Let's imagine we want to perform handwriting recognition. From time to time, we get a new column of data. Is it the end of a letter? If yes, which one? Is it the end of a word? Is it punctuation? All these questions can be answered with a recurrent network.
For our test example, we will go back to our 10-digit dataset and use LSTMs instead of convolution layers.
We use similar hyperparameters:
import tensorflow as tf
from tensorflow.contrib import rnn
# rows of 28 pixels
n_input=28
# unrolled through 28 time steps (our images are (28,28))
time_steps=28
# hidden LSTM units
num_units=128
# learning rate for adam
learning_rate=0.001
n_classes=10
batch_size=128
n_epochs = 10
step = 100
Setting up training and testing data is almost similar to our CNN example, except for the way we reshape the images:
import os
import numpy as np
from sklearn.datasets import fetch_mldata...
