def __init__(self):
# Instantiate Embeddings
self.embeddings = Embeddings(WORD_EMBEDDING_DIMENSION, WORD_EMBEDDING_WINDOW_SIZE, 1, 4)
# Gets word2vec_model, word2index, index2word, word2vec_weights, tokenized_indexed_sentences
self.word2vec_model = self.embeddings.get_intersected_model()
word2index = self.embeddings.get_vocabulary()[0]
word2vec_weights = self.word2vec_model.wv.syn0
indexed_sentences = self.embeddings.get_indexed_sentences()
# Shifting the indexes by 1 so as to reserve space for Masking
self.word2index = {word:index + 1 for word, index in word2index.items()}
self.index2word = {index:word for word, index in self.word2index.items()}
self.vocab_size = len(word2index)
indexed_sentences = [np.array(sentence) + 1
for sentence in indexed_sentences
if len(sentence) > 0]
# Creating a zero vector for masking and then appending with word2vec_weights
mask_vector = np.zeros((1, word2vec_weights.shape[1]))
self.word2vec_weights = np.append(mask_vector, self.word2vec_weights, axis=0)
# Padding Sentences
sentence_with_max_len = max([len(sentence) for sentence in indexed_sentences])
self.indexed_sentences = sequence.pad_sequences(indexed_sentences,
maxlen=sentence_with_max_len,
padding='post')
from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from keras.layers import Embedding
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.preprocessing import sequence
from intersect_embeddings import Embeddings
from keras.callbacks import ModelCheckpoint
from nltk.tokenize import word_tokenize
import random
from itertools import groupby
# ## Instantiate Embeddings
embeddings = Embeddings(300, 4, 1, 4)
# ### Getting data from preprocessing
word2vec_model = embeddings.get_intersected_model()
word2index, index2word = embeddings.get_vocabulary()
word2vec_weights = word2vec_model.wv.syn0
tokenized_indexed_sentences = embeddings.get_indexed_sentences()
word2index = {word: index + 1 for word, index in word2index.items()}
index2word = {index: word for word, index in word2index.items()}
new_weights = np.zeros((1, word2vec_weights.shape[1]))
new_weights = np.append(new_weights, word2vec_weights, axis=0)
# ## Defining model
# Changes to the model to be done here
import random
model_name = "lstm-1024-512-epochs-25-batchsize-128-acc-1"
word_embedding_dimension = 300
word_embedding_window_size = 4
batch_size = 256
epochs = 15
window_size = 5
accuracy_threshold = 1
activation = 'relu'
custom_accuracy = 0
loss_function = 'mse'
# ## Instantiate Embeddings
embeddings = Embeddings(word_embedding_dimension, word_embedding_window_size,
1, 4)
# ### getting data from preprocessing
word2vec_model = embeddings.get_intersected_model()
word2vec_weights = word2vec_model.wv.syn0
word2index, index2word = embeddings.get_vocabulary()
tokenized_indexed_sentences = embeddings.get_indexed_sentences()
# ### generating training data
vocab_size = len(word2index)
print(vocab_size)
seq_in = []
seq_out = []
# generating dataset
for sentence in tokenized_indexed_sentences:
class LSTMModel():
def __init__(self):
# Instantiate Embeddings
self.embeddings = Embeddings(WORD_EMBEDDING_DIMENSION, WORD_EMBEDDING_WINDOW_SIZE, 1, 4)
# Gets word2vec_model, word2index, index2word, word2vec_weights, tokenized_indexed_sentences
self.word2vec_model = self.embeddings.get_intersected_model()
word2index = self.embeddings.get_vocabulary()[0]
word2vec_weights = self.word2vec_model.wv.syn0
indexed_sentences = self.embeddings.get_indexed_sentences()
# Shifting the indexes by 1 so as to reserve space for Masking
self.word2index = {word:index + 1 for word, index in word2index.items()}
self.index2word = {index:word for word, index in self.word2index.items()}
self.vocab_size = len(word2index)
indexed_sentences = [np.array(sentence) + 1
for sentence in indexed_sentences
if len(sentence) > 0]
# Creating a zero vector for masking and then appending with word2vec_weights
mask_vector = np.zeros((1, word2vec_weights.shape[1]))
self.word2vec_weights = np.append(mask_vector, self.word2vec_weights, axis=0)
# Padding Sentences
sentence_with_max_len = max([len(sentence) for sentence in indexed_sentences])
self.indexed_sentences = sequence.pad_sequences(indexed_sentences,
maxlen=sentence_with_max_len,
padding='post')
def generate_sequences(self):
for seq_in in self.indexed_sentences:
seq_in_len = len(seq_in)
seq_out = np.append(seq_in[1:], seq_in[seq_in_len - 1])
one_hot_encoded_y = [np_utils.to_categorical(index, num_classes=self.vocab_size)
for index in seq_out]
yield (seq_in, one_hot_encoded_y)
def train_model(self):
# Defining model
model = Sequential()
model.add(Embedding(input_dim=self.word2vec_weights.shape[0],
output_dim=self.word2vec_weights.shape[1],
weights=[self.word2vec_weights], mask_zero=True))
model.add(LSTM(1024, return_sequences=True))
model.add(LSTM(1024))
model.add(Dense(self.vocab_size, activation='sigmoid'))
model.compile(loss='cross_entropy', optimizer='adam', metrics=['accuracy'])
model_weights_path = "../weights/lstm-2-1024-1024-batchsize-256-epochs-30-classification"
if not os.path.exists(model_weights_path):
os.makedirs(model_weights_path)
checkpoint_path = model_weights_path + '/weights.{epoch:02d}.hdf5'
checkpoint = ModelCheckpoint(filepath=checkpoint_path,
verbose=1,
save_best_only=False,
mode='max')
print("Model Summary")
print(model.summary())
model.fit(seq_in, seq_out, epochs=EPOCHS, verbose=1, batch_size=BATCH_SIZE, callbacks=[checkpoint])
return model
def predict(self):
model = self.train_model()
sentence_test = "In which regions in particular did"
indexed_sentences = self.embeddings.get_indexed_query(sentence_test)
sent = np.array(indexed_sentences) + 1
pattern = list(sent)
print(' '.join(self.index2word[index] for index in pattern))
# for i in range(10):
prediction = model.predict(np.array([pattern]))
pred_word = self.word2vec_model.similar_by_vector(prediction[0][prediction.shape[1] - 1])[0][0]
sys.stdout.write(pred_word + " ")
pattern.append(self.word2index[pred_word])
pattern = pattern[:len(pattern)]
def accuracy(self):
count = 0
correct = 0
for sub_sample_in, sub_sample_out in zip(seq_in, seq_out):
ypred = model.predict_on_batch(np.expand_dims(sub_sample_in, axis = 0))[0]
ytrue = sub_sample_out
pred_word = word2vec_model.similar_by_vector(ypred)[0][0]
true_word = word2vec_model.similar_by_vector(ytrue)[0][0]
similarity = word2vec_model.similarity(pred_word, true_word)
if similarity == 1:
correct += 1
count += 1
print("Accuracy {0}".format(correct/count))
def model_summary(self):
# model_results = model_fit_summary.history
# model_results.update(model_fit_summary.params)
model_results["word_embedding_dimension"] = WORD_EMBEDDING_DIMENSION
model_results["word_embedding_window_size"] = WORD_EMBEDDING_WINDOW_SIZE
model_results["window_size"] = WINDOW_SIZE
model_results["batch_size"] = BATCH_SIZE
model_results["epochs"] = EPOCHS
model_results["model_name"] = MODEL_NAME
model_results["accuracy_threshold"] = ACCURACY_THRESHOLD
model_results["activation"] = ACTIVATION
model_results["custom_accuracy"] = CUSTOM_ACCURACY
model_results["loss_function"] = LOSS_FUNCTION
model_results["layers"] = []
model_results["dropouts"] = []
for layer in model.layers:
if hasattr(layer, "units"):
layer_summary = {}
layer_summary["units"] = layer.get_config()["units"]
layer_summary["name"] = layer.name
model_results["layers"].append(layer_summary)
if hasattr(layer, "rate"):
dropout_summary = {}
dropout_summary["rate"] = layer.get_config()["rate"]
model_results["dropouts"].append(dropout_summary)
text_file_path = "../weights/{0}/model_results.json".format(model_name)
with open(text_file_path, "w") as f:
json.dump(model_results, f)