def init_training_dnn_single(self, graph, optimizer_conf):
''' initialze training graph;
assumes self.logits, self.labels_holder in place'''
with graph.as_default():
# record variables we have already initialized
variables_before = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
loss = nnet.loss_dnn(self.logits, self.labels_holder)
learning_rate_holder = tf.placeholder(tf.float32,
shape=[],
name='learning_rate')
# train op may introduce new variables
#train_op = nnet.training(optimizer_conf, loss, learning_rate_holder)
opt = nnet.prep_optimizer(optimizer_conf, learning_rate_holder)
grads = nnet.get_gradients(opt, loss)
train_op = nnet.apply_gradients(optimizer_conf, opt, grads)
eval_acc = nnet.evaluation_dnn(self.logits, self.labels_holder)
# and thus we need to intialize them
variables_after = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
new_variables = list(set(variables_after) - set(variables_before))
init_train_op = tf.variables_initializer(new_variables)
self.loss = loss
self.learning_rate_holder = learning_rate_holder
self.train_op = train_op
self.eval_acc = eval_acc
self.init_train_op = init_train_op
def init_training_lstm_multi(self, graph, optimizer_conf):
tower_losses = []
tower_grads = []
tower_accs = []
with graph.as_default(), tf.device('/cpu:0'):
# record variables we have already initialized
variables_before = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
learning_rate_holder = tf.placeholder(tf.float32,
shape=[],
name='learning_rate')
opt = nnet.prep_optimizer(optimizer_conf, learning_rate_holder)
for i in range(self.num_towers):
with tf.device('/gpu:%d' % i):
with tf.name_scope('Tower_%d' % (i)) as scope:
tower_start_index = i * self.batch_size
tower_end_index = (i + 1) * self.batch_size
tower_mask_holder = self.mask_holder[
tower_start_index:tower_end_index, :]
tower_labels_holder = self.labels_holder[
tower_start_index:tower_end_index, :]
loss = nnet.loss_lstm(self.tower_logits[i],
tower_labels_holder,
tower_mask_holder)
tower_losses.append(loss)
grads = nnet.get_gradients(opt, loss)
tower_grads.append(grads)
eval_acc = nnet.evaluation_lstm(
self.tower_logits[i], tower_labels_holder,
tower_mask_holder)
tower_accs.append(eval_acc)
grads = nnet.average_gradients(tower_grads)
train_op = nnet.apply_gradients(optimizer_conf, opt, grads)
losses = tf.reduce_sum(tower_losses)
accs = tf.reduce_sum(tower_accs)
# we need to intialize variables that are newly added
variables_after = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
new_variables = list(set(variables_after) - set(variables_before))
init_train_op = tf.variables_initializer(new_variables)
self.loss = losses
self.learning_rate_holder = learning_rate_holder
self.train_op = train_op
self.eval_acc = accs
self.init_train_op = init_train_op
def init_training_single(self, graph, optimizer_conf, learning_rate=None):
''' initialze training graph;
assumes self.asr_logits, self.sid_logits, self.labels_holder in place'''
with graph.as_default():
# record variables we have already initialized
variables_before = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
learning_rate_holder = tf.placeholder(tf.float32,
shape=[],
name='learning_rate')
if learning_rate is None:
opt = nnet.prep_optimizer(optimizer_conf, learning_rate_holder)
else:
opt = nnet.prep_optimizer(optimizer_conf, learning_rate)
if self.alpha_holder is None:
self.alpha_holder = tf.placeholder(tf.float32,
shape=[],
name='alpha_holder')
self.learning_rate_holder = learning_rate_holder
self.bucket_tr_loss = []
self.bucket_tr_asr_loss = []
self.bucket_tr_sid_loss = []
self.bucket_tr_train_op = []
self.bucket_tr_asr_train_op = []
self.bucket_tr_sid_train_op = []
self.bucket_tr_asr_eval_acc = []
self.bucket_tr_sid_eval_acc = []
assert len(self.bucket_tr_asr_logits) == len(
self.bucket_tr_sid_logits)
for (asr_logits, sid_logits, asr_labels_holder, sid_labels_holder, mask_holder) in \
zip(self.bucket_tr_asr_logits, self.bucket_tr_sid_logits,
self.bucket_tr_asr_labels_holders, self.bucket_tr_sid_labels_holders,
self.bucket_tr_mask_holders):
asr_loss = nnet.loss_dnn(asr_logits, asr_labels_holder,
mask_holder)
sid_loss = nnet.loss_dnn(sid_logits, sid_labels_holder)
loss = self.alpha_holder * asr_loss + self.beta_holder * sid_loss
grads = nnet.get_gradients(opt, loss)
asr_grads = nnet.get_gradients(opt, asr_loss)
sid_grads = nnet.get_gradients(opt, sid_loss)
train_op = nnet.apply_gradients(optimizer_conf, opt, grads)
asr_train_op = nnet.apply_gradients(optimizer_conf, opt,
asr_grads)
sid_train_op = nnet.apply_gradients(optimizer_conf, opt,
sid_grads)
asr_eval_acc = nnet.evaluation_dnn(asr_logits,
asr_labels_holder,
mask_holder)
sid_eval_acc = nnet.evaluation_dnn(sid_logits,
sid_labels_holder)
self.bucket_tr_loss.append(loss)
self.bucket_tr_asr_loss.append(asr_loss)
self.bucket_tr_sid_loss.append(sid_loss)
self.bucket_tr_train_op.append(train_op)
self.bucket_tr_asr_train_op.append(asr_train_op)
self.bucket_tr_sid_train_op.append(sid_train_op)
self.bucket_tr_asr_eval_acc.append(asr_eval_acc)
self.bucket_tr_sid_eval_acc.append(sid_eval_acc)
variables_after = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
new_variables = list(set(variables_after) - set(variables_before))
init_train_op = tf.variables_initializer(new_variables)
self.init_train_op = init_train_op