What does it mean when train and validation loss diverge from epoch 1?

Possible explanations

1. Coding error
2. Overfitting due to differences in the training / validation data
3. Skewed classes (and differences in the training / validation data)

Things I would try

1. Swapping the training and the validation set. Does the problem still occur?
2. Plot the curves in more detail for the first ~10 epochs (e.g. directly after initialization; each few training iterations, not only per epoch). Do you still start at > 75%? Then your classes might be skewed and you might also want to check if your training-validation split is stratified.

Code

1. This is useless: np.concatenate(X_train)
2. Make your code as readable as possible when you post it here. This includes removing lines which are commented out.

This looks suspicious for a coding error to me:

if test:
    X_test.append(input)
    y_test.append(output)
else:
    #if((output == 0 and np.average(y_train) > 0.5) or output == 1):
    X_train.append(input)
    y_train.append(output)

use sklearn.model_selection.train_test_split instead. Do all transformations to the data before, then make the split with this method.