def main(nn_type, data_type):
print(" Starting... ")
filename_train = 'qa1_single-supporting-fact_train.txt'
filename_test = 'qa1_single-supporting-fact_test.txt'
directory = 'data/babi_tasks/tasks_1-20_v1-2/en/'
num_epochs = 500
#processor = Preprocessor(directory, filename_train, filename_test, data_type)
#X_train, y_train, mask_train, X_test, y_test, mask_test, input_size, max_seq_len, idx2word = processor.extract_data()
#wProc = WikiProcessor('C:/Users/Dan/Desktop/Crore/6.864/Project/Data/wiki_qa/')
#wProc.process()
#proc = CNNProcessor()
# proc.process()
if nn_type == "lstm":
proc = BabiProcessor(data_type)
X_train, y_train, mask_train, X_test, y_test, mask_test, input_size, max_seq_len, idx2word = proc.process()
lstm = LSTM(X_train, y_train, mask_train, X_test, y_test, mask_test, idx2word)
network, l_mask, l_in = lstm.build_model(input_size, max_seq_len)
lstm.optimize(network, l_mask, l_in)
elif nn_type == "mem_net" and data_type == "babi":
mn = MemNet()
mn.run('babi')
elif nn_type == "mem_net" and data_type == "wiki_qa":
mn = MemNet()
mn.run(data_type)
elif nn_type == "mem_net" and data_type == "cnn":
mn = MemNet()
mn.run('cnn_qa')
elif nn_type == "dynam_net":
proc = BabiProcessor(data_type, "dynam_net")
X_train, Q_train, Y_train, mask_train, X_test, Q_test, Y_test, mask_test, input_size, max_seqlen, idx2word, max_queslen = proc.process()
dn = DynamicMemNet(X_train, Q_train, Y_train, mask_train, X_test, Q_test, Y_test, mask_test, input_size, max_seqlen, idx2word, max_queslen)
dn.build()
dn.train()
elif nn_type == "dynam_net_theano":
#num_fact_hidden_units, number_classes, number_fact_embeddings, dimension_fact_embeddings, num_episode_hidden_units
dmn_t = DMN_full_babi()
dmn_t.train()
print("Finished DMN Theano")
