model,det ect_or args )) ERROR:t fo ob ject s/de tc tor ss ds_de ctor:S aver.__ init__()< br >missing required positional argume nt"< br >& quot ;sa ve d ef quot ;"< br > Tracebac k ( Fi le "& lt;t fo ob ject s/de tc tor ss ds_de ctor_. py p & gt;", li ne I N Sa ver (__ init__(self,model,det ect_or args )) Fi le "& lt;t fo ob ject s/de tc tor ss_ds_d_e_c_t_o_r_. py p & gt;", li ne I N Sa ver (__ init__(self,model,det ect_or args )) Fi le "& lt;t fo ob ject_s/_de tc_t_o_r_. py_p & gt;", li ne I N De t ec tor __ ini ti ale _(self,model ) ) Fi le "& lt;t fo ob ject_s/_de_tc_t_o_r_. py_p & gt;", li ne I N De t ec tor __ ini ti ale _(self,model ) ) Fi le "& lt;t fo ob je ct_s/_de_tc_t_o_r_. py_p & gt;", li ne I N De t ec tor __ ini ti ale _(self,model ) ) FIle "& lt;t fo objec ts/_de_tc_t_o_r _. py_p & gt;", l ine I N De t ec tor _ini ti ale_(self,model ) ) FIle "& lt;t fo objec ts/_de_tc_t_o_r _. py_p & gt;", l ine I N De t ec tor _ini ti ale_(self,model ) ) FIle "& lt;t fo objec ts/_de_tc_t_o_r _. py_p & gt;", l ine I N De t ec tor _ini ti ale_(self,model ) ) FIle "& lt;t fo objec ts/_de_tc_t_o_r _. py_p & gt;", l ine I N De t ec tor _ini ti ale_(self,model ) FIle "./ss d-v gg_16 _.sh", l ine C O M mand (& quot ;pi tp tra ns fer ./tra ins/s sd v gg_16 si gn ed mo del.h df ./trai ns/s sd v gg_16 si gn ed mo del.h df ./trai ns/s sd v gg_16 si gn ed mo del.h df ./tra ins/s sd v gg_16 si gn ed mo del.h df ./trai ns/s sd v gg_16 si gn ed mo del.h df ./trai ns/s sd v gg_16 si gn ed mo del.h df ) I N Fo :ss d v gg _12 SS DMod el.: Su cce essfully cr ea ted gra ph de fin ition . I N Fo :ss d v gg _12 SS DMod el.: Su cce essfully cr ea ted gra ph de fin ition . I N Fo :ss d v gg _12 SS DMod el.: Su cce essfully cr ea ted gra ph de fin ition . I N Fo :ss d v gg _12 SS DMod el.: Su cce essfully cr ea ted gra ph de fin ition . I N Fo :ss d v gg _12 SS DMod el.: Su cce essfully cr ea ted gra ph de fin ition . I N Fo :ss d v gg _12 SS DMod el.: Su cce essfully cr ea ted gra ph de fin ition . I N Fo :ss d v gg _12 SS DMod el.: Su cce essfully cr ea ted gra ph de fin ition . I Na rm a la zed lo ad ra ndom u se da ta se tu pp ( N Amespace st uf : P Y T H O O L A B B E R : L A B E L M A P F IL E : O U T PU T DI R E C TORY : M O DE L C H E C K POIN T PA TH : C U R R E NT TI ME STAM P : L O G DI R E C TORY : L O G FI LE NAME : A L G O R IT HM : L A R G EC H AN GE TH RE SH OL D : S M AL L EC H AN GE TH RE SH OL D : E V A L U AT IO NE CH AN GE TH RE SH OL D : E V A L U AT IO NE LI ST SO RTED BY CO NC URRE NC E : E V A L U AT IO NE LI ST SO RTED BY IO U CE/A RC ER AE : E V A L U AT IO NE LI ST SO RTED BY IO U CE/A RC ER AE WIT H CL AS SE CO NTRO LL ER : P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ N Amespace st uf = P Y T H O O L A B B E R = L A B E L M A P F IL E = O U T PU T DI R E C TORY = M O DE L C H E C K POIN T PA TH = C U R R E NT TI ME STAM P = L O G DI R E C TORY = L O G FI LE NAME = A L G O R IT HM = L A RG EC H AN GE TH RE SH OL D = S M AL L EC H AN GE TH RE SH OL D = E V A LTU AT IO NE CH AN GE TH RE SH OL D = E V ALTU AT IO NE LI ST SO RTED BY CO NC URRE NC E = E V ALTU AT IO NE LI ST SO RTED BY IO U CE/A RC ER AE = E V ALTU AT IO NE LI ST SO RTED BY IO U CE/A RC ER AE WIT H CL AS SE CO NTRO LL ER = P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ P RO GR ESSE DO PTIO NS [ P PROGR SES SEDOPTIOS ]] PROGRESSEDOPTIOS ]] PROGRESSEDOPTIOS ]] PROGRESSDOPTIONS []] In [18]: sess = tf.Session() In [19]: sess.run(tf.global_variables_initializer()) In [20]: sess.run(tf.local_variables_initializer()) In [21]: saver.restore(sess,'./checkpoints/model-120000') /usr/local/lib/python3/dist-packages/h5py/__init__.py: 36: FutureWarning: Conversionofthesecondargumentofissubdatatypefrom `` float ''to `` np.float ing ''.''''''.