CODEWITHSUNDEEP
python-2.7tensorflowconv-neural-networkvgg-net
Alberto Merciai
Alberto Merciai

Reputation: 494

fine tuning vgg raise memory error

Hi i'm trying to fine tuning vgg on my problem but when i try to train the net i get this error.

OOM when allocating tensor with shape[25088,4096]

The net has this structure:

enter image description here

I take this tensorflow pretrained vgg implementation code from this site.

I only add this procedure to train the net:

with tf.name_scope('joint_loss'):
   joint_loss = ya_loss+yb_loss+yc_loss+yd_loss+ye_loss+yf_loss+yg_loss+yh_loss+yi_loss+yl_loss+ym_loss+yn_loss

    # Loss with weight decay
    l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
    self.joint_loss = joint_loss + self.weights_decay * l2_loss
    self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(joint_loss)

i try to reduce the batch size to 2 but not works i get the same error. The error is due to the big tensor that cannot be allocated in memory. I get this error only in train cause if i feed a value without minimize the net works. How i can avoid this error? how can i save memory of graphic card(Nvidia GeForce GTX 970)?

UPDATE: if i use the GradientDescentOptimizer the training process start, instead if i use AdamOptimizer i get the memory error, seems that the GradientDescentOptimizer use less memory.

Upvotes: 0

Views: 322

Answers (1)

Allen Lavoie
Allen Lavoie

Reputation: 5808

Without a backward pass ("feed a value without minimizing"), TensorFlow can immediately de-allocate intermediate activations. With a backward pass, the graph has a giant U-shape, where activations from the forward pass need to be kept in memory for the backward pass. There are some tricks (such as swapping to host memory), but in general backprop means that memory usage will be higher.

Adam does keep some extra bookkeeping variables around, so it will increase memory usage proportional to the amount of memory your weight variables are already using. If your training steps take quite a while (in which case having the variable updates on the GPU isn't important), you could instead locate the optimization ops in host memory.

If you need a larger batch size and can't reduce image resolution or model size, combining gradients from multiple workers/GPUs using something like SyncReplicasOptimizer can be a good option. Looking at the paper associated with this model, it looks like they were training on 4 GPUs each with 12GB of memory.

Upvotes: 1

Related Questions