def get_average_loss_value_of_test_sample(test_order_list, nn, dictionary,
source_file_name, n_best_list_file_name,
sbleu_score_list_file_name, smoothing_factor):
loss_value_test_set = 0
for t_i in test_order_list:
(phrase_pair_dict_n_listase_pair_dict_all, phrase_pair_dict_n_list,
total_base_score_list, sbleu_score_list) \
= get_everything(t_i, source_file_name, n_best_list_file_name,
sbleu_score_list_file_name)
xBlue_t_i, _ = xbleu(nn, total_base_score_list, sbleu_score_list,
phrase_pair_dict_n_list, dictionary, smoothing_factor)
sys.stdout.write(str(-xBlue_t_i) + ", ")
sys.stdout.flush()
loss_value_test_set -= xBlue_t_i
print
return loss_value_test_set/len(test_order_list) if len(test_order_list) > 0 else float('nan')
def get_average_loss_value_of_test_sample(test_order_list, nn, dictionary,
source_file_name,
n_best_list_file_name,
sbleu_score_list_file_name,
smoothing_factor):
loss_value_test_set = 0
for t_i in test_order_list:
(phrase_pair_dict_n_listase_pair_dict_all, phrase_pair_dict_n_list,
total_base_score_list, sbleu_score_list) \
= get_everything(t_i, source_file_name, n_best_list_file_name,
sbleu_score_list_file_name)
xBlue_t_i, _ = xbleu(nn, total_base_score_list, sbleu_score_list,
phrase_pair_dict_n_list, dictionary,
smoothing_factor)
sys.stdout.write(str(-xBlue_t_i) + ", ")
sys.stdout.flush()
loss_value_test_set -= xBlue_t_i
print
return loss_value_test_set / len(test_order_list) if len(
test_order_list) > 0 else float('nan')
def main(source_file_name, n_best_list_file_name, sbleu_score_list_file_name,
learning_rate, smoothing_factor):
print "Loading and initializing neural network"
W1 = np.loadtxt("data/weight_initialization.gz")
W2 = np.identity(100)
nn = CPTMNeuralNetwork([W1.shape[0], 100, 100], [W1, W2])
dictionary = corpora.Dictionary.load("data/dictionary.dict")
training_set_size = 0
# Each line in source_file is a source sentence.
# source_file should end with an empty line
with open(source_file_name, 'r') as source_file:
for _ in source_file:
training_set_size += 1
training_set_size -= 1 # ends with empty line
# uncomment to manually set training_set_size for testing purposes
#training_set_size = 30
training_order_list = range(training_set_size)
# uncomment to randomize training samples
# (should be done for deployment, however might be good with reproducable results while testing)
#random.shuffle(training_order_list)
test_set_size = max(10, int(0.1 * training_set_size))
# uncomment to manually set test_set_size for testing purposes
#test_set_size = 5
test_order_list = training_order_list[-test_set_size:]
training_order_list = training_order_list[:-test_set_size]
print "Training sample size:", training_set_size, "(nr of source sentences)"
print "Test sample size:", test_set_size, "(nr of source sentences)"
# initialize variables
d_theta_old = [0, 0] # momentum terms
# calculate average loss function value of test samples
print "Calculating average loss function value of test samples using initial weights"
initial_loss_value_test_set = get_average_loss_value_of_test_sample(
test_order_list, nn, dictionary, source_file_name,
n_best_list_file_name, sbleu_score_list_file_name, smoothing_factor)
print "Average loss function value:", initial_loss_value_test_set
loss_value_history = [initial_loss_value_test_set]
converged = False
epoch_count = 0
# For debug
xBleu_history = []
xBleu_change_history = []
seen_nan = 0
# train until overfit (early stop)
print
print "Start training..."
while not converged:
theta_previous = nn.weights
for list_index, i in enumerate(training_order_list):
(phrase_pair_dict_all, phrase_pair_dict_n_list,
total_base_score_list, sbleu_score_list) \
= get_everything(i, source_file_name,
n_best_list_file_name, sbleu_score_list_file_name)
xblue_i, Ej_translation_probability_list = xbleu(
nn, total_base_score_list, sbleu_score_list,
phrase_pair_dict_n_list, dictionary, smoothing_factor)
error_term_dict_i = get_error_term_dict(
phrase_pair_dict_all, phrase_pair_dict_n_list,
sbleu_score_list, xblue_i, Ej_translation_probability_list)
d_theta_old = nn.update_mini_batch(phrase_pair_dict_all,
learning_rate, dictionary,
error_term_dict_i, d_theta_old)
if debug_mode:
debug_print_weights_after_update(nn, d_theta_old,
error_term_dict_i)
# check if xBleu increases after each iteration for testing purposes
# change to >> if True: << if you want to make this check and see output
if False:
xblue_i_after, _ = xbleu(nn, total_base_score_list,
sbleu_score_list,
phrase_pair_dict_n_list, dictionary,
smoothing_factor)
if np.isnan(xblue_i_after):
converged = True
xBleu_history.append((i, xblue_i))
xBleu_change_history.append(xblue_i_after - xblue_i)
print "-------------------------------------------------------------"
print "xBleu history: [(xBleu_before_gradient_descent, xBleu_after_gradient_descent)]"
print xBleu_history
print
print "xBleu_change_history [xBleu_after - xBleu_before]"
print xBleu_change_history
print "-------------------------------------------------------------"
print "Finished epoch nr", epoch_count, "training sample nr", list_index + 1,\
"(of %d)" % (training_set_size - test_set_size), "| source sentence nr", i+1
epoch_count += 1
print "====================================="
print "Finished epoch number:", epoch_count
print "====================================="
# calculate loss function on test set after each epoch using updated weights
print "Calculating loss function value on test set (%d samples) using new weights" % test_set_size
loss_value_test_set = get_average_loss_value_of_test_sample(
test_order_list, nn, dictionary, source_file_name,
n_best_list_file_name, sbleu_score_list_file_name,
smoothing_factor)
loss_value_history.append(loss_value_test_set)
print_loss_value_history(loss_value_history, test_set_size)
# TODO: CONVERGENCE TEST, a simple approach is to stop once the loss function value
# of this iteration is worse than the previous one
#if loss_value_history[-2] > loss_value_history[-1]:
if False: # as of now, run forever. Change this once you have determined a good convergence criteria
converged = True
print "CONVERGED!!!!!!!!!!!!"
print "Saving weights from previous epoch to file"
np.savetxt('W1.gz', theta_previous[0])
np.savetxt('W2.gz', theta_previous[1])
else:
print "No overfitting, keep training..."
def main(source_file_name, n_best_list_file_name, sbleu_score_list_file_name,
learning_rate, smoothing_factor):
print "Loading and initializing neural network"
W1 = np.loadtxt("data/weight_initialization.gz")
W2 = np.identity(100)
nn = CPTMNeuralNetwork([W1.shape[0], 100, 100], [W1, W2])
dictionary = corpora.Dictionary.load("data/dictionary.dict")
training_set_size = 0
# Each line in source_file is a source sentence.
# source_file should end with an empty line
with open(source_file_name, 'r') as source_file:
for _ in source_file:
training_set_size += 1
training_set_size -= 1 # ends with empty line
# uncomment to manually set training_set_size for testing purposes
#training_set_size = 30
training_order_list = range(training_set_size)
# uncomment to randomize training samples
# (should be done for deployment, however might be good with reproducable results while testing)
#random.shuffle(training_order_list)
test_set_size = max(10, int(0.1*training_set_size))
# uncomment to manually set test_set_size for testing purposes
#test_set_size = 5
test_order_list = training_order_list[-test_set_size:]
training_order_list = training_order_list[:-test_set_size]
print "Training sample size:", training_set_size, "(nr of source sentences)"
print "Test sample size:", test_set_size, "(nr of source sentences)"
# initialize variables
d_theta_old = [0, 0] # momentum terms
# calculate average loss function value of test samples
print "Calculating average loss function value of test samples using initial weights"
initial_loss_value_test_set = get_average_loss_value_of_test_sample(
test_order_list, nn, dictionary, source_file_name,
n_best_list_file_name, sbleu_score_list_file_name,
smoothing_factor)
print "Average loss function value:", initial_loss_value_test_set
loss_value_history = [initial_loss_value_test_set]
converged = False
epoch_count = 0
# For debug
xBleu_history = []
xBleu_change_history = []
seen_nan = 0
# train until overfit (early stop)
print
print "Start training..."
while not converged:
theta_previous = nn.weights
for list_index, i in enumerate(training_order_list):
(phrase_pair_dict_all, phrase_pair_dict_n_list,
total_base_score_list, sbleu_score_list) \
= get_everything(i, source_file_name,
n_best_list_file_name, sbleu_score_list_file_name)
xblue_i, Ej_translation_probability_list = xbleu(
nn, total_base_score_list, sbleu_score_list,
phrase_pair_dict_n_list, dictionary, smoothing_factor)
error_term_dict_i = get_error_term_dict(
phrase_pair_dict_all, phrase_pair_dict_n_list,
sbleu_score_list, xblue_i,
Ej_translation_probability_list)
d_theta_old = nn.update_mini_batch(
phrase_pair_dict_all, learning_rate, dictionary, error_term_dict_i, d_theta_old)
if debug_mode:
debug_print_weights_after_update(nn, d_theta_old, error_term_dict_i)
# check if xBleu increases after each iteration for testing purposes
# change to >> if True: << if you want to make this check and see output
if False:
xblue_i_after, _ = xbleu(nn, total_base_score_list, sbleu_score_list,
phrase_pair_dict_n_list, dictionary, smoothing_factor)
if np.isnan(xblue_i_after):
converged = True
xBleu_history.append((i, xblue_i))
xBleu_change_history.append(xblue_i_after - xblue_i)
print "-------------------------------------------------------------"
print "xBleu history: [(xBleu_before_gradient_descent, xBleu_after_gradient_descent)]"
print xBleu_history
print
print "xBleu_change_history [xBleu_after - xBleu_before]"
print xBleu_change_history
print "-------------------------------------------------------------"
print "Finished epoch nr", epoch_count, "training sample nr", list_index + 1,\
"(of %d)" % (training_set_size - test_set_size), "| source sentence nr", i+1
epoch_count += 1
print "====================================="
print "Finished epoch number:", epoch_count
print "====================================="
# calculate loss function on test set after each epoch using updated weights
print "Calculating loss function value on test set (%d samples) using new weights" % test_set_size
loss_value_test_set = get_average_loss_value_of_test_sample(
test_order_list, nn, dictionary,
source_file_name, n_best_list_file_name, sbleu_score_list_file_name,
smoothing_factor)
loss_value_history.append(loss_value_test_set)
print_loss_value_history(loss_value_history, test_set_size)
# TODO: CONVERGENCE TEST, a simple approach is to stop once the loss function value
# of this iteration is worse than the previous one
#if loss_value_history[-2] > loss_value_history[-1]:
if False: # as of now, run forever. Change this once you have determined a good convergence criteria
converged = True
print "CONVERGED!!!!!!!!!!!!"
print "Saving weights from previous epoch to file"
np.savetxt('W1.gz', theta_previous[0])
np.savetxt('W2.gz', theta_previous[1])
else:
print "No overfitting, keep training..."
