弄浪的鱼

ResNet

深度残差网络(Deep residual network, ResNet)的提出是CNN图像史上的一件里程碑事件,ResNet取得了5项第一,并又一次刷新了CNN模型在ImageNet上的历史。

本文介绍如何用 TensorFlow2.0 来实现 ResNet18,并用其训练 cifar100 的分类模型。

ResNet 的实现

a residual block

ResNet 的基本结构如上图所示,为了解决网络层次过深而导致的退化问题(Degradation problem)。ResNet 设计了一种短路连接(shortcut connection)来解决这个问题。

在编程实现的角度来看,首先我们要建一个 BasicBlock ,它包括两个 layer,以及一个 shortcut connection。ResNet 则是由多个 BasicBlock 堆叠而成。

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import  tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, Sequential

class BasicBlock(layers.Layer):

def __init__(self, filter_num, stride=1):
super(BasicBlock, self).__init__()

self.conv1 = layers.Conv2D(filter_num, (3, 3), strides=stride, padding='same')
self.bn1 = layers.BatchNormalization()
self.relu = layers.Activation('relu')

self.conv2 = layers.Conv2D(filter_num, (3, 3), strides=1, padding='same')
self.bn2 = layers.BatchNormalization()

if stride != 1:
self.downsample = Sequential()
self.downsample.add(layers.Conv2D(filter_num, (1, 1), strides=stride))
else:
self.downsample = lambda x:x

def call(self, inputs, training=None):

# [b, h, w, c]
out = self.conv1(inputs)
out = self.bn1(out)
out = self.relu(out)

out = self.conv2(out)
out = self.bn2(out)

identity = self.downsample(inputs)

output = layers.add([out, identity])
output = tf.nn.relu(output)

return output

class ResNet(keras.Model):

def __init__(self, layer_dims, num_classes=100): # [2, 2, 2, 2]
super(ResNet, self).__init__()

self.stem = Sequential([layers.Conv2D(64, (3, 3), strides=(1, 1)),
layers.BatchNormalization(),
layers.Activation('relu'),
layers.MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding='same')
])

self.layer1 = self.build_resblock(64, layer_dims[0])
self.layer2 = self.build_resblock(128, layer_dims[1], stride=2)
self.layer3 = self.build_resblock(256, layer_dims[2], stride=2)
self.layer4 = self.build_resblock(512, layer_dims[3], stride=2)

# output: [b, 512, h, w],
self.avgpool = layers.GlobalAveragePooling2D()
self.fc = layers.Dense(num_classes)

def call(self, inputs, training=None):

x = self.stem(inputs)

x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)

# [b, c]
x = self.avgpool(x)
# [b, 100]
x = self.fc(x)

return x

def build_resblock(self, filter_num, blocks, stride=1):

res_blocks = Sequential()
# may down sample
res_blocks.add(BasicBlock(filter_num, stride))

for _ in range(1, blocks):
res_blocks.add(BasicBlock(filter_num, stride=1))

return res_blocks

def resnet18():
return ResNet([2, 2, 2, 2])

def resnet34():
return ResNet([3, 4, 6, 3])

使用 ResNet

ResNet 的使用和一般的模型使用没有什么区别,如果不清楚可以看这篇文章:【TensorFlow2.0】手撕前向传播算法

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import  tensorflow as tf
from tensorflow.keras import layers, optimizers, datasets, Sequential
import os
from resnet1 import resnet18

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
tf.random.set_seed(2345)

def preprocess(x, y):
# [-1~1]
x = tf.cast(x, dtype=tf.float32) / 255. - 0.5
y = tf.cast(y, dtype=tf.int32)
return x,y


(x,y), (x_test, y_test) = datasets.cifar100.load_data()
y = tf.squeeze(y, axis=1)
y_test = tf.squeeze(y_test, axis=1)
print(x.shape, y.shape, x_test.shape, y_test.shape)


train_db = tf.data.Dataset.from_tensor_slices((x,y))
train_db = train_db.shuffle(1000).map(preprocess).batch(512)

test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test))
test_db = test_db.map(preprocess).batch(512)

sample = next(iter(train_db))
print('sample:', sample[0].shape, sample[1].shape,
tf.reduce_min(sample[0]), tf.reduce_max(sample[0]))

def main():

# [b, 32, 32, 3] => [b, 1, 1, 512]
model = resnet18()
model.build(input_shape=(None, 32, 32, 3))
model.summary()
optimizer = optimizers.Adam(lr=1e-3)

for epoch in range(500):

for step, (x,y) in enumerate(train_db):

with tf.GradientTape() as tape:
# [b, 32, 32, 3] => [b, 100]
logits = model(x)
# [b] => [b, 100]
y_onehot = tf.one_hot(y, depth=100)
# compute loss
loss = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True)
loss = tf.reduce_mean(loss)

grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))

if step %50 == 0:
print(epoch, step, 'loss:', float(loss))

total_num = 0
total_correct = 0
for x,y in test_db:

logits = model(x)
prob = tf.nn.softmax(logits, axis=1)
pred = tf.argmax(prob, axis=1)
pred = tf.cast(pred, dtype=tf.int32)

correct = tf.cast(tf.equal(pred, y), dtype=tf.int32)
correct = tf.reduce_sum(correct)

total_num += x.shape[0]
total_correct += int(correct)

acc = total_correct / total_num
print(epoch, 'acc:', acc)

if __name__ == '__main__':
main()