Tensorflow2.0笔记 - ResNet实践

本笔记记录使用ResNet18网络结构，进行CIFAR100数据集的训练和验证。由于参数较多，训练时间会比较长，因此只跑了10个epoch，准确率还没有提升上去。

import os
import time
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics, Inputos.environ['TF_CPP_MIN_LOG_LEVEL']='2'
#tf.random.set_seed(12345)
tf.__version__#关于ResNet的描述，可以参考如下链接：
#https://blog.csdn.net/qq_39770163/article/details/126169080
#代码基于ResNet18结构，有少许不一样
class BasicBlock(layers.Layer):def __init__(self, filter_num, strides = 1):super(BasicBlock, self).__init__()#卷积层1self.conv1 = layers.Conv2D(filter_num, (3,3), strides = strides, padding='same')#BN层self.bn1 = layers.BatchNormalization()#Relu层self.relu = layers.Activation('relu')#卷积层2,BN层2,self.conv2 = layers.Conv2D(filter_num, (3,3), strides = 1, padding='same')self.bn2 = layers.BatchNormalization()#Shortcutif strides != 1:#如果strides不为1，需要下采样self.downsample = Sequential()self.downsample.add(layers.Conv2D(filter_num, (1,1), strides=strides))else:#strides为1， 直接返回原始值即可self.downsample = lambda x:xdef call(self, inputs, training = None):#经过第一个卷积层,BN和Reluout = self.conv1(inputs)out = self.bn1(out)out = self.relu(out)#经过第二个卷积层out = self.conv2(out)out = self.bn2(out)#Shortt处理，out和输入相加identity = self.downsample(inputs)output = layers.add([out, identity])#再经过一个reluoutput = tf.nn.relu(output)return outputclass ResNet(keras.Model):#layer_dims表示对应位置的ResBlock包含了几个BasicBlock#比如[2,2,2,2] => 总共4个ResBlock，每个ResBlock包含两个BasicBlock#num_classes表示输出的类别的个数def __init__(self, layer_dims, num_classes=100):super(ResNet, self).__init__()#预处理单元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')])#创建中间ResBlock层self.layer1 = self.buildResBlock(64, layer_dims[0])self.layer2 = self.buildResBlock(128, layer_dims[1], strides=2)self.layer3 = self.buildResBlock(256, layer_dims[2], strides=2)self.layer4 = self.buildResBlock(512, layer_dims[3], strides=2)#自适应输出层self.avgpool = layers.GlobalAveragePooling2D()#全连接层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)#经过avgpool => [b, 512]x = self.avgpool(x)#经过Dense => [b, 100]x = self.fc(x)return xdef buildResBlock(self, filter_num, blocks, strides = 1):resBlocks = Sequential()resBlocks.add(BasicBlock(filter_num, strides))#后续的resBlock的strides都设置为1for _ in range(1, blocks):resBlocks.add(BasicBlock(filter_num))return resBlocks;def ResNet18():return ResNet([2, 2, 2 ,2]);def ResNet34():return ResNet([3, 4, 6, 3])#加载CIFAR100数据集
#如果下载很慢，可以使用迅雷下载到本地，迅雷的链接也可以直接用官网URL：
#      https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
#下载好后，将cifar-100.python.tar.gz放到 .keras\datasets 目录下（我的环境是C:\Users\Administrator\.keras\datasets）
# 参考：https://blog.csdn.net/zy_like_study/article/details/104219259
(x_train,y_train), (x_test, y_test) = datasets.cifar100.load_data()
print("Train data shape:", x_train.shape)
print("Train label shape:", y_train.shape)
print("Test data shape:", x_test.shape)
print("Test label shape:", y_test.shape)def preprocess(x, y):x = tf.cast(x, dtype=tf.float32) / 255.y = tf.cast(y, dtype=tf.int32)return x,yy_train = tf.squeeze(y_train, axis=1)
y_test = tf.squeeze(y_test, axis=1)batch_size = 128
train_db = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_db = train_db.shuffle(1000).map(preprocess).batch(batch_size)test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test))
test_db = test_db.map(preprocess).batch(batch_size)sample = next(iter(train_db))
print("Train data sample：", sample[0].shape, sample[1].shape, tf.reduce_min(sample[0]), tf.reduce_max(sample[0]))def main():#创建ResNetresNet = ResNet18()resNet.build(input_shape=[None, 32, 32, 3])resNet.summary()#设置优化器optimizer = optimizers.Adam(learning_rate=1e-3)#进行训练num_epoches = 10for epoch in range(num_epoches):for step, (x,y) in enumerate(train_db):with tf.GradientTape() as tape:#[b, 32, 32, 3] => [b, 100]logits = resNet(x)#标签做one_hot encodingy_onehot = tf.one_hot(y, depth=100)#计算损失loss = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True)loss = tf.reduce_mean(loss)#计算梯度grads = tape.gradient(loss, resNet.trainable_variables)#更新参数optimizer.apply_gradients(zip(grads, resNet.trainable_variables))if (step % 100 == 0):print("Epoch[", epoch + 1, "/", num_epoches, "]: step - ", step, " loss:", float(loss))#进行验证total_samples = 0total_correct = 0for x,y in test_db:logits = resNet(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_samples += x.shape[0]total_correct += int(correct)#统计准确率acc = total_correct / total_samplesprint("Epoch[", epoch + 1, "/", num_epoches, "]: accuracy:", acc)if __name__ == '__main__':main()

运行结果：