标签：Term Short tensor train Long plt test input size

# Define LSTM 
class Lstm(nn.Module):
    
    def __init__(self, input_size, hidden_size=2, output_size=1, num_layers=1):
        super().__init__()
 
        self.layer1 = nn.LSTM(input_size, hidden_size, num_layers) 
        self.layer2 = nn.Linear(hidden_size, output_size)
 
    def forward(self, _x):
        x, _ = self.layer1(_x) 
        s, b, h = x.shape 
        
        x = x.view(s*b, h)
        x = self.layer2(x)
        x = x.view(s, b, -1)
        return x

# Generate data
n = 100
t = np.linspace(0,10.0*np.pi,n)
X = np.sin(t) 
X = X.astype('float32')
# Set window of past points for LSTM model
input_N = 5
output_N = 1
batch = 5
# Split into train/test data
last = int(n/2.5)
Xtrain = X[:-last]

Xtest = X[-last-input_N:]

# Store window number of points as a sequence
xin = []
next_X = []
for i in range(input_N,len(Xtrain)):
    xin.append(Xtrain[i-input_N:i])
    next_X.append(Xtrain[i])

# Reshape data to format for LSTM
xin, next_X = np.array(xin), np.array(next_X)
xin = xin.reshape(-1,1,input_N)


train_x = torch.from_numpy(xin)
train_y = torch.from_numpy(next_X)

train_x_tensor = train_x.reshape(-1,batch,input_N) # set batch size to 5
train_y_tensor = train_y.reshape(-1,batch,output_N) # set batch size to 5


model = Lstm(input_N, 5, output_N,1) # 5 hidden units
loss_function = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)

max_epochs = 1000
for epoch in range(max_epochs):
	output = model(train_x_tensor)
	loss = loss_function(output, train_y_tensor)
	
	loss.backward()
	optimizer.step()
	optimizer.zero_grad()
	if loss.item() < 1e-4:
		print('Epoch [{}/{}], Loss: {:.5f}'.format(epoch+1, max_epochs, loss.item()))
		print("The loss value is reached")
		break
	elif (epoch+1) % 100 == 0:
		print('Epoch: [{}/{}], Loss:{:.5f}'.format(epoch+1, max_epochs, loss.item()))
        
    # prediction on training dataset
predictive_y_for_training = model(train_x_tensor)
predictive_y_for_training = predictive_y_for_training.view(-1, 1).data.numpy()

# ----------------- plot -------------------
plt.figure()
plt.plot(train_y, label='Actual')
plt.plot(predictive_y_for_training, 'm--', label='Lstm')
plt.title('Train')
plt.legend(loc=1)


x_test_in = []
test_X =[]
for i in range(input_N,len(Xtest)):
    x_test_in.append(Xtrain[i-input_N:i])
    test_X.append(Xtrain[i])
    
x_test_in = np.array(x_test_in) 
test_x_tensor = x_test_in.reshape(-1, batch, input_N) # set batch size to 5
test_x_tensor = torch.from_numpy(test_x_tensor)
 
predictive_y_for_testing = model(test_x_tensor)
predictive_y_for_testing = predictive_y_for_testing.view(-1, 1).data.numpy()

plt.figure()
plt.plot(test_X,label = 'Actual')
plt.plot(predictive_y_for_testing,'r-.',label='Lstm')
plt.title('Test')
plt.legend(loc=0)
plt.show()

标签：Term,Short,tensor,train,Long,plt,test,input,size
来源： https://www.cnblogs.com/conpi/p/16114080.html

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。