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fn = FirstNetwork_v1()
fit_v1()

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def fit_v1(epochs = 1000, learning_rate = 1):
  loss_arr = []
  acc_arr = []
  opt = optim.SGD(fn.parameters(), lr=learning_rate)
  
  for epoch in range(epochs):
    y_hat = fn(X_train)
    loss = F.cross_entropy(y_hat, Y_train)
    loss_arr.append(loss.item())
    acc_arr.append(accuracy(y_hat, Y_train))

vaibhavkumar049

from torch import optim

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fn = FirstNetwork_v1()
fit()

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class FirstNetwork_v1(nn.Module):
  
  def __init__(self):
    super().__init__()
    torch.manual_seed(0)
    self.lin1 = nn.Linear(2, 2)
    self.lin2 = nn.Linear(2, 4)
    
  def forward(self, X):
    a1 = self.lin1(X)

vaibhavkumar049

fn = FirstNetwork()
fit()

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def fit(epochs = 1000, learning_rate = 1):
  loss_arr = []
  acc_arr = []
  for epoch in range(epochs):
    y_hat = fn(X_train)
    loss = F.cross_entropy(y_hat, Y_train)
    loss_arr.append(loss.item())
    acc_arr.append(accuracy(y_hat, Y_train))

    loss.backward()

vaibhavkumar049

class FirstNetwork(nn.Module):
  
  def __init__(self):
    super().__init__()
    torch.manual_seed(0)
    self.weights1 = nn.Parameter(torch.randn(2, 2) / math.sqrt(2))
    self.bias1 = nn.Parameter(torch.zeros(2))
    self.weights2 = nn.Parameter(torch.randn(2, 4) / math.sqrt(2))
    self.bias2 = nn.Parameter(torch.zeros(4))
    

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import torch.nn as nn

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import torch.nn.functional as F