代码语言：python

所属分类：人工智能

代码描述：python pytorch使用LSTM通过正弦函数值来预测余弦函数的值分布代码，我们取正弦函数的值作为LSTM的输入，来预测余弦函数的值。基于Pytorch来构建LSTM模型，采用1个输入神经元，1个输出神经元，16个隐藏神经元作为LSTM网络的构成参数，平均绝对误差（LMSE）作为损失误差，使用Adam优化算法来训练LSTM神经网络。

代码标签： python pytorch LSTM 正弦 函数 预测 余弦 函数 分布代

下面为部分代码预览，完整代码请点击下载或在bfwstudio webide中打开

#!/usr/local/python3/bin/python3
# -*- coding: utf-8 -*
import numpy as np
import torch
from torch import nn
import matplotlib.pyplot as plt

# Define LSTM Neural Networks
class LstmRNN(nn.Module):
    """
        Parameters：
        - input_size: feature size
        - hidden_size: number of hidden units
        - output_size: number of output
        - num_layers: layers of LSTM to stack
    """
    def __init__(self, input_size, hidden_size=1, output_size=1, num_layers=1):
        super().__init__()
 
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers) # utilize the LSTM model in torch.nn 
        self.forwardCalculation = nn.Linear(hidden_size, output_size)
 
    def forward(self, _x):
        x, _ = self.lstm(_x)  # _x is input, size (seq_len, batch, input_size)
        s, b, h = x.shape  # x is output, size (seq_len, batch, hidden_size)
        x = x.view(s*b, h)
        x = self.forwardCalculation(x)
        x = x.view(s, b, -1)
        return x

if __name__ == '__main__':
    # create database
    data_len = 200
    t = np.linspace(0, 12*np.pi, data_len)
    sin_t = np.sin(t)
    cos_t = np.cos(t)

    dataset = np.zeros((data_len, 2))
    dataset[:,0] = sin_t
    dataset[:,1] = cos_t
    dataset = dataset.astype('float32')

    # plot part of the original dataset
    plt.figure()
    plt.plot(t[0:60], dataset[0:60,0], label='sin(t)')
    plt.plot(t[0:60], dataset[0:60,1], label = 'cos(t)')
    plt.plot([2.5, 2.5], [-1.3, 0.55], 'r--', label='t = 2.5') # t = 2.5
    plt.plot([6.8, 6.8], [-1.3, 0.85], 'm--', label='t = 6.8') # t = 6.8
    plt.xlabel('t')
    plt.ylim(-1.2, 1.2)
    plt.ylabel('sin(t) and cos(t)')
    plt.legend(loc='upper right')

    # choose dataset for training and testing
    train_data_ratio = 0.5 # Choose 80% of the data for testing
    train_data_len = int(data_len*train_data_ratio)
    train_x = dataset[:train_data_len, 0]
    train_y = dataset[:train_data_len, 1]
    INPUT_FEATURES_NUM = 1
    OUTPUT_FEATURES_NUM = 1
    t_for_training = t[:train_data_len]

    # test_x = train_x
    # test_y = train_y
    test_x = dataset[train_data_len:, 0]
    test_y = dataset[train_data_len:, 1]
    t_for_testing = t[train_data_len:]

    # ----------------- train -------------------
    train_x_tensor = train_x.reshape(-1, 5, INPUT_FEATURES_NUM) # set batch size to 5
    train_y_tensor = train_y.reshape(-1, 5, OUTPUT_FEATURES_NUM) # set batch size to 5
 
    # transfer data to pytorch tensor
    train_x_tensor = torch.from_numpy(train_x_tensor)
    train_y_tensor = torch.from_numpy(train_y_tensor)
    # test_x_tensor = torch.from_numpy(test_x)
 
    lstm_model = LstmRNN(INPUT_FEATURES_NUM, 16, output_size=OUTPUT_FEATURES_NUM, num_layers=1) # 16 hidden units
    print(.........完整代码请登录后点击上方下载按钮下载查看

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