From 91d4e1c83f9abb1ca8fcd94a65d6b74aaa3458da Mon Sep 17 00:00:00 2001 From: Mistobaan Date: Fri, 27 Oct 2017 12:25:41 -0700 Subject: [PATCH] fix mispells --- tensor2tensor/data_generators/README.md | 2 +- tensor2tensor/data_generators/generator_utils.py | 4 ++-- tensor2tensor/data_generators/problem.py | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/tensor2tensor/data_generators/README.md b/tensor2tensor/data_generators/README.md index 04a90a778..0ccbfe1c1 100644 --- a/tensor2tensor/data_generators/README.md +++ b/tensor2tensor/data_generators/README.md @@ -42,7 +42,7 @@ for an example of usage. The generators should yield dictionaries with string keys and values being lists of {int, float, str}. Here is a very simple generator for a data-set where -inputs are lists of 2s with length upto 100 and targets are lists of length 1 +inputs are lists of 2s with length up to 100 and targets are lists of length 1 with an integer denoting the length of the input list. ``` diff --git a/tensor2tensor/data_generators/generator_utils.py b/tensor2tensor/data_generators/generator_utils.py index 55ccf117e..8ce66dc6e 100644 --- a/tensor2tensor/data_generators/generator_utils.py +++ b/tensor2tensor/data_generators/generator_utils.py @@ -190,7 +190,7 @@ def maybe_download(directory, filename, url): print() tf.gfile.Rename(inprogress_filepath, filepath) statinfo = os.stat(filepath) - tf.logging.info("Succesfully downloaded %s, %s bytes." % (filename, + tf.logging.info("Successfully downloaded %s, %s bytes." % (filename, statinfo.st_size)) else: tf.logging.info("Not downloading, file already found: %s" % filepath) @@ -242,7 +242,7 @@ def maybe_download_from_drive(directory, filename, url): # Print newline to clear the carriage return from the download progress print() statinfo = os.stat(filepath) - tf.logging.info("Succesfully downloaded %s, %s bytes." % (filename, + tf.logging.info("Successfully downloaded %s, %s bytes." % (filename, statinfo.st_size)) return filepath diff --git a/tensor2tensor/data_generators/problem.py b/tensor2tensor/data_generators/problem.py index 657a5b18b..c826e29dd 100644 --- a/tensor2tensor/data_generators/problem.py +++ b/tensor2tensor/data_generators/problem.py @@ -130,7 +130,7 @@ class Problem(object): Data generation: * generate_data(data_dir, tmp_dir) - Generate training and dev datasets into data_dir. - - Additonal files, e.g. vocabulary files, should also be written to + - Additional files, e.g. vocabulary files, should also be written to data_dir. Vocab files are newline-separated files with each line containing a token. The standard convention for the filename is to set it to be @@ -515,7 +515,7 @@ def _default_hparams(): return tf.contrib.training.HParams( # Use this parameter to get comparable perplexity numbers with different # tokenizations. This value should be set to the ratio of the number of - # tokens in the test set according to the tokeization used to the number + # tokens in the test set according to the tokenization used to the number # of tokens in the test set in the "official" tokenization. For # example, if we are using a word-piece based model and we want to # compute per-word perplexity, then we set loss_multiplier to the number