+ "abcddefabcddefabcddefabcddefabcddefabcddefabcddefabcddefabcddefabcddefabcddefabcddefabcddefabcddefabcddefabcddef"""""
#input_text = "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, ipsum sed diam"
#with os.open("lstm_training_data/vanishing_vocables_de.txt", "r"):
#with os.open("lstm_training_data/loremipsum.txt", "r"):
with os.fdopen(os.open("lstm_training_data/schachnovelle.txt", os.O_RDONLY)) as f:
input_text = f.read()
# initialize data store object
data_store = DataStore(
input_data_set=set(input_text),
output_data_set=set(input_text),
input_data=input_text)
# set data_store configuration
data_store.configure({
"sequence_length": config["time_steps"]
})
# samples is a generator object for sequences of length data_store.config['time_steps']
samples = data_store.samples()
# Uncomment to use random samples for training. The amount specifies the number of sequences
# to be trained during one iteration. After each iteration the sequences will be shuffled again.
#samples = data_store.random_samples(amount=20)
lstm = LSTMNetwork()
# apply configuration to lstm config
lstm.configure(config)
# tell the network the function to use for printing status information while training
lstm.get_status = util.get_status_function(data_store, lstm, config["status_frequency"])
