loss/val_loss decrease but acc/val_acc are consistent
In your code:

model.compile(optimizer = 'adam', loss = 'mean_absolute_error', metrics=['accuracy'])


You are using accuracy as a metric, metrics=['accuracy']. This does not work when you are doing regression (which you are doing). Regression is when you have continuous (floats) labels.

So instead your code should look like:

model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics=['mean_absolute_error'])


Then when you train you should look for both loss and your metric to be decreasing during training. If they are, then your network is behaving normally.