How to load pre-processed .txt data and use it for training convolutional networks? I have a lot of '.txt' (pre-processed) files related to C3D and I need to use this to train and test while using CNN model.

How does one go about this? 

Most of data in each .txt files are formatted like this:

{0.00001478 0.00000006 0.00000133 0.00000038 0.00000001 0.00000001 0.00000000 0.00000001 0.00000004 0.00000011 0.00000000 0.00000002 0.00095226 0.00005343 0.00000002 0.00000000 0.00000000 0.00000342 0.00000002 0.00000036 0.00000000 0.00000046 0.00000000 0.00000000 0.00001069 0.00000000 0.00007515 0.00000000 0.00000000 0.00000000 0.00000000 0.00000009 0.00000000 0.01169860 0.00000017 0.00000000 0.00000010 0.00000113 0.00000472 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000003 0.00000150 0.00001475 0.00000000 0.00000000 0.00000023 0.00000005 0.00000011 0.00000000 0.00000000 0.00001333 0.00000000 0.00000002 0.00000017 0.00000076 0.00000051 0.00000015 0.00000440 0.00000001 0.00000079 0.00000000 0.00000000 0.00000008 0.00000000 0.00000001 0.00000002 0.98503888 0.00000013 0.00000000 0.00000000 0.00208691 0.00000000 0.00000003 0.00000051 0.00000001 0.00001191 0.00000000 0.00000000 0.00000001 0.00000001 0.00000000 0.00000000 0.00000003 0.00000000 0.00000001 0.00000000 0.00000000 0.00000021 0.00000000 0.00000000 0.00000000 0.00000000 0.00000027 0.00000675}
I did not really feel clear on what you were trying to accomplish. But here is how you load the format you showed into a numpy array:

import numpy as np

# Read file from disk
with open('path/to/file.txt') as f:
    txt = f.read()

# Make a list of values in string format 
vals = txt.replace('{','').replace('}','').strip().split()

# Cast values to floats
vals = [np.float32(val) for val in vals]

# Store in numpy array
vals = np.array(vals)