在今年的3月7号，谷歌在 Tensorflow Developer Summit 2019 大会上发布 TensorFlow 2.0 Alpha
版，随后又发布了Beta版本。
Resnet18结构
Tensorflow搭建Resnet18
导入第三方库
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers,Sequential搭建BasicBlock
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:xdef call(self,input,training=None):out=self.conv1(input)out=self.bn1(out)out=self.relu(out)out=self.conv2(out)out=self.bn2(out)identity=self.downsample(input)output=layers.add([out,identity])output=tf.nn.relu(output)return output
搭建ResNet
class ResNet(keras.Model):def __init__(self,layer_dims,num_classes=10):super(ResNet, self).__init__()# 预处理层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')])# resblockself.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)# there are [b,512,h,w]# 自适应self.avgpool=layers.GlobalAveragePooling2D()self.fc=layers.Dense(num_classes)def call(self,input,training=None):x=self.stem(input)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 xdef build_resblock(self,filter_num,blocks,stride=1):res_blocks= Sequential()# may down sampleres_blocks.add(BasicBlock(filter_num,stride))# just down sample one timefor pre in range(1,blocks):res_blocks.add(BasicBlock(filter_num,stride=1))return res_blocks
def resnet18():return ResNet([2,2,2,2])训练数据
为了数据获取方便，这里使用的是CIFAR10的数据，可以在代码中直接使用keras.datasets.cifar10.load_data()方法获取，非常的方便
训练代码如下：
import os
import tensorflow as tf
from Resnet import resnet18
from tensorflow.keras import datasets,layers,optimizers,Sequential,metricsos.environ["TF_CPP_MIN_LOG_LEVEL"]='2'
tf.random.set_seed(2345)def preprocess(x,y):x=2*tf.cast(x,dtype=tf.float32)/255.-1y=tf.cast(y,dtype=tf.int32)return x,y
(x_train,y_train),(x_test,y_test)=datasets.cifar10.load_data()
y_train=tf.squeeze(y_train,axis=1)
y_test=tf.squeeze(y_test,axis=1)
# print(x_train.shape,y_train.shape,x_test.shape,y_test.shape)
train_data=tf.data.Dataset.from_tensor_slices((x_train,y_train))
train_data=train_data.shuffle(1000).map(preprocess).batch(64)test_data=tf.data.Dataset.from_tensor_slices((x_test,y_test))
test_data=test_data.map(preprocess).batch(64)sample=next(iter(train_data))
print('sample:',sample[0].shape,sample[1].shape,tf.reduce_min(sample[0]),tf.reduce_max(sample[0]))def main():model=resnet18()model.build(input_shape=(None,32,32,3))model.summary()optimizer=optimizers.Adam(lr=1e-3)for epoch in range(50):for step,(x,y) in enumerate(train_data):with tf.GradientTape() as tape:logits=model(x)y_onehot=tf.one_hot(y,depth=10)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%100==0:print(epoch,step,'loss',float(loss))total_num=0total_correct=0for x,y in test_data: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_numprint(epoch,'acc:',acc)
if __name__ == '__main__':main()
训练数据
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
sequential (Sequential) multiple 2048
_________________________________________________________________
sequential_1 (Sequential) multiple 148736
_________________________________________________________________
sequential_2 (Sequential) multiple 526976
_________________________________________________________________
sequential_4 (Sequential) multiple 2102528
_________________________________________________________________
sequential_6 (Sequential) multiple 8399360
_________________________________________________________________
global_average_pooling2d (Gl multiple 0
_________________________________________________________________
dense (Dense) multiple 5130
=================================================================
Total params: 11,184,778
Trainable params: 11,176,970
Non-trainable params: 7,808
_________________________________________________________________
0 0 loss 2.2936558723449707
0 100 loss 1.855604887008667
0 200 loss 1.9335857629776
0 300 loss 1.508711576461792
0 400 loss 1.5679863691329956
0 500 loss 1.5649926662445068
0 600 loss 1.147849202156067
0 700 loss 1.3818628787994385
0 acc: 0.5424
1 0 loss 1.3022596836090088
1 100 loss 1.4624202251434326
1 200 loss 1.3188159465789795
1 300 loss 1.1521495580673218
1 400 loss 0.9550357460975647
1 500 loss 1.2304189205169678
1 600 loss 0.7009983062744141
1 700 loss 0.8488335609436035
1 acc: 0.644
2 0 loss 0.9625152945518494
2 100 loss 1.174363374710083
2 200 loss 1.1750390529632568
2 300 loss 0.7221378087997437
2 400 loss 0.7162064909934998
2 500 loss 0.926654040813446
2 600 loss 0.6159981489181519
2 700 loss 0.6437114477157593
2 acc: 0.6905
3 0 loss 0.7495195865631104
3 100 loss 0.9840961694717407
3 200 loss 0.9429250955581665
3 300 loss 0.5575872659683228
3 400 loss 0.5735365152359009
3 500 loss 0.7843905687332153
3 600 loss 0.6125107407569885
3 700 loss 0.6241222620010376
3 acc: 0.6933
4 0 loss 0.7694090604782104
4 100 loss 0.5488263368606567
4 200 loss 0.9142876863479614
4 300 loss 0.4908181428909302
4 400 loss 0.5889899730682373
4 500 loss 0.7341771125793457
4 600 loss 0.4880038797855377
4 700 loss 0.5088012218475342
4 acc: 0.7241
5 0 loss 0.5378311276435852
5 100 loss 0.5630106925964355
5 200 loss 0.8578733205795288
5 300 loss 0.3617972433567047
5 400 loss 0.29359108209609985
5 500 loss 0.5915042757987976
5 600 loss 0.3684327006340027
5 700 loss 0.40654802322387695
5 acc: 0.7005
6 0 loss 0.5005596280097961
6 100 loss 0.40528279542922974
6 200 loss 0.4127967953681946
6 300 loss 0.4062516987323761
6 400 loss 0.40751856565475464
6 500 loss 0.45849910378456116
6 600 loss 0.4571283459663391
6 700 loss 0.32558882236480713
6 acc: 0.7119
可以看到使用ResNet18网络结构，参数量是非常大的，有 11,184,778，所以训练起来的话，很耗时间，这里笔者没有训练完，有兴趣的同学，可以训练一下
