def train_theano():
model = RNNTheano(Config._VOCABULARY_SIZE, hidden_dim=Config._HIDDEN_DIM)
t1 = time.time()
model.sgd_step(X_train[10], y_train[10], Config._LEARNING_RATE)
t2 = time.time()
print "SGD Step time: %f milliseconds" % ((t2 - t1) * 1000.)
model.train_with_sgd(X_train, y_train, nepoch=Config._NEPOCH, learning_rate=Config._LEARNING_RATE)
if Config._MODEL_FILE != None:
print "start saving model..."
save_model_parameters_theano(Config._MODEL_FILE, model)
print "model saved!"
def generate_sentence(model, index_to_word, word_to_index, min_length=5):
# We start the sentence with the start token
new_sentence = [word_to_index[SENTENCE_START_TOKEN]]
# Repeat until we get an end token
while not new_sentence[-1] == word_to_index[SENTENCE_END_TOKEN]:
next_word_probs = model.predict(new_sentence)[-1]
samples = np.random.multinomial(1, [next_word_probs])
sampled_word = np.argmax(samples)
new_sentence.append(sampled_word)
# Seomtimes we get stuck if the sentence becomes too long, e.g. "........" :(
# And: We don't want sentences with UNKNOWN_TOKEN's
if len(new_sentence) > 100 or sampled_word == word_to_index[UNKNOWN_TOKEN]:
return None
if len(new_sentence) < min_length:
return None
return new_sentence
# cProfile.run("model.numpy_sdg_step(X_train[10], y_train[10], 0.005)")
# print("----------------------------------------------------------------")
np.random.seed(10)
model_theano = RNNTheano(vocabulary_size)
# cProfile.run("model_theano.train_with_sgd(X_train[10], y_train[10], 0.005)")
print ("----------------------------------------------------------------")
# losses_numpy = model.train_with_sgd(X_train[:100], y_train[:100], nepoch=5, evaluate_loss_after=1)
losses_theano = model_theano.train_with_sgd(X_train[:100], y_train[:100], nepoch=5, evaluate_loss_after=1)
generated_sentence = generate_sentence(model, index_to_word, word_to_index)
print generated_sentence
import preprocess
from rnn_numpy import RNNNumpy
from rnn_theano import RNNTheano
import numpy as np
import cProfile
X_train, y_train, vocabulary_size = preprocess.create_train_data()
np.random.seed(10)
model = RNNNumpy(vocabulary_size)
np.random.seed(10)
model = RNNNumpy(vocabulary_size)
#cProfile.run("model.numpy_sdg_step(X_train[10], y_train[10], 0.005)")
#print("----------------------------------------------------------------")
np.random.seed(10)
model_theano = RNNTheano(vocabulary_size)
#cProfile.run("model_theano.train_with_sgd(X_train[10], y_train[10], 0.005)")
print("----------------------------------------------------------------")
losses_numpy = model.train_with_sgd(X_train[:100],
y_train[:100],
nepoch=5,
evaluate_loss_after=1)
losses_theano = model_theano.train_with_sgd(X_train[:100],
y_train[:100],
nepoch=5,
evaluate_loss_after=1)
