fix bug and upload time series

Author: L1aoXingyu

chapter5_RNN/time_series/international-airline-passengers.csv renamed to chapter5_RNN/time_series/data.csv

@@ -1,4 +1,105 @@
 import pandas as pd
 import matplotlib.pyplot as plt
+import torch
+from torch import nn, optim
+from torch.autograd import Variable
+import numpy as np
 
-data_csv = pd.read_csv('')
+np.random.seed(2017)
+# load data
+data_csv = pd.read_csv('./data.csv', usecols=[1])
+
+# data preprocessing
+data_csv = data_csv.dropna()
+dataset = data_csv.values
+dataset = dataset.astype('float32')
+max_value = np.max(dataset)
+min_value = np.min(dataset)
+scalar = max_value - min_value
+dataset = list(map(lambda x: x / scalar, dataset))
+
+
+# create dataset
+def create_dataset(dataset, look_back=2):
+    dataX, dataY = [], []
+    for i in range(len(dataset) - look_back):
+        a = dataset[i:(i + look_back)]
+        dataX.append(a)
+        dataY.append(dataset[i + look_back])
+    return np.array(dataX), np.array(dataY)
+
+
+data_X, data_Y = create_dataset(dataset)
+
+# split train set and test set
+train_size = int(len(data_X) * 0.7)
+test_size = len(data_X) - train_size
+train_X = data_X[:train_size]
+train_Y = data_Y[:train_size]
+test_X = data_X[train_size:]
+test_Y = data_Y[train_size:]
+
+train_X = train_X.reshape(-1, 1, 2)
+train_Y = train_Y.reshape(-1, 1, 1)
+test_X = test_X.reshape(-1, 1, 2)
+
+train_x = torch.from_numpy(train_X)
+train_y = torch.from_numpy(train_Y)
+test_x = torch.from_numpy(test_X)
+
+
+# define network
+class lstm(nn.Module):
+    def __init__(self, input_size=2, hidden_size=4, output_size=1,
+                 num_layer=2):
+        super(lstm, self).__init__()
+        self.layer1 = nn.LSTM(input_size, hidden_size, num_layer)
+        self.layer2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x, _ = self.layer1(x)  # seq, batch, hidden
+        s, b, h = x.size()
+        x = x.view(s * b, h)
+        x = self.layer2(x)
+        x = x.view(s, b, -1)
+        return x
+
+
+model = lstm()
+if torch.cuda.is_available():
+    model = model.cuda()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=1e-2)
+
+# In[195]:
+
+total_epoch = 1000
+for epoch in range(total_epoch):
+    if torch.cuda.is_available():
+        train_x = train_x.cuda()
+        train_y = train_y.cuda()
+    var_x = Variable(train_x)
+    var_y = Variable(train_y)
+    out = model(var_x)
+    loss = criterion(out, var_y)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    if (epoch + 1) % 100 == 0:
+        print('epoch {}, loss is {}'.format(epoch + 1, loss.data[0]))
+
+torch.save(model.state_dict(), './lstm.pth')
+model = model.eval()
+
+data_X = data_X.reshape(-1, 1, 2)
+data_X = torch.from_numpy(data_X)
+var_data = Variable(data_X)
+predict = model(var_data)
+
+predict = predict.cpu().data.numpy()
+
+predict = predict.reshape(-1)
+
+plt.plot(predict, 'r')
+plt.plot(dataset, 'b')
+plt.show()