Memory error when using more layers in CNN model On my dell core i7 - 16GB RAM - 4gb 960m GPU laptop, I am working on a project to the classify lung CT images using 3d CNN. I'm using the CPU version of tensorflow. The images are prepared as numpy array size (25,50,50). 

My CNN model had 2 conv layers, two maxpool layer, one FC layer and output layer. With this architecture I could train the model with approximately (5000 to 6000) samples. After adding more layers my model now has 6 conv layers, 3 max-pool layers, FC and output layer. My problem is after changing the architecture with just more than 1000 samples my memory gets filled and I get memory error. I tried to make smaller batches, but every time getting same error. I have two questions:


Why by adding more layers the model needs more memory?
Is there any way to deal with this type of problem?
More layers mean more parameters for your network, which in turn means more required space in memory to store those parameters.  
The only solution (besides increasing the memory of your computer) is reducing the size of your network. A few pointers on this: Firstly, 3-D CNNs require much more space than 2-D ones. One idea could be to shift to a 2-D one. Other than that, the layers with the most parameters are the FC ones. These are sometimes redundant. I'd suggest reducing the size of those first.