tensorflow2 手写数字识别

1、conda运行环境加载fashion-mnist数据集，存放在如下路径中，C:UsersAdministrator.keras 数据网盘下载链接： 链接：https://pan.baidu.com/s/1sCCOdscPRVwgbJSKShJl0A 提取码：4wpq

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

def data_press(x,y):
    x=tf.cast(x,tf.float32)/255
    y=tf.cast(y,tf.int32)

    return  x,y
(x,y),(x_test,y_test)=datasets.fashion_mnist.load_data()

traindata=tf.data.Dataset.from_tensor_slices((x,y))
traindata=traindata.map(data_press).shuffle(100000).batch(100)
testdata=tf.data.Dataset.from_tensor_slices((x_test,y_test))
testdata=testdata.map(data_press).batch(100)



# d=next(iter(traindata))
# print(d)
model=Sequential([
    layers.Dense(256,activation=relu)
    ,layers.Dense(126,activation=relu)
    ,layers.Dense(64,activation=relu)
    ,layers.Dense(32,activation=relu)
    ,layers.Dense(10)
])
model.build(input_shape=[None,28*28])
model.summary()
optim=optimizers.Adam(lr=1e-3)
import datetime
current_time=datetime.datetime.now().strftime(("%Y%m%d-%H%M%S"))
log_dir=logs/+current_time
summary_writer=tf.summary.create_file_writer(log_dir)
count={cnt:0,test:0}
def train(epoch,count):

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

        with tf.GradientTape() as tape:
            # print(x_trainshape,x.shape,y.shape)
            x=tf.reshape(x,[-1,28*28])
            y=tf.one_hot(y,depth=10)
            y_pred=model(x)
            mse_loss=tf.reduce_mean(tf.square(y,y_pred))
            mse_loss2=tf.reduce_mean(tf.losses.MSE(y,y_pred))
            mse_ce=tf.reduce_mean(keras.losses.categorical_crossentropy(y,y_pred,from_logits=True))

        grads=tape.gradient(mse_ce,model.trainable_variables)
        optim.apply_gradients(zip(grads,model.trainable_variables))
        if step%100==0:
            print(epoch,step,,float(mse_loss),float(mse_loss2),float(mse_ce))
            with summary_writer.as_default():
                tf.summary.scalar(train-loss,float(mse_ce),step=count[cnt])
        count[cnt]+=1
    #test
    toteal_c=0
    total_num=0
    for step,(x,y) in enumerate(testdata):
        # print(x.shape)
        # print(y.shape,y.shape)
        x=tf.reshape(x,[-1,28*28])

        x=model(x)
        pred=tf.argmax(x,axis=1)
        pred=tf.cast(pred,dtype=tf.int32)
        correct=tf.equal(pred,y)
        correct=tf.reduce_sum(tf.cast(correct,tf.int32))
        toteal_c+=int(correct)
        total_num+=x.shape[0]
        with summary_writer.as_default():
            tf.summary.scalar(test-acc,float(toteal_c/total_num),step=count[test])
        count[test]+=1
    acc=toteal_c/total_num
    print(acc)
if __name__==__main__:
    for i in range(30):
        train(i,count)

tensorboard 展示： 1、开启tensorboard --logdir logs 2、定义summary,

current_time=datetime.datetime.now().strftime(("%Y%m%d-%H%M%S"))
log_dir=logs/+current_time
summary_writer=tf.summary.create_file_writer(log_dir)

with summary_writer.as_default():
                tf.summary.scalar(train-loss,float(mse_ce),step=count[cnt])

3、http://localhost:6006/ 展示