I need to do some preprocessing before feeding the input to my network. I am working on my data iterator and making some functions to make the data ready. My train set is a large file and each line has following scheme. Each line is semicolon seperated which could also be empty. Each split then contains different events which are como seperated and finaly each observation has 5 singnals which are colon seperated.
1:1560629595635:183.94:z1:Happy,2:1560629505635:100:z1:Sad;5:1561929595635:1:z1:Happy,13:1561629595635:12:j1:Sad;50:15616295956351:10:f1:Sad
My objective is to split each line in following order:
1. ';' Split
2. ',' Split
3. ':' Split
*Note: length after the last split = 5
In following function that I wrote, if I specify indx for the function it works fine for only one fragment output of ';' split.
def __semicolon_coma_colon_split(line, table, indx):
sources = tf.strings.split(line, ';')
pairs = tf.strings.split(sources[indx], ',')
items = tf.strings.split(pairs, ':').to_tensor()
return (tf.strings.to_number(items[:, 0], out_type=tf.int32),
tf.strings.to_number(items[:, 1], out_type=tf.int64),
tf.strings.to_number(items[:, 2], out_type=tf.float32),
tf.cast(table.lookup(items[:, 3]), tf.int32),
tf.cast(table.lookup(items[:, 4]), tf.int32))
And the current output shape is
(<tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>)
However, I need to run this cuncurrently on all the splits after ';' and the expected output tensor would look like a tuple of tuples that each contains 5 tensors;
((<tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>),
(<tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>, <tf.Tensor: shape=(2,)>),
(<tf.Tensor: shape=(1,)>, <tf.Tensor: shape=(1,)>, <tf.Tensor: shape=(1,)>, <tf.Tensor: shape=(1,)>, <tf.Tensor: shape=(1,)>))