foo.py

import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np

BATCH_SIZE = 1
INPUT_DIM = 1
OUTPUT_DIM = 1
DTYPE = np.float32

class Net(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, hidden_layers):
        super(Net, self).__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.hidden_layers = hidden_layers

        self.rnn = nn.RNN(input_dim, hidden_dim, hidden_layers)
        self.h2o = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h_0 = Variable(torch.zeros(self.hidden_layers, BATCH_SIZE, self.hidden_dim))
        if DTYPE == np.float32:
            h_0.float()
        else:
            h_0.double()

        output, h_t = self.rnn(x, h_0)
        output = self.h2o(output)
        return output


def weights_init(m):
    if isinstance(m, nn.RNN):
        nn.init.xavier_uniform(m.weight_ih_l0.data)
        nn.init.orthogonal(m.weight_hh_l0.data)
        nn.init.constant(m.bias_ih_l0.data, 0)
        nn.init.constant(m.bias_hh_l0.data, 0)
    if isinstance(m, nn.Linear):
        nn.init.xavier_uniform(m.weight.data)
        nn.init.constant(m.bias.data, 0)


data = np.loadtxt('mg17.csv', delimiter=',', dtype=DTYPE)
trX = torch.from_numpy(np.expand_dims(data[:4000, [0]], axis=1))
trY = torch.from_numpy(np.expand_dims(data[:4000, [1]], axis=1))

loss_fcn = nn.MSELoss()
model = Net(INPUT_DIM, 10, OUTPUT_DIM, 1)
model.apply(weights_init)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

for e in range(1000):
    model.train()
    x = Variable(trX)
    y = Variable(trY)
    model.zero_grad()
    output = model(x)
    loss = loss_fcn(output, y)
    loss.backward()
    optimizer.step()
    print("Epoch", e + 1, "TR:", loss.cpu().data.numpy()[0])
    y = Variable(trY)
    model.zero_grad()
    output = model(x)
    loss = loss_fcn(output, y)
    loss.backward()
    optimizer.step()

    print("Epoch", e + 1, "TR:", loss.cpu().data.numpy()[0])