Question: for my input in the feed forward neural network that I have implemented in Keras, I just wanted to check that my understanding is correct. 

```
[[ 25.26000023  26.37000084  24.67000008  23.30999947]
[ 26.37000084  24.67000008  23.30999947  21.36000061]
[ 24.67000008  23.30999947  21.36000061  19.77000046]...]

```

So in the data above it is a time window of 4 inputs in an array. My input layer is

```
model.add(Dense(4, input_dim=4, activation='sigmoid')) 

model.fit(trainX, trainY, nb_epoch=10000,verbose=2,batch_size=4)

```

and batch\_size is 4, in theory when I call the fit function will the function go over all these inputs in each nb\_epoch? and does the batch\_size need to be 4 in order for this time window to work? 

Thanks John

Answer: 
You can store it as String. The issue you are facing - I assume - is when you want to return that JSON as structure within the Solr JSON response. By default Solr does not know that you want to **embed** the content rather than **present** it.

To tell it otherwise, try using a [**[json]** document transformer](https://lucene.apache.org/solr/guide/6_6/transforming-result-documents.html#TransformingResultDocuments-_json_xml_) in your **fl** parameter.
`String` type stores a word/sentence as an exact string without performing tokenization etc. Commonly useful for storing exact matches, e.g, for facetting.

`Text` typically performs `tokenization`, and secondary processing (such as lower-casing etc.). More of use when you want to match a word which is part of that a sentence.

With the text you can use tokenizers and filter made available by `solr` in order to create more useful token in order have proper search.

I would suggest to store it as the `text`.

It would be helpful for you to `tokenize` the data and can be helpful in the search as well.

Also as its a `Json`, it would be bigger in size and may grow bigger in size, so again can be helpful to be stored as `text` instead of `string`.