Tensorflow: 6 layer CNN: OOM (use 10Gb GPU memory) - out-of-memory

Question

I am using the following code for running a 6 layer CNN with 2 FC layers on top (on Tesla K-80 GPU).
Somehow, it consumes entire memory 10GB and died out of memory.I know that i can reduce the batch_size and then run , but i also want to run with 15 or 20 CNN layers.Whats wrong with the following code and why it takes all the memory? How should i run the code for 15 layers CNN.
Code:
import model
with tf.Graph().as_default() as g_train:
filenames = tf.train.match_filenames_once(FLAGS.train_dir+'*.tfrecords')
filename_queue = tf.train.string_input_producer(filenames, shuffle=True, num_epochs=FLAGS.num_epochs)
feats,labels = get_batch_input(filename_queue, batch_size=FLAGS.batch_size)
### feats size=(batch_size, 100, 50)
logits = model.inference(feats, FLAGS.batch_size)
loss = model.loss(logits, labels, feats)
tvars = tf.trainable_variables()
global_step = tf.Variable(0, name='global_step', trainable=False)
# Add to the Graph operations that train the model.
train_op = model.training(loss, tvars, global_step, FLAGS.learning_rate, FLAGS.clip_gradients)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = model.evaluation(logits, labels, feats)
summary_op = tf.merge_all_summaries()
saver = tf.train.Saver(tf.all_variables(), max_to_keep=15)
# The op for initializing the variables.
init_op = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init_op)
summary_writer = tf.train.SummaryWriter(FLAGS.model_dir,
graph=sess.graph)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = 0
while not coord.should_stop():
_, loss_value = sess.run([train_op, loss])
if step % 100 == 0:
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value))
# Update the events file.
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
if (step == 0) or (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
ckpt_model = os.path.join(FLAGS.model_dir, 'model.ckpt')
saver.save(sess, ckpt_model, global_step=step)
#saver.save(sess, FLAGS.model_dir, global_step=step)
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
finally:
coord.join(threads)
sess.close()
###################### File model.py ####################
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1],
padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2,s=2):
# MaxPool2D wrapper
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, s,
s,1],padding='SAME')
def inference(feats,batch_size):
#feats size (batch_size,100,50,1) #batch_size=256
conv1_w=tf.get_variable("conv1_w", [filter_size,filter_size,1,256],initializer=tf.uniform_unit_scaling_initializer())
conv1_b=tf.get_variable("conv1_b",[256])
conv1 = conv2d(feats, conv1_w, conv1_b,2)
conv1 = maxpool2d(conv1, k=2,s=2)
### This was replicated for 6 layers and the 2 FC connected layers are added
return logits
def training(loss, train_vars, global_step, learning_rate, clip_gradients):
# Add a scalar summary for the snapshot loss.
tf.scalar_summary(loss.op.name, loss)
grads, _ = tf.clip_by_global_norm(tf.gradients(loss, train_vars,aggregation_method=1), clip_gradients)
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.apply_gradients(zip(grads, train_vars), global_step=global_step)
return train_op

Answer 1

I am not too sure what the model python library is. If it is something you wrote and can change the setting in the optimizer I would suggest the following which I use in my own code
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cost, aggregation_method = tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
By default the aggeragetion_method is ADD_N but if you change it to EXPERIMENTAL_ACCUMULATE_N or EXPERIMENTAL_TREE this will greatly save memory. The main memory hog in these programs is that tensorflow must save the output values at every neuron so that it can compute the gradients. Changing the aggregation_method helps a lot from my experience.
Also BTW I don't think there is anything wrong with your code. I can run out of memory on small cov-nets as well.