def train(net: NeuralNetwork,
inputs: Tensor,
targets: Tensor,
num_epochs: int = 5000,
iterator: DataIterator = BatchIterator(),
loss: Loss = CrossEntropy(),
optimizer: Optimizer = MBGD(),
showGraph: bool = False) -> None:
losses = []
for epoch in range(num_epochs):
epoch_loss = 0.0
for batch in iterator(inputs, targets):
for X, Y in zip(batch.inputs, batch.targets):
predicted = net.forward(X)
epoch_loss += loss.loss(predicted, Y)
grad = loss.grad(predicted, Y)
net.backwards(grad)
optimizer.step(net)
print(epoch, epoch_loss)
losses.append(epoch_loss)
if epoch_loss < 300:
pass
if showGraph:
plt.plot(losses)
plt.show()
