TensorFlow2.0 搭建簡單的ResNet和訓練

1.resnet.py

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

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.Acativation('relu')
        
        self.conv2 = layers.Conv2D(filter_num, (3,3), strides=1, padding='same')
        self.bn2 = layers.BatchNormalization()
         
        if stride != 1: #維數不爲1的時候需要進行下采樣
            self.downsample = Sequential()
            self.downsample.add(layers.Conv2D(filter_num, (1,1), strides=stride))
            self.downsample.add(layers.BatchNormalization())
        
        else:
            self.downsample = lambda x: x
        
       # self.stride = stride
    
    def call(self, inputs, training=None):
        residual = self.downsample(inputs) #原來的x
        
        conv1 = self.conv1(inputs)
        bn1 = self.bn1(conv1)
        relu1 = self.relu(bn1)
        conv2 = self.conv2(relu1)
        bn2 = self.bn2(conv2)
        
        add = layers.add([bn2, residual]) #shortcut
        out = self.relu(add) #等價於out = tf.nn.relu(add)
        return out

class ResNet(keras.Model):
    
    def __int__(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() #功能層
        self.fc = layers.Dense(num_classes) 
        
            
        
    def call(self, inputs, trianing=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)
        x = self.fc(x)
        
        return x
        
    
    def build_resblock(self, filter_num, blocks, stride=1):
        #may downsample
        res_blocks = keras.Sequential()
        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])

2.resnet_train.py

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

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.random.set_seed(2345)
from resnet import resnet18

def preprocess(x, y):
    # [0~1]
    x = tf.cast(x, dtype=tf.float32) / 255. - 1
    y = tf.cast(y, dtype=tf.int32)
    return x, y

batchsz = 256
#[32, 32, 3], [10k, 1]
(x,y), (x_val, y_val) = datasets.cifar100.load_data()
y = tf.squeeze(y,axis=1)
y_val = tf.squeeze(y_val,axis=1) #注意維度變換
print(x.shape,y.shape,x_val.shape,y_val.shape)

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

test_db = tf.data.Dataset.from_tensor_slices((x_val,y_val))
test_db = test_db.map(preprocess).batch(batchsz)

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

def main():
    
    model = resnet18()
    model.summary()
    model.build(input_shape=(None,32,32,3))
    optimizer = optimizers.Adam(lr=1e-4)
    
    #拼接需要訓練的參數 [1,2] + [3,4] = [1,2,3,4]
    for epoch in range(50):
        
        for step, (x,y) in enumerate(train_db):
            
            with tf.GradientTape() as tape:
                #[b,32,32,3] => [b,1,1,512]
                logits = model(x)
                
                y_onehot = tf.one_hot(y, depth=100) #[50k, 10]
               # y_val_onehot = tf.one_hot(y_val, depth=100)
                
                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.trianabel_variables))
            
            if step % 100 == 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('acc: ',acc)
        
if __name__ == '__main__':
    main()

代碼借鑑了網易雲課堂龍老師的tensorflow2.0教材，僅供交流使用.