写一个卷积神经网络与RNN结合做多元时间序列预测的模型代码

：

import numpy as np
from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, LSTM
from keras.models import Sequential

# 定义模型
model = Sequential()

# 加入卷积层
model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(None, 1)))

# 加入池化层
model.add(MaxPooling1D(pool_size=2))

# 加入LSTM层
model.add(LSTM(64, activation='relu'))

# 加入全连接层
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))

# 编译模型
model.compile(loss='mse', optimizer='adam')

相关问题

写一段rnn模型对时间序列进行预测的代码

以下是一个简单的RNN模型，用于预测一个时间序列的未来值：

import numpy as np
import tensorflow as tf

# 生成时间序列数据
def generate_time_series(batch_size, n_steps):
    freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)
    time = np.linspace(0, 1, n_steps)
    series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10))  # wave 1
    series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20))  # wave 2
    series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5)    # noise
    return series[..., np.newaxis].astype(np.float32)

n_steps = 50
n_inputs = 1
n_neurons = 100
n_outputs = 1

# 定义模型
X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_outputs])
basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)
outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32)
logits = tf.layers.dense(states, n_outputs)
loss = tf.reduce_mean(tf.square(logits - y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
training_op = optimizer.minimize(loss)

# 训练模型
n_iterations = 1000
batch_size = 50
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for iteration in range(n_iterations):
        X_batch, y_batch = generate_time_series(batch_size, n_steps)
        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
        if iteration % 100 == 0:
            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})
            print(iteration, "\tMSE:", mse)

    # 使用训练好的模型进行预测
    X_new, _ = generate_time_series(1, n_steps)
    y_pred = sess.run(logits, feed_dict={X: X_new.reshape(1, n_steps, n_inputs)})

该模型使用了一个基本的RNN单元，并在最后加上了一个全连接层，将最后一个时间步的输出转换成一个预测值。在训练过程中，我们使用Adam优化器来最小化均方误差。在训练完后，我们使用训练好的模型来预测一个新的时间序列。

请为我写一个使用pytorch中的RNN对于时间序列数据进行预测的代码

import torch
import torch.nn as nnclass RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNN, self).__init__()

self.hidden_size = hidden_size

self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
self.i2o = nn.Linear(input_size + hidden_size, output_size)
self.softmax = nn.LogSoftmax(dim=1)

def forward(self, input, hidden):
combined = torch.cat((input, hidden), 1)
hidden = self.i2h(combined)
output = self.i2o(combined)
output = self.softmax(output)
return output, hidden def initHidden(self):
return torch.zeros(1, self.hidden_size)# 定义属性
n_hidden = 128
rnn = RNN(n_inputs, n_hidden, n_outputs)# 预测函数
def predict(input_line, n_predictions=3):
output, hidden = rnn(Variable(line_tensor), hidden)

topv, topi = output.data.topk(n_predictions, 1, True)
predictions = [] for i in range(n_predictions):
value = topv[0][i]
category_index = topi[0][i]
predictions.append([value, category_index])

return predictions