When I train my transformer using t2t-trainer, I see the targets/accuracy go up to around 65% on tensorboard. But then when I try:
t2t-decoder --data_dir=$DATA_DIR --problem=$PROBLEM --model=$MODEL --hparams_set=$HPARAMS --output_dir=$TRAIN_DIR --t2t_usr_dir='.'
It looks like none of the OUTPUTs matches the corresponding TARGETs. Am I missing something obvious?
for i in xrange(self._model_hparams.audio_compression):
--data_dir=$DATA_DIR \
--problem=translate_enfr_wmt_small8k\
--model=transformer \
--hparams='max_length=100,eval_drop_long_sequences=True'\
--hparams='batch_size=1024'\
--worker_gpu=8 \
--train_steps=350000 \
--hparams_set=$HPARAMS \
--eval_steps=5000 \
--output_dir=$TRAIN_DIR \
--schedule=continuous_train_and_eval
pip install -q -e .
Traceback (most recent call last):
File "/home/travis/virtualenv/python3.6.7/lib/python3.6/site-packages/pkg_resources/__init__.py", line 567, in _build_master
ws.require(__requires__)
File "/home/travis/virtualenv/python3.6.7/lib/python3.6/site-packages/pkg_resources/__init__.py", line 884, in require
needed = self.resolve(parse_requirements(requirements))
File "/home/travis/virtualenv/python3.6.7/lib/python3.6/site-packages/pkg_resources/__init__.py", line 775, in resolve
raise VersionConflict(dist, req).with_context(dependent_req)
pkg_resources.ContextualVersionConflict: (tensorflow-probability 0.7.0 (/home/travis/virtualenv/python3.6.7/lib/python3.6/site-packages), Requirement.parse('tensorflow-probability==0.8'), {'kfac'})It seems requirements installation is failing during Travis build
Hi All, I am trying to run tensor2tensor in colab and I am getting following error. Any help is appreciated.
'''
ImportError Traceback (most recent call last)
<ipython-input-2-00e41dd9537e> in <module>()
9 import collections
10
---> 11 from tensor2tensor import models
12 from tensor2tensor import problems
13 from tensor2tensor.layers import common_layers
7 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/tf_compat.py in ensure_tf_install()
57 if tf_version < min_tf_version:
58 raise ImportError(
---> 59 "This version of TensorFlow Datasets requires TensorFlow "
60 f"version >= {MIN_TF_VERSION}; Detected an installation of version "
61 f"{tf.version}. Please upgrade TensorFlow to proceed."
ImportError: This version of TensorFlow Datasets requires TensorFlow version >= 2.1.0; Detected an installation of version 1.15.2. Please upgrade TensorFlow to proceed.
'''
to work around it i had to uninstall rogue packages and ensure they are consistent with tf 1.5.2.
but that's too much of a hassle. In the end i've stayed with tf 2.0 version but instead of installing t2t from pip directly i used my own fork. That was necessary because t2t in master branch is incommpatble with tf2. It shows errors like flags ar not part of tf and so on. The only changes that were necessary was to change import statement in tensor2tensor/bin/*.py from "import tensorflow as tf" to "import tensorflow.compat.v1 as tf"
then you just install run "pip install ." from root repo dir.
t2t-trainer \
--data_dir=$DATA_DIR \
--problem=$PROBLEM \
--model=$MODEL \
--hparams='max_length=70,batch_size=1024,eval_drop_long_sequences=true'\
--worker_gpu=8 \
--train_steps=500000 \
--hparams_set=$HPARAMS \
--output_dir=$TRAIN_DIR \