Tensorflow dynamic RNN (LSTM): how to format input?

Question

I have been given some data of this format and the following details:

person1, day1, feature1, feature2, ..., featureN, label
person1, day2, feature1, feature2, ..., featureN, label
...
person1, dayN, feature1, feature2, ..., featureN, label
person2, day1, feature1, feature2, ..., featureN, label
person2, day2, feature1, feature2, ..., featureN, label
...
person2, dayN, feature1, feature2, ..., featureN, label
...

• there is always the same number of features but each feature might be a 0 representing nothing
• there is a varying amount of days available for each person, e.g. person1 has 20 days of data, person2 has 50

The goal is to predict the label of the person the following day, so the label for dayN+1, either on a per-person basis, or overall (per-person makes more sense to me). I can freely reformat the data (it is not large). Based on the above after some reading I thought a dynamic RNN (LSTM) could work best:

• recurrent neural network: because the next day relies on the previous day
• lstm: because the model builds up with each day
• dynamic: because not all features are present each day

If it does not make sense for the data I have, please stop me here. The question is then:

How to give/format this data for tensorflow/tflearn?

I have looked at this example using tflearn but I do not understand its input format so that I can 'mirror' it to mine. Similarly, have found this post on a very similar question yet it seems like the samples the poster has are not related between each-other as they are in mine. My experience with tensorflow is limited to its get started page.

Answer 1

dynamic: because not all features are present each day

You've got the wrong concept of dynamic here. Dynamic RNN in Tensorflow means the graph is dynamically created during execution, but the inputs are always the same size (0 as the lack of a feature should work ok).

Anyways, what you've got here are sequences of varying length (day1 ... day?) of feature vectors (feature1 ... featureN). First, you need a LSTM cell

cell = tf.contrib.rnn.LSTMcell(size)

so you can then create a dynamically unrolled rnn graph using tf.nn.dynamic_rnn. From the docs:

inputs: The RNN inputs.

If time_major == False (default), this must be a Tensor of shape: [batch_size, max_time, ...], or a nested tuple of such elements.

where max_time refers to the input sequence length. Because we're using dynamic_rnn, the sequence length doesn't need to be defined during compile time, so your input placeholder could be:

x = tf.placeholder(tf.float32, shape=(batch_size, None, N))

Which is then fed into the rnn like

outputs, state = tf.nn.dynamic_rnn(cell, x)

Meaning your input data should have the shape (batch_size, seq_length, N). If examples in one batch have varying length, you should pad them with 0-vectors to the max length and pass the appropriate sequence_length parameter to dynamic_rnn

Obviously I've skipped a lot of details, so to fully understand RNNs you should probably read one of the many excellent RNN tutorials, like this one for example.