# Copyright 2015 Conchylicultor. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Main script. See README.md for more information
Use python 3
"""
import argparse # Command line parsing
import configparser # Saving the models parameters
import datetime # Chronometer
import os # Files management
import tensorflow as tf
import numpy as np
import math
from tqdm import tqdm # Progress bar
from tensorflow.python import debug as tf_debug
from textdata import TextData
from model import Model
class Chatbot:
"""
Main class which launch the training or testing mode
"""
class TestMode:
""" Simple structure representing the different testing modes
"""
ALL = 'all'
INTERACTIVE = 'interactive' # The user can write his own questions
DAEMON = 'daemon' # The chatbot runs on background and can regularly be called to predict something
def __init__(self):
"""
"""
# Model/dataset parameters
self.args = None
# Task specific object
self.textData = None # Dataset
self.model = None # Sequence to sequence model
# Tensorflow utilities for convenience saving/logging
self.writer = None
self.saver = None
self.modelDir = '' # Where the model is saved
self.globStep = 0 # Represent the number of iteration for the current model
# TensorFlow main session (we keep track for the daemon)
self.sess = None
# Filename and directories constants
self.MODEL_DIR_BASE = 'save/model'
self.MODEL_NAME_BASE = 'model'
self.MODEL_EXT = '.ckpt'
self.CONFIG_FILENAME = 'params.ini'
self.CONFIG_VERSION = '0.5'
self.TEST_IN_NAME = 'data/test/samples.txt'
self.TEST_OUT_SUFFIX = '_predictions.txt'
self.SENTENCES_PREFIX = ['Q: ', 'A: ']
@staticmethod
def parseArgs(args):
"""
Parse the arguments from the given command line
Args:
args (list<str>): List of arguments to parse. If None, the default sys.argv will be parsed
"""
parser = argparse.ArgumentParser()
# Global options
globalArgs = parser.add_argument_group('Global options')
globalArgs.add_argument('--test',
nargs='?',
choices=[Chatbot.TestMode.ALL, Chatbot.TestMode.INTERACTIVE, Chatbot.TestMode.DAEMON],
const=Chatbot.TestMode.ALL, default=None,
help='if present, launch the program try to answer all sentences from data/test/ with'
' the defined model(s), in interactive mode, the user can wrote his own sentences,'
' use daemon mode to integrate the chatbot in another program')
globalArgs.add_argument('--createDataset', action='store_true',
help='if present, the program will only generate the dataset from the corpus (no training/testing)')
globalArgs.add_argument('--playDataset', type=int, nargs='?', const=10, default=None,
help='if set, the program will randomly play some samples(can be use conjointly with createDataset if this is the only action you want to perform)')
globalArgs.add_argument('--reset', action='store_true',
help='use this if you want to ignore the previous model present on the model directory (Warning: the model will be destroyed with all the folder content)')
globalArgs.add_argument('--verbose', action='store_true',
help='When testing, will plot the outputs at the same time they are computed')
globalArgs.add_argument('--debug', action='store_true',
help='run DeepQA with Tensorflow debug mode. Read TF documentation for more details on this.')
globalArgs.add_argument('--keepAll', action='store_true',
help='If this option is set, all saved model will be kept (Warning: make sure you have enough free disk space or increase saveEvery)') # TODO: Add an option to delimit the max size
globalArgs.add_argument('--modelTag', type=str, default=None,
help='tag to differentiate which model to store/load')
globalArgs.add_argument('--rootDir', type=str, default=None,
help='folder where to look for the models and data')
globalArgs.add_argument('--watsonMode', action='store_true',
help='Inverse the questions and answer when training (the network try to guess the question)')
globalArgs.add_argument('--autoEncode', action='store_true',
help='Randomly pick the question or the answer and use it both as input and output')
globalArgs.add_argument('--device', type=str, default=None,
help='\'gpu\' or \'cpu\' (Warning: make sure you have enough free RAM), allow to choose on which hardware run the model')
globalArgs.add_argument('--seed', type=int, default=None, help='random seed for replication')
globalArgs.add_argument('--beam-search', action='store_true', help='beam search in test')
globalArgs.add_argument('--beam-size', type=int, default=3, help='beam search Size')
# Dataset options
datasetArgs = parser.add_argument_group('Dataset options')
datasetArgs.add_argument('--corpus', choices=TextData.corpusChoices(), default=TextData.corpusChoices()[0],
help='corpus on which extract the dataset.')
datasetArgs.add_argument('--datasetTag', type=str, default='',
help='add a tag to the dataset (file where to load the vocabulary and the precomputed samples, not the original corpus). Useful to manage multiple versions. Also used to define the file used for the lightweight format.') # The samples are computed from the corpus if it does not exist already. There are saved in \'data/samples/\'
datasetArgs.add_argument('--ratioDataset', type=float, default=1.0,
help='ratio of dataset used to avoid using the whole dataset') # Not implemented, useless ?
datasetArgs.add_argument('--maxLength', type=int, default=10,
help='maximum length of the sentence (for input and output), define number of maximum step of the RNN')
datasetArgs.add_argument('--filterVocab', type=int, default=2,
help='remove rarelly used words (by default words used only once). 0 to keep all words.')
datasetArgs.add_argument('--skipLines', action='store_true',
help='Generate training samples by only using even conversation lines as questions (and odd lines as answer). Useful to train the network on a particular person.')
datasetArgs.add_argument('--vocabularySize', type=int, default=40000,
help='Limit the number of words in the vocabulary (0 for unlimited)')
# Network options (Warning: if modifying something here, also make the change on save/loadParams() )
nnArgs = parser.add_argument_group('Network options', 'architecture related option')
nnArgs.add_argument('--hiddenSize', type=int, default=512, help='number of hidden units in each RNN cell')
nnArgs.add_argument('--numLayers', type=int, default=2, help='number of rnn layers')
nnArgs.add_argument('--softmaxSamples', type=int, default=2048,
help='Number of samples in the sampled softmax loss function. A value of 0 deactivates sampled softmax')
nnArgs.add_argument('--initEmbeddings', action='store_true',
help='if present, the program will initialize the embeddings with pre-trained word2vec vectors')
nnArgs.add_argument('--embeddingSize', type=int, default=50, help='embedding size of the word representation')
nnArgs.add_argument('--embeddingSource', type=str, default="GoogleNews-vectors-negative300.bin",
help='embedding file to use for the word representation')
# Training options
trainingArgs = parser.add_argument_group('Training options')
trainingArgs.add_argument('--numEpochs', type=int, default=30, help='maximum number of epochs to run')
trainingArgs.add_argument('--saveEvery', type=int, default=2000,
help='nb of mini-batch step before creating a model checkpoint')
trainingArgs.add_argument('--batchSize', type=int, default=128, help='mini-batch size')
trainingArgs.add_argument('--learningRate', type=float, default=0.002, help='Learning rate')
trainingArgs.add_argument('--dropout', type=float, default=0.9, help='Dropout rate (keep probabilities)')
return parser.parse_args(args)
def main(self, args=None):
"""
Launch the training and/or the interactive mode
"""
print('Welcome to DeepQA v0.1 !')
print
print('TensorFlow detected: v{}'.format(tf.__version__))
# General initialisation
self.args = self.parseArgs(args)
if not self.args.rootDir:
self.args.rootDir = os.getcwd() # Use the current working directory
# tf.logging.set_verbosity(tf.logging.INFO) # DEBUG, INFO, WARN (default), ERROR, or FATAL
self.loadModelParams() # Update the self.modelDir and self.globStep, for now, not used when loading Model (but need to be called before _getSummaryName)
self.textData = TextData(self.args)
# TODO: Add a mode where we can force the input of the decoder // Try to visualize the predictions for
# each word of the vocabulary / decoder input
# TODO: For now, the model are trained for a specific dataset (because of the maxLength which define the
# vocabulary). Add a compatibility mode which allow to launch a model trained on a different vocabulary (
# remap the word2id/id2word variables).
if self.args.createDataset:
print('Dataset created! Thanks for using this program')
return # No need to go further
# Prepare the model
with tf.device(self.getDevice()):
self.model = Model(self.args, self.textData)
# Saver/summaries
self.writer = tf.summary.FileWriter(self._getSummaryName())
self.saver = tf.train.Saver(max_to_keep=200)
# TODO: Fixed seed (WARNING: If dataset shuffling, make sure to do that after saving the
# dataset, otherwise, all which cames after the shuffling won't be replicable when
# reloading the dataset). How to restore the seed after loading ??
# Also fix seed for random.shuffle (does it works globally for all files ?)
# Running session
self.sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True, # Allows backup device for non GPU-available operations (when forcing GPU)
log_device_placement=False) # Too verbose ?
) # TODO: Replace all sess by self.sess (not necessary a good idea) ?
if self.args.debug:
self.sess = tf_debug.LocalCLIDebugWrapperSession(self.sess)
self.sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
print('Initialize variables...')
self.sess.run(tf.global_variables_initializer())
# Reload the model eventually (if it exist.), on testing mode, the models are not loaded here (but in predictTestset)
if self.args.test != Chatbot.TestMode.ALL:
self.managePreviousModel(self.sess)
# Initialize embeddings with pre-trained word2vec vectors
if self.args.initEmbeddings:
self.loadEmbedding(self.sess)
if self.args.test:
if self.args.test == Chatbot.TestMode.INTERACTIVE:
self.mainTestInteractive(self.sess)
elif self.args.test == Chatbot.TestMode.ALL:
print('Start predicting...')
self.predictTestset(self.sess)
print('All predictions done')
elif self.args.test == Chatbot.TestMode.DAEMON:
print('Daemon mode, running in background...')
else:
raise RuntimeError('Unknown test mode: {}'.format(self.args.test)) # Should never happen
else:
self.mainTrain(self.sess)
if self.args.test != Chatbot.TestMode.DAEMON:
self.sess.close()
print("The End! Thanks for using this program")
def mainTrain(self, sess):
""" Training loop
Args:
sess: The current running session
"""
# Specific training dependent loading
self.textData.makeLighter(self.args.ratioDataset) # Limit the number of training samples
mergedSummaries = tf.summary.merge_all() # Define the summary operator (Warning: Won't appear on the tensorboard graph)
if self.globStep == 0: # Not restoring from previous run
self.writer.add_graph(sess.graph) # First time only
# If restoring a model, restore the progression bar ? and current batch ?
print('Start training (press Ctrl+C to save and exit)...')
try:
for e in range(self.args.numEpochs):
print
print("----- Epoch {}/{} ; (lr={}) -----".format(e + 1, self.args.numEpochs, self.args.learningRate))
batches = self.textData.getBatches()
# TODO: Also update learning parameters eventually
tic = datetime.datetime.now()
for nextBatch in tqdm(batches, desc="Training"):
# Training pass
ops, feedDict = self.model.step(nextBatch)
assert len(ops) == 2 # training, loss
_, loss, summary = sess.run(ops + (mergedSummaries,), feedDict)
self.writer.add_summary(summary, self.globStep)
self.globStep += 1
# Output training status
if self.globStep % 100 == 0:
perplexity = math.exp(float(loss)) if loss < 300 else float("inf")
tqdm.write("----- Step %d -- Loss %.2f -- Perplexity %.2f" % (self.globStep, loss, perplexity))
# Checkpoint
if self.globStep % self.args.saveEvery == 0:
self._saveSession(sess)
toc = datetime.datetime.now()
print("Epoch finished in {}".format(
toc - tic)) # Warning: Will overflow if an epoch takes more than 24 hours, and the output isn't really nicer
except (KeyboardInterrupt, SystemExit): # If the user press Ctrl+C while testing progress
print('Interruption detected, exiting the program...')
def predictTestset(self, sess):
""" Try predicting the sentences from the samples.txt file.
The sentences are saved on the modelDir under the same name
Args:
sess: The current running session
"""
# Loading the file to predict
with open(os.path.join(self.args.rootDir, self.TEST_IN_NAME), 'r') as f:
lines = f.readlines()
modelList = self._getModelList()
if not modelList:
print(
'Warning: No model found in \'{}\'. Please train a model before trying to predict'.format(
self.modelDir))
return
# Predicting for each model present in modelDir
for modelName in sorted(modelList): # TODO: Natural sorting
print('Restoring previous model from {}'.format(modelName))
self.saver.restore(sess, modelName)
print('Testing...')
saveName = modelName[:-len(
self.MODEL_EXT)] + self.TEST_OUT_SUFFIX # We remove the model extension and add the prediction suffix
with open(saveName, 'w') as f:
nbIgnored = 0
for line in tqdm(lines, desc='Sentences'):
question = line[:-1] # Remove the endl character
if bool(self.args.beam_search):
answers = self.beamSearchPredict(question)
if not answers:
nbIgnored += 1
continue
f.write(self.SENTENCES_PREFIX[0] + question + '\n')
for answer in reversed(answers):
f.write(self.SENTENCES_PREFIX[1] + self.textData.sequence2str(answer, clean=True) + '\n')
f.write('\n')
else:
answer = self.singlePredict(question)
if not answer:
nbIgnored += 1
continue
predString = '{x[0]}{0}\n{x[1]}{1}\n\n'.format(question,
self.textData.sequence2str(answer, clean=True),
x=self.SENTENCES_PREFIX)
f.write(predString)
if self.args.verbose:
tqdm.write(predString)
print('Prediction finished, {}/{} sentences ignored (too long)'.format(nbIgnored, len(lines)))
def mainTestInteractive(self, sess):
""" Try predicting the sentences that the user will enter in the console
Args:
sess: The current running session
"""
# TODO: If verbose mode, also show similar sentences from the training set with the same words (include in mainTest also)
# TODO: Also show the top 10 most likely predictions for each predicted output (when verbose mode)
# TODO: Log the questions asked for latter re-use (merge with test/samples.txt)
print('Testing: Launch interactive mode:')
print('')
print('Welcome to the interactive mode, here you can ask to Deep Q&A the sentence you want. Don\'t have high '
'expectation. Type \'exit\' or just press ENTER to quit the program. Have fun.')
while True:
question = input(self.SENTENCES_PREFIX[0])
if question == '' or question == 'exit':
break
questionSeq = [] # Will be contain the question as seen by the encoder
if bool(self.args.beam_search):
answers = self.beamSearchPredict(question, questionSeq)
if not answers:
print('Warning: sentence too long, sorry. Maybe try a simpler sentence.')
continue
for answer in reversed(answers):
print('{}{}'.format(str(self.SENTENCES_PREFIX[1]),
self.textData.sequence2str(answer, clean=True)))
else:
answer = self.singlePredict(question, questionSeq)
if not answer:
print('Warning: sentence too long, sorry. Maybe try a simpler sentence.')
continue
print('{}{}'.format(self.SENTENCES_PREFIX[1], self.textData.sequence2str(answer, clean=True)))
if self.args.verbose:
print(self.textData.batchSeq2str(questionSeq, clean=True, reverse=True))
print(self.textData.sequence2str(answer))
print
def singlePredict(self, question, questionSeq=None):
""" Predict the sentence
Args:
question (str): the raw input sentence
questionSeq (List<int>): output argument. If given will contain the input batch sequence
Return:
list <int>: the word ids corresponding to the answer
"""
# Create the input batch
batch = self.textData.sentence2enco(question)
if not batch:
return None
if questionSeq is not None: # If the caller want to have the real input
questionSeq.extend(batch.encoderSeqs)
# Run the model
ops, feedDict = self.model.step(batch)
output = self.sess.run(ops[0], feedDict) # TODO: Summarize the output too (histogram, ...)
answer = self.textData.deco2sentence(output)
return answer
def beamSearchPredict(self, question, questionSeq=None):
# Create the input batch
batch = self.textData.sentence2enco(question)
if not batch:
return None
if questionSeq is not None: # If the caller want to have the real input
questionSeq.extend(batch.encoderSeqs)
# Run the model
ops, feedDict = self.model.step(batch)
def softmax(x):
return np.exp(x) / np.sum(np.exp(x), axis=0)
beam_size = self.args.beam_size
path = [[] for _ in range(beam_size)]
probs = [[] for _ in range(beam_size)]
output = self.sess.run(ops[0][0], feedDict)
for k in range(len(path)):
prob = softmax(output[-1].reshape(-1, ))
log_probs = np.log(np.clip(a=prob, a_min=1e-5, a_max=1))
top_k_indexs = np.argsort(log_probs)[-beam_size:]
path[k].extend([top_k_indexs[k]])
probs[k].extend([log_probs[top_k_indexs[k]]])
for i in range(2, self.args.maxLengthDeco):
tmp = []
for k in range(len(path)):
for j in range(len(path[k])):
feedDict[self.model.decoderInputs[j + 1]] = [path[k][j]]
output = self.sess.run(ops[0][0:i], feedDict)
prob = softmax(output[-1].reshape(-1, ))
log_probs = np.log(np.clip(a=prob, a_min=1e-5, a_max=1))
tmp.extend(list(log_probs + probs[k][-1]))
top_k_indexs = np.argsort(tmp)[-beam_size:]
indexs = top_k_indexs % self.textData.getVocabularySize()
for k in range(len(path)):
probs[k].extend([tmp[top_k_indexs[k]]])
path[k].extend([indexs[k]])
return path
def daemonPredict(self, sentence):
""" Return the answer to a given sentence (same as singlePredict() but with additional cleaning)
Args:
sentence (str): the raw input sentence
Return:
str: the human readable sentence
"""
return self.textData.sequence2str(
self.singlePredict(sentence),
clean=True
)
def daemonClose(self):
""" A utility function to close the daemon when finish
"""
print('Exiting the daemon mode...')
self.sess.close()
print('Daemon closed.')
def loadEmbedding(self, sess):
""" Initialize embeddings with pre-trained word2vec vectors
Will modify the embedding weights of the current loaded model
Uses the GoogleNews pre-trained values (path hardcoded)
"""
# Fetch embedding variables from model
with tf.variable_scope("embedding_rnn_seq2seq/rnn/embedding_wrapper", reuse=True):
em_in = tf.get_variable("embedding")
with tf.variable_scope("embedding_rnn_seq2seq/embedding_rnn_decoder", reuse=True):
em_out = tf.get_variable("embedding")
# Disable training for embeddings
variables = tf.get_collection_ref(tf.GraphKeys.TRAINABLE_VARIABLES)
variables.remove(em_in)
variables.remove(em_out)
# If restoring a model, we can leave here
if self.globStep != 0:
return
# New model, we load the pre-trained word2vec data and initialize embeddings
embeddings_path = os.path.join(self.args.rootDir, 'data', 'embeddings', self.args.embeddingSource)
embeddings_format = os.path.splitext(embeddings_path)[1][1:]
print("Loading pre-trained word embeddings from %s " % embeddings_path)
with open(embeddings_path, "rb") as f:
header = f.readline()
vocab_size, vector_size = map(int, header.split())
binary_len = np.dtype('float32').itemsize * vector_size
initW = np.random.uniform(-0.25, 0.25, (len(self.textData.word2id), vector_size))
for line in tqdm(range(vocab_size)):
word = []
while True:
ch = f.read(1)
if ch == b' ':
word = b''.join(word).decode('utf-8')
break
if ch != b'\n':
word.append(ch)
if word in self.textData.word2id:
if embeddings_format == 'bin':
vector = np.fromstring(f.read(binary_len), dtype='float32')
elif embeddings_format == 'vec':
vector = np.fromstring(f.readline(), sep=' ', dtype='float32')
else:
raise Exception("Unkown format for embeddings: %s " % embeddings_format)
initW[self.textData.word2id[word]] = vector
else:
if embeddings_format == 'bin':
f.read(binary_len)
elif embeddings_format == 'vec':
f.readline()
else:
raise Exception("Unkown format for embeddings: %s " % embeddings_format)
# PCA Decomposition to reduce word2vec dimensionality
if self.args.embeddingSize < vector_size:
U, s, Vt = np.linalg.svd(initW, full_matrices=False)
S = np.zeros((vector_size, vector_size), dtype=complex)
S[:vector_size, :vector_size] = np.diag(s)
initW = np.dot(U[:, :self.args.embeddingSize], S[:self.args.embeddingSize, :self.args.embeddingSize])
# Initialize input and output embeddings
sess.run(em_in.assign(initW))
sess.run(em_out.assign(initW))
def managePreviousModel(self, sess):
""" Restore or reset the model, depending of the parameters
If the destination directory already contains some file, it will handle the conflict as following:
* If --reset is set, all present files will be removed (warning: no confirmation is asked) and the training
restart from scratch (globStep & cie reinitialized)
* Otherwise, it will depend of the directory content. If the directory contains:
* No model files (only summary logs): works as a reset (restart from scratch)
* Other model files, but modelName not found (surely keepAll option changed): raise error, the user should
decide by himself what to do
* The right model file (eventually some other): no problem, simply resume the training
In any case, the directory will exist as it has been created by the summary writer
Args:
sess: The current running session
"""
modelName = self._getModelName()
if os.listdir(self.modelDir):
if self.args.reset:
print('Reset: Destroying previous model at {}'.format(self.modelDir))
# Analysing directory content
elif os.path.exists(modelName): # Restore the model
print('Restoring previous model from {}'.format(modelName))
self.saver.restore(sess,
modelName) # Will crash when --reset is not activated and the model has not been saved yet
elif self._getModelList():
print('Conflict with previous models.')
raise RuntimeError(
'Some models are already present in \'{}\'. You should check them first (or re-try with the keepAll flag)'.format(
self.modelDir))
else: # No other model to conflict with (probably summary files)
print('No previous model found, but some files found at {}. Cleaning...'.format(
self.modelDir)) # Warning: No confirmation asked
self.args.reset = True
if self.args.reset:
fileList = [os.path.join(self.modelDir, f) for f in os.listdir(self.modelDir)]
for f in fileList:
print('Removing {}'.format(f))
os.remove(f)
else:
print('No previous model found, starting from clean directory: {}'.format(self.modelDir))
def _saveSession(self, sess):
""" Save the model parameters and the variables
Args:
sess: the current session
"""
tqdm.write('Checkpoint reached: saving model (don\'t stop the run)...')
self.saveModelParams()
model_name = self._getModelName()
with open(model_name, 'w') as f: # HACK: Simulate the old model existance to avoid rewriting the file parser
f.write('This file is used internally by DeepQA to check the model existance. Please do not remove.\n')
self.saver.save(sess, model_name) # TODO: Put a limit size (ex: 3GB for the modelDir)
tqdm.write('Model saved.')
def _getModelList(self):
""" Return the list of the model files inside the model directory
"""
return [os.path.join(self.modelDir, f) for f in os.listdir(self.modelDir) if f.endswith(self.MODEL_EXT)]
def loadModelParams(self):
""" Load the some values associated with the current model, like the current globStep value
For now, this function does not need to be called before loading the model (no parameters restored). However,
the modelDir name will be initialized here so it is required to call this function before managePreviousModel(),
_getModelName() or _getSummaryName()
Warning: if you modify this function, make sure the changes mirror saveModelParams, also check if the parameters
should be reset in managePreviousModel
"""
# Compute the current model path
self.modelDir = os.path.join(self.args.rootDir, self.MODEL_DIR_BASE)
if self.args.modelTag:
self.modelDir += '-' + self.args.modelTag
# If there is a previous model, restore some parameters
configName = os.path.join(self.modelDir, self.CONFIG_FILENAME)
if not self.args.reset and not self.args.createDataset and os.path.exists(configName):
# Loading
config = configparser.ConfigParser()
config.read(configName)
# Check the version
currentVersion = config.get('General', 'version')
if currentVersion != self.CONFIG_VERSION:
raise UserWarning(
'Present configuration version {0} does not match {1}. You can try manual changes on \'{2}\''.format(
currentVersion, self.CONFIG_VERSION, configName))
# Restoring the the parameters
self.globStep = config.getint('General', 'globStep')
self.args.watsonMode = config.getboolean('General', 'watsonMode')
self.args.autoEncode = config.getboolean('General', 'autoEncode')
self.args.corpus = config.get('General', 'corpus')
self.args.datasetTag = config.get('Dataset', 'datasetTag')
self.args.maxLength = config.getint('Dataset',
'maxLength') # We need to restore the model length because of the textData associated and the vocabulary size (TODO: Compatibility mode between different maxLength)
self.args.filterVocab = config.getint('Dataset', 'filterVocab')
self.args.skipLines = config.getboolean('Dataset', 'skipLines')
self.args.vocabularySize = config.getint('Dataset', 'vocabularySize')
self.args.hiddenSize = config.getint('Network', 'hiddenSize')
self.args.numLayers = config.getint('Network', 'numLayers')
self.args.softmaxSamples = config.getint('Network', 'softmaxSamples')
self.args.initEmbeddings = config.getboolean('Network', 'initEmbeddings')
self.args.embeddingSize = config.getint('Network', 'embeddingSize')
self.args.embeddingSource = config.get('Network', 'embeddingSource')
# No restoring for training params, batch size or other non model dependent parameters
# Show the restored params
print
print('Warning: Restoring parameters:')
print('globStep: {}'.format(self.globStep))
print('watsonMode: {}'.format(self.args.watsonMode))
print('autoEncode: {}'.format(self.args.autoEncode))
print('corpus: {}'.format(self.args.corpus))
print('datasetTag: {}'.format(self.args.datasetTag))
print('maxLength: {}'.format(self.args.maxLength))
print('filterVocab: {}'.format(self.args.filterVocab))
print('skipLines: {}'.format(self.args.skipLines))
print('vocabularySize: {}'.format(self.args.vocabularySize))
print('hiddenSize: {}'.format(self.args.hiddenSize))
print('numLayers: {}'.format(self.args.numLayers))
print('softmaxSamples: {}'.format(self.args.softmaxSamples))
print('initEmbeddings: {}'.format(self.args.initEmbeddings))
print('embeddingSize: {}'.format(self.args.embeddingSize))
print('embeddingSource: {}'.format(self.args.embeddingSource))
print
# For now, not arbitrary independent maxLength between encoder and decoder
self.args.maxLengthEnco = self.args.maxLength
self.args.maxLengthDeco = self.args.maxLength + 2
if self.args.watsonMode:
self.SENTENCES_PREFIX.reverse()
def saveModelParams(self):
""" Save the params of the model, like the current globStep value
Warning: if you modify this function, make sure the changes mirror loadModelParams
"""
config = configparser.ConfigParser()
config.add_section('General')
config.set('General', 'version', self.CONFIG_VERSION)
config.set('General', 'globStep', str(self.globStep))
config.set('General', 'watsonMode', str(self.args.watsonMode))
config.set('General', 'autoEncode', str(self.args.autoEncode))
config.set('General', 'corpus', str(self.args.corpus))
config.add_section('Dataset')
config.set('Dataset', 'datasetTag', str(self.args.datasetTag))
config.set('Dataset', 'maxLength', str(self.args.maxLength))
config.set('Dataset', 'filterVocab', str(self.args.filterVocab))
config.set('Dataset', 'skipLines', str(self.args.skipLines))
config.set('Dataset', 'vocabularySize', str(self.args.vocabularySize))
config.add_section('Network')
config.set('Network', 'hiddenSize', str(self.args.hiddenSize))
config.set('Network', 'numLayers', str(self.args.numLayers))
config.set('Network', 'softmaxSamples', str(self.args.softmaxSamples))
config.set('Network', 'initEmbeddings', str(self.args.initEmbeddings))
config.set('Network', 'embeddingSize', str(self.args.embeddingSize))
config.set('Network', 'embeddingSource', str(self.args.embeddingSource))
# Keep track of the learning params (but without restoring them)
config.add_section('Training (won\'t be restored)')
config.set('Training (won\'t be restored)', 'learningRate', str(self.args.learningRate))
config.set('Training (won\'t be restored)', 'batchSize', str(self.args.batchSize))
config.set('Training (won\'t be restored)', 'dropout', str(self.args.dropout))
with open(os.path.join(self.modelDir, self.CONFIG_FILENAME), 'w') as configFile:
config.write(configFile)
def _getSummaryName(self):
""" Parse the argument to decide were to save the summary, at the same place that the model
The folder could already contain logs if we restore the training, those will be merged
Return:
str: The path and name of the summary
"""
return self.modelDir
def _getModelName(self):
""" Parse the argument to decide were to save/load the model
This function is called at each checkpoint and the first time the model is load. If keepAll option is set, the
globStep value will be included in the name.
Return:
str: The path and name were the model need to be saved
"""
modelName = os.path.join(self.modelDir, self.MODEL_NAME_BASE)
if self.args.keepAll: # We do not erase the previously saved model by including the current step on the name
modelName += '-' + str(self.globStep)
return modelName + self.MODEL_EXT
def getDevice(self):
""" Parse the argument to decide on which device run the model
Return:
str: The name of the device on which run the program
"""
if self.args.device == 'cpu':
return '/cpu:0'
elif self.args.device == 'gpu':
return '/gpu:0'
elif self.args.device is None: # No specified device (default)
return None
else:
print('Warning: Error in the device name: {}, use the default device'.format(self.args.device))
return None
