rnn = RNNTheano(VOCAB_SIZE+SPEC_SYMBOLS_COUNT, hidden_dim = HIDDEN_LAYER_SIZE)
if PRELOAD_WEIGHTS:
print "preloading weights"
rnn.preload_weights(weights_file)
train_loss = []
test_loss = []
else:
print "training the model"
train_loss = []
test_loss = []
for e in range(EPOCHS):
i = 0
print("--- Epoch "+str(e+1)+" ---")
train_loss.append(rnn.total_loss(itertools.islice(load_songs(train_file,word_to_index),MAX_L_SENTENCES)))
test_loss.append(rnn.total_loss(itertools.islice(load_songs(test_file,word_to_index),MAX_L_SENTENCES)))
sentences = load_songs(train_file,word_to_index)
for sentence in itertools.islice(sentences, MAX_SENTENCES):
i+=1
sentence.insert(0,word_to_index[sentence_start_token])
y = copy.copy(sentence)
y.pop(0)
y.append(word_to_index[sentence_end_token])
rnn.train(sentence, y, ALPHA)
if i % 10 == 0:
print("processed "+str(i))
# saving weights
rnn.save_weights(weights_file)
# read the vocab
index_to_word, word_to_index = read_vocabulary(vocab_file, 8000)
# adding special symbols
index_to_word.append(sentence_end_token)
index_to_word.append(sentence_start_token)
word_to_index[sentence_start_token] = VOCAB_SIZE+1
word_to_index[sentence_end_token] = VOCAB_SIZE+2
if THEANO:
rnn = RNNTheano(VOCAB_SIZE+SPEC_SYMBOLS_COUNT, hidden_dim = 50)
else:
rnn = RNN(VOCAB_SIZE+SPEC_SYMBOLS_COUNT, VOCAB_SIZE+SPEC_SYMBOLS_COUNT,hidden_dim = 100)
# generate sentences
print("training the model")
loss = [rnn.total_loss(itertools.islice(tokenize_file(word_to_index, train_file), MAX_L_SENTENCES))]
for e in range(EPOCHS):
i = 0
print("--- Epoch "+str(e+1)+" ---")
loss.append(rnn.total_loss(itertools.islice(tokenize_file(word_to_index, train_file), MAX_L_SENTENCES)))
sentences = tokenize_file(word_to_index, train_file)
for sentence in itertools.islice(sentences, MAX_SENTENCES):
i+=1
sentence.insert(0,word_to_index[sentence_start_token])
y = copy.copy(sentence)
y.pop(0)
y.append(word_to_index[sentence_end_token])
rnn.train(sentence, y, ALPHA)
if i % 10 == 0:
print("preprocessed "+str(i))
index_to_word.append(sentence_end_token)
index_to_word.append(sentence_start_token)
word_to_index[sentence_start_token] = VOCAB_SIZE + 1
word_to_index[sentence_end_token] = VOCAB_SIZE + 2
if THEANO:
rnn = RNNTheano(VOCAB_SIZE + SPEC_SYMBOLS_COUNT, hidden_dim=50)
else:
rnn = RNN(VOCAB_SIZE + SPEC_SYMBOLS_COUNT,
VOCAB_SIZE + SPEC_SYMBOLS_COUNT,
hidden_dim=100)
# generate sentences
print("training the model")
loss = [
rnn.total_loss(
itertools.islice(tokenize_file(word_to_index, train_file),
MAX_L_SENTENCES))
]
for e in range(EPOCHS):
i = 0
print("--- Epoch " + str(e + 1) + " ---")
loss.append(
rnn.total_loss(
itertools.islice(tokenize_file(word_to_index, train_file),
MAX_L_SENTENCES)))
sentences = tokenize_file(word_to_index, train_file)
for sentence in itertools.islice(sentences, MAX_SENTENCES):
i += 1
sentence.insert(0, word_to_index[sentence_start_token])
y = copy.copy(sentence)
y.pop(0)
