Critical ops hack android 0.6.0
![critical ops hack android 0.6.0 critical ops hack android 0.6.0](https://hackgames.top/wp-content/uploads/CriticalOpsReloadedHack-830x450.jpg)
16:57:11,245 root INFO model_dir: checkpoints 16:57:11.201493: W tensorflow/core/platform/cpu_feature_: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.
![critical ops hack android 0.6.0 critical ops hack android 0.6.0](https://i.pinimg.com/originals/89/2a/4d/892a4d00a48a043873333c8b2518cb1a.jpg)
16:57:11.201375: W tensorflow/core/platform/cpu_feature_: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. While training following error occured, assertion failed: checkpoints\model.ckpt-9000 Synth 90k training error 10:37:42,850 root INFO Reading model parameters from. 10:37:39,294 root INFO clip_gradients: True 10:37:39.292842: I C:\tf_jenkins\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 10:37:33,182 root INFO Longest label (6): MONTHSĬ:\Users\kimduknam>aocr test -max-prediction 30 -visualize. 10:37:33,182 root INFO Dataset is ready: 1 pairs. 10:37:33,182 root INFO Building a dataset from. 10:37:33.179317: I C:\tf_jenkins\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 I aocr dataset and make testing.tfrecords file and tried below and it does not make results folder and there is no step process.Ĭ:\Users\kimduknam>aocr dataset. (The reason I set the background color is aocr test did not work at the image which was applied only for size edit, so I just tried)Īnyway it did not work at all. I took a picture of a word(it was 'months' word) and edit size(to 187 x 31) and set background color to the same of the picture at ADOBE PHOTOSHOP. The result was great!, so I decided to aocr test command for my own picture. I trained and made model(-00000-of-00001,, ) and aocr test command for the rest of the link sample data. show AssertionError when I run "aocr train training.tfrecords" Separately I tried to run this under python2 at first and there was some issue with that too.Īnyway, if you have fixes, please go ahead, or if you have suggestions on the best way you want it fixed, I'll proceed and send in a PR. Similarly, -visualize doesn't work at all because the "filename" being passed in isn't actually a string, but a byte literal.
#CRITICAL OPS HACK ANDROID 0.6.0 CODE#
I have a hack to work around it but I'm just learning my way around this code so you might have a better answer. I'm seeing things like b"B'test'" vs b'test (where the correct word is test), and the extra b and quotes are reducing the reported quality of the results. The 'test' command is reporting incorrect results due to some byte / string issue, even if the prediction is correct. With the very latest code (I pulled on morning of oct-7-2017), there are a few string issues.
![critical ops hack android 0.6.0 critical ops hack android 0.6.0](https://img.happymod.com/reviews/202012/24/c270908e3415526407f11592b297baef.jpg)
Hi-this is a great repo, thanks to the original authors and the great work you have done to make it more usable. What You Get Is What You See: A Visual Markup Decompiler max-prediction: Maximum length of the predicted word/phrase.max-height: Maximum height for the input images.WARNING: images with the width higher than maximum will be discarded. max-width: Maximum width for the input images.use-gru: Use GRU cells instead of LSTM.no-gradient-clipping: Do not perform gradient clipping.max-gradient-norm: Clip gradients to this norm.no-resume: Create new weights even if there are checkpoints present.(Encoder number of hidden units will be attn-num-hidden* attn-num-layers). attn-num-layers: Number of layers in attention decoder cell.attn-num-hidden: Number of hidden units in attention decoder cell.target-embedding-size: Embedding dimension for each target.initial-learning-rate: Initial learning rate, note the we use AdaDelta, so the initial value does not matter much.num-epoch: The number of whole data passes.steps-per-checkpoint: Checkpointing (print perplexity, save model) per how many steps.format: Format for the export (either savedmodel or frozengraph).visualize: Output the attention maps on the original image.Gcloud ml-engine jobs submit training $JOB_NAME \