Python 我的LSTM代码给出了平面预测和don'；I don’我不知道出了什么问题，有人能帮忙看看吗？
这是代码。我想类Python 我的LSTM代码给出了平面预测和don'；I don’我不知道出了什么问题，有人能帮忙看看吗？,python,machine-learning,lstm,pytorch,rnn,Python,Machine Learning,Lstm,Pytorch,Rnn,这是代码。我想类mylstm有问题，但我找不到它。。。输入很简单，只有7列数据 我试着把所有的张量都打印出来，但没发现哪里不对。谢谢你的帮助 class mylstm(nn.Module): def __init__(self, input_size, hidden_size, T, logger): super(mylstm, self).__init__() self.T = T self.input_size = input_siz
mylstmclass mylstm(nn.Module):
def __init__(self, input_size, hidden_size, T, logger):
super(mylstm, self).__init__()
self.T = T
self.input_size = input_size
self.hidden_size = hidden_size
self.logger = logger
self.lstm_layer = nn.LSTM(input_size = 7, hidden_size = hidden_size)
self.fc = nn.Linear(hidden_size, 1)
#self.fc.weight.data.normal_()
def forward(self, input_data):
hidden = self.init_hidden(input_data)
cell = self.init_hidden(input_data)
for t in range(self.T - 1):
if t < self.T - 1:
self.lstm_layer.flatten_parameters()
_, lstm_output = self.lstm_layer(input_data[:,t,:].unsqueeze(0), (hidden, cell))
hidden = lstm_output[0]
cell = lstm_output[1]
y_pred = self.fc(hidden[0])
return y_pred
def init_hidden(self, x):
return Variable(x.data.new(1, x.size(0), self.hidden_size).zero_())
# Train the model
class rnn:
def __init__(self, file_data, logger, input_size = 7, hidden_size = 64, T = 10,
learning_rate = 0.01, batch_size = 128, parallel = True, debug = False):
self.T = T
dat = pd.read_csv(file_data, nrows = 100 if debug else None)
self.logger = logger
self.logger.info("Shape of data: %s.\nMissing in data: %s.", dat.shape, dat.isnull().sum().sum())
self.X = dat.loc[:, [x for x in dat.columns.tolist()]].values
self.y = np.array(dat.rtm_spp)
self.batch_size = batch_size
self.lstm1 = mylstm(input_size = input_size,
hidden_size = hidden_size,
T = T, logger = logger)
if parallel:
self.lstm1 = nn.DataParallel(self.lstm1)
self.lstm1_optimizer = optim.Adam(params = filter(lambda p: p.requires_grad, self.lstm1.parameters()),
lr = learning_rate)
self.train_size = 20000
self.y = self.y - np.mean(self.y[:self.train_size]) # Question: why Adam requires data to be normalized?
self.logger.info("Training size: %d.", self.train_size)
def train(self, n_epochs = 10):
iter_per_epoch = int(np.ceil(self.train_size * 1. / self.batch_size))
logger.info("Iterations per epoch: %3.3f ~ %d.", self.train_size * 1. / self.batch_size, iter_per_epoch)
self.iter_losses = np.zeros(n_epochs * iter_per_epoch)
self.epoch_losses = np.zeros(n_epochs)
self.loss_func = nn.MSELoss()
n_iter = 0
learning_rate = 1.
for i in range(n_epochs):
perm_idx = np.random.permutation(self.train_size - self.T-1)
j = 0
while j < self.train_size:
batch_idx = perm_idx[j:(j + self.batch_size)]
X = np.zeros((len(batch_idx), self.T - 1, self.X.shape[1]))
#y_history = np.zeros((len(batch_idx), self.T - 1))
y_target = self.y[batch_idx + self.T]
for k in range(len(batch_idx)):
X[k, :, :] = self.X[batch_idx[k] : (batch_idx[k] + self.T - 1), :]
loss = self.train_iteration(X, y_target)
self.iter_losses[i * iter_per_epoch + j // self.batch_size] = loss
#if (j / self.batch_size) % 50 == 0:
j += self.batch_size
n_iter += 1
if n_iter % 10000 == 0 and n_iter > 0:
for param_group in self.lstm1_optimizer.param_groups:
param_group['lr'] = param_group['lr'] * 0.9
self.epoch_losses[i] = np.mean(self.iter_losses[range(i * iter_per_epoch, (i + 1) * iter_per_epoch)])
if i % 10 == 0:
self.logger.info("Epoch %d, loss: %3.3f.", i, self.epoch_losses[i])
y_train_pred = self.predict(on_train = True)
y_test_pred = self.predict(on_train = False)
def train_iteration(self, X,y_target):
self.lstm1_optimizer.zero_grad()
y_pred = self.lstm1(Variable(torch.from_numpy(X).type(torch.FloatTensor)))
y_true = Variable(torch.from_numpy(y_target).type(torch.FloatTensor))
y_true = y_true.view(y_true.shape[0],1)
y_pred=y_pred.squeeze(0)
print(y_pred)
loss = self.loss_func(y_pred, y_true)
loss.backward()
self.lstm1_optimizer.step()
return loss.data[0]
def predict(self, on_train = False):
if on_train:
y_pred = np.zeros(self.train_size - self.T +1)
else:
y_pred = np.zeros(self.X.shape[0] - self.train_size)
i = 0
while i < len(y_pred):
batch_idx = np.array(range(len(y_pred)))[i : (i + self.batch_size)]
X = np.zeros((len(batch_idx), self.T - 1, self.X.shape[1]))
#y_history = np.zeros((len(batch_idx), self.T - 1))
for j in range(len(batch_idx)):
if on_train:
X[j, :, :] = self.X[range(batch_idx[j], batch_idx[j] + self.T - 1), :]
else:
X[j, :, :] = self.X[range(batch_idx[j] + self.train_size - self.T, batch_idx[j] + self.train_size - 1), :]
input_data = Variable(torch.from_numpy(X).type(torch.FloatTensor))
# print(self.lstm1(torch.randn(128,9,7)))
#print(self.lstm1(X).data.numpy())
y_pred[i:(i + self.batch_size)] = self.lstm1(input_data).data.numpy()[:,0]
i += self.batch_size
return y_pred
model = rnn(file_data = 'L.csv', logger = logger, parallel = False,
learning_rate = .001)
model.train(n_epochs = 1000)
y_pred = model.predict()