Alexnet可以說是使深度學習大火的深度模型。它在2012年被Hinton等人提出,該模型憑藉一個8層卷積神經網絡而贏得了ImageNet的圖像識別挑戰,這個模型與經典的LeNet-5有點類似。
這個模型有一些顯著的特性:
1)網絡層數比LeNet-5深,包含5層卷積和3層全連接。
2)第一層卷積核大小爲,第二層爲,之後均爲,此外,第一、二和五層卷積層之後都跟隨這池化核大小爲,步長爲2的池化層。
具體的理論部分可以去閱讀原論文:ImageNet Classification with Deep Convolutional Neural Networks
下面是AlexNet的相關實現代碼:
import mxnet.gluon as gn
import mxnet.image as im
import mxnet.autograd as ag
import mxnet.ndarray as nd
import mxnet.initializer as init
'''---定義模型---'''
# AlexNet中的LRN其實沒有太多用,反而會增加計算時間,所以刪去
net=gn.nn.Sequential()
with net.name_scope():
# 第一階段
net.add(gn.nn.Conv2D(channels=96,kernel_size=11,strides=(4,4),activation="relu"))
net.add(gn.nn.MaxPool2D(pool_size=(3,3),strides=2))
# 第二階段
net.add(gn.nn.Conv2D(channels=256, kernel_size=5, strides=(1, 1),padding=2,activation="relu"))
net.add(gn.nn.MaxPool2D(pool_size=(3, 3), strides=2))
# 第三階段
net.add(gn.nn.Conv2D(channels=384, kernel_size=3, strides=(1, 1), padding=1, activation="relu"))
net.add(gn.nn.Conv2D(channels=384, kernel_size=3, strides=(1, 1), padding=1, activation="relu"))
net.add(gn.nn.Conv2D(channels=256, kernel_size=3, strides=(1, 1), padding=1, activation="relu"))
net.add(gn.nn.MaxPool2D(pool_size=(3, 3), strides=2))
# 第四階段
net.add(gn.nn.Flatten())
net.add(gn.nn.Dense(4096,activation="relu"))
net.add(gn.nn.Dropout(0.5))
# 第五階段
net.add(gn.nn.Dense(4096, activation="relu"))
net.add(gn.nn.Dropout(0.5))
# 第六階段
net.add(gn.nn.Dense(10)) # 真實AlexNet的輸出其實是1000
'''---讀取數據和預處理---'''
def load_data_fashion_mnist(batch_size, resize=None):
transformer = []
if resize:
transformer += [gn.data.vision.transforms.Resize(resize)]
transformer += [gn.data.vision.transforms.ToTensor()]
transformer = gn.data.vision.transforms.Compose(transformer)
mnist_train = gn.data.vision.FashionMNIST( train=True)
mnist_test = gn.data.vision.FashionMNIST( train=False)
train_iter = gn.data.DataLoader(
mnist_train.transform_first(transformer), batch_size, shuffle=True)
test_iter = gn.data.DataLoader(
mnist_test.transform_first(transformer), batch_size, shuffle=False)
return train_iter, test_iter
batch_size=32
train_iter,test_iter=load_data_fashion_mnist(batch_size,resize=224)
net.initialize(init=init.Xavier()) # 隨機初始化
# softmax和交叉熵損失函數
# 由於將它們分開會導致數值不穩定(前兩章博文的結果可以對比),所以直接使用gluon提供的API
cross_loss=gn.loss.SoftmaxCrossEntropyLoss()
# 定義準確率
def accuracy(output,label):
return nd.mean(output.argmax(axis=1)==label).asscalar()
def evaluate_accuracy(data_iter,net):# 定義測試集準確率
acc=0
for data,label in data_iter:
label = label.astype('float32')
output=net(data)
acc+=accuracy(output,label)
return acc/len(data_iter)
# softmax和交叉熵分開的話數值可能會不穩定
cross_loss=gn.loss.SoftmaxCrossEntropyLoss()
# 優化
train_step=gn.Trainer(net.collect_params(),'sgd',{"learning_rate":0.01})
# 訓練
lr=0.1
epochs=20
for epoch in range(epochs):
train_loss=0
train_acc=0
for image,y in train_iter:
y = y.astype('float32')
with ag.record():
output = net(image)
loss = cross_loss(output, y)
loss.backward()
train_step.step(batch_size)
train_loss += nd.mean(loss).asscalar()
train_acc += accuracy(output, y)
test_acc = evaluate_accuracy(test_iter, net)
print("Epoch %d, Loss:%f, Train acc:%f, Test acc:%f"
%(epoch,train_loss/len(train_iter),train_acc/len(train_iter),test_acc))
受訓練設備的影響,本文不附上訓練過程(訓練時間太長…)