implement

#RNN
import torch
import torchvision
from torch import nn
import matplotlib.pyplot as plt
import torchvision.transforms as transforms

'''
Data preprocessing
'''

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class RnnModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, layer_num):
        super(RnnModel, self).__init__()
        self.hidden_size = hidden_size
        self.layer_num = layer_num
        #batch_first – If True, then the input and output tensors are provided as (batch, seq, feature).
        self.rnn = nn.RNN(input_size, hidden_size, layer_num, batch_first=True)   
        self.full_connect = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        batch_size = x.size(0)
        # Initializing hidden state for the first input
        hidden = torch.zeros(self.layer_num, batch_size, self.hidden_size)
        # hidden_new = F(input_new,hidden)
        out, hidden = self.rnn(x, hidden)
        
        out = self.full_connect(out)
        return out, hidden
        
model = RnnModel(input_size=, output_size=, hidden_size =, layer_num = )


‘’‘
Training model
’‘’
epoch = 1000
lr=0.01
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)   
     
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