loss = criterion.forward(
output,
train_target_hot.narrow(0, b, mini_batch_size).t())
# put to zero weights and bias
model.zero_grad()
##Backpropagation
#Calculate grad of loss
loss_grad = criterion.backward()
#Grad of the model
model.backward(loss_grad)
#Update parameters
model.grad_step(lr=lr)
if e % print_step == 0:
print(f'Epoc : {e}, Loss: {loss}')
#Save loss in vector
loss_at_print.append(float(loss))
test_prediction = model.forward(test_input.t())
test_accuracy.append(
float(compute_nb_errors(model, test_input, test_target)) /
test_target.size(0) * 100)
print(f'\tTest Accuracy : {100-test_accuracy[-1]}%')
training_prediction = model.forward(train_input.t())
training_accuracy.append(
float(compute_nb_errors(model, train_input, train_target)) /
train_target.size(0) * 100)
print(f'\tTraining Accuracy : {100-training_accuracy[-1]}%')
#Calculate loss
framework_loss = framework_criterion.forward(framework_output,train_target_hot.narrow(0,b,mini_batch_size).t())
# put to zero weights and bias
framework_model.zero_grad()
##Backpropagation
#Calculate grad of loss
loss_grad = framework_criterion.backward()
#Grad of the framework_model
framework_model.backward(loss_grad)
#Update parameters
framework_model.grad_step(lr=lr)
#Train the model using pytorch NN
pytorch_output = pytorch_model(train_input.narrow(0, b, mini_batch_size))
pytorch_loss = pytorch_criterion(pytorch_output, train_target_hot.narrow(0, b, mini_batch_size))
pytorch_model.zero_grad()
pytorch_loss.backward()
pytorch_optimizer.step()
if e % print_step ==0 :
print(f'Epoc : {e}, Mini Deeplearning Framework Loss: {framework_loss}, Pytorch Loss:{pytorch_loss}')
#Save loss in vector
framework_loss_at_print.append(float(framework_loss))
framework_test_accuracy.append(float(compute_nb_errors(framework_model,test_input, test_target)) / test_target.size(0) * 100)
print(f'\tMini Deeplearning Framework: tTest Accuracy : {100-framework_test_accuracy[-1]}%')