  Deep learning models are trained and deployed in multiple domains. Increasing
usage of deep learning models alarms the usage of memory consumed while
computation by deep learning models. Existing approaches for reducing memory
consumption like model compression, hardware changes are specific. We propose a
generic analysis of memory consumption while training deep learning models in
comparison with hyperparameters used for training. Hyperparameters which
includes the learning rate, batchsize, number of hidden layers and depth of
layers decide the model performance, accuracy of the model. We assume the
optimizers and type of hidden layers as a known values. The change in
hyperparamaters and the number of hidden layers are the variables considered in
this proposed approach. For better understanding of the computation cost, this
proposed analysis studies the change in memory consumption with respect to
hyperparameters as main focus. This results in general analysis of memory
consumption changes during training when set of hyperparameters are altered.
