Understanding LSTM in Tensorflow

Long Short Term Memory(LSTM) are the most common types of Recurrent Neural Networks used these days.They are mostly used with sequential data.An in depth look at LSTMs can be found in this incredible blog post. As the title suggests,the main aim of this blogpost is to make the reader comfortable with the implementation details of basic LSTM network in tensorflow. For fulfilling this aim we will take MNIST as our dataset. The MNIST dataset consists of images of handwritten digits and their corresponding labels.We can download and read the data in tensorflow with the help of following in built functionality- Let us discuss the shape with respect to training data of MNIST dataset.Shapes of all three splits are identical. The training set consists of 55000 images of 28 pixels X 28 pixels each.These 784(28X28) pixel values are flattened in form of a single vector of dimensionality 784.The collection of all such 55000 pixel vectors(one for each image) is stored in form of a numpy array of shape (55000,784) and is referred to as mnist.train.images.