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model.py
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model.py
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from modules import init_hidden, Encoder, Decoder
import numpy as np
import torch
import math
from sklearn.utils import shuffle
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def toTorch(data):
return torch.from_numpy(data).float().to(device)
class DA_RNN:
def __init__(self, X_dim, Y_dim, encoder_hidden_size=64, decoder_hidden_size=64,
linear_dropout=0, T=10, learning_rate=1e-5, batch_size=128, decay_rate=0.95):
self.T = T
self.decay_rate = decay_rate
self.batch_size = batch_size
self.X_dim = X_dim
self.Y_dim = Y_dim
self.encoder = Encoder(X_dim, encoder_hidden_size, T, linear_dropout).to(device)
self.decoder = Decoder(encoder_hidden_size, decoder_hidden_size, T, linear_dropout, Y_dim).to(device)
self.encoder_optim = torch.optim.Adam(params=self.encoder.parameters(), lr=learning_rate)
self.decoder_optim = torch.optim.Adam(params=self.decoder.parameters(), lr=learning_rate)
self.loss_func = torch.nn.MSELoss()
def adjust_learning_rate(self):
for enc_params, dec_params in zip(self.encoder_optim.param_groups, self.decoder_optim.param_groups):
enc_params['lr'] = enc_params['lr'] * self.decay_rate
dec_params['lr'] = dec_params['lr'] * self.decay_rate
def ToTrainingBatches(self, X, Y, shuffle_slice=True):
X_batches = []
Y_batches = []
N = X.shape[0]
batch_num = math.ceil((N-self.T)/self.batch_size)
i = self.T-1
for b in range(batch_num):
# number of output = N - T + 1
# N is length, i is an index
_batch_size = self.batch_size if N-i >= self.batch_size else N-i
X_batch = np.empty((_batch_size, self.T, self.X_dim))
Y_batch = np.empty((_batch_size, self.Y_dim))
for b_idx in range(_batch_size):
# print(N, i, i-self.T+1, i+1)
# print(X[i-self.T+1:i+1].shape)
X_batch[b_idx, :, :] = X[i-self.T+1:i+1]
Y_batch[b_idx, :] = Y[i]
i += 1
X_batches.append(X_batch)
Y_batches.append(Y_batch)
# TODO: zero padding
# print(X.shape[0], np.sum([_.shape[0] for _ in X_batches]))
if shuffle_slice:
return shuffle(X_batches, Y_batches)
else:
return X_batches, Y_batches
def ToTestingBatch(self, X):
N = X.shape[0]
X_batch = np.empty((N-self.T+1, self.T, self.X_dim))
i = self.T-1
b_idx = 0
while i < N:
X_batch[b_idx, :, :] = X[i-self.T+1:i+1]
i += 1
b_idx += 1
# TODO: zero padding
return X_batch
def train(self, X_train, Y_train, X_val, Y_val, epochs):
if len(Y_train.shape) == 1:
Y_train = Y_train[:, np.newaxis]
if len(Y_val.shape) == 1:
Y_val = Y_val[:, np.newaxis]
assert len(X_train) == len(Y_train)
assert len(X_val) == len(Y_val)
epoch_loss_hist = []
iter_loss_hist = []
N = X_train.shape[0]
for _e in range(epochs):
X_train_batches, Y_train_batches = self.ToTrainingBatches(X_train, Y_train)
for X_train_batch, Y_train_batch in zip(X_train_batches, Y_train_batches):
X_train_loss = self.train_iter(X_train_batch, Y_train_batch)
iter_loss_hist.append(np.mean(X_train_loss))
# decay learning rate
# if _e % 20 == 0:
# self.adjust_learning_rate()
epoch_loss_hist.append(iter_loss_hist[-len(X_train_batches):])
if _e % 2 == 0:
print("Epoch: {}\t".format(_e), end="")
Y_val_pred = self.predict(X_val, on_train=True)
Y_val_loss = self.loss_func(Y_val_pred, toTorch(Y_val[-(N-self.T+1):]))
print("train_loss: {:.4f} val_loss: {:.4f}".format(X_train_loss, Y_val_loss))
return epoch_loss_hist, iter_loss_hist
def train_iter(self, X, Y):
self.encoder.train(), self.decoder.train()
self.encoder_optim.zero_grad(), self.decoder_optim.zero_grad()
_, X_encoded = self.encoder(toTorch(X))
Y_pred = self.decoder(X_encoded)
loss = self.loss_func(Y_pred, toTorch(Y))
loss.backward()
self.encoder_optim.step()
self.decoder_optim.step()
return loss.item()
def predict(self, X, on_train=False):
self.encoder.eval(), self.decoder.eval()
X_batch = self.ToTestingBatch(X)
_, X_encoded = self.encoder(toTorch(X_batch))
Y_pred = self.decoder(X_encoded)
if on_train == False:
Y_pred = Y_pred.cpu().detach().numpy()
return Y_pred