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train_seq2seq.py
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train_seq2seq.py
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#importing necessary libraries and classes
from keras.models import Model
from keras.layers.recurrent import LSTM
from keras.layers import Dense, Input
from keras.callbacks import ModelCheckpoint
from keras.preprocessing.sequence import pad_sequences
from collections import Counter
import nltk
import numpy as np
from sklearn.model_selection import train_test_split
import urllib.request
import os
import sys
import zipfile
np.random.seed(42)
#Hyperparameter tuning
BATCH_SIZE = 16
NUM_EPOCHS = 250
GLOVE_EMBEDDING_SIZE = 100
HIDDEN_UNITS = 256
MAX_INPUT_SEQ_LENGTH = 30
MAX_TARGET_SEQ_LENGTH = 30
MAX_VOCAB_SIZE = 10000
DATA_SET_NAME = 'my_data'
DATA_DIR_PATH = 'E:/chatbot/ChatCrazie/my_data'
WEIGHT_FILE_PATH = 'E:/chatbot/ChatCrazie/support files/model-weights.h5'
GLOVE_MODEL = "E:/chatbot/ChatCrazie/glove.6B." + str(GLOVE_EMBEDDING_SIZE) + "d.txt"
WHITELIST = 'abcdefghijklmnopqrstuvwxyz1234567890?.,'
#defines the valid characters for the chatbot
def in_white_list(_word):
for char in _word:
if char in WHITELIST:
return True
return False
#it is done to load the glove embeddings file
def load_glove_embeddings():
_word2em = {}
file = open(GLOVE_MODEL, mode='rt', encoding='utf8')
for line in file:
words = line.strip().split()
word = words[0]
embeds = np.array(words[1:], dtype=np.float32)
_word2em[word] = embeds
file.close()
return _word2em
word2em = load_glove_embeddings()
target_counter = Counter()
input_texts = []
target_texts = []
for file in os.listdir(DATA_DIR_PATH):
filepath = os.path.join(DATA_DIR_PATH, file)
if os.path.isfile(filepath):
print('processing file: ', file)
lines = open(filepath, 'rt', encoding='utf8').read().split('\n')
prev_words = []
for line in lines:
if line.startswith('- - '):
prev_words = []
if line.startswith('- - ') or line.startswith(' - '):
line = line.replace('- - ', '')
line = line.replace(' - ', '')
next_words = [w.lower() for w in nltk.word_tokenize(line)]
next_words = [w for w in next_words if in_white_list(w)]
if len(next_words) > MAX_TARGET_SEQ_LENGTH:
next_words = next_words[0:MAX_TARGET_SEQ_LENGTH]
if len(prev_words) > 0:
input_texts.append(prev_words)
target_words = next_words[:]
target_words.insert(0, 'start')
target_words.append('end')
for w in target_words:
target_counter[w] += 1
target_texts.append(target_words)
prev_words = next_words
for idx, (input_words, target_words) in enumerate(zip(input_texts, target_texts)):
if idx > 10:
break
print([input_words, target_words])
target_word2idx = dict()
for idx, word in enumerate(target_counter.most_common(MAX_VOCAB_SIZE)):
target_word2idx[word[0]] = idx + 1
if 'unknown' not in target_word2idx:
target_word2idx['unknown'] = 0
target_idx2word = dict([(idx, word) for word, idx in target_word2idx.items()])
num_decoder_tokens = len(target_idx2word)
np.save('E:/chatbot/ChatCrazie/support files/target-word2idx.npy', target_word2idx)
np.save('E:/chatbot/ChatCrazie/support files/target-idx2word.npy', target_idx2word)
input_texts_word2em = []
encoder_max_seq_length = 0
decoder_max_seq_length = 0
for input_words, target_words in zip(input_texts, target_texts):
encoder_input_wids = []
for w in input_words:
emb = np.zeros(shape=GLOVE_EMBEDDING_SIZE)
if w in word2em:
emb = word2em[w]
encoder_input_wids.append(emb)
input_texts_word2em.append(encoder_input_wids)
encoder_max_seq_length = max(len(encoder_input_wids), encoder_max_seq_length)
decoder_max_seq_length = max(len(target_words), decoder_max_seq_length)
context = dict()
context['num_decoder_tokens'] = num_decoder_tokens
context['encoder_max_seq_length'] = encoder_max_seq_length
context['decoder_max_seq_length'] = decoder_max_seq_length
print(context)
np.save('E:/chatbot/ChatCrazie/support files/word-glove-context.npy', context)
def generate_batch(input_word2em_data, output_text_data):
num_batches = len(input_word2em_data) // BATCH_SIZE
while True:
for batchIdx in range(0, num_batches):
start = batchIdx * BATCH_SIZE
end = (batchIdx + 1) * BATCH_SIZE
encoder_input_data_batch = pad_sequences(input_word2em_data[start:end], encoder_max_seq_length)
decoder_target_data_batch = np.zeros(shape=(BATCH_SIZE, decoder_max_seq_length, num_decoder_tokens))
decoder_input_data_batch = np.zeros(shape=(BATCH_SIZE, decoder_max_seq_length, GLOVE_EMBEDDING_SIZE))
for lineIdx, target_words in enumerate(output_text_data[start:end]):
for idx, w in enumerate(target_words):
w2idx = target_word2idx['unknown'] # default unknown
if w in target_word2idx:
w2idx = target_word2idx[w]
if w in word2em:
decoder_input_data_batch[lineIdx, idx, :] = word2em[w]
if idx > 0:
decoder_target_data_batch[lineIdx, idx - 1, w2idx] = 1
yield [encoder_input_data_batch, decoder_input_data_batch], decoder_target_data_batch
#Encoder layers,inputs,outputs
encoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE), name='encoder_inputs')
encoder_lstm = LSTM(units=HIDDEN_UNITS, return_state=True, name='encoder_lstm')
encoder_outputs, encoder_state_h, encoder_state_c = encoder_lstm(encoder_inputs)
encoder_states = [encoder_state_h, encoder_state_c]
#Decoder layers - input,output,LSTM,Dense
decoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE), name='decoder_inputs')
decoder_lstm = LSTM(units=HIDDEN_UNITS, return_state=True, return_sequences=True, name='decoder_lstm')
decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(decoder_inputs,
initial_state=encoder_states)
decoder_dense = Dense(units=num_decoder_tokens, activation='softmax', name='decoder_dense')
decoder_outputs = decoder_dense(decoder_outputs)
#model
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop',metrics=['accuracy'])
json = model.to_json()
open('E:/chatbot/ChatCrazie/support files/word-architecture.json', 'w').write(json)
#train_test split
Xtrain, Xtest, Ytrain, Ytest = train_test_split(input_texts_word2em, target_texts, test_size=0.2, random_state=42)
#execution and updation of wt in batches
train_gen = generate_batch(Xtrain, Ytrain)
test_gen = generate_batch(Xtest, Ytest)
train_num_batches = len(Xtrain) // BATCH_SIZE
test_num_batches = len(Xtest) // BATCH_SIZE
#saving of model at checks through checkpoint
checkpoint = ModelCheckpoint(filepath=WEIGHT_FILE_PATH, save_best_only=True)
#fitting of chatbot moel
model.fit_generator(generator=train_gen, steps_per_epoch=train_num_batches,
epochs=NUM_EPOCHS,
verbose=1, validation_data=test_gen, validation_steps=test_num_batches, callbacks=[checkpoint])
#final weights saved at desired location
model.save_weights(WEIGHT_FILE_PATH)