购物网站开发流程,公司怎么做网页,品牌建设是一个循序渐进的过程,免费好用的云电脑本笔记主要记录metrics相关的内容#xff0c;详细内容请参考代码注释#xff0c;代码本身只使用了Accuracy和Mean。本节的代码基于上篇笔记FashionMnist的代码经过简单修改而来#xff0c;上篇笔记链接如下#xff1a;
Tensorflow2.0笔记 - FashionMnist数据集训练-CSDN博… 本笔记主要记录metrics相关的内容详细内容请参考代码注释代码本身只使用了Accuracy和Mean。本节的代码基于上篇笔记FashionMnist的代码经过简单修改而来上篇笔记链接如下
Tensorflow2.0笔记 - FashionMnist数据集训练-CSDN博客文章浏览阅读339次。本笔记使用FashionMnist数据集搭建一个5层的神经网络进行训练并统计测试集的精度。本笔记中FashionMnist数据集是直接下载到本地加载的方式不涉及用梯子。关于FashionMnist的介绍请自行百度。https://blog.csdn.net/vivo01/article/details/136921592?spm1001.2014.3001.5502
#Fashion Mnist数据集本地下载和加载不用梯子
#https://blog.csdn.net/scar2016/article/details/115361245 百度网盘
#https://blog.csdn.net/weixin_43272781/article/details/110006990 github
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers, Sequential, metricstf.__version__#加载fashion mnist数据集
def load_mnist(path, kindtrain):import osimport gzipimport numpy as npLoad MNIST data from pathlabels_path os.path.join(path,%s-labels-idx1-ubyte.gz% kind)images_path os.path.join(path,%s-images-idx3-ubyte.gz% kind)with gzip.open(labels_path, rb) as lbpath:labels np.frombuffer(lbpath.read(), dtypenp.uint8,offset8)with gzip.open(images_path, rb) as imgpath:images np.frombuffer(imgpath.read(), dtypenp.uint8,offset16).reshape(len(labels), 784)return images, labels#预处理数据
def preprocess(x, y):x tf.cast(x, dtypetf.float32)x tf.convert_to_tensor(x, dtypetf.float32) / 255.y tf.cast(y, dtypetf.int32)y tf.convert_to_tensor(y, dtypetf.int32)return x, y
#训练数据
train_data, train_labels load_mnist(./datasets)
print(train_data.shape, train_labels.shape)
#测试数据
test_data, test_labels load_mnist(./datasets, t10k)
print(test_data.shape, test_labels.shape)batch_size 128train_db tf.data.Dataset.from_tensor_slices((train_data, train_labels))
train_db train_db.map(preprocess).shuffle(10000).batch(batch_size)test_db tf.data.Dataset.from_tensor_slices((test_data, test_labels))
test_db test_db.map(preprocess).batch(batch_size)train_db_iter iter(train_db)
sample next(train_db_iter)
print(Batch:, sample[0].shape, sample[1].shape)#定义网络模型
model Sequential([#Layer 1: [b, 784] [b, 256]layers.Dense(256, activationtf.nn.relu),#Layer 2: [b, 256] [b, 128]layers.Dense(128, activationtf.nn.relu),#Layer 3: [b, 128] [b, 64]layers.Dense(64, activationtf.nn.relu),#Layer 4: [b, 64] [b, 32]layers.Dense(32, activationtf.nn.relu),#Layer 5: [b, 32] [b, 10], 输出类别结果layers.Dense(10)
])#编译网络
model.build(input_shape[None, 28*28])
model.summary()#进行训练
total_epoches 5
learn_rate 0.01#Metrics统计
#参考资料https://zhuanlan.zhihu.com/p/42438077
#1. 新建meter
#acc_meter metrics.Accuracy()
#loss_meter metrics.Mean()
#2. 更新状态, update_state()
#loss_meter.update_state(loss)
#acc_meter.update_state(y, pred)
#3.获取结果, result()
#print(step, loss:, loss_meter.result().numpy())
#print(step, Evaluate Acc:, total_correct/total, acc_meter.result().numpy())
#4.清除度量信息reset_states()
#loss_meter.reset_states()
#acc_meter.reset_states()#新建准确度和loss度量对象
acc_meter metrics.Accuracy()
loss_meter metrics.Mean()optimizer optimizers.Adam(learning_rate learn_rate)
for epoch in range(total_epoches):for step, (x,y) in enumerate(train_db):with tf.GradientTape() as tape:logits model(x)y_onehot tf.one_hot(y, depth10)#使用交叉熵作为lossloss_ce tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot, logits, from_logitsTrue))#调用update_state更新loss度量信息loss_meter.update_state(loss_ce)#计算梯度grads tape.gradient(loss_ce, model.trainable_variables)#更新梯度optimizer.apply_gradients(zip(grads, model.trainable_variables))if step % 100 0:print(Epoch[, epoch, ]: step-, step, \tloss: , loss_meter.result().numpy())loss_meter.reset_states()#使用测试集进行验证total_correct 0total_num 0#清除准确度的统计信息acc_meter.reset_states()for x,y in test_db:logits model(x)#使用softmax得到各个类别的概率prob tf.nn.softmax(logits, axis1)#求出概率最大的结果参数位置作为预测的分类结果pred tf.cast(tf.argmax(prob, axis1), dtypetf.int32)#比较结果correct tf.equal(pred, y)correct tf.reduce_sum(tf.cast(correct, dtypetf.int32))#计算精度total_correct int(correct)total_num x.shape[0]#使用metircs的update_state进行更新acc_meter.update_state(y, pred)acc total_correct / total_numprint(Epoch[, epoch, ] Manual Accuracy:, acc, Metrics Accuracy:, acc_meter.result().numpy())运行结果
