def trainModelPipe(self, loss_type):
encoder = EncoderRNN(input_size=self.n_features,
hidden_size=self.hidden_size,
num_grulstm_layers=self.num_grulstm_layers,
batch_size=self.batch_size).to(self.device)
decoder = DecoderRNN(input_size=1,
hidden_size=self.hidden_size,
num_grulstm_layers=self.num_grulstm_layers,
fc_units=16,
output_size=1).to(self.device)
net_gru = Net_GRU(encoder, decoder, self.N_output,
self.device).to(self.device)
self.train_model(net_gru,
batch_size=self.batch_size,
loss_type=loss_type,
learning_rate=0.001,
epochs=500,
gamma=self.gamma,
print_every=50,
eval_every=50,
verbose=1,
alpha=self.alpha)
return net_gru
np.array(losses_tdi).mean())
print(f"batch_size: {batch_size}")
## TODO run with dtw implementation
encoder = EncoderRNN(input_size=3,
hidden_size=128,
num_grulstm_layers=2,
batch_size=batch_size).to(device)
decoder = DecoderRNN(input_size=1,
hidden_size=128,
num_grulstm_layers=2,
fc_units=16,
output_size=1).to(device)
net_gru_dtw = Net_GRU(encoder, decoder, N_output, device).to(device)
train_model(net_gru_dtw,
batch_size=batch_size,
loss_type='dtw',
learning_rate=0.001,
epochs=500,
gamma=gamma,
print_every=50,
eval_every=50,
verbose=1,
alpha=alpha,
target_mean=target_log_mean,
target_std=target_log_std)
encoder = EncoderRNN(input_size=3,
print(' Eval mse= ',
np.array(losses_mse).mean(), ' dtw= ',
np.array(losses_dtw).mean(), ' tdi= ',
np.array(losses_tdi).mean())
encoder = EncoderRNN(input_size=1,
hidden_size=128,
num_grulstm_layers=1,
batch_size=batch_size).to(device)
decoder = DecoderRNN(input_size=1,
hidden_size=128,
num_grulstm_layers=1,
fc_units=16,
output_size=1).to(device)
net_gru_dilate = Net_GRU(encoder, decoder, N_output, device).to(device)
train_model(net_gru_dilate,
loss_type='dilate',
learning_rate=0.001,
epochs=500,
gamma=gamma,
print_every=50,
eval_every=50,
verbose=1)
encoder = EncoderRNN(input_size=1,
hidden_size=128,
num_grulstm_layers=1,
batch_size=batch_size).to(device)
decoder = DecoderRNN(input_size=1,
hidden_size=128,