dataset = np.reshape(dataset, (-1, 1))
时间: 2023-08-18 17:10:45 浏览: 121
这段代码将 "dataset" 数组进行了重塑,将其从原先的一维数组转换为二维数组,其中第一维的长度由原数组的长度自动计算得出,第二维的长度为 1。
具体来说,它使用了 NumPy 库的 ".reshape()" 方法,其中第一个参数为 "-1",表示将数组的第一维长度设为自动计算的值,第二个参数为 "1",表示将数组的第二维长度设为 1。这样做的目的通常是为了将数据转换为模型能够接受的输入形式。例如,对于一些深度学习模型,输入数据的维度需要满足一定的要求,如果数据维度不符合要求,就需要进行重塑操作。
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import numpy import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn from torch.utils.data import DataLoader, Dataset import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value print(scalar) dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): 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) train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') train_X = torch.from_numpy(train_X) train_Y = torch.from_numpy(train_Y) test_X = torch.from_numpy(test_X) class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): out, h = self.rnn(x) out = self.linear(out[0]) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()请适当修改代码,并写出预测值和真实值的代码
以下是适当修改后的代码以及预测值和真实值的代码:
```
import numpy as np
import matplotlib.pyplot as plt
import math
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
dataset = []
for data in np.arange(0, 3, .01):
data = math.sin(data * math.pi)
dataset.append(data)
dataset = np.array(dataset)
dataset = dataset.astype('float32')
max_value = np.max(dataset)
min_value = np.min(dataset)
scalar = max_value - min_value
print(scalar)
dataset = list(map(lambda x: x / scalar, dataset))
def create_dataset(dataset, look_back=3):
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)
train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))]
test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):]
train_X = train_X.reshape(-1, 1, 3).astype('float32')
train_Y = train_Y.reshape(-1, 1, 1).astype('float32')
test_X = test_X.reshape(-1, 1, 3).astype('float32')
train_X = torch.from_numpy(train_X)
train_Y = torch.from_numpy(train_Y)
test_X = torch.from_numpy(test_X)
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size=1, num_layer=2):
super(RNN, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.num_layer = num_layer
self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
self.linear = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, h = self.rnn(x)
out = self.linear(out[:, -1, :])
return out
net = RNN(3, 20)
criterion = nn.MSELoss(reduction='mean')
optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
train_loss = []
test_loss = []
for e in range(1000):
pred = net(train_X)
loss = criterion(pred, train_Y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (e + 1) % 100 == 0:
print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item()))
train_loss.append(loss.item())
plt.plot(train_loss, label='train_loss')
plt.legend()
plt.show()
# 预测值和真实值的代码
net.eval()
pred_test = net(test_X)
pred_test = pred_test.detach().numpy() * scalar
test_Y = test_Y.reshape(-1, 1) * scalar
plt.plot(pred_test, label='predict')
plt.plot(test_Y, label='true')
plt.legend()
plt.show()
```
dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) 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)) def create_dataset(dataset, look_back=3): 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)以这段代码为基础写预测正弦函数的RNN代码,绘图真实值和预测值
好的,以下是基于给出代码完成预测正弦函数的RNN代码:
```
import numpy as np
import math
import tensorflow as tf
import matplotlib.pyplot as plt
# 生成数据
dataset = []
for data in np.arange(0, 3, .01):
data = math.sin(data * math.pi)
dataset.append(data)
dataset = np.array(dataset)
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))
# 创建数据集
look_back = 5
trainX, trainY = create_dataset(dataset, look_back)
# 转换为张量
trainX = tf.convert_to_tensor(trainX)
trainY = tf.convert_to_tensor(trainY)
# 定义模型
model = tf.keras.Sequential([
tf.keras.layers.LSTM(32, input_shape=(look_back, 1)),
tf.keras.layers.Dense(1)
])
# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.MeanSquaredError())
# 训练模型
history = model.fit(trainX, trainY, epochs=100, batch_size=16, verbose=2)
# 预测结果
predicted = []
for i in range(len(dataset) - look_back):
x = np.array(dataset[i:i+look_back])
x = np.reshape(x, (1, look_back, 1))
y = model.predict(x)
predicted.append(y[0][0])
# 反归一化
predicted = np.array(predicted)
predicted = predicted * scalar
# 绘图
plt.plot(dataset[look_back:], label='True Data')
plt.plot(predicted, label='Predicted Data')
plt.legend()
plt.show()
```
在这个 RNN 模型中,我们使用 LSTM 层来学习时间序列数据的特征,然后使用全连接层来进行预测。训练过程中我们使用了 Adam 优化器和均方误差损失函数。最后,我们使用模型对测试集进行预测,并将预测结果和真实值绘制在同一张图上,以便进行比较。