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seqeval's Issues

Different Classification Reports for IOBES and BILOU

I compared the results of the same model on the same test data with both IOBES and BILOU schemes. I get exactly the same precision, recall, and F1 scores which I expect:

Precision = 0.6762295081967213
Recall = 0.5045871559633027
F1 = 0.5779334500875658

However, I get different classification reports as shown below! Any explanation for this?
BILOU:

              precision    recall  f1-score   support

         LOC      0.676     0.505     0.578       327

   micro avg      0.676     0.505     0.578       327
   macro avg      0.676     0.505     0.578       327
weighted avg      0.676     0.505     0.578       327

IOBES:

              precision    recall  f1-score   support

         LOC      0.667     0.503     0.574       314

   micro avg      0.667     0.503     0.574       314
   macro avg      0.667     0.503     0.574       314
weighted avg      0.667     0.503     0.574       314

My Environment

  • Operating System: Windows 10
  • Python Version: 3.8.3
  • Package Version: 1.2.2

Raise error while using tf.keras eager execution

When I using seqeval with tf2.0-alpha, it will raise Error.

Code:

from seqeval.metrics import accuracy_score

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=[accuracy_score])

Log:

/usr/local/lib/python3.6/dist-packages/seqeval/metrics/sequence_labeling.py in accuracy_score(y_true, y_pred)
    173         0.80
    174     """
--> 175     if any(isinstance(s, list) for s in y_true):
    176         y_true = [item for sublist in y_true for item in sublist]
    177         y_pred = [item for sublist in y_pred for item in sublist]

/usr/local/lib/python3.6/dist-packages/seqeval/metrics/sequence_labeling.py in <genexpr>(.0)
    173         0.80
    174     """
--> 175     if any(isinstance(s, list) for s in y_true):
    176         y_true = [item for sublist in y_true for item in sublist]
    177         y_pred = [item for sublist in y_pred for item in sublist]

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in __iter__(self)
    447     if not context.executing_eagerly():
    448       raise TypeError(
--> 449           "Tensor objects are only iterable when eager execution is "
    450           "enabled. To iterate over this tensor use tf.map_fn.")
    451     shape = self._shape_tuple()

TypeError: Tensor objects are only iterable when eager execution is enabled. To iterate over this tensor use tf.map_fn.

Classification_report is going really slow for mode='strict'

I have a dummy dataset in my local machine.
While my sklearn token-level evaluation (strict mode on/off) and my seqeval entity-level evaluation (strict mode off) run all together in 5 seconds, for some reason the seqeval entity-level evaluation with arg mode='strict' takes around 70 seconds, which is too much.

Is there any way to speed it up somehow? Maybe the code needs to get more optimized?

I can't run experiments with more data on my AWS machine using mode='strict'.
The evaluation on mode='strict' takes more time than the training of the neural models.

Many thanks!

  • Operating System: Ubuntu 18 (LTS)
  • Python Version: 3.8
  • Package Version: 1.1.0

simple 'I', 'O', 'B' tagging throws error

Since the last commit, the simple 'I', 'O', 'B' tags are no longer supported.

from seqeval.metrics import f1_score

y_true = [['O', 'O', 'O', 'B', 'I', 'I', 'O'], ['B', 'I', 'O']]
y_pred = [['O', 'O', 'B', 'I', 'I', 'I', 'O'], ['B', 'I', 'O']]
f1_score(y_true, y_pred)

Throws error:
ValueError: Invalid tag is found: B

Is this behaviour intended?

Bug - labels are only taken from true data

Example:
from seqeval.metrics import classification_report
y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'B-TEST']]

print(classification_report(y_true, y_pred))

The result is:
           precision    recall  f1-score   support

      PER       1.00      1.00      1.00         1
     MISC       0.00      0.00      0.00         1

micro avg       0.33      0.50      0.40         2
macro avg       0.50      0.50      0.50         2

As you can see B-TEST does not appear in the table even though it's a false positive (it only appears in the predicted labels). The complete label list should be comprised from the union of true_labels + predicted_labels.

IO scheme encoding support

IO scheme encoding

Hi, hope you are doing well.
I'd like to ask you if you could incorporate simple IO scheme encoding in seqeval.

Thank you for this awesome library.

How to reproduce conlleval results?

May I ask how to reproduce conlleval results?

According to https://www.clips.uantwerpen.be/conll2000/chunking/output.html, the conlleval results are:

accuracy:  84.08%; precision:  68.83%; recall:  80.83%; FB1:  74.35
             ADJP: precision:   0.00%; recall:   0.00%; FB1:   0.00  1
             ADVP: precision:  45.45%; recall:  62.50%; FB1:  52.63  11
               NP: precision:  64.98%; recall:  78.63%; FB1:  71.16  317
               PP: precision:  83.18%; recall:  98.89%; FB1:  90.36  107
             SBAR: precision:  66.67%; recall:  33.33%; FB1:  44.44  3
               VP: precision:  69.00%; recall:  79.31%; FB1:  73.80  100

While I use

from seqeval.metrics import classification_report
classification_report(conll_tags_true, conll_tags_pred, digits=4, mode="strict")

It returns:

              precision    recall  f1-score   support

        ADJP     0.0000    0.0000    0.0000         6
        ADVP     0.4545    0.6250    0.5263         8
          NP     0.6442    0.7821    0.7065       257
          PP     0.8302    0.9888    0.9026        89
        SBAR     0.6667    0.3333    0.4444         6
          VP     0.6869    0.7907    0.7351        86

   micro avg     0.6842    0.8053    0.7398       452
   macro avg     0.5471    0.5866    0.5525       452
weighted avg     0.6773    0.8053    0.7345       452

There are some differences, such as the NP line.

classification_report works uncorrectly

When running classification_report, I suppose the support column is the number of tags in y_true list. I have two lists y_true.npy and y_pred.npy , they are attached bellow
when I run classification_report it gives me support: 1379 for "LOC" tag, but the true number is 1377 tags

      precision    recall  f1-score   support\n\n        

PER 0.4862 0.8423 0.6165 1294\n
LOC 0.5914 0.3002 0.3983 1379\n
ORG 0.2000 0.0474 0.0767 274\n
MISC 0.4902 0.5102 0.5000 49\n\n

avg / total 0.5085 0.5147 0.4648 2996\n'

y_true and y_pred.zip

Classification report can return a dictionary

Right now, the classification report only gives a formatted text output. But users might want to get a dictionary containing class wise precision, recall and f1-score (like it is in sklearn.metrics.classification_report)

numpy and scikit-learn versions are very restrictive

They both use == which is very limiting when you are trying to install seqeval with other packages

'numpy==1.19.2', 'scikit-learn==0.23.2'

If these could be relaxed to >= of whatever the true minimum version needed that would help a great deal.

Different result for a dataset

When I evaluate ner model, the result from classification_report is alway different with last result. I read the source code, but the support item is the true labels of dataset, but it can not get the same result with last result.

can not install in aliyun ubuntu

can not install seqeval of version greater than 0.0.10
installing from the source code (via pip install -e .) report this error

pip install seqeval
Collecting seqeval
Using cached seqeval-1.2.2.tar.gz (43 kB)
ERROR: Command errored out with exit status 1:
command: /root/anaconda3/envs/layoutxlm/bin/python -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-3968sdw2/seqeval_4fdb6867e5d54d70ae3b03c96a8dec1d/setup.py'"'"'; file='"'"'/tmp/pip-install-3968sdw2/seqeval_4fdb6867e5d54d70ae3b03c96a8dec1d/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(file) if os.path.exists(file) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-pip-egg-info-8vwsc1dz
cwd: /tmp/pip-install-3968sdw2/seqeval_4fdb6867e5d54d70ae3b03c96a8dec1d/
Complete output (33 lines):
WARNING: The repository located at mirrors.cloud.aliyuncs.com is not a trusted or secure host and is being ignored. If this repository is available via HTTPS we recommend you use HTTPS instead, otherwise you may silence this warning and allow it anyway with '--trusted-host mirrors.cloud.aliyuncs.com'.
ERROR: Could not find a version that satisfies the requirement setuptools_scm (from versions: none)
ERROR: No matching distribution found for setuptools_scm
Traceback (most recent call last):
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/site-packages/setuptools/installer.py", line 75, in fetch_build_egg
subprocess.check_call(cmd)
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/subprocess.py", line 363, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['/root/anaconda3/envs/layoutxlm/bin/python', '-m', 'pip', '--disable-pip-version-check', 'wheel', '--no-deps', '-w', '/tmp/tmpqrwh8afv', '--quiet', '--index-url', 'http://mirrors.cloud.aliyuncs.com/pypi/simple/', 'setuptools_scm']' returned non-zero exit status 1.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "", line 1, in
File "/tmp/pip-install-3968sdw2/seqeval_4fdb6867e5d54d70ae3b03c96a8dec1d/setup.py", line 56, in
'Programming Language :: Python :: Implementation :: PyPy'
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/site-packages/setuptools/init.py", line 152, in setup
_install_setup_requires(attrs)
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/site-packages/setuptools/init.py", line 147, in _install_setup_requires
dist.fetch_build_eggs(dist.setup_requires)
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/site-packages/setuptools/dist.py", line 689, in fetch_build_eggs
replace_conflicting=True,
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/site-packages/pkg_resources/init.py", line 768, in resolve
replace_conflicting=replace_conflicting
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/site-packages/pkg_resources/init.py", line 1051, in best_match
return self.obtain(req, installer)
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/site-packages/pkg_resources/init.py", line 1063, in obtain
return installer(requirement)
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/site-packages/setuptools/dist.py", line 745, in fetch_build_egg
return fetch_build_egg(self, req)
File "/root/anaconda3/envs/layoutxlm/lib/python3.7/site-packages/setuptools/installer.py", line 77, in fetch_build_egg
raise DistutilsError(str(e)) from e
distutils.errors.DistutilsError: Command '['/root/anaconda3/envs/layoutxlm/bin/python', '-m', 'pip', '--disable-pip-version-check', 'wheel', '--no-deps', '-w', '/tmp/tmpqrwh8afv', '--quiet', '--index-url', 'http://mirrors.cloud.aliyuncs.com/pypi/simple/', 'setuptools_scm']' returned non-zero exit status 1.

  • Operating System: ubuntu
  • Python Version: 3.7
  • Package Version: 1.2.2

macro avg calculations

Hi, I've used seqval in a NER system and it predicted me this classification report. Screenshot of the report is here
Based on my knowledge, the macro avg of whatever metric (e.g. precision) would be in this case like the following
macro avg - pr = (0.74 + 0.85 + 0.96 + 0.65 + 0.87 + 0.54 + 0.33 + 0.00) / 8 = 0.6175, which is not the case.

Accuracy Vs F1-score

Hi,

I am working on a sequence labeling problem. I am using seqeval to obtain accuracy and F1 scores. However, is it normal to get an accuracy significantly higher than F1-score (Acc 95.2% and average F-1 score : 52.4%)

I am very confused

Thank u for ur help

seqeval 1.2.2: module 'enum' has no attribute 'Flag'

When I use the old latest version within my python code I get the following error. The problem is solved when I uninstall it and installed the old version 0.0.12!

    from seqeval.metrics import f1_score, precision_score, recall_score, classification_report
  File "/opt/anaconda3/envs/lm_training/lib/python3.5/site-packages/seqeval/metrics/__init__.py", line 1, in <module>
    from seqeval.metrics.sequence_labeling import (accuracy_score,
  File "/opt/anaconda3/envs/lm_training/lib/python3.5/site-packages/seqeval/metrics/sequence_labeling.py", line 14, in <module>
    from seqeval.metrics.v1 import SCORES, _precision_recall_fscore_support
  File "/opt/anaconda3/envs/lm_training/lib/python3.5/site-packages/seqeval/metrics/v1.py", line 8, in <module>
    from seqeval.scheme import Entities, Token, auto_detect
  File "/opt/anaconda3/envs/lm_training/lib/python3.5/site-packages/seqeval/scheme.py", line 27, in <module>
    class Prefix(enum.Flag):
AttributeError: module 'enum' has no attribute 'Flag'

I need the new version that has the fixes of issues reported in the "issues" tab of this repo so I can't use the old version.

My environment:

  • OS: Ubuntu 18.04
  • Python Version: 3.5.6
  • Package Version: seqeval 1.2.2

Unable to install

Please provide a detailed step by step procedure for installing seqeval from your repo. I also tried to install from .whl file available in pypi.org

seqeval-0.0.6-py3-none-any.whl is not a supported wheel on this platform This is the error that it is showing. Please let me know how to use it. Thanks.

classification_report does not work on a single list/example

Hi, since the last update, the classification_report method works only with a list of lists and it does not support a single list.

How to reproduce the behaviour

from seqeval.metrics import classification_report
from seqeval.scheme import IOB2

# Tutorial example
# OK, works
print('Example #1 (from tutorial) Works')
y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]


print(classification_report(y_true, y_pred, output_dict=False, mode=None, scheme=IOB2))

# OK, works
print('Example #2 Works')
y_true2 = [['O', 'O', 'B-LOC']]
y_pred2 = [['O', 'B-LOC', 'O']]


print(classification_report(y_true2, y_pred2, output_dict=False, mode='strict', scheme=IOB2))

# NOT OK, DOES NOT WORK
print('Example #3 DOES NOT WORK')
y_true2 = ['O', 'O', 'B-LOC']
y_pred2 = ['O', 'B-LOC', 'O']
print(classification_report(y_true2, y_pred2, output_dict=False, mode='strict', scheme=IOB2))

Your Environment

  • Operating System: Ubuntu 18.04.05 (LTS)
  • Python Version: 3.8
  • Package Version: 1.1.0

PS

Probably the same will happen in precision, recall, f1 scores methods too. Haven't tested.

Proposal to get TP, FP, FN of all targets

Feature description

Proposal to get TP, FP, FN of all targets:

At a certain point, we have three different arrays for all the targets,
tp_sum, pred_sum, true_sum

e.g.,

target_names = ['person', 'org', 'place']
tp_sum   = [1 0 0]
pred_sum = [1 0 1]
true_sum = [1 1 0]

Here, tp_sum is used as numerator of both Precision and Recall calculations.

So, tp_sum is nothing but a collection of True Positive counts of each target.


pred_sum is being used as denominator of Precision.
As per Precision's definition then, pred_sum == TP + FP

Here, we already know the count of TP (from tp_sum).
To get FP, we shall do, FP = pred_sum - tp_sum


true_sum is being used as denominator of Recall.
As per Recall's definition then, true_sum == TP + FN

Here, we already know the count of TP again (from tp_sum)
To get FN, we shall do, FN = true_sum - tp_sum


Does this sound logical?

If so, better to add a function for this, like performance_measure?

ZeroDivisionError when y_true has only 'O' tokens

As the title says, when your y_true contains only 'O' tokens, the classification_report method will return the following error:

ZeroDivisionError: Weights sum to zero, can't be normalized

This can be confusing when testing on a small, local dataset where the chances of having 'O' tokens is really high, thus, confusing the user.

Maybe there should be a try catch returning an appropriate message for that case.

How to reproduce the behaviour

from seqeval.metrics import classification_report
from seqeval.scheme import IOB2

y_pred = [['B-LOC', 'I-LOC', 'I-PER', 'B-PER']]
y_true = [['O', 'O', 'O', 'O']]

print(classification_report(y_true=y_true, y_pred=y_pred, output_dict=False, scheme=IOB2))
/home/kaslou/anaconda3/envs/my_env/lib/python3.8/site-packages/numpy/lib/function_base.py:380: RuntimeWarning: Mean of empty slice.
  avg = a.mean(axis)
/home/kaslou/anaconda3/envs/my_env/lib/python3.8/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
Traceback (most recent call last):
  File "/home/kaslou/.config/JetBrains/PyCharm2020.2/scratches/check-eval.py", line 9, in <module>
    print(classification_report(y_true=y_true, y_pred=y_pred, output_dict=False, scheme=IOB2))
  File "/home/kaslou/anaconda3/envs/my_env/lib/python3.8/site-packages/seqeval/metrics/sequence_labeling.py", line 425, in classification_report
    p = np.average(ps, weights=s)
  File "<__array_function__ internals>", line 5, in average
  File "/home/kaslou/anaconda3/envs/my_env/lib/python3.8/site-packages/numpy/lib/function_base.py", line 409, in average
    raise ZeroDivisionError(
ZeroDivisionError: Weights sum to zero, can't be normalized

Your Environment

  • Operating System: Ubuntu 18 (LTS
  • Python Version: 3.8
  • Package Version: 1.1

Is there a way to print the confusion matrix

Hey,

I want to print the FP and FN for my system. I checked the code and it seems you don't use them in the calculation and just use pred_sum and true_sum. Is there an easy way to get these numbers?

Thanks!

classification_report do not report for each label correctly

I have two model with diffirent BIO tags in same data,
My first data format,

hung_khí        O
quay    O
lại     O
đường   O
lộ_tẻ   O
tìm     O
đối_thủ O
trả_thù P-X
thấy    O
anh     O
chung   O
đang    O
dọn_dẹp O
shop    O
quần_áo C-X
chúng   O
tưởng   O

and classification_report show me,

           precision    recall  f1-score   support

        X       0.79      0.75      0.77    584887

micro avg       0.78      0.75      0.77    584887
macro avg       0.79      0.75      0.77    584887

And my second BIO label,

hung_khí        O
quay    O
lại     O
đường   O
lộ_tẻ   O
tìm     O
đối_thủ O
trả_thù .PUNCTUATION
thấy    O
anh     O
chung   O
đang    O
dọn_dẹp O
shop    O
quần_áo ,COMMA
chúng   O
tưởng   O

and eval report of seqeval,

               precision    recall  f1-score   support

       ,COMMA       0.72      0.65      0.68    364918
?QUESTIONMARK       0.60      0.29      0.39      3687

    micro avg       0.71      0.64      0.68    368605
    macro avg       0.72      0.64      0.68    368605

What am I do wrong?

Error defining model with tensorflow.keras

In both tensorflow 1.15 and tensorflow 2.x the keras callback does not work if the model is defined using keras from tensorflow, i.e.:

import tensorflow.keras as keras

this is due to the incompatibility of both improting keras directly and through tensorflow. But as far as I know, the later is the recommended way of doing so.

is there any way of making the latter compatible? I tried changing the importing line of your library and it didn't work.

Weighted average versus macro average?

I noticed that in computing the 'macro' average in the classification report, the result is weighted by the support for each class. My understanding of macro-averaging is that it treats each class equally (in contrast to micro-averaging, where large classes have a greater impact on the final result). e.g. see Manning (2009) p.280: "Macroaveraging
gives equal weight to each class" https://nlp.stanford.edu/IR-book/pdf/13bayes.pdf

As a suggestion, to avoid confusion you could follow the sklearn model and have options for all three of micro-average, macro-average, and weighted average: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score

string index out of range

image
I don't know why string index out of range
['L', 'D', '1', '0', '_', '1', 'W', 'H', 'P', 'C', '下', '甲', '板', 'C', '1', '2', '井', '注', '水', '井', '采', '油', '树', '服', '务', '管', '汇', '处', '无', '堵', '头', ',', '注', '水', '压', '力', '高', ',', '存', '在', '安', '全', '隐', '患', '。'] ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-STATUS', 'I-STATUS', 'I-STATUS', 'O', 'B-STATUS', 'I-STATUS', 'I-STATUS', 'I-STATUS', 'I-STATUS', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']

digits doesn't work when set `output_dict=True`

How to reproduce the behaviour

When set output_dict=True in classification_report, the arg digits doesn't work
Your Environment

  • Operating System: Linux
  • Python Version: 3.8.10
  • Package Version: 1.2.2

Mute warnings when computing metrics for non-NER labels?

Is it possible to mute warnings when computing metrics for e.g. non-NER tasks, e.g. POS tagging? (Or optionally validate labels for NER?) I believe the implementations of precision_score, recall_score, etc. should still work fine for other sequence labeling tasks.

If not, it might be a nice feature to add. Since a warning is raised for each non-NER label, they can add up for larger label sets.

Keras callback for a single output

I have a multi-output Keras model (one input for sequence classification and another for sequence labeling). Is there a way to apply the callback to a single output only? For now I've hacked together

import numpy as np
from seqeval.callbacks import F1Metrics


class SingleOutputF1(F1Metrics):
    def __init__(self, id2label, output_index):
        super().__init__(id2label)
        self.output_index = output_index

    def predict(self, X, y):
        """Predict sequences.

        Args:
            X (list): input data.
            y (list): tags.

        Returns:
            y_true: true sequences.
            y_pred: predicted sequences.
        """
        y_pred = self.model.predict_on_batch(X)[self.output_index]

        # reduce dimension.
        y_true = np.argmax(y, -1)
        y_pred = np.argmax(y_pred, -1)

        lens = self.get_length(y_true)

        y_true = self.convert_idx_to_name(y_true, lens)
        y_pred = self.convert_idx_to_name(y_pred, lens)

        return y_true, y_pred

    def on_epoch_end_fit(self, epoch, logs={}):
        X = self.validation_data[0]
        y = self.validation_data[self.output_index + 1]
        y_true, y_pred = self.predict(X, y)
        score = self.score(y_true, y_pred)
        logs['f1'] = score

    def on_epoch_end_fit_generator(self, epoch, logs={}):
        y_true = []
        y_pred = []
        for validation_data in self.validation_data:
            X = validation_data[0]
            y = validation_data[self.output_index + 1]
            y_true_batch, y_pred_batch = self.predict(X, y)
            y_true.extend(y_true_batch)
            y_pred.extend(y_pred_batch)
        score = self.score(y_true, y_pred)
        logs['f1'] = score

which seems to work.

Error during offline installation

In our company we can't use pip for online installation, we can only do offline installation. So pip install seqeval[cpu] doesnt work. So we downloaded the seqeval v0.0.12 package from the releases. And tried installing using the following three commands:

python -m pip install --no-index --user --find-links="J:\USB\OPD\Repository\Python Pack\Latest Python Package\PythonPackage" "J:\USB\OPD\Repository\Python Pack\Latest Python Package\PythonPackage\seqeval-0.0.12.tar.gz"

and

python setup.py install

and

python -m pip install -e .

In all the cases we were getting the following error:

Looking in links: j:\USB\OPD\Repository\Python Pack\Latest Python Package\Python
Package
Processing j:\usb\opd\repository\python pack\latest python package\pythonpackage
\seqeval-0.0.12.tar.gz
    ERROR: Command errored out with exit status 1:
     command: 'C:\ProgramData\Anaconda3\python.exe' -c 'import sys, setuptools,
tokenize; sys.argv[0] = '"'"'C:\\Users\\jk2446\\AppData\\Local\\Temp\\pip-req-bu
ild-5a2amt18\\setup.py'"'"'; __file__='"'"'C:\\Users\\jk2446\\AppData\\Local\\Te
mp\\pip-req-build-5a2amt18\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', op
en)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec
(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base 'C:\Users\jk2446\
AppData\Local\Temp\pip-req-build-5a2amt18\pip-egg-info'
         cwd: C:\Users\jk2446\AppData\Local\Temp\pip-req-build-5a2amt18\
    Complete output (27 lines):
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "C:\Users\jk2446\AppData\Local\Temp\pip-req-build-5a2amt18\setup.py",
 line 57, in <module>
        'Programming Language :: Python :: Implementation :: PyPy'
      File "C:\Users\jk2446\AppData\Roaming\Python\Python36\site-packages\setupt
ools\__init__.py", line 145, in setup
        return distutils.core.setup(**attrs)
      File "C:\ProgramData\Anaconda3\lib\distutils\core.py", line 108, in setup
        _setup_distribution = dist = klass(attrs)
      File "C:\Users\jk2446\AppData\Roaming\Python\Python36\site-packages\setupt
ools\dist.py", line 448, in __init__
        k: v for k, v in attrs.items()
      File "C:\ProgramData\Anaconda3\lib\distutils\dist.py", line 281, in __init
__
        self.finalize_options()
      File "C:\Users\jk2446\AppData\Roaming\Python\Python36\site-packages\setupt
ools\dist.py", line 740, in finalize_options
        ep.load()(self)
      File "C:\Users\jk2446\AppData\Roaming\Python\Python36\site-packages\setupt
ools\dist.py", line 747, in _finalize_setup_keywords
        ep.load()(self, ep.name, value)
      File "C:\Users\jk2446\AppData\Roaming\Python\Python36\site-packages\setupt
ools_scm\integration.py", line 17, in version_keyword
        dist.metadata.version = get_version(**value)
      File "C:\Users\jk2446\AppData\Roaming\Python\Python36\site-packages\setupt
ools_scm\__init__.py", line 150, in get_version
        parsed_version = _do_parse(config)
      File "C:\Users\jk2446\AppData\Roaming\Python\Python36\site-packages\setupt
ools_scm\__init__.py", line 113, in _do_parse
        "use git+https://github.com/user/proj.git#egg=proj" % config.absolute_ro
ot
    LookupError: setuptools-scm was unable to detect version for 'C:\\Users\\jk2
446\\AppData\\Local\\Temp\\pip-req-build-5a2amt18'.

    Make sure you're either building from a fully intact git repository or PyPI
tarballs. Most other sources (such as GitHub's tarballs, a git checkout without
the .git folder) don't contain the necessary metadata and will not work.

    For example, if you're using pip, instead of https://github.com/user/proj/ar
chive/master.zip use git+https://github.com/user/proj.git#egg=proj
    ----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check th
e logs for full command output.

Version details:

OS: Windows 7
Python: 3.6.3
Setuptools_scn: 3.3.3
Pip: 19.3.1
seqeval: 0.0.12

What might be the problem. Kindly help.

Non-NER tags are missing one letter

How to reproduce the behaviour

If I execute the next code with POS tags

y_pred = [['ADJ', 'CONJ', 'VERB', 'AUX', 'NOUN', 'ADJ', 'SCONJ'], ['CONJ', 'SCONJ', 'X']]
y_true = [['ADJ', 'DET', 'VERB', 'AUX', 'NOUN', 'ADJ', 'SCONJ'], ['CONJ', 'ART', 'X']]
print(classification_report(y_true, y_pred))

What I get is:

              precision    recall  f1-score   support

        CONJ       0.50      1.00      0.67         1
          DJ       1.00      1.00      1.00         2
         ERB       1.00      1.00      1.00         1
          ET       0.00      0.00      0.00         1
         ONJ       0.50      1.00      0.67         1
         OUN       1.00      1.00      1.00         1
          RT       0.00      0.00      0.00         1
          UX       1.00      1.00      1.00         1

   micro avg       0.78      0.78      0.78         9
   macro avg       0.62      0.75      0.67         9
weighted avg       0.67      0.78      0.70         9

Here, all tags are missing the first letter. If I pass in suffix=True, now the missing letter of the tags is the last one:

              precision    recall  f1-score   support

          AD       1.00      1.00      1.00         2
          AR       0.00      0.00      0.00         1
          AU       1.00      1.00      1.00         1
         CON       0.50      1.00      0.67         1
          DE       0.00      0.00      0.00         1
         NOU       1.00      1.00      1.00         1
        SCON       0.50      1.00      0.67         1
         VER       1.00      1.00      1.00         1

   micro avg       0.78      0.78      0.78         9
   macro avg       0.62      0.75      0.67         9
weighted avg       0.67      0.78      0.70         9

Moreover, one letter tags are ignored.

Your Environment

  • Operating System: Ubuntu 20.10
  • Python Version: Python 3.8.6
  • Package Version: seqeval==1.2.2

Different f1 score in f1_score() and classification_report()

I am using above functions to compute f1 score for Named entity recognition where I am using 10 entities with BIO encoding.

f1_score() showing
0.437542201215395

where as Classification_report() showing
precision recall f1-score support

         loc                0.11          0.75      0.19       102
   person               0.77          0.72      0.75        90
company               0.85         0.74      0.79        70
product                0.84          0.62      0.71        42

facility 0.72 0.81 0.76 26
other 0.76 0.65 0.70 83
musicartist 0.75 0.55 0.63 22
sportsteam 0.91 0.67 0.77 15
movie 1.00 0.57 0.73 7
tvshow 1.00 0.50 0.67 6

avg / total 0.65 0.70 0.61 463

Please remove tensorflow from setup.py

Since tensorflow is not directly required in this project, please remove it from the setup.py.
I have a gpu virtualenv, when I install seqeval it will install tensorflow-cpu to this environment, then I have to delete the tensorflow-cpu and re-install tensorflow-gpu.

Biomedical named entity evaluation has a problem

image
image

Above of the two examples are the same in the criteria of entity evaluation,
however, it shows different evaluation scores.
Most of the biomedical named entity datasets are composed of the first case.
I think this should be fixed for the further usage of seqeval framework.

Plans for a Confusion matrix?

Are there any plans to implement a confusion matrix computation for NER labels?
I see that the classification report already gives a lot of information required for evaluation. A confusion matrix would be a nice addition to present the prediction errors.

why classification_report can not count label "ORGANIZATION"

the result is
precision recall f1-score support

PRODUCT 0.884 0.840 0.862 1007
LOCATION 0.927 0.760 0.835 50
PERSON 0.000 0.000 0.000 2

micro avg 0.885 0.835 0.859 1059
macro avg 0.884 0.835 0.859 1059

but in the data set,i have:
pred:['ORGANIZATION', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
true:['ORGANIZATION', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']

Not IOB2 evaluation schema

In [1]: y_true = [['B-Phone', "I-Phone"]]

In [2]: y_pred = [["I-Phone", "I-Phone"]]

In [3]: from seqeval.metrics import accuracy_score

In [4]: from seqeval.metrics import classification_report

In [5]: from seqeval.metrics import f1_score

In [6]: classification_report(y_true, y_pred)
Out[6]: ' precision recall f1-score support\n\n Phone 1.00 1.00 1.00 1\n\nmicro avg 1.00 1.00 1.00 1\nmacro avg 1.00 1.00 1.00 1\n'

This is what I tested. In iob2, the above data should be 0 precision and 0 recall.

Keras Callback Error specific GPU

I am having the following issues after first epoch of the training

Note that, I am using forcefully only 1 gpu in the machine with this

os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

Program runs fine without the callback, but including callbacks having this issue.


`2019-09-04 10:04:01.301387: W tensorflow/core/common_runtime/bfc_allocator.cc:314] Allocator (GPU_0_bfc) ran out of memory trying to allocate 9.58GiB (rounded to 10290790400).  Current allocation summary follows.
2019-09-04 10:04:01.301716: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (256): 	Total Chunks: 200, Chunks in use: 199. 50.0KiB allocated for chunks. 49.8KiB in use in bin. 2.6KiB client-requested in use in bin.
2019-09-04 10:04:01.301921: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (512): 	Total Chunks: 8, Chunks in use: 7. 4.0KiB allocated for chunks. 3.5KiB in use in bin. 2.4KiB client-requested in use in bin.
2019-09-04 10:04:01.302028: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (1024): 	Total Chunks: 2, Chunks in use: 2. 2.2KiB allocated for chunks. 2.2KiB in use in bin. 2.0KiB client-requested in use in bin.
2019-09-04 10:04:01.302127: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (2048): 	Total Chunks: 23, Chunks in use: 23. 46.0KiB allocated for chunks. 46.0KiB in use in bin. 45.0KiB client-requested in use in bin.
2019-09-04 10:04:01.302224: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (4096): 	Total Chunks: 4, Chunks in use: 3. 16.5KiB allocated for chunks. 12.5KiB in use in bin. 12.5KiB client-requested in use in bin.
2019-09-04 10:04:01.302358: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (8192): 	Total Chunks: 19, Chunks in use: 19. 156.0KiB allocated for chunks. 156.0KiB in use in bin. 156.0KiB client-requested in use in bin.
2019-09-04 10:04:01.302453: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (16384): 	Total Chunks: 1, Chunks in use: 1. 16.5KiB allocated for chunks. 16.5KiB in use in bin. 16.3KiB client-requested in use in bin.
2019-09-04 10:04:01.302548: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (32768): 	Total Chunks: 1, Chunks in use: 1. 32.0KiB allocated for chunks. 32.0KiB in use in bin. 32.0KiB client-requested in use in bin.
2019-09-04 10:04:01.302644: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (65536): 	Total Chunks: 5, Chunks in use: 5. 336.0KiB allocated for chunks. 336.0KiB in use in bin. 336.0KiB client-requested in use in bin.
2019-09-04 10:04:01.302783: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (131072): 	Total Chunks: 5, Chunks in use: 5. 992.0KiB allocated for chunks. 992.0KiB in use in bin. 992.0KiB client-requested in use in bin.
2019-09-04 10:04:01.302874: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (262144): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2019-09-04 10:04:01.302969: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (524288): 	Total Chunks: 1, Chunks in use: 1. 794.5KiB allocated for chunks. 794.5KiB in use in bin. 448.0KiB client-requested in use in bin.
2019-09-04 10:04:01.303064: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (1048576): 	Total Chunks: 32, Chunks in use: 32. 32.00MiB allocated for chunks. 32.00MiB in use in bin. 32.00MiB client-requested in use in bin.
2019-09-04 10:04:01.303166: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (2097152): 	Total Chunks: 34, Chunks in use: 34. 68.46MiB allocated for chunks. 68.46MiB in use in bin. 68.46MiB client-requested in use in bin.
2019-09-04 10:04:01.303263: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (4194304): 	Total Chunks: 18, Chunks in use: 18. 79.56MiB allocated for chunks. 79.56MiB in use in bin. 72.00MiB client-requested in use in bin.
2019-09-04 10:04:01.303358: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (8388608): 	Total Chunks: 18, Chunks in use: 17. 146.89MiB allocated for chunks. 136.00MiB in use in bin. 136.00MiB client-requested in use in bin.
2019-09-04 10:04:01.303453: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (16777216): 	Total Chunks: 4, Chunks in use: 4. 67.54MiB allocated for chunks. 67.54MiB in use in bin. 64.00MiB client-requested in use in bin.
2019-09-04 10:04:01.303546: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (33554432): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
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2019-09-04 10:04:01.303737: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (134217728): 	Total Chunks: 2, Chunks in use: 1. 372.16MiB allocated for chunks. 197.90MiB in use in bin. 174.25MiB client-requested in use in bin.
2019-09-04 10:04:01.303833: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (268435456): 	Total Chunks: 2, Chunks in use: 0. 3.00GiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2019-09-04 10:04:01.303925: I tensorflow/core/common_runtime/bfc_allocator.cc:780] Bin for 9.58GiB was 256.00MiB, Chunk State: 
2019-09-04 10:04:01.304017: I tensorflow/core/common_runtime/bfc_allocator.cc:786]   Size: 1.00GiB | Requested Size: 275.0KiB | in_use: 0 | bin_num: 20
2019-09-04 10:04:01.304103: I tensorflow/core/common_runtime/bfc_allocator.cc:786]   Size: 2.00GiB | Requested Size: 528.95MiB | in_use: 0 | bin_num: 20
2019-09-04 10:04:01.304183: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 2147483648
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2019-09-04 10:04:01.310418: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2428c79200 next 413 of size 256
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2019-09-04 10:04:01.310888: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2429c7b000 next 2629 of size 1048576
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2019-09-04 10:04:01.311046: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f242ac7b000 next 2607 of size 256
2019-09-04 10:04:01.311061: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242ac7b100 next 2590 of size 2097152
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2019-09-04 10:04:01.311217: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f242bc7b100 next 4798 of size 512
2019-09-04 10:04:01.311229: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242bc7b300 next 3834 of size 256
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2019-09-04 10:04:01.311270: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f242bc7b600 next 3946 of size 4096
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2019-09-04 10:04:01.311312: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242c761a00 next 4900 of size 2338816
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2019-09-04 10:04:01.311334: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f242cbd7a00 next 4097 of size 182720000
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2019-09-04 10:04:01.312430: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2452847800 next 115 of size 4194304
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2019-09-04 10:04:01.312554: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2478000000 next 40 of size 16777216
2019-09-04 10:04:01.312568: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2479000000 next 43 of size 8388608
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2019-09-04 10:04:01.312674: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a412300 next 51 of size 196608
2019-09-04 10:04:01.312687: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a442300 next 54 of size 8192
2019-09-04 10:04:01.312699: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a444300 next 55 of size 8388608
2019-09-04 10:04:01.312710: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac44300 next 58 of size 65536
2019-09-04 10:04:01.312722: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac54300 next 59 of size 4096
2019-09-04 10:04:01.312734: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac55300 next 60 of size 65536
2019-09-04 10:04:01.312747: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac65300 next 61 of size 65536
2019-09-04 10:04:01.312761: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac75300 next 18446744073709551615 of size 20491520
2019-09-04 10:04:01.312775: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 33554432
2019-09-04 10:04:01.312788: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24be000000 next 32 of size 8388608
2019-09-04 10:04:01.312802: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24be800000 next 35 of size 8388608
2019-09-04 10:04:01.312814: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24bf000000 next 18446744073709551615 of size 16777216
2019-09-04 10:04:01.312825: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 16777216
2019-09-04 10:04:01.312841: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da000000 next 24 of size 4194304
2019-09-04 10:04:01.312855: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da400000 next 26 of size 256
2019-09-04 10:04:01.312868: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da400100 next 27 of size 12288
2019-09-04 10:04:01.312881: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da403100 next 28 of size 8192
2019-09-04 10:04:01.312893: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da405100 next 29 of size 4194304
2019-09-04 10:04:01.312904: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da805100 next 30 of size 2048
2019-09-04 10:04:01.312917: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da805900 next 33 of size 16896
2019-09-04 10:04:01.312932: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da809b00 next 34 of size 8192
2019-09-04 10:04:01.312946: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da80bb00 next 36 of size 2048
2019-09-04 10:04:01.312959: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da80c300 next 37 of size 81920
2019-09-04 10:04:01.312972: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da820300 next 38 of size 256
2019-09-04 10:04:01.312983: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da820400 next 41 of size 4096
2019-09-04 10:04:01.312994: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da821400 next 42 of size 32768
2019-09-04 10:04:01.313007: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da829400 next 18446744073709551615 of size 8219648
2019-09-04 10:04:01.313020: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 1048576
2019-09-04 10:04:01.313035: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400000 next 1 of size 1280
2019-09-04 10:04:01.313049: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400500 next 2 of size 256
2019-09-04 10:04:01.313062: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400600 next 3 of size 256
2019-09-04 10:04:01.313073: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400700 next 4 of size 256
2019-09-04 10:04:01.313085: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400800 next 5 of size 256
2019-09-04 10:04:01.313097: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400900 next 6 of size 256
2019-09-04 10:04:01.313111: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400a00 next 7 of size 256
2019-09-04 10:04:01.313124: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400b00 next 8 of size 512
2019-09-04 10:04:01.313137: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400d00 next 10 of size 256
2019-09-04 10:04:01.313150: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400e00 next 11 of size 256
2019-09-04 10:04:01.313162: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400f00 next 12 of size 8192
2019-09-04 10:04:01.313175: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549402f00 next 16 of size 204800
2019-09-04 10:04:01.313189: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549434f00 next 17 of size 256
2019-09-04 10:04:01.313203: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549435000 next 18 of size 512
2019-09-04 10:04:01.313217: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549435200 next 19 of size 8192
2019-09-04 10:04:01.313230: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549437200 next 20 of size 8192
2019-09-04 10:04:01.313242: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549439200 next 21 of size 256
2019-09-04 10:04:01.313254: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549439300 next 22 of size 256
2019-09-04 10:04:01.313267: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549439400 next 25 of size 512
2019-09-04 10:04:01.313282: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549439600 next 18446744073709551615 of size 813568
2019-09-04 10:04:01.313295: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 4194304
2019-09-04 10:04:01.313309: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f254b200000 next 18446744073709551615 of size 4194304
2019-09-04 10:04:01.313322: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 4194304
2019-09-04 10:04:01.313334: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f254b600000 next 18446744073709551615 of size 4194304
2019-09-04 10:04:01.313345: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 8388608
2019-09-04 10:04:01.313360: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2553000000 next 15 of size 4194304
2019-09-04 10:04:01.313374: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2553400000 next 18446744073709551615 of size 4194304
2019-09-04 10:04:01.313387: I tensorflow/core/common_runtime/bfc_allocator.cc:809]      Summary of in-use Chunks by size: 
2019-09-04 10:04:01.313413: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 199 Chunks of size 256 totalling 49.8KiB
2019-09-04 10:04:01.313427: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 7 Chunks of size 512 totalling 3.5KiB
2019-09-04 10:04:01.313440: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 1024 totalling 1.0KiB
2019-09-04 10:04:01.313455: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 1280 totalling 1.2KiB
2019-09-04 10:04:01.313471: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 23 Chunks of size 2048 totalling 46.0KiB
2019-09-04 10:04:01.313485: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 2 Chunks of size 4096 totalling 8.0KiB
2019-09-04 10:04:01.313498: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 4608 totalling 4.5KiB
2019-09-04 10:04:01.313512: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 18 Chunks of size 8192 totalling 144.0KiB
2019-09-04 10:04:01.313525: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 12288 totalling 12.0KiB
2019-09-04 10:04:01.313540: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 16896 totalling 16.5KiB
2019-09-04 10:04:01.313555: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 32768 totalling 32.0KiB
2019-09-04 10:04:01.313570: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 4 Chunks of size 65536 totalling 256.0KiB
2019-09-04 10:04:01.313585: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 81920 totalling 80.0KiB
2019-09-04 10:04:01.313598: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 196608 totalling 192.0KiB
2019-09-04 10:04:01.313611: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 4 Chunks of size 204800 totalling 800.0KiB
2019-09-04 10:04:01.313626: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 813568 totalling 794.5KiB
2019-09-04 10:04:01.313642: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 32 Chunks of size 1048576 totalling 32.00MiB
2019-09-04 10:04:01.313657: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 32 Chunks of size 2097152 totalling 64.00MiB
2019-09-04 10:04:01.313670: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 2 Chunks of size 2338816 totalling 4.46MiB
2019-09-04 10:04:01.313682: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 16 Chunks of size 4194304 totalling 64.00MiB
2019-09-04 10:04:01.313694: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 8095744 totalling 7.72MiB
2019-09-04 10:04:01.313707: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 8219648 totalling 7.84MiB
2019-09-04 10:04:01.313722: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 17 Chunks of size 8388608 totalling 136.00MiB
2019-09-04 10:04:01.313738: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 3 Chunks of size 16777216 totalling 48.00MiB
2019-09-04 10:04:01.313753: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 20491520 totalling 19.54MiB
2019-09-04 10:04:01.313766: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 4 Chunks of size 67108864 totalling 256.00MiB
2019-09-04 10:04:01.313779: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 207515648 totalling 197.90MiB
2019-09-04 10:04:01.313793: I tensorflow/core/common_runtime/bfc_allocator.cc:816] Sum Total of in-use chunks: 839.85MiB
2019-09-04 10:04:01.313806: I tensorflow/core/common_runtime/bfc_allocator.cc:818] total_region_allocated_bytes_: 4296015872 memory_limit_: 10979036365 available bytes: 6683020493 curr_region_allocation_bytes_: 4294967296
2019-09-04 10:04:01.313829: I tensorflow/core/common_runtime/bfc_allocator.cc:824] Stats: 
Limit:                 10979036365
InUse:                   880645376
MaxInUse:               3286797056
NumAllocs:                 5943743
MaxAllocSize:           1107755008

2019-09-04 10:04:01.314288: W tensorflow/core/common_runtime/bfc_allocator.cc:319] __________________________________________________________________________*****___******************
2019-09-04 10:04:01.314450: W tensorflow/core/framework/op_kernel.cc:1502] OP_REQUIRES failed at conv_ops.cc:486 : Resource exhausted: OOM when allocating tensor with shape[1142,1024,50,44] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
2019-09-04 10:04:11.583601: W tensorflow/core/common_runtime/bfc_allocator.cc:314] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.50GiB (rounded to 2689638400).  Current allocation summary follows.
2019-09-04 10:04:11.583934: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (256): 	Total Chunks: 200, Chunks in use: 199. 50.0KiB allocated for chunks. 49.8KiB in use in bin. 2.6KiB client-requested in use in bin.
2019-09-04 10:04:11.584147: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (512): 	Total Chunks: 8, Chunks in use: 7. 4.0KiB allocated for chunks. 3.5KiB in use in bin. 2.4KiB client-requested in use in bin.
2019-09-04 10:04:11.584269: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (1024): 	Total Chunks: 2, Chunks in use: 2. 2.2KiB allocated for chunks. 2.2KiB in use in bin. 2.0KiB client-requested in use in bin.
2019-09-04 10:04:11.584295: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (2048): 	Total Chunks: 23, Chunks in use: 23. 46.0KiB allocated for chunks. 46.0KiB in use in bin. 45.0KiB client-requested in use in bin.
2019-09-04 10:04:11.584311: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (4096): 	Total Chunks: 4, Chunks in use: 3. 16.5KiB allocated for chunks. 12.5KiB in use in bin. 12.5KiB client-requested in use in bin.
2019-09-04 10:04:11.584394: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (8192): 	Total Chunks: 19, Chunks in use: 19. 156.0KiB allocated for chunks. 156.0KiB in use in bin. 156.0KiB client-requested in use in bin.
2019-09-04 10:04:11.584412: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (16384): 	Total Chunks: 1, Chunks in use: 1. 16.5KiB allocated for chunks. 16.5KiB in use in bin. 16.3KiB client-requested in use in bin.
2019-09-04 10:04:11.584422: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (32768): 	Total Chunks: 1, Chunks in use: 1. 32.0KiB allocated for chunks. 32.0KiB in use in bin. 32.0KiB client-requested in use in bin.
2019-09-04 10:04:11.584432: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (65536): 	Total Chunks: 5, Chunks in use: 5. 336.0KiB allocated for chunks. 336.0KiB in use in bin. 336.0KiB client-requested in use in bin.
2019-09-04 10:04:11.584441: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (131072): 	Total Chunks: 5, Chunks in use: 5. 992.0KiB allocated for chunks. 992.0KiB in use in bin. 992.0KiB client-requested in use in bin.
2019-09-04 10:04:11.584450: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (262144): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2019-09-04 10:04:11.584460: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (524288): 	Total Chunks: 1, Chunks in use: 1. 794.5KiB allocated for chunks. 794.5KiB in use in bin. 448.0KiB client-requested in use in bin.
2019-09-04 10:04:11.584469: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (1048576): 	Total Chunks: 32, Chunks in use: 32. 32.00MiB allocated for chunks. 32.00MiB in use in bin. 32.00MiB client-requested in use in bin.
2019-09-04 10:04:11.584479: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (2097152): 	Total Chunks: 34, Chunks in use: 34. 68.46MiB allocated for chunks. 68.46MiB in use in bin. 68.46MiB client-requested in use in bin.
2019-09-04 10:04:11.584489: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (4194304): 	Total Chunks: 18, Chunks in use: 18. 79.56MiB allocated for chunks. 79.56MiB in use in bin. 72.00MiB client-requested in use in bin.
2019-09-04 10:04:11.584499: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (8388608): 	Total Chunks: 18, Chunks in use: 17. 146.89MiB allocated for chunks. 136.00MiB in use in bin. 136.00MiB client-requested in use in bin.
2019-09-04 10:04:11.584532: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (16777216): 	Total Chunks: 4, Chunks in use: 4. 67.54MiB allocated for chunks. 67.54MiB in use in bin. 64.00MiB client-requested in use in bin.
2019-09-04 10:04:11.584550: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (33554432): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2019-09-04 10:04:11.584564: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (67108864): 	Total Chunks: 4, Chunks in use: 4. 256.00MiB allocated for chunks. 256.00MiB in use in bin. 256.00MiB client-requested in use in bin.
2019-09-04 10:04:11.584574: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (134217728): 	Total Chunks: 2, Chunks in use: 1. 372.16MiB allocated for chunks. 197.90MiB in use in bin. 174.25MiB client-requested in use in bin.
2019-09-04 10:04:11.584584: I tensorflow/core/common_runtime/bfc_allocator.cc:764] Bin (268435456): 	Total Chunks: 4, Chunks in use: 1. 9.22GiB allocated for chunks. 4.90GiB in use in bin. 4.90GiB client-requested in use in bin.
2019-09-04 10:04:11.584599: I tensorflow/core/common_runtime/bfc_allocator.cc:780] Bin for 2.50GiB was 256.00MiB, Chunk State: 
2019-09-04 10:04:11.584615: I tensorflow/core/common_runtime/bfc_allocator.cc:786]   Size: 1.00GiB | Requested Size: 940.98MiB | in_use: 0 | bin_num: 20
2019-09-04 10:04:11.584631: I tensorflow/core/common_runtime/bfc_allocator.cc:786]   Size: 1.32GiB | Requested Size: 128B | in_use: 0 | bin_num: 20, prev:   Size: 4.90GiB | Requested Size: 4.90GiB | in_use: 1 | bin_num: -1
2019-09-04 10:04:11.584645: I tensorflow/core/common_runtime/bfc_allocator.cc:786]   Size: 2.00GiB | Requested Size: 528.95MiB | in_use: 0 | bin_num: 20
2019-09-04 10:04:11.584659: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 6683020288
2019-09-04 10:04:11.584683: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f21b4000000 next 4517 of size 5262336000
2019-09-04 10:04:11.584698: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f22eda8e000 next 18446744073709551615 of size 1420684288
2019-09-04 10:04:11.584709: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 2147483648
2019-09-04 10:04:11.584721: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f2344000000 next 18446744073709551615 of size 2147483648
2019-09-04 10:04:11.584733: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 1073741824
2019-09-04 10:04:11.584756: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f23c4000000 next 18446744073709551615 of size 1073741824
2019-09-04 10:04:11.584771: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 536870912
2019-09-04 10:04:11.584789: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424000000 next 120 of size 4194304
2019-09-04 10:04:11.584810: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424400000 next 121 of size 256
2019-09-04 10:04:11.584825: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424400100 next 122 of size 256
2019-09-04 10:04:11.584850: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424400200 next 123 of size 2048
2019-09-04 10:04:11.584869: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424400a00 next 124 of size 256
2019-09-04 10:04:11.584885: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424400b00 next 125 of size 8192
2019-09-04 10:04:11.584906: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424402b00 next 126 of size 4194304
2019-09-04 10:04:11.584923: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424802b00 next 127 of size 256
2019-09-04 10:04:11.584939: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424802c00 next 128 of size 2048
2019-09-04 10:04:11.584957: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424803400 next 129 of size 8192
2019-09-04 10:04:11.584976: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424805400 next 130 of size 256
2019-09-04 10:04:11.584994: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424805500 next 131 of size 256
2019-09-04 10:04:11.585015: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2424805600 next 132 of size 256
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2019-09-04 10:04:11.586939: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2428c77500 next 2772 of size 256
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2019-09-04 10:04:11.587893: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2429c7b000 next 2629 of size 1048576
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2019-09-04 10:04:11.587966: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242a07b000 next 392 of size 1048576
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2019-09-04 10:04:11.588095: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242aa7b000 next 1157 of size 2097152
2019-09-04 10:04:11.588109: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f242ac7b000 next 2607 of size 256
2019-09-04 10:04:11.588123: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242ac7b100 next 2590 of size 2097152
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2019-09-04 10:04:11.588178: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242b47b100 next 624 of size 1048576
2019-09-04 10:04:11.588191: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242b57b100 next 4272 of size 1048576
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2019-09-04 10:04:11.588242: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242b97b100 next 2844 of size 1048576
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2019-09-04 10:04:11.588268: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242bb7b100 next 3441 of size 1048576
2019-09-04 10:04:11.588281: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f242bc7b100 next 4798 of size 512
2019-09-04 10:04:11.588293: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242bc7b300 next 3834 of size 256
2019-09-04 10:04:11.588307: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242bc7b400 next 499 of size 256
2019-09-04 10:04:11.588318: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242bc7b500 next 4935 of size 256
2019-09-04 10:04:11.588339: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f242bc7b600 next 3946 of size 4096
2019-09-04 10:04:11.588360: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242bc7c600 next 2203 of size 4608
2019-09-04 10:04:11.588382: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f242bc7d800 next 2157 of size 11420160
2019-09-04 10:04:11.588405: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242c761a00 next 4900 of size 2338816
2019-09-04 10:04:11.588427: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f242c99ca00 next 1658 of size 2338816
2019-09-04 10:04:11.588445: I tensorflow/core/common_runtime/bfc_allocator.cc:800] Free  at 0x7f242cbd7a00 next 4097 of size 182720000
2019-09-04 10:04:11.588466: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2437a19000 next 18446744073709551615 of size 207515648
2019-09-04 10:04:11.588481: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 268435456
2019-09-04 10:04:11.588495: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2444000000 next 57 of size 67108864
2019-09-04 10:04:11.588508: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2448000000 next 62 of size 2048
2019-09-04 10:04:11.588522: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2448000800 next 63 of size 8388608
2019-09-04 10:04:11.588534: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2448800800 next 64 of size 1024
2019-09-04 10:04:11.588545: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2448800c00 next 65 of size 4194304
2019-09-04 10:04:11.588556: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2448c00c00 next 66 of size 8388608
2019-09-04 10:04:11.588568: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2449400c00 next 67 of size 8192
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2019-09-04 10:04:11.588596: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2449404c00 next 69 of size 256
2019-09-04 10:04:11.588607: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2449404d00 next 70 of size 8388608
2019-09-04 10:04:11.588619: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2449c04d00 next 71 of size 2048
2019-09-04 10:04:11.588632: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2449c05500 next 72 of size 16777216
2019-09-04 10:04:11.588643: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244ac05500 next 73 of size 67108864
2019-09-04 10:04:11.588656: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244ec05500 next 74 of size 8388608
2019-09-04 10:04:11.588664: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f405500 next 75 of size 8192
2019-09-04 10:04:11.588672: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f407500 next 76 of size 4194304
2019-09-04 10:04:11.588684: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f807500 next 77 of size 256
2019-09-04 10:04:11.588695: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f807600 next 78 of size 256
2019-09-04 10:04:11.588708: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f807700 next 79 of size 512
2019-09-04 10:04:11.588721: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f807900 next 80 of size 2048
2019-09-04 10:04:11.588736: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f808100 next 81 of size 256
2019-09-04 10:04:11.588749: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f808200 next 82 of size 204800
2019-09-04 10:04:11.588762: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83a200 next 83 of size 256
2019-09-04 10:04:11.588773: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83a300 next 84 of size 256
2019-09-04 10:04:11.588789: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83a400 next 85 of size 256
2019-09-04 10:04:11.588803: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83a500 next 86 of size 256
2019-09-04 10:04:11.588817: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83a600 next 87 of size 256
2019-09-04 10:04:11.588831: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83a700 next 88 of size 512
2019-09-04 10:04:11.588844: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83a900 next 89 of size 512
2019-09-04 10:04:11.588857: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83ab00 next 90 of size 256
2019-09-04 10:04:11.588869: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83ac00 next 91 of size 256
2019-09-04 10:04:11.588883: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83ad00 next 92 of size 256
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2019-09-04 10:04:11.588911: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83af00 next 94 of size 256
2019-09-04 10:04:11.588924: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f244f83b000 next 95 of size 8388608
2019-09-04 10:04:11.588936: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245003b000 next 96 of size 8388608
2019-09-04 10:04:11.588947: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245083b000 next 97 of size 8388608
2019-09-04 10:04:11.588956: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245103b000 next 98 of size 8388608
2019-09-04 10:04:11.588969: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245183b000 next 99 of size 8388608
2019-09-04 10:04:11.588983: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245203b000 next 100 of size 256
2019-09-04 10:04:11.588997: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245203b100 next 101 of size 256
2019-09-04 10:04:11.589010: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245203b200 next 102 of size 256
2019-09-04 10:04:11.589024: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245203b300 next 103 of size 256
2019-09-04 10:04:11.589036: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245203b400 next 104 of size 256
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2019-09-04 10:04:11.589075: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245203b700 next 107 of size 8388608
2019-09-04 10:04:11.589089: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245283b700 next 108 of size 8192
2019-09-04 10:04:11.589102: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245283d700 next 109 of size 8192
2019-09-04 10:04:11.589116: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f245283f700 next 110 of size 8192
2019-09-04 10:04:11.589126: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2452841700 next 111 of size 8192
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2019-09-04 10:04:11.589152: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2452843800 next 113 of size 8192
2019-09-04 10:04:11.589165: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2452845800 next 114 of size 8192
2019-09-04 10:04:11.589179: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2452847800 next 115 of size 4194304
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2019-09-04 10:04:11.589219: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2453447800 next 118 of size 4194304
2019-09-04 10:04:11.589234: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2453847800 next 18446744073709551615 of size 8095744
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2019-09-04 10:04:11.589262: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2464000000 next 53 of size 67108864
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2019-09-04 10:04:11.589315: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2479000000 next 43 of size 8388608
2019-09-04 10:04:11.589329: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2479800000 next 44 of size 8388608
2019-09-04 10:04:11.589343: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a000000 next 45 of size 8192
2019-09-04 10:04:11.589354: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a002000 next 46 of size 256
2019-09-04 10:04:11.589368: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a002100 next 47 of size 4194304
2019-09-04 10:04:11.589381: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a402100 next 48 of size 256
2019-09-04 10:04:11.589392: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a402200 next 49 of size 256
2019-09-04 10:04:11.589405: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a402300 next 50 of size 65536
2019-09-04 10:04:11.589420: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a412300 next 51 of size 196608
2019-09-04 10:04:11.589434: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a442300 next 54 of size 8192
2019-09-04 10:04:11.589448: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247a444300 next 55 of size 8388608
2019-09-04 10:04:11.589461: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac44300 next 58 of size 65536
2019-09-04 10:04:11.589477: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac54300 next 59 of size 4096
2019-09-04 10:04:11.589490: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac55300 next 60 of size 65536
2019-09-04 10:04:11.589502: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac65300 next 61 of size 65536
2019-09-04 10:04:11.589516: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f247ac75300 next 18446744073709551615 of size 20491520
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2019-09-04 10:04:11.589544: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24be000000 next 32 of size 8388608
2019-09-04 10:04:11.589558: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24be800000 next 35 of size 8388608
2019-09-04 10:04:11.589570: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24bf000000 next 18446744073709551615 of size 16777216
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2019-09-04 10:04:11.589594: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da000000 next 24 of size 4194304
2019-09-04 10:04:11.589608: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da400000 next 26 of size 256
2019-09-04 10:04:11.589623: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da400100 next 27 of size 12288
2019-09-04 10:04:11.589636: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da403100 next 28 of size 8192
2019-09-04 10:04:11.589650: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da405100 next 29 of size 4194304
2019-09-04 10:04:11.589663: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da805100 next 30 of size 2048
2019-09-04 10:04:11.589676: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da805900 next 33 of size 16896
2019-09-04 10:04:11.589690: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da809b00 next 34 of size 8192
2019-09-04 10:04:11.589704: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da80bb00 next 36 of size 2048
2019-09-04 10:04:11.589718: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da80c300 next 37 of size 81920
2019-09-04 10:04:11.589731: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da820300 next 38 of size 256
2019-09-04 10:04:11.589745: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da820400 next 41 of size 4096
2019-09-04 10:04:11.589755: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da821400 next 42 of size 32768
2019-09-04 10:04:11.589767: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f24da829400 next 18446744073709551615 of size 8219648
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2019-09-04 10:04:11.589795: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400000 next 1 of size 1280
2019-09-04 10:04:11.589809: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400500 next 2 of size 256
2019-09-04 10:04:11.589822: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400600 next 3 of size 256
2019-09-04 10:04:11.589835: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400700 next 4 of size 256
2019-09-04 10:04:11.589848: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400800 next 5 of size 256
2019-09-04 10:04:11.589862: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400900 next 6 of size 256
2019-09-04 10:04:11.589875: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400a00 next 7 of size 256
2019-09-04 10:04:11.589888: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400b00 next 8 of size 512
2019-09-04 10:04:11.589901: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400d00 next 10 of size 256
2019-09-04 10:04:11.589913: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400e00 next 11 of size 256
2019-09-04 10:04:11.589923: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549400f00 next 12 of size 8192
2019-09-04 10:04:11.589937: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549402f00 next 16 of size 204800
2019-09-04 10:04:11.589951: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549434f00 next 17 of size 256
2019-09-04 10:04:11.589965: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549435000 next 18 of size 512
2019-09-04 10:04:11.589988: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549435200 next 19 of size 8192
2019-09-04 10:04:11.589999: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549437200 next 20 of size 8192
2019-09-04 10:04:11.590011: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549439200 next 21 of size 256
2019-09-04 10:04:11.590022: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549439300 next 22 of size 256
2019-09-04 10:04:11.590033: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549439400 next 25 of size 512
2019-09-04 10:04:11.590044: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2549439600 next 18446744073709551615 of size 813568
2019-09-04 10:04:11.590059: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 4194304
2019-09-04 10:04:11.590074: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f254b200000 next 18446744073709551615 of size 4194304
2019-09-04 10:04:11.590087: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 4194304
2019-09-04 10:04:11.590100: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f254b600000 next 18446744073709551615 of size 4194304
2019-09-04 10:04:11.590108: I tensorflow/core/common_runtime/bfc_allocator.cc:793] Next region of size 8388608
2019-09-04 10:04:11.590119: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2553000000 next 15 of size 4194304
2019-09-04 10:04:11.590130: I tensorflow/core/common_runtime/bfc_allocator.cc:800] InUse at 0x7f2553400000 next 18446744073709551615 of size 4194304
2019-09-04 10:04:11.590144: I tensorflow/core/common_runtime/bfc_allocator.cc:809]      Summary of in-use Chunks by size: 
2019-09-04 10:04:11.590170: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 199 Chunks of size 256 totalling 49.8KiB
2019-09-04 10:04:11.590185: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 7 Chunks of size 512 totalling 3.5KiB
2019-09-04 10:04:11.590199: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 1024 totalling 1.0KiB
2019-09-04 10:04:11.590212: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 1280 totalling 1.2KiB
2019-09-04 10:04:11.590226: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 23 Chunks of size 2048 totalling 46.0KiB
2019-09-04 10:04:11.590235: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 2 Chunks of size 4096 totalling 8.0KiB
2019-09-04 10:04:11.590249: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 4608 totalling 4.5KiB
2019-09-04 10:04:11.590262: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 18 Chunks of size 8192 totalling 144.0KiB
2019-09-04 10:04:11.590279: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 12288 totalling 12.0KiB
2019-09-04 10:04:11.590295: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 16896 totalling 16.5KiB
2019-09-04 10:04:11.590310: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 32768 totalling 32.0KiB
2019-09-04 10:04:11.590325: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 4 Chunks of size 65536 totalling 256.0KiB
2019-09-04 10:04:11.590338: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 81920 totalling 80.0KiB
2019-09-04 10:04:11.590349: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 196608 totalling 192.0KiB
2019-09-04 10:04:11.590360: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 4 Chunks of size 204800 totalling 800.0KiB
2019-09-04 10:04:11.590375: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 813568 totalling 794.5KiB
2019-09-04 10:04:11.590390: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 32 Chunks of size 1048576 totalling 32.00MiB
2019-09-04 10:04:11.590406: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 32 Chunks of size 2097152 totalling 64.00MiB
2019-09-04 10:04:11.590420: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 2 Chunks of size 2338816 totalling 4.46MiB
2019-09-04 10:04:11.590434: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 16 Chunks of size 4194304 totalling 64.00MiB
2019-09-04 10:04:11.590448: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 8095744 totalling 7.72MiB
2019-09-04 10:04:11.590459: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 8219648 totalling 7.84MiB
2019-09-04 10:04:11.590475: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 17 Chunks of size 8388608 totalling 136.00MiB
2019-09-04 10:04:11.590491: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 3 Chunks of size 16777216 totalling 48.00MiB
2019-09-04 10:04:11.590506: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 20491520 totalling 19.54MiB
2019-09-04 10:04:11.590520: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 4 Chunks of size 67108864 totalling 256.00MiB
2019-09-04 10:04:11.590534: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 207515648 totalling 197.90MiB
2019-09-04 10:04:11.590549: I tensorflow/core/common_runtime/bfc_allocator.cc:812] 1 Chunks of size 5262336000 totalling 4.90GiB
2019-09-04 10:04:11.590565: I tensorflow/core/common_runtime/bfc_allocator.cc:816] Sum Total of in-use chunks: 5.72GiB
2019-09-04 10:04:11.590588: I tensorflow/core/common_runtime/bfc_allocator.cc:818] total_region_allocated_bytes_: 10979036160 memory_limit_: 10979036365 available bytes: 205 curr_region_allocation_bytes_: 8589934592
2019-09-04 10:04:11.590614: I tensorflow/core/common_runtime/bfc_allocator.cc:824] Stats: 
Limit:                 10979036365
InUse:                  6142981376
MaxInUse:               7216919808
NumAllocs:                 5943748
MaxAllocSize:           5262336000

2019-09-04 10:04:11.590690: W tensorflow/core/common_runtime/bfc_allocator.cc:319] ************************************************__________________________________________**_*******
2019-09-04 10:04:11.590739: W tensorflow/core/framework/op_kernel.cc:1502] OP_REQUIRES failed at conv_ops.cc:486 : Resource exhausted: OOM when allocating tensor with shape[1142,256,50,46] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
2019-09-04 10:04:11.628917: W tensorflow/core/common_runtime/bfc_allocator.cc:237] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.08GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-09-04 10:04:11.655198: W tensorflow/core/common_runtime/bfc_allocator.cc:237] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.30GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
Traceback (most recent call last):
  File "main.py", line 531, in <module>
    TErecognizer(ip.MAX_LEN, ip.x_tr, ip.x_te, ip.y_tr, ip.y_te, ip.n_tag, ip.tag2idx, ip.testing_value).recognizerModel(False)
  File "/home/alamtl/Share/temptagger/model.py", line 184, in recognizerModel
    history = model.fit( [self.x_tr], self.y_tr, validation_split=0.1, batch_size=self.BATCH_SIZE, epochs=self.n_epochs, verbose=1, callbacks=[es, mc, self.sc])
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/keras/engine/training.py", line 1039, in fit
    validation_steps=validation_steps)
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/keras/engine/training_arrays.py", line 217, in fit_loop
    callbacks.on_epoch_end(epoch, epoch_logs)
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/keras/callbacks.py", line 79, in on_epoch_end
    callback.on_epoch_end(epoch, logs)
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/seqeval/callbacks.py", line 80, in on_epoch_end
    self.on_epoch_end_fit(epoch, logs)
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/seqeval/callbacks.py", line 87, in on_epoch_end_fit
    y_true, y_pred = self.predict(X, y)
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/seqeval/callbacks.py", line 49, in predict
    y_pred = self.model.predict_on_batch(X)
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/keras/engine/training.py", line 1274, in predict_on_batch
    outputs = self.predict_function(ins)
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__
    return self._call(inputs)
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py", line 2675, in _call
    fetched = self._callable_fn(*array_vals)
  File "/home/alamtl/.local/miniconda3/envs/base_alamtl/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1458, in __call__
    run_metadata_ptr)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
  (0) Resource exhausted: OOM when allocating tensor with shape[1142,1024,50,44] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[{{node lambda_1/module_apply_default/bilm/CNN/Conv2D_6}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

	 [[crf_1/while_1/Identity/_898]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

  (1) Resource exhausted: OOM when allocating tensor with shape[1142,1024,50,44] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[{{node lambda_1/module_apply_default/bilm/CNN/Conv2D_6}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

0 successful operations.
0 derived errors ignored.
`

Confusion matrix in addition to other metrics

Hi,

While f1_score and other metrics are very informative, can confusion matrix be also added?
It is very useful to look on it in order to identify where exactly is mismatch in predictions.

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