def main():
print("Initalizing Network")
for run in RunBuilder.get_runs(params):
comment = f'-{run}'
device = torch.device(run.device)
cnn = CNN()
cnn = cnn.to(device)
#cnn.load_state_dict(torch.load("E:\LearningStuff\DLcode\Pytorch\Mnist\CNN3MODEL9967.pkl"))
#extending_loader=get_expanding_data_loader([8],length=2000,batch_size=run.batch_size)
train_loader = get_train_data_loader(run.batch_size)
test_loader = get_test_data_loader(run.batch_size)
optimizer = optim.Adam(cnn.parameters(), lr=run.learning_rate)
''' Initializing tensorboard '''
tb = SummaryWriter(comment=comment, flush_secs=1)
images, labels = next(iter(train_loader))
grid = torchvision.utils.make_grid(images)
'''begin to train'''
for epoch in range(num_epochs):
total_loss = 0
total_correct = 0
adjust_learning_rate(optimizer, epoch, run.learning_rate)
for batch in train_loader:
images = batch[0].to(device)
labels = batch[1].to(device)
preds = cnn(images)
loss = F.cross_entropy(preds, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
total_correct += get_num_correct(preds, labels)
tb.add_scalar('Loss', total_loss, epoch)
tb.add_scalar('Number Correct', total_correct, epoch)
tb.add_scalar('Accuracy', total_correct / 60000, epoch)
path = SAVED_PATH + "\MODEL" + str(epoch) + ".pkl"
torch.save(cnn.state_dict(), path)
print("epoch", epoch, "loss", total_loss, "Accuracy",
total_correct / 60000)
cnn.to('cpu').eval()
for epoch in range(num_epochs):
cnn.load_state_dict(
torch.load(SAVED_PATH + "\MODEL" + str(epoch) + ".pkl"))
total_correct = 0
for batchs in test_loader:
images, labels = batchs
preds = cnn(images)
total_correct += get_num_correct(preds, labels)
print("The Accuracy of the ", epoch, "model on test-set is:",
total_correct / 60000)
tb.add_scalar('Accuracy on testset', total_correct / 10000, epoch)
tb.close()
def train():
model = CNN(gConfig['rate'])
model = model.creatModel()
check_point = ModelCheckpoint(filepath='./model_dir/train.h5',
save_best_only=True)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
image = Image()
model.fit_generator(
image.gen(),
steps_per_epoch=10000, #每次迭代需要多少个Batch
epochs=5, #共需迭代几次
validation_data=image.gen(),
validation_steps=10,
callbacks=[check_point])
def SingleTest():
test_loader = get_test_data_loader(batch_size=256)
cnn = CNN()
''' 防止权重改变 '''
cnn.eval()
cnn.load_state_dict(torch.load('newMODEL9915.pkl'))
print("load cnn net.")
total_correct = 0
for batchs in test_loader:
images, labels = batchs
preds = cnn(images)
total_correct += get_num_correct(preds, labels)
print('The Accuracy of the model on test-set is:', total_correct / 10000)
def predict(image):
if not torch.is_tensor(image):
image = image.resize((28, 28))
'''将图像转为tensor'''
loader = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(0.1307, 0.3081)])
image = loader(image).unsqueeze(dim=0)
''' 预测 '''
network = CNN()
network.eval()
network.load_state_dict(torch.load(PATH + "\Model1.pkl"))
pred = network(image).argmax(dim=1)
return pred
def plot_confusion_matrix():
cnn = CNN()
cnn.load_state_dict(torch.load(path + "\Model1.pkl"))
loader = get_test_data_loader(10)
ConfusionMatrix(cnn, loader)
def CombinationTest():
'''将九个预训练的模型级联进行预测，以达到99.7%的准确率'''
cnn1 = CNN()
cnn1.load_state_dict(torch.load(path + "\Model1.pkl"))
cnn1.eval()
cnn2 = CNN()
cnn2.load_state_dict(torch.load(path + "\Model2.pkl"))
cnn2.eval()
cnn3 = CNN()
cnn3.load_state_dict(torch.load(path + "\Model3.pkl"))
cnn3.eval()
cnn4 = CNN()
cnn4.load_state_dict(torch.load(path + "\Model4.pkl"))
cnn4.eval()
cnn5 = CNN()
cnn5.load_state_dict(torch.load(path + "\Model5.pkl"))
cnn5.eval()
cnn6 = CNN()
cnn6.load_state_dict(torch.load(path + "\Model6.pkl"))
cnn6.eval()
cnn7 = CNN()
cnn7.load_state_dict(torch.load(path + "\Model7.pkl"))
cnn7.eval()
cnn8 = CNN()
cnn8.load_state_dict(torch.load(path + "\Model8.pkl"))
cnn8.eval()
cnn9 = CNN()
cnn9.load_state_dict(torch.load(path + "\Model9.pkl"))
cnn9.eval()
total_correct = 0
test_loader = get_test_data_loader(256)
for batchs in test_loader:
images, labels = batchs
preds1 = cnn1(images).argmax(dim=1).unsqueeze(dim=1)
preds2 = cnn2(images).argmax(dim=1).unsqueeze(dim=1)
preds3 = cnn3(images).argmax(dim=1).unsqueeze(dim=1)
preds4 = cnn4(images).argmax(dim=1).unsqueeze(dim=1)
preds5 = cnn5(images).argmax(dim=1).unsqueeze(dim=1)
preds6 = cnn6(images).argmax(dim=1).unsqueeze(dim=1)
preds7 = cnn7(images).argmax(dim=1).unsqueeze(dim=1)
preds8 = cnn8(images).argmax(dim=1).unsqueeze(dim=1)
preds9 = cnn9(images).argmax(dim=1).unsqueeze(dim=1)
preds = torch.cat((preds1, preds2, preds3, preds4, preds5, preds6,
preds7, preds8, preds9),
dim=1)
preds = preds.numpy()
pred = []
for i in range(preds.shape[0]):
temp = preds[i]
pred.append(sorted(temp)[len(temp) // 2])
preds = torch.tensor(pred)
total_correct += new_get_num_correct(preds, labels)
print("The Accuracy of the combination model on test-set is:",
total_correct / 10000)