def main():
# generate data and translate labels
train_features, train_targets = generate_all_datapoints_and_labels()
test_features, test_targets = generate_all_datapoints_and_labels()
train_labels, test_labels = convert_labels(train_targets), convert_labels(test_targets)
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('Model: Linear + ReLU + Linear +ReLU + Linear + ReLU + Linear + Tanh')
print('Loss: MSE')
print('Optimizer: SGD')
print('*************************************************************************')
print('Training')
print('*************************************************************************')
# build network, loss and optimizer for Model 1
my_model_design_1=[Linear(2,25), ReLU(), Linear(25,25), Dropout(p=0.5), ReLU(),
Linear(25,25), ReLU(),Linear(25,2),Tanh()]
my_model_1=Sequential(my_model_design_1)
optimizer_1=SGD(my_model_1,lr=1e-3)
criterion_1=LossMSE()
# train Model 1
batch_size=1
for epoch in range(50):
temp_train_loss_sum=0.
temp_test_loss_sum=0.
num_train_correct=0
num_test_correct=0
# trained in batch-fashion: here batch size = 1
for temp_batch in range(0,len(train_features), batch_size):
temp_train_features=train_features.narrow(0, temp_batch, batch_size)
temp_train_labels=train_labels.narrow(0, temp_batch, batch_size)
for i in range(batch_size):
# clean parameter gradient before each batch
optimizer_1.zero_grad()
temp_train_feature=temp_train_features[i]
temp_train_label=temp_train_labels[i]
# forward pass to compute loss
temp_train_pred=my_model_1.forward(temp_train_feature)
temp_train_loss=criterion_1.forward(temp_train_pred,temp_train_label)
temp_train_loss_sum+=temp_train_loss
_, temp_train_pred_cat=torch.max(temp_train_pred,0)
_, temp_train_label_cat=torch.max(temp_train_label,0)
if temp_train_pred_cat==temp_train_label_cat:
num_train_correct+=1
# calculate gradient according to loss gradient
temp_train_loss_grad=criterion_1.backward(temp_train_pred,temp_train_label)
# accumulate parameter gradient in each batch
my_model_1.backward(temp_train_loss_grad)
# update parameters by optimizer
optimizer_1.step()
# evaluate the current model on testing set
# only forward pass is implemented
for i_test in range(len(test_features)):
temp_test_feature=test_features[i_test]
temp_test_label=test_labels[i_test]
temp_test_pred=my_model_1.forward(temp_test_feature)
temp_test_loss=criterion_1.forward(temp_test_pred,temp_test_label)
temp_test_loss_sum+=temp_test_loss
_, temp_test_pred_cat=torch.max(temp_test_pred,0)
_, temp_test_label_cat=torch.max(temp_test_label,0)
if temp_test_pred_cat==temp_test_label_cat:
num_test_correct+=1
temp_train_loss_mean=temp_train_loss_sum/len(train_features)
temp_test_loss_mean=temp_test_loss_sum/len(test_features)
temp_train_accuracy=num_train_correct/len(train_features)
temp_test_accuracy=num_test_correct/len(test_features)
print("Epoch: {}/{}..".format(epoch+1, 50),
"Training Loss: {:.4f}..".format(temp_train_loss_mean),
"Training Accuracy: {:.4f}..".format(temp_train_accuracy),
"Validation/Test Loss: {:.4f}..".format(temp_test_loss_mean),
"Validation/Test Accuracy: {:.4f}..".format(temp_test_accuracy), )
# # visualize the classification performance of Model 1 on testing set
test_pred_labels_1=[]
for i in range(1000):
temp_test_feature=test_features[i]
temp_test_label=test_labels[i]
temp_test_pred=my_model_1.forward(temp_test_feature)
_, temp_train_pred_cat=torch.max(temp_test_pred,0)
if test_targets[i].int() == temp_train_pred_cat.int():
test_pred_labels_1.append(int(test_targets[i]))
else:
test_pred_labels_1.append(2)
fig,axes = plt.subplots(1,1,figsize=(6,6))
axes.scatter(test_features[:,0], test_features[:,1], c=test_pred_labels_1)
axes.set_title('Classification Performance of Model 1')
plt.show()
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('*************************************************************************')
print('Model: Linear + ReLU + Linear + Dropout+ SeLU + Linear + Dropout + ReLU + Linear + Sigmoid')
print('Loss: Cross Entropy')
print('Optimizer: Adam')
print('*************************************************************************')
print('Training')
print('*************************************************************************')
# build network, loss function and optimizer for Model 2
my_model_design_2=[Linear(2,25), ReLU(), Linear(25,25), Dropout(p=0.5), SeLU(),
Linear(25,25),Dropout(p=0.5), ReLU(),Linear(25,2),
Sigmoid()]
my_model_2=Sequential(my_model_design_2)
optimizer_2=Adam(my_model_2,lr=1e-3)
criterion_2=CrossEntropy()
# train Model 2
batch_size=1
epoch=0
while(epoch<25):
temp_train_loss_sum=0.
temp_test_loss_sum=0.
num_train_correct=0
num_test_correct=0
# trained in batch-fashion: here batch size = 1
for temp_batch in range(0,len(train_features), batch_size):
temp_train_features=train_features.narrow(0, temp_batch, batch_size)
temp_train_labels=train_labels.narrow(0, temp_batch, batch_size)
for i in range(batch_size):
# clean parameter gradient before each batch
optimizer_2.zero_grad()
temp_train_feature=temp_train_features[i]
temp_train_label=temp_train_labels[i]
# forward pass to compute loss
temp_train_pred=my_model_2.forward(temp_train_feature)
temp_train_loss=criterion_2.forward(temp_train_pred,temp_train_label)
temp_train_loss_sum+=temp_train_loss
_, temp_train_pred_cat=torch.max(temp_train_pred,0)
_, temp_train_label_cat=torch.max(temp_train_label,0)
if temp_train_pred_cat==temp_train_label_cat:
num_train_correct+=1
# calculate gradient according to loss gradient
temp_train_loss_grad=criterion_2.backward(temp_train_pred,temp_train_label)
'''
if (not temp_train_loss_grad[0]>=0) and (not temp_train_loss_grad[0]<0):
continue
'''
# accumulate parameter gradient in each batch
my_model_2.backward(temp_train_loss_grad)
# update parameters by optimizer
optimizer_2.step()
# evaluate the current model on testing set
# only forward pass is implemented
for i_test in range(len(test_features)):
temp_test_feature=test_features[i_test]
temp_test_label=test_labels[i_test]
temp_test_pred=my_model_2.forward(temp_test_feature)
temp_test_loss=criterion_2.forward(temp_test_pred,temp_test_label)
temp_test_loss_sum+=temp_test_loss
_, temp_test_pred_cat=torch.max(temp_test_pred,0)
_, temp_test_label_cat=torch.max(temp_test_label,0)
if temp_test_pred_cat==temp_test_label_cat:
num_test_correct+=1
temp_train_loss_mean=temp_train_loss_sum/len(train_features)
temp_test_loss_mean=temp_test_loss_sum/len(test_features)
temp_train_accuracy=num_train_correct/len(train_features)
temp_test_accuracy=num_test_correct/len(test_features)
# in case there is gradient explosion problem, initiliza model again and restart training
# but the situation seldom happens
if (not temp_train_loss_grad[0]>=0) and (not temp_train_loss_grad[0]<0):
epoch=0
my_model_design_2=[Linear(2,25), ReLU(), Linear(25,25), Dropout(p=0.5), ReLU(),
Linear(25,25),Dropout(p=0.5), ReLU(),Linear(25,2),Sigmoid()]
my_model_2=Sequential(my_model_design_2)
optimizer_2=Adam(my_model_2,lr=1e-3)
criterion_2=CrossEntropy()
print('--------------------------------------------------------------------------------')
print('--------------------------------------------------------------------------------')
print('--------------------------------------------------------------------------------')
print('--------------------------------------------------------------------------------')
print('--------------------------------------------------------------------------------')
print('Restart training because of gradient explosion')
continue
print("Epoch: {}/{}..".format(epoch+1, 25),
"Training Loss: {:.4f}..".format(temp_train_loss_mean),
"Training Accuracy: {:.4f}..".format(temp_train_accuracy),
"Validation/Test Loss: {:.4f}..".format(temp_test_loss_mean),
"Validation/Test Accuracy: {:.4f}..".format(temp_test_accuracy), )
epoch+=1
# visualize the classification performance of Model 2 on testing set
test_pred_labels_2=[]
for i in range(1000):
temp_test_feature=test_features[i]
temp_test_label=test_labels[i]
temp_test_pred=my_model_2.forward(temp_test_feature)
_, temp_train_pred_cat=torch.max(temp_test_pred,0)
if test_targets[i].int() == temp_train_pred_cat.int():
test_pred_labels_2.append(int(test_targets[i]))
else:
test_pred_labels_2.append(2)
fig,axes = plt.subplots(1,1,figsize=(6,6))
axes.scatter(test_features[:,0], test_features[:,1], c=test_pred_labels_2)
axes.set_title('Classification Performance of Model 2')
plt.show()
# Define training parameters
nb_epochs = 1000
lr = 1e-5
mini_batch_size = 100
optimizer1 = SGD(model1.param(), lr=lr)
optimizer2 = SGD(model2.param(), lr=lr)
print("#" * 50)
print("Training model 1")
# Train model 1
for e in range(nb_epochs):
sum_loss = 0
for b in range(0, train_input.size(0), mini_batch_size):
output, loss = model1.forward(train_input.narrow(0, b, mini_batch_size), train_target.narrow(0, b, mini_batch_size))
optimizer1.zero_grad()
grad = model1.backward()
sum_loss = sum_loss + loss.item()
optimizer1.step()
print("Iteration {0:}: loss = {1:.3f}".format(e+1, sum_loss / (train_input.shape[0]/mini_batch_size)),
end='\r',
flush=True)
loss_train1 = sum_loss / (train_input.shape[0]/mini_batch_size)
print()
print("#" * 50)
# Test model 1
output_test1, loss_test1 = model1.forward(test_input, test_target)
max_iter = 10000
# batcher parameters
batch_size = 64
lenet = LeNet(layers)
lenet.save_model("../models/my_model.model")
#optimizer = SGDMomentum(lenet, **opt_params)
optimizer = SGD(lenet.parameters(), lr=0.1)
epochs = 10
per_epoch = -(-xtrain.shape[0] // batch_size)
iter_cnt = 0
for epoch in range(epochs):
for ix in tqdm(range(per_epoch)):
optimizer.update_lr(iter_cnt)
rand_ix = np.random.randint(0, xtrain.shape[0], (batch_size,))
batch_x = xtrain[rand_ix]
batch_y = ytrain[rand_ix]
my_loss = lenet.forward(batch_x.reshape((batch_size, -1)), batch_y)
lenet.backward(my_loss)
optimizer.step()
optimizer.zero_grad()
iter_cnt += 1
print("Epoch {0} of {1} Done. Starting Testing".format(
epoch + 1, epochs))
start = time.time()
test_loss = lenet.forward(xtest, ytest)
print("Testing Done. Took {0:.2f}s. Accuracy: {1:.4f}, Loss: {2:.2f}".format(
time.time() - start, test_loss["acc"], test_loss["loss"]))
lenet.save_model("weights.npy")