def train(FLAGS):
# Load the data
en_token_ids, en_seq_lens, en_vocab_dict, en_rev_vocab_dict = \
process_data('data/en.p', max_vocab_size=5000, target_lang=False)
sp_token_ids, sp_seq_lens, sp_vocab_dict, sp_rev_vocab_dict = \
process_data('data/sp.p', max_vocab_size=5000, target_lang=True)
# Split into train and validation sets
train_encoder_inputs, train_decoder_inputs, train_targets, \
train_en_seq_lens, train_sp_seq_len, \
valid_encoder_inputs, valid_decoder_inputs, valid_targets, \
valid_en_seq_lens, valid_sp_seq_len = \
split_data(en_token_ids, sp_token_ids, en_seq_lens, sp_seq_lens,
train_ratio=0.8)
# Update parameters
FLAGS.en_vocab_size = len(en_vocab_dict)
FLAGS.sp_vocab_size = len(sp_vocab_dict)
# Start session
with tf.Session() as sess:
# Create new model or load old one
model = create_model(sess, FLAGS)
# Training begins
losses = []
for epoch_num, epoch in enumerate(
generate_epoch(train_encoder_inputs, train_decoder_inputs,
train_targets, train_en_seq_lens,
train_sp_seq_len, FLAGS.num_epochs,
FLAGS.batch_size)):
print "EPOCH: %i" % (epoch_num)
# Decay learning rate
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(epoch):
loss, _ = model.step(sess, FLAGS, batch_encoder_inputs,
batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
FLAGS.dropout)
batch_loss.append(loss)
losses.append(np.mean(batch_loss))
plt.plot(losses, label='loss')
plt.legend()
plt.show()
def train(params):
hindi_token_ids, hindi_seq_lens, hindi_vocab_dict, hindi_rev_vocab_dict = process_data('../data/hindi_dump.p', max_vocab_size=100000, target_lang=False)
bengali_token_ids, bengali_seq_lens, bengali_vocab_dict, bengali_rev_vocab_dict = process_data('../data/bengali_dump.p', max_vocab_size=100000, target_lang=True)
train_encoder_inputs, train_decoder_inputs, train_targets, train_hindi_seq_lens, train_bengali_seq_len, valid_encoder_inputs, valid_decoder_inputs, valid_targets, valid_hindi_seq_lens, valid_bengali_seq_lens = split_data(hindi_token_ids, bengali_token_ids, hindi_seq_lens, bengali_seq_lens,train_ratio=0.8)
params.hindi_vocab_size = len(hindi_vocab_dict)
params.bengali_vocab_size = len(bengali_vocab_dict)
print params.hindi_vocab_size, params.bengali_vocab_size
with tf.Session() as sess:
_model = model(params)
sess.run(tf.global_variables_initializer())
losses = []
accs = []
for epoch_num, epoch in enumerate(generate_epoch(train_encoder_inputs,train_decoder_inputs, train_targets,train_hindi_seq_lens, train_bengali_seq_len,params.num_epochs, params.batch_size)):
print "EPOCH : ", epoch_num
sess.run(tf.assign(_model.lr, 0.01 * (0.99 ** epoch_num)))
batch_loss = []
batch_acc = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,batch_targets, batch_hindi_seq_lens,batch_bengali_seq_lens) in enumerate(epoch):
loss, _,acc = _model.step(sess, params,batch_encoder_inputs, batch_decoder_inputs, batch_targets,batch_hindi_seq_lens, batch_bengali_seq_lens,params.dropout)
batch_loss.append(loss)
batch_acc.append(acc)
losses.append(np.mean(batch_loss))
accs.append(np.mean(batch_acc))
print "Training Loss: ",losses[-1]
print "Training Accuracy",accs[-1]
plt.plot(losses, label='loss')
plt.legend()
# plt.show()
plt.title('Plot for Training Error versus Epochs', fontsize='20', style='oblique')
plt.xlabel('Epochs', fontsize='16', color='green')
plt.ylabel('Training Error', fontsize='16', color='green')
plt.savefig('../output/plot.png')
plt.show()
acc = _model.test(sess, params, valid_encoder_inputs, valid_decoder_inputs, valid_targets, valid_hindi_seq_lens, valid_bengali_seq_lens, params.dropout)
print acc
def train(FLAGS):
"""
Train the model on the associative retrieval task.
"""
# Load the train/valid datasets
print("Loading datasets:")
with open(os.path.join(FLAGS.data_dir, 'train.p'), 'rb') as f:
train_X, train_y = pickle.load(f)
print("train_X:", np.shape(train_X), ",train_y:", np.shape(train_y))
with open(os.path.join(FLAGS.data_dir, 'valid.p'), 'rb') as f:
valid_X, valid_y = pickle.load(f)
print("valid_X:", np.shape(valid_X), ",valid_y:", np.shape(valid_y))
with tf.Session() as sess:
# Load the model
model = create_model(sess, FLAGS)
start_time = time.time()
# Start training
train_epoch_loss = []
valid_epoch_loss = []
train_epoch_accuracy = []
valid_epoch_accuracy = []
train_epoch_gradient_norm = []
for train_epoch_num, train_epoch in enumerate(
generate_epoch(train_X, train_y, FLAGS.num_epochs,
FLAGS.batch_size)):
print("EPOCH:", train_epoch_num)
# Assign the learning rate
sess.run(tf.assign(model.lr, FLAGS.learning_rate))
#sess.run(tf.assign(model.lr, FLAGS.learning_rate))
# Decay the learning rate
#sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
# (FLAGS.learning_rate_decay_factor ** epoch_num)))
#if epoch_num < 1000:
# sess.run(tf.assign(model.lr, FLAGS.learning_rate))
#elif epoch_num >= 1000: # slow down now
# sess.run(tf.assign(model.lr, 1e-4))
# Custom decay (empirically decided)
#if (epoch_num%1000 == 0):
# sess.run(tf.assign(model.lr,
# FLAGS.learning_rate/(10**(epoch_num//1000))))
# Train set
train_batch_loss = []
train_batch_accuracy = []
train_batch_gradient_norm = []
for train_batch_num, (batch_X, batch_y) in enumerate(train_epoch):
loss, accuracy, norm, _ = model.step(sess,
batch_X,
batch_y,
FLAGS.l,
FLAGS.e,
forward_only=False)
train_batch_loss.append(loss)
train_batch_accuracy.append(accuracy)
train_batch_gradient_norm.append(norm)
train_epoch_loss.append(np.mean(train_batch_loss))
train_epoch_accuracy.append(np.mean(train_batch_accuracy))
train_epoch_gradient_norm.append(
np.mean(train_batch_gradient_norm))
print('Epoch: [%i/%i] time: %.4f, loss: %.7f,'
' acc: %.7f, norm: %.7f' %
(train_epoch_num, FLAGS.num_epochs, time.time() - start_time,
train_epoch_loss[-1], train_epoch_accuracy[-1],
train_epoch_gradient_norm[-1]))
# Validation set
valid_batch_loss = []
valid_batch_accuracy = []
for valid_epoch_num, valid_epoch in enumerate(
generate_epoch(valid_X,
valid_y,
num_epochs=1,
batch_size=FLAGS.batch_size)):
for valid_batch_num, (batch_X,
batch_y) in enumerate(valid_epoch):
loss, accuracy = model.step(sess,
batch_X,
batch_y,
FLAGS.l,
FLAGS.e,
forward_only=True)
valid_batch_loss.append(loss)
valid_batch_accuracy.append(accuracy)
valid_epoch_loss.append(np.mean(valid_batch_loss))
valid_epoch_accuracy.append(np.mean(valid_batch_accuracy))
# Save the model
if (train_epoch_num % FLAGS.save_every == 0 or
train_epoch_num == (FLAGS.num_epochs-1)) and \
(train_epoch_num > 0):
if not os.path.isdir(FLAGS.ckpt_dir):
os.makedirs(FLAGS.ckpt_dir)
checkpoint_path = os.path.join(FLAGS.ckpt_dir,
"%s.ckpt" % model_name)
print("Saving the model.")
model.saver.save(sess,
checkpoint_path,
global_step=model.global_step)
plt.plot(train_epoch_accuracy, label='train accuracy')
plt.plot(valid_epoch_accuracy, label='valid accuracy')
plt.legend(loc=4)
plt.title('%s_Accuracy' % FLAGS.model_name)
plt.show()
plt.plot(train_epoch_loss, label='train loss')
plt.plot(valid_epoch_loss, label='valid loss')
plt.legend(loc=3)
plt.title('%s_Loss' % FLAGS.model_name)
plt.show()
plt.plot(train_epoch_gradient_norm, label='gradient norm')
plt.legend(loc=4)
plt.title('%s_Gradient Norm' % FLAGS.model_name)
plt.show()
# Store results for global plot
with open('%s_results.p' % FLAGS.model_name, 'wb') as f:
pickle.dump([
train_epoch_accuracy, valid_epoch_accuracy, train_epoch_loss,
valid_epoch_loss, train_epoch_gradient_norm
], f)
def test(FLAGS):
"""
Sample inputs of your own.
"""
# Corpus for indexing
corpus = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '0', '1',
'2', '3', '4', '5', '6', '7', '8', '9', '?'
]
# Render the sample to proper input format
sample = 'g5o8k1??g'
X = []
for item in sample:
X.append(corpus.index(item))
X_one_hot = np.eye(26 + 10 + 1)[np.array(X).astype('int')]
with tf.Session() as sess:
if FLAGS.model_name == 'RNN-LN-FW':
# Inputs need to real inputs of batch_size 128
# because we use A(t) which updates even during testing
# Load the model
model = create_model(sess, FLAGS)
# Load real samples
with open(os.path.join(FLAGS.data_dir, 'train.p'), 'rb') as f:
train_X, train_y = pickle.load(f)
for train_epoch_num, train_epoch in enumerate(
generate_epoch(train_X, train_y, 1, FLAGS.batch_size)):
for train_batch_num, (batch_X,
batch_y) in enumerate(train_epoch):
batch_X[0] = X_one_hot
logits = model.logits.eval(feed_dict={
model.X: batch_X,
model.l: FLAGS.l,
model.e: FLAGS.e
})
print("INPUT:", sample)
print("PREDICTION:", corpus[np.argmax(logits[0])])
return
else:
# Reset from train sizes to sample sizes
FLAGS.batch_size = 1
# Load the model
model = create_model(sess, FLAGS)
logits = model.logits.eval(feed_dict={
model.X: [X_one_hot],
model.l: FLAGS.l,
model.e: FLAGS.e
})
print("INPUT:", sample)
print("PREDICTION:", corpus[np.argmax(logits)])
def train(FLAGS):
# Load the data
en_token_ids, en_seq_lens, en_vocab_dict, en_rev_vocab_dict = \
process_data('data/en.p', max_vocab_size=5000, target_lang=False)
sp_token_ids, sp_seq_lens, sp_vocab_dict, sp_rev_vocab_dict = \
process_data('data/sp.p', max_vocab_size=5000, target_lang=True)
# Split into train and validation sets
train_encoder_inputs, train_decoder_inputs, train_targets, \
train_en_seq_lens, train_sp_seq_len, \
valid_encoder_inputs, valid_decoder_inputs, valid_targets, \
valid_en_seq_lens, valid_sp_seq_len = \
split_data(en_token_ids, sp_token_ids, en_seq_lens, sp_seq_lens,
train_ratio=0.8)
# Update parameters
FLAGS.en_vocab_size = len(en_vocab_dict)
FLAGS.sp_vocab_size = len(sp_vocab_dict)
FLAGS.sp_max_len = max(sp_seq_lens) + 1 # GO token
# Start session
with tf.Session() as sess:
# Create new model or load old one
model = create_model(sess, FLAGS, forward_only=False)
# Training begins
train_losses = []
valid_losses = []
for epoch_num, epoch in enumerate(generate_epoch(train_encoder_inputs,
train_decoder_inputs, train_targets,
train_en_seq_lens, train_sp_seq_len,
FLAGS.num_epochs, FLAGS.batch_size)):
print "EPOCH: %i" % (epoch_num)
# Decay learning rate
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(epoch):
y_pred, loss, _ = model.step(sess, FLAGS,
batch_encoder_inputs, batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
FLAGS.dropout, forward_only=False)
batch_loss.append(loss)
train_losses.append(np.mean(batch_loss))
for valid_epoch_num, valid_epoch in enumerate(generate_epoch(valid_encoder_inputs,
valid_decoder_inputs, valid_targets,
valid_en_seq_lens, valid_sp_seq_len,
num_epochs=1, batch_size=FLAGS.batch_size)):
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(valid_epoch):
loss = model.step(sess, FLAGS,
batch_encoder_inputs, batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
dropout=0.0, forward_only=True, sampling=False)
batch_loss.append(loss)
valid_losses.append(np.mean(batch_loss))
# Save checkpoint.
if not os.path.isdir(FLAGS.ckpt_dir):
os.makedirs(FLAGS.ckpt_dir)
checkpoint_path = os.path.join(FLAGS.ckpt_dir, "model.ckpt")
print "Saving the model."
model.saver.save(sess, checkpoint_path,
global_step=model.global_step)
plt.plot(train_losses, label='train_loss')
plt.plot(valid_losses, label='valid_loss')
plt.legend()
plt.show()
def train(FLAGS):
# Load the data
en_token_ids, en_seq_lens, en_vocab_dict, en_rev_vocab_dict = \
process_data('data/tst2013.en', max_vocab_size=30000, target_lang=False)
sp_token_ids, sp_seq_lens, sp_vocab_dict, sp_rev_vocab_dict = \
process_data('data/tst2013.tr', max_vocab_size=30000, target_lang=True)
# Split into train and validation sets
train_encoder_inputs, train_decoder_inputs, train_targets, \
train_en_seq_lens, train_sp_seq_len, \
valid_encoder_inputs, valid_decoder_inputs, valid_targets, \
valid_en_seq_lens, valid_sp_seq_len = \
split_data(en_token_ids, sp_token_ids, en_seq_lens, sp_seq_lens,
train_ratio=0.8)
output = open('data/vocab_en.pkl', 'wb')
pickle.dump(en_vocab_dict, output)
output.close()
output = open('data/vocab_sp.pkl', 'wb')
pickle.dump(sp_vocab_dict, output)
output.close()
# Update parameters
FLAGS.en_vocab_size = len(en_vocab_dict)
FLAGS.sp_vocab_size = len(sp_vocab_dict)
print 'len(en_vocab_dict)', len(en_vocab_dict)
print 'len(sp_vocab_dict)', len(sp_vocab_dict)
# Start session
with tf.Session() as sess:
model = None
# Create new model or load old one
f = checkpoint_path + ".index"
print f
exit()
if os.path.isfile(f):
model = restore_model(sess)
else:
model = create_model(sess, FLAGS)
# Training begins
losses = []
for epoch_num, epoch in enumerate(generate_epoch(train_encoder_inputs,
train_decoder_inputs, train_targets,
train_en_seq_lens, train_sp_seq_len,
FLAGS.num_epochs, FLAGS.batch_size)):
print "EPOCH: %i" % (epoch_num)
# Decay learning rate
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(epoch):
loss, _ = model.step(sess, FLAGS,
batch_encoder_inputs, batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
FLAGS.dropout)
print loss
batch_loss.append(loss)
print 'mean: ', np.mean(batch_loss)
print "Saving the model."
model.saver.save(sess, checkpoint_path)
def train(FLAGS):
# Load the data
en_token_ids, en_seq_lens, en_vocab_dict, en_rev_vocab_dict = \
process_data('data/my_en.txt', max_vocab_size=5000, target_lang=False)
sp_token_ids, sp_seq_lens, sp_vocab_dict, sp_rev_vocab_dict = \
process_data('data/my_sp.txt', max_vocab_size=5000, target_lang=True)
# Split into train and validation sets
train_encoder_inputs, train_decoder_inputs, train_targets, \
train_en_seq_lens, train_sp_seq_len, \
valid_encoder_inputs, valid_decoder_inputs, valid_targets, \
valid_en_seq_lens, valid_sp_seq_len = \
split_data(en_token_ids, sp_token_ids, en_seq_lens, sp_seq_lens,
train_ratio=0.8)
output = open('data/vocab_en.pkl', 'wb')
pickle.dump(en_vocab_dict, output)
output.close()
output = open('data/vocab_sp.pkl', 'wb')
pickle.dump(sp_vocab_dict, output)
output.close()
# Update parameters
FLAGS.en_vocab_size = len(en_vocab_dict)
FLAGS.sp_vocab_size = len(sp_vocab_dict)
print 'len(en_vocab_dict)', len(en_vocab_dict)
print 'len(sp_vocab_dict)', len(sp_vocab_dict)
# Start session
with tf.Session() as sess:
# Create new model or load old one
model = create_model(sess, FLAGS)
# Training begins
losses = []
for epoch_num, epoch in enumerate(
generate_epoch(train_encoder_inputs, train_decoder_inputs,
train_targets, train_en_seq_lens,
train_sp_seq_len, FLAGS.num_epochs,
FLAGS.batch_size)):
print "EPOCH: %i" % (epoch_num)
# Decay learning rate
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(epoch):
loss, _ = model.step(sess, FLAGS, batch_encoder_inputs,
batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
FLAGS.dropout)
batch_loss.append(loss)
losses.append(np.mean(batch_loss))
checkpoint_path = "/tmp/model.ckpt"
print "Saving the model."
model.saver.save(sess, checkpoint_path)
plt.plot(losses, label='loss')
plt.legend()
plt.savefig('seq_01.png')
def train():
X, y = load_data_and_labels()
vocab_list, vocab_dict, rev_vocab_dict = create_vocabulary(
X, FLAGS.en_vocab_size)
X, seq_lens = data_to_token_ids(X, vocab_dict)
train_X, train_y, train_seq_lens, valid_X, valid_y, valid_seq_lens = \
split_data(X, y, seq_lens)
FLAGS.max_sequence_length = len(train_X[0])
with tf.Session() as sess:
# Load old model or create new one
model = create_model(sess, FLAGS)
# Train results
for epoch_num, epoch in enumerate(
generate_epoch(train_X, train_y, train_seq_lens,
FLAGS.num_epochs, FLAGS.batch_size)):
print "EPOCH:", epoch_num
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
train_loss = []
train_accuracy = []
for batch_num, (batch_X, batch_y,
batch_seq_lens) in enumerate(epoch):
_, loss, accuracy = model.step(
sess,
batch_X,
batch_seq_lens,
batch_y,
dropout_keep_prob=FLAGS.dropout_keep_prob,
forward_only=False,
sampling=False)
train_loss.append(loss)
train_accuracy.append(accuracy)
print
print "EPOCH %i SUMMARY" % epoch_num
print "Training loss %.3f" % np.mean(train_loss)
print "Training accuracy %.3f" % np.mean(train_accuracy)
print "----------------------"
# Validation results
for valid_epoch_num, valid_epoch in enumerate(
generate_epoch(valid_X,
valid_y,
valid_seq_lens,
num_epochs=1,
batch_size=FLAGS.batch_size)):
valid_loss = []
valid_accuracy = []
for valid_batch_num, \
(valid_batch_X, valid_batch_y, valid_batch_seq_lens) in \
enumerate(valid_epoch):
loss, accuracy = model.step(sess,
valid_batch_X,
valid_batch_seq_lens,
valid_batch_y,
dropout_keep_prob=1.0,
forward_only=True,
sampling=False)
valid_loss.append(loss)
valid_accuracy.append(accuracy)
print "Validation loss %.3f" % np.mean(valid_loss)
print "Validation accuracy %.3f" % np.mean(valid_accuracy)
print "----------------------"
# Save checkpoint every epoch.
if not os.path.isdir(FLAGS.ckpt_dir):
os.makedirs(FLAGS.ckpt_dir)
checkpoint_path = os.path.join(FLAGS.ckpt_dir, "model.ckpt")
print "Saving the model."
model.saver.save(sess,
checkpoint_path,
global_step=model.global_step)
