out of memory when using BatchNormalization on convolutional layers

[guyk1971]

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Hi,
I'm trying to train a small model to classify mnist on TitanX GPU.
here's the gist of the script:

data loading and preparation as in the mnist_cnn example and then :

x = Input(shape=input_shape)
layer = Conv2D(32, kernel_size=(3, 3),activation='relu')(x)
layer = BatchNormalization()(layer)
layer = Conv2D(64, kernel_size=(3, 3),activation='relu')(layer)
layer = BatchNormalization()(layer)
layer = MaxPooling2D(pool_size=(2,2))(layer)
layer = Dropout(0.25)(layer)
layer = Flatten()(layer)
layer = Dense(128,activation='relu')(layer)
layer = Dropout(0.5)(layer)
predictions = Dense(num_classes,activation='softmax')(layer)
model = Model(inputs=x,outputs=predictions)
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

I get a message error about running out of memory (on TitanX ?! for such a small model ???)
some experiments:

1. when commenting out the BatchNormaliation, it works .
2. When using sequentioal model, same phenomenon happens - fails with Batchnorm
3. when adding the batchnorm right after the Dense (128,...) it also works
4. as expected, when using only dense layers, batchnorm works

Am I missing something ? (yes, I know, Batchnorm should allegedly come before activation, but its arguable. I'd want to see it works and not crash first).

Thanks,
Guy

[StefanoD]

I had a performance issue, too, with BN on Convonutional Layers.
I'm using Theano as backend.

[Dref360]

BatchNorm is pretty memory intensive.
Let's say you have an image 300x300x512, BN initiate 2 vectors of (512,) + keep a running mean so 4 vector of 512. Then it creates another image of 300x300x512 normalized. So basically, you more than double the memory usage. Expected.

[stale]

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[JiaShengLiu111]

BatchNorm is pretty memory intensive.
Let's say you have an image 300x300x512, BN initiate 2 vectors of (512,) + keep a running mean so 4 vector of 512. Then it creates another image of 300x300x512 normalized. So basically, you more than double the memory usage. Expected.

The memory you said is the memory of GPU.