I’m trying to model converting from Keras to Pytorch.
I think the problem is to train model part code.
So, can anyone help this part for me.
I’m newb at Pytorch…
This is a Keras model configuration.
The objective of this model is many to one model for prediction.
The data ( [ a1 a2 a3 a4 a5] ) is fed into this model. And model predict a6.
    model = Sequential()
    model.add(LSTM(10, return_sequences=True, input_shape=(windowsize,1)))
    model.add(LSTM(10))
    model.add(Dense(outputsize))
    model.compile(loss='mse',optimizer='adam')

This is a pytorch code.
import numpy as np
import torch.nn as nn
import torch
from torch.autograd import Variable
from numpy import array
from matplotlib import pyplot
from pandas import Series
from pandas import DataFrame
from pandas import concat
from pandas import read_csv
from sklearn.preprocessing import MinMaxScaler
import torch.nn.functional as F


def timeseries_to_supervised(data, lag):

    dataX, dataY = [], []
    for i in range(len(data) - lag - 1):
        dataX.append(data[i:(i + lag)])
        dataY.append(data[i + lag])
    return dataX, dataY


def difference(dataset, interval=1):
    diff = list()
    for i in range(interval, len(dataset)):
        value = dataset[i] - dataset[i - interval]
        diff.append(value)
    #One-dimensional ndarray with axis labels
    return np.asarray(diff)

#89page
def loaddata(path):
    series = read_csv(path, header=0, parse_dates=[0], index_col=0,
                      squeeze=True)
    return series

class mod(nn.Module):
    def __init__(self, feature_size, hidden_size, output_size):
        super(mod, self).__init__()

        self.hidden_size = hidden_size
        self.rnn1 = nn.RNN(input_size=feature_size,
                            hidden_size=hidden_size,
                            num_layers=1)
        self.dense1 = nn.Linear(hidden_size, output_size)

    def forward(self,input, hidden):
        x, hidden = self.rnn1(input, hidden)
        x = x[- 1]
        x = x.view(-1, self.hidden_size)
        x = F.softmax(self.dense1(x))
        return x
    def init_hidden(self, num_layer, batch_size, hidden_size):
        return Variable(torch.zeros(num_layer, batch_size, hidden_size))

if __name__ == "__main__":

    #---------------------------------#
    #timestep = windowsize
    windowsize = 5
    #windowsize ...  x = [ 1, 2, 3, 4, 5 ] y = [ 6 ]
    outputsize = 1
    featuresize = 1
    #---------------------------------#
    scaler = MinMaxScaler(feature_range=(0, 1))
    #---------------------------------#


    series = loaddata('C:/Users/asdfw/Desktop/ICC_Example/data/BTCUSD.csv')
    diff_data = difference(series, 1)
    diff_data = diff_data.reshape(-1, 1)
    scaler = scaler.fit(diff_data)
    diff_raw = diff_data
    #diff_data = scaler.transform(diff_data)
    X, y = timeseries_to_supervised(diff_data, windowsize)


    # ----------------------------------------------------------#
    X = np.asarray(X)
    y = np.asarray(y)
    print('X is ', X.shape)
    print('Y is ', y.shape)

    #----------------------------------------------------------#

    batch_size = 1
    num_layer = 1
    hidden_size = 20

    #----------------------------------------------------------#

    model = mod(featuresize, hidden_size, outputsize)
    criterion = nn.L1Loss()
    hidden = model.init_hidden(num_layer, batch_size, hidden_size)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)


    #----------------------------------------------------------#
    #-------------------------------------------Probelm is started here---------------------------------------------------------------
    X = np.asarray(X[0,:,:])
    y = y[0,:]

    print(X, y)
    #----------------------------------------------------------#

    for epoch in range(0, 10):
        model.zero_grad()
        print('Input X/Y Size is ' ,X.size(), y.size())

        output = model(X, hidden)

        loss = criterion(output, y)
        loss.backward()
        optimizer.step()