hamelsmu

test

hamelsmu

# reference: https://hub.docker.com/_/ubuntu/
FROM ubuntu:16.04

# Adds metadata to the image as a key value pair example LABEL version="1.0"
LABEL maintainer="Hamel Husain <[email protected]>"

##Set environment variables
ENV LANG=C.UTF-8 LC_ALL=C.UTF-8

RUN apt-get update --fix-missing && apt-get install -y wget bzip2 ca-certificates \

hamelsmu

SELECT
  url as issue_url
  -- replace more than one white-space character in a row with a single space
, REGEXP_REPLACE(title, r"\s{2,}", ' ') as issue_title
, REGEXP_REPLACE(body, r"\s{2,}", ' ') as body

FROM(
    SELECT
        JSON_EXTRACT(payload, '$.issue.html_url') as url
        -- extract the title and body removing parentheses, brackets, and quotes

hamelsmu

from ktext.preprocess import processor
# instantiate data processing object
body_pp = processor(keep_n=8000, padding_maxlen=70)
# process data
train_body_vecs = body_pp.fit_transform(train_body_raw)

hamelsmu

# instantiate the pre-processor for titles
title_pp = processor(append_indicators=True, keep_n=4500, 
                     padding_maxlen=12, padding ='post')
# process the titles
train_title_vecs = title_pp.fit_transform(train_title_raw)

hamelsmu

from keras.models import Model
from keras.layers import Input, LSTM, GRU, Dense, Embedding, Bidirectional, BatchNormalization
from keras import optimizers

#arbitrarly set latent dimension for embedding and hidden units
latent_dim = 300

##### Define Model Architecture ######

########################

hamelsmu

from keras.callbacks import CSVLogger, ModelCheckpoint

#setup callbacks for model logging
script_name_base = 'tutorial_seq2seq'
csv_logger = CSVLogger('{:}.log'.format(script_name_base))
model_checkpoint = ModelCheckpoint('{:}.epoch{{epoch:02d}}-val{{val_loss:.5f}}.hdf5'.format(script_name_base),
                                   save_best_only=True)

# pass arguments to model.fit
batch_size = 1200

hamelsmu

def extract_decoder_model(model):
    """
    Extract the decoder from the original model.

    Inputs:
    ------
    model: keras model object

    Returns:
    -------

hamelsmu

def build_seq2seq_model(word_emb_dim,
                        hidden_state_dim,
                        encoder_seq_len,
                        num_encoder_tokens,
                        num_decoder_tokens):
    """
    Builds architecture for sequence to sequence model.

    Encoder and Decoder layer consist of one GRU Layer each.  User
    can specify the dimensionality of the word embedding and the hidden state.

hamelsmu

def cleanup_virtualenv(bare=True):
    if not bare:
        click.echo(crayons.red('Environment creation aborted.'))
    try:
        # Delete the virtualenv.
        rmtree(project.virtualenv_location)
    except OSError as e:
        click.echo(
            '{0} An error occurred while removing {1}!'.format(
                crayons.red('Error: ', bold=True),