Pooya

TensorFlow:

TensorFlow is an open source software library for numerical computation using data flow graphs in which nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. TensorFlow is Google Brain's second generation machine learning system, with a reference implementation released as open source software on November 9, 2015. It provides a Python API, as well as a less documented C/C++ API. TensorFlow can run on multiple CPUs and GPUs (with optional CUDA extensions for general-purpose computing on graphics processing units). It runs on 64-bit Linux or Mac OS X desktop or server systems, as well as on mobile computing platforms, including Android and Apple's iOS. TensorFlow computations are expressed as stateful dataflow graphs. Many teams at Google have migrated from DistBelief to TensorFlow for research and production uses.This library of algorithms originated from Google's need to instruct computer systems, known as neural networks, to learn and reason similarly to how humans do, so that new applications can be derived which are able to assume roles and functions previously reserved only for capable humans; the name TensorFlow itself derives from the operations in which such neural networks perform on multidimensional data arrays. These multidimensional arrays are referred to as "tensors" but this concept is not identical to the mathematical concept of tensors. The purpose is to train neural networks to detect and decipher patterns and correlations. There are some other libraries for numerical computations in python. One of these libraries is Theano, which is one of the strong python libraries for machine learning tasks. However, Theano's applicability in different GPU's is not experimentally verified yet, which was one of the major reasons for pushing us through TensorFlow.

Evaluation:

In our project we want to implement different architectures of neural networks with different complexities on a large dataset to spot the effect of complexity of neural networks on its accuracy on large datasets. There is an assumption in machine learning which says simple machine architectures don't learn from extra provided data. However, if we increase the complexity of machine then the performance would increase by the increase in amount of data. It is very important to consider that it is not applicable to train very complex neural networks on very large datasets, since it needs a lot of time. Therefore, it is very important to take advantage of some high performance computing techniques like using multiple GPUs/CPUs to split the dataset in different batches and load these batches to the designed complex neural network while it is training on some other data batches. In this projects we aim to measure the