How to fix “ResourceExhaustedError: OOM when allocating tensor”

Question

I wanna make a model with multiple inputs. So, I try to build a model like this.

# define two sets of inputs
inputA = Input(shape=(32,64,1))
inputB = Input(sh         


        

Answer 1

From [800000,32,30,62] it seems your model put all the data in one batch.

Try specified batch size like

history = model.fit([trainimage, train_product_embd],train_label, validation_data=([validimage,valid_product_embd],valid_label), epochs=10, steps_per_epoch=100, validation_steps=10, batch_size=32)

If it still OOM then try reduce the batch_size

Answer 2

OOM stands for "out of memory". Your GPU is running out of memory, so it can't allocate memory for this tensor. There are a few things you can do:

• Decrease the number of neurons in your Dense, Conv2D layers
• Use a smaller batch_size (or increase steps_per_epoch and validation_steps)
• Use grayscale images (there will be one channel instead of three)
• Reduce the number of layers
• Use more MaxPooling2D layers, and increase their pool size
• Use larger strides in your Conv2D layers
• Reduce the size of your images (you can use PIL or cv2 for that)
• Apply dropout
• Use smaller float precision, namely np.float32 if you accidentally used np.float64
• If you're using a pre-trained model, freeze the first layers

There is more useful information about this error:

OOM when allocating tensor with shape[800000,32,30,62]

This is a weird shape. If you're working with images, you should normally have 3 or 1 channels. On top of that, it seems like you are passing your entire dataset at once; you should instead pass it in batches.

Answer 3

Happened to me as well.

You can try reducing trainable parameters by using some form of Transfer Learning - try freezing the initial few layers and use lower batch sizes.