def compareFixed():
t = Tasks()
x_test, y_test = t.sequence_type_1(100)
add_params, mul_params = torch.load('program_memory/add.pt'), torch.load(
'program_memory/mul.pt')
hnm = HNM(10, 20, add_params, mul_params)
hnm.load_state_dict(torch.load("learned_params/hnm_arch_2.pt"))
ntm = NTM(10, 20)
ntm.load_state_dict(torch.load("learned_params/ntm.pt"))
lstm = LSTM(14, 256, 325, 1)
lstm.load_state_dict(torch.load("learned_params/lstm.pt"))
hnm_diff, lstm_diff, ntm_diff = 0, 0, 0
for i in range(len(x_test)):
hnm_out = hnm.recurrent_forward(x_test[i:i + 1])
ntm_out = ntm.recurrent_forward(x_test[i:i + 1])
lstm_out = lstm.recurrent_forward(x_test[i:i + 1])
answer = np.argmax(y_test[i:i + 1].detach().numpy())
hnm_diff += abs(answer - np.argmax(hnm_out.detach().numpy()))
ntm_diff += abs(answer - np.argmax(ntm_out.detach().numpy()))
lstm_diff += abs(answer - np.argmax(lstm_out.detach().numpy()))
print(hnm_diff / len(y_test), ntm_diff / len(y_test),
lstm_diff / len(y_test))
def trainNTM():
t = Tasks()
x_train, y_train = t.sequence_type_1(2000)
ntm = NTM(10, 20)
ntm.train(x_train, y_train, 1, maxEpoch=25, learning_rate=0.0006)
def trainHarvard():
t1 = Tasks()
x_train, y_train = t1.sequence_type_1(2000)
add_params, mul_params = torch.load('program_memory/add.pt'), torch.load(
'program_memory/mul.pt')
hnm = HNM(10, 20, add_params, mul_params)
hnm.train(x_train, y_train, 1)
def trainLSTM():
t = Tasks()
x_train, y_train = t.sequence_type_1(2000)
lstm = LSTM(14, 256, 325, 1)
lstm.train(x_train, y_train, maxEpoch=25, learning_rate=0.0003)
