params['normalise_data'] = 4 # adding more values to params, what does this value signify? #
params['reset_state']=res # adding more values to params, what does this value signify? #
params['seq_length']=seq # adding more values to params, what does this value signify? #
params["reload_data"] = 0 # adding more values to params, what does this value signify? #
params = config.update_params(params) # New param values are updated#
#params["model"] = "kfl_QRf"
if params["model"] == "lstm":
Model = lstm(params=params)
elif params["model"] == "kfl_QRf":
Model = kfl_QRf(params=params)
elif params["model"] == "kfl_Rf":
Model = kfl_Rf(params=params)
elif params["model"] == "kfl_QRFf":
Model = kfl_QRFf(params=params)
elif params["model"] == "kfl_K":
Model = kfl_K(params=params)
params["rn_id"]="dobuleloss081500_nrm4_seq%i_res%i_keep%f_lr%f"%(seq,res,rnn_keep_prob,params["lr"]) # adding more values to params, what does this value signify? #
params=config.update_params(params) # New param values are updated#
# (params, X_train, Y_train, F_list_train, G_list_train, S_Train_list, R_L_Train_list,
# X_test, Y_test, F_list_test, G_list_test, S_Test_list, R_L_Test_list) = \
# dut.prepare_training_set(params) # This is where the observation set will be given#
show_every = 1
#(index_train_list, S_Train_list) = dut.get_seq_indexes(params, S_Train_list)
#(index_test_list, S_Test_list) = dut.get_seq_indexes(params, S_Test_list)
batch_size = params['batch_size']
#n_train_batches = len(index_train_list)
#n_train_batches /= batch_size
#n_test_batches = len(index_test_list)
#n_test_batches /= batch_size
#params['training_size'] = len(X_train) * params['seq_length']
params['Qn_hidden'] = h
params['Rn_hidden'] = h
params['Kn_hidden'] = h
if params["model"] == "lstm":
tracker = lstm(params=params)
elif params["model"] == "kfl_QRf":
tracker = kfl_QRf(params=params)
elif params["model"] == "kfl_Rf":
tracker = kfl_Rf(params=params)
elif params["model"] == "kfl_f":
tracker = kfl_f(params=params)
elif params["model"] == "kfl_QRFf":
tracker = kfl_QRFf(params=params)
elif params["model"] == "kfl_K":
tracker = kfl_K(params=params)
params = config.update_params(params)
show_every = 1
ut.start_log(params)
ut.log_write("Model training started", params)
median_result_lst, mean_result_lst = train(tracker, params)
# if params["data_mode"]=="David":
# np.savetxt('/home/coskun/PycharmProjects/poseft/trials/res/'+params["sequence"]+'/'+msg,mean_result_lst)
# else:
# np.savetxt('/home/coskun/PycharmProjects/poseft/trials/res/klstm/'+params["sequence"]+'/'+msg,median_result_lst)
# np.min(median_result_lst,axis=1)
# print median_result_lst
# print mean_result_lst
# print 'Min...'