解释这段代码 model = Sequential() model.add(LSTM(25, input_shape=(1, look_back))) model.add(Dropout(0.1)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(X, y, epochs=epochs, batch_size=50, validation_data=(testX, testY), verbose=1)
时间: 2023-06-19 14:07:54 浏览: 69
这段代码是使用Keras建立了一个LSTM模型,并进行了训练和验证。逐行解释如下:
1. `model = Sequential()`:建立一个序列模型,即按照顺序把不同的层堆叠起来。
2. `model.add(LSTM(25, input_shape=(1, look_back)))`:添加一个LSTM层,其中25是LSTM层的神经元数量,input_shape是输入数据的形状,1表示时间步长,look_back表示输入数据的特征数量。
3. `model.add(Dropout(0.1))`:添加一个Dropout层,以减少过拟合。
4. `model.add(Dense(1))`:添加一个全连接层,输出维度为1。
5. `model.compile(loss='mean_squared_error', optimizer='adam')`:编译模型,设置损失函数为均方误差,优化器为Adam。
6. `history = model.fit(X, y, epochs=epochs, batch_size=50, validation_data=(testX, testY), verbose=1)`:训练模型,其中X和y分别表示训练数据和目标数据,epochs表示迭代次数,batch_size表示每个批次的样本数量,validation_data表示验证数据,verbose表示是否显示训练过程。训练完成后,将训练误差和验证误差保存在history变量中。
相关问题
model = Sequential() model.add(LSTM(64, activation='relu', input_shape=(look_back, 6))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse')
这段代码是使用Keras搭建一个LSTM模型,其中包括一个LSTM层和一个全连接层。LSTM层有64个神经元,使用relu作为激活函数,输入形状为(look_back,6),即输入的数据是一个look_back长度,每个时间步有6个特征。全连接层只有一个神经元,用于输出模型的预测结果。模型使用Adam优化器和均方误差作为损失函数进行训练。
将冒号后面的代码改写成一个nn.module类:import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, LSTM data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])
以下是将代码改写成一个nn.module类:
```python
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, LSTM
class LSTMModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers, look_back):
super().__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.look_back = look_back
self.lstm1 = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.lstm2 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
x = x.view(-1, self.look_back, 1)
out, _ = self.lstm1(x, (h0, c0))
out, _ = self.lstm2(out, (h0, c0))
out = self.fc(out[:, -1, :])
return out
data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030')
df = data1.fillna(method='ffill')
data = df.values.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train, test = data[0:train_size, :], data[train_size:len(data), :]
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
look_back = 30
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTMModel(input_size=1, hidden_size=50, output_size=1, num_layers=2, look_back=look_back).to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
num_epochs = 6
for epoch in range(num_epochs):
for i, (inputs, labels) in enumerate(zip(trainX, trainY)):
inputs = torch.from_numpy(inputs).float().to(device)
labels = torch.from_numpy(labels).float().to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(trainX)}], Loss: {loss.item():.4f}')
model.eval()
trainPredict = scaler.inverse_transform(model(torch.from_numpy(trainX).float().to(device)).detach().cpu().numpy())
testPredict = scaler.inverse_transform(model(torch.from_numpy(testX).float().to(device)).detach().cpu().numpy())
trainY = scaler.inverse_transform([trainY])
testY = scaler.inverse_transform([testY])
```
这个类接受5个参数,分别是:
- input_size: LSTM的输入维度,对于这个例子来说,input_size=1
- hidden_size: LSTM的隐藏层维度
- output_size: LSTM的输出维度,对于这个例子来说,output_size=1
- num_layers: LSTM的层数
- look_back: 每个样本的序列长度,对于这个例子来说,look_back=30