def autoencode_procedure(option, path, z_size, examples, fold_inputs,
encoder_graph, decoder_graph, mus_and_log_sigs,
look_behind, token_probs_t, hidden_state_t, sess):
# Autoencode_bit
examples_dir = join(BASEDIR, option + '_examples')
mkdir_p(examples_dir)
autoencoded_examples_path = join(examples_dir, 'autoencoded')
message = 'Autencoding {} examples'.format(len(examples))
for i, (fold_batch, input_sequence) in enumerate(
tqdm(zip(td.group_by_batches(fold_inputs, 1), examples),
desc=message,
total=len(examples))):
dir_for_example = join(autoencoded_examples_path, str(i))
mkdir_p(dir_for_example)
p_of_z = sess.run(
mus_and_log_sigs,
feed_dict={encoder_graph.loom_input_tensor: fold_batch})
mus = p_of_z[:, :z_size]
input_code = example_to_code(input_sequence[look_behind:])
write_to_file(join(dir_for_example, 'input.hs'), input_code)
imsave(join(dir_for_example, 'input.png'), input_sequence.T)
imsave(join(dir_for_example, 'z.png'), mus.reshape(
(mus.size // 32, 32)))
# DECODER PART
if look_behind > 0:
decoder_input = padded_look_behind_generator(
input_sequence, look_behind, TOKEN_EMB_SIZE)
else:
decoder_input = zeros_generator() # NOQA
fd = decoder_graph.build_feed_dict([(mus.squeeze(axis=0),
[next(decoder_input)])])
token_probs, hidden_state = sess.run([token_probs_t, hidden_state_t],
feed_dict=fd)
tokens_probs = [token_probs]
text_so_far = [one_hot(TOKEN_EMB_SIZE, token_probs.squeeze().argmax())]
for _ in range(len(input_sequence) - 1):
fd = decoder_graph.build_feed_dict([(hidden_state.squeeze(),
[next(decoder_input)])])
token_probs, hidden_state = sess.run(
[token_probs_t, hidden_state_t], feed_dict=fd)
tokens_probs += [token_probs]
text_so_far += [
one_hot(TOKEN_EMB_SIZE,
token_probs.squeeze().argmax())
]
decoder_output = np.concatenate(tokens_probs)
imsave(join(dir_for_example, 'decoder_output.png'), decoder_output.T)
write_to_file(join(dir_for_example, 'autoencoded_code.hs'),
example_to_code(text_so_far))
def train_epoch(self,sess, train_set):
if not self.is_compiled:
return 0, 0
t = time.time()
print("starttrainepoch")
loss = sum(self.train_step(sess,ba)
for ba in td.group_by_batches(train_set, self.BATCH_SIZE))
t = time.time() - t
return loss, t
def train_epoch(self, train_set, batch_size):
loss = 0
for batch in td.group_by_batches(train_set, batch_size):
train_feed_dict = {
self.keep_prob_ph: self.ModelConfig.keep_prob,
self.compiler.loom_input_tensor: batch
}
loss += self.train_step(train_feed_dict)
return loss
def test_eval():
test_loss = tf.reduce_sum(compiler.metric_tensors['root_loss'])
_test_logits = compiler.metric_tensors['root_logits']
test_loss_whole = 0.
test_pred_whole = []
test_labels_whole = []
test_logits_whole = []
# f1 = open("logTmp", "w")
for batch in td.group_by_batches(test_set, BATCH_SIZE):
test_feed_dict[compiler.loom_input_tensor] = batch
test_loss_batch, test_pred_batch, test_labels_batch, test_logits_batch = sess.run(
[test_loss, pred, labels, _test_logits], test_feed_dict)
test_loss_whole = test_loss_whole + test_loss_batch
test_pred_whole = test_pred_whole + test_pred_batch.tolist()
test_labels_whole = test_labels_whole + test_labels_batch.tolist()
test_logits_whole = test_logits_whole + test_logits_batch.tolist()
f1score = metrics.f1_score(test_labels_whole,
test_pred_whole,
average=None)
print('test_loss_avg: %.3e, test_f1score:\n [%s]' %
(test_loss_whole, f1score))
return test_labels_whole, test_pred_whole, test_logits_whole
def train_epoch(train_set):
return sum(
train_step(batch)
for batch in td.group_by_batches(train_set, BATCH_SIZE))
def get_batches(self, dataset=None, batch_size=100):
if dataset == None:
dataset = self.get_dataset()
import tensorflow_fold as td
return td.group_by_batches(dataset, batch_size)
def main():
apputil.initialize(variable_scope='embedding')
# load data early so we can initialize hyper parameters accordingly
ds = data.load_dataset('../data/statements')
hyper.node_type_num = len(ds.word2int)
hyper.dump()
# create model variables
param.initialize_embedding_weights()
# Compile the block
tree_sum = td.GetItem(0) >> tree_sum_blk(l2loss_blk)
compiler = td.Compiler.create(tree_sum)
(batched_loss, ) = compiler.output_tensors
loss = tf.reduce_mean(batched_loss)
opt = tf.train.AdamOptimizer(learning_rate=hyper.learning_rate)
global_step = tf.Variable(0, trainable=False, name='global_step')
train_step = opt.minimize(loss, global_step=global_step)
# Attach summaries
tf.summary.histogram('Wl', param.get('Wl'))
tf.summary.histogram('Wr', param.get('Wr'))
tf.summary.histogram('B', param.get('B'))
tf.summary.histogram('Embedding', param.get('We'))
tf.summary.scalar('loss', loss)
summary_op = tf.summary.merge_all()
# create missing dir
if not os.path.exists(hyper.train_dir):
os.makedirs(hyper.train_dir)
# train loop
saver = tf.train.Saver()
train_set = compiler.build_loom_inputs(ds.get_split('all')[1])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(hyper.log_dir, graph=sess.graph)
write_embedding_metadata(summary_writer, ds.word2int)
for epoch, shuffled in enumerate(
td.epochs(train_set, hyper.num_epochs), 1):
for step, batch in enumerate(
td.group_by_batches(shuffled, hyper.batch_size), 1):
train_feed_dict = {compiler.loom_input_tensor: batch}
start_time = default_timer()
_, loss_value, summary, gstep = sess.run(
[train_step, loss, summary_op, global_step],
train_feed_dict)
duration = default_timer() - start_time
logger.info(
'global %d epoch %d step %d loss = %.2f (%.1f samples/sec; %.3f sec/batch)',
gstep, epoch, step, loss_value,
hyper.batch_size / duration, duration)
if gstep % 10 == 0:
summary_writer.add_summary(summary, gstep)
if gstep % 10 == 0:
saver.save(sess,
os.path.join(hyper.train_dir, "model.ckpt"),
global_step=gstep)
def do_evaluation():
# load data early to get node_type_num
ds = data.load_dataset('data/statements')
hyper.node_type_num = len(ds.word2int)
(compiler, _, _, _, raw_accuracy, batch_size_op) = build_model()
# restorer for embedding matrix
embedding_path = tf.train.latest_checkpoint(hyper.embedding_dir)
if embedding_path is None:
raise ValueError('Path to embedding checkpoint is incorrect: ' +
hyper.embedding_dir)
# restorer for other variables
checkpoint_path = tf.train.latest_checkpoint(hyper.train_dir)
if checkpoint_path is None:
raise ValueError('Path to tbcnn checkpoint is incorrect: ' +
hyper.train_dir)
restored_vars = tf.get_collection_ref('restored')
restored_vars.append(param.get('We'))
restored_vars.extend(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES))
embeddingRestorer = tf.train.Saver({'embedding/We': param.get('We')})
restorer = tf.train.Saver(
tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES))
# train loop
total_size, test_gen = ds.get_split('test')
test_set = compiler.build_loom_inputs(test_gen)
with tf.Session() as sess:
# Restore embedding matrix first
embeddingRestorer.restore(sess, embedding_path)
# Restore others
restorer.restore(sess, checkpoint_path)
# Initialize other variables
gvariables = [
v for v in tf.global_variables()
if v not in tf.get_collection('restored')
]
sess.run(tf.variables_initializer(gvariables))
num_epochs = 1 if not hyper.warm_up else 3
for shuffled in td.epochs(test_set, num_epochs):
logger.info('')
logger.info(
'======================= Evaluation ===================================='
)
accumulated_accuracy = 0.
start_time = default_timer()
for step, batch in enumerate(
td.group_by_batches(shuffled, hyper.batch_size), 1):
feed_dict = {compiler.loom_input_tensor: batch}
accuracy_value, actual_bsize = sess.run(
[raw_accuracy, batch_size_op], feed_dict)
accumulated_accuracy += accuracy_value * actual_bsize
logger.info(
'evaluation in progress: running accuracy = %.2f, processed = %d / %d',
accuracy_value,
(step - 1) * hyper.batch_size + actual_bsize, total_size)
duration = default_timer() - start_time
total_accuracy = accumulated_accuracy / total_size
logger.info(
'evaluation accumulated accuracy = %.2f%% (%.1f samples/sec; %.2f seconds)',
total_accuracy * 100, total_size / duration, duration)
logger.info(
'======================= Evaluation End ================================='
)
logger.info('')
def do_train():
# load data early to get node_type_num
ds = data.load_dataset('data/statements')
hyper.node_type_num = len(ds.word2int)
hyper.dump()
(compiler, unscaled_logits, logits, batched_labels, raw_accuracy,
batch_size_op) = build_model()
(loss, global_step, train_step,
summary_op) = train_with_val(unscaled_logits, batched_labels,
raw_accuracy)
val_summary_op = tf.summary.scalar('val_accuracy', raw_accuracy)
# create missing dir
if not os.path.exists(hyper.train_dir):
os.makedirs(hyper.train_dir)
# restorer for embedding matrix
restorer = tf.train.Saver({'embedding/We': param.get('We')})
embedding_path = tf.train.latest_checkpoint(hyper.embedding_dir)
if embedding_path is None:
raise ValueError('Path to embedding checkpoint is incorrect: ' +
hyper.embedding_dir)
# train loop
saver = tf.train.Saver()
train_set = compiler.build_loom_inputs(ds.get_split('train')[1])
val_set = compiler.build_loom_inputs(ds.get_split('val')[1])
with tf.Session() as sess:
# Restore embedding matrix first
restorer.restore(sess, embedding_path)
# Initialize other variables
gvariables = tf.global_variables()
gvariables.remove(param.get('We')) # exclude We
sess.run(tf.variables_initializer(gvariables))
summary_writer = tf.summary.FileWriter(hyper.log_dir, graph=sess.graph)
val_step_counter = 0
shuffled = zip(td.epochs(train_set, hyper.num_epochs),
td.epochs(val_set, hyper.num_epochs))
for epoch, (train_shuffled, val_shuffled) in enumerate(shuffled, 1):
for step, batch in enumerate(
td.group_by_batches(train_shuffled, hyper.batch_size), 1):
train_feed_dict = {compiler.loom_input_tensor: batch}
start_time = default_timer()
(_, loss_value, summary, gstep, actual_bsize) = sess.run(
[train_step, loss, summary_op, global_step, batch_size_op],
train_feed_dict)
duration = default_timer() - start_time
logger.info(
'global %d epoch %d step %d loss = %.2f (%.1f samples/sec; %.3f sec/batch)',
gstep, epoch, step, loss_value, actual_bsize / duration,
duration)
if gstep % 10 == 0:
summary_writer.add_summary(summary, gstep)
# do a validation test
logger.info('')
logger.info(
'======================= Validation ===================================='
)
accumulated_accuracy = 0.
total_size = 0
start_time = default_timer()
for batch in td.group_by_batches(val_shuffled, hyper.batch_size):
feed_dict = {compiler.loom_input_tensor: batch}
accuracy_value, actual_bsize, val_summary = sess.run(
[raw_accuracy, batch_size_op, val_summary_op], feed_dict)
summary_writer.add_summary(val_summary, val_step_counter)
accumulated_accuracy += accuracy_value * actual_bsize
total_size += actual_bsize
val_step_counter += 1
logger.info(
'validation step, accuracy = %.2f, current batch = %d, processed = %d',
accuracy_value, actual_bsize, total_size)
duration = default_timer() - start_time
total_accuracy = accumulated_accuracy / total_size
logger.info(
'validation acc = %.2f%% (%.1f samples/sec; %.2f seconds)',
total_accuracy * 100, total_size / duration, duration)
saved_path = saver.save(sess,
os.path.join(hyper.train_dir,
"model.ckpt"),
global_step=gstep)
logger.info('validation saved path: %s', saved_path)
logger.info(
'======================= Validation End ================================='
)
logger.info('')
