I have downloaded a program from github: https://github.com/pytorch/examples/tree/master/regression 6 .
The network has  a single fully-connected layer to fit a 4th degree polynomial. Then I want to design a two layers network with the same construct to fit a more complicated function. But it could not work completely. Since I am a newer, could someone help me and give me some suggestion?
Firstly, let me say thank you to the author.
# !/usr/bin/env python
from __future__ import print_function
from itertools import count
import torch
import torch.autograd
import torch.nn.functional as F
from torch.autograd import Variable

# get the coefficient of the polynomial
POLY_DEGREE = 4
W_target = torch.randn(POLY_DEGREE, 1) * 5
b_target = torch.randn(1) * 5

# Builds features i.e. a matrix with columns [x, x^2, x^3, x^4]  
def make_features(x):
    x = x.unsqueeze(1)
    return torch.cat([x ** i for i in range(1, POLY_DEGREE+1)], 1)

    # generate polynomial
def f(x):
"""Approximated function."""
    return x.mm(W_target) + b_target[0]

# Builds a batch i.e. (x, f(x)) pair.
def get_batch(batch_size=32):
    random = torch.randn(batch_size)
    x = make_features(random)
    y = f(x)
    return Variable(x), Variable(y)

# Define model: it is error!!!!!!
fc = torch.nn.Linear(W_target.size(0), 1)
x1 = make_features(fc.eval())
y1 = f(x1)
fc1 = torch.nn.Linear(W_target.size(0), 1)

for batch_idx in count(1):
# Get data with the Variable type
    batch_x, batch_y = get_batch()

    # Reset gradients
    fc1.zero_grad()

    # Forward pass
    output = F.smooth_l1_loss(fc1(y1), batch_y)

    # Backward pass
    output.backward()

    # Apply gradients
    for param in fc.parameters():
        param.data.add_(-0.1 * param.grad.data)

    # Stop criterion
    loss = output.data[0]
    print(batch_idx, "is: ", loss)
    if loss < 1e-3:
        break

``