手写数字问题

import  os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'      #使tensorflow少打印一些不必要的信息

import  tensorflow.compat.v1 as tf
from    tensorflow import keras
from    tensorflow.keras import layers, optimizers, datasets
tf.enable_eager_execution() #保证sess.run()能够正常运行

#数据集加载
(x, y), (x_val, y_val) = datasets.mnist.load_data()
x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.
y = tf.convert_to_tensor(y, dtype=tf.int32)
y = tf.one_hot(y, depth=10)
print(x.shape, y.shape)
train_dataset = tf.data.Dataset.from_tensor_slices((x, y))
train_dataset = train_dataset.batch(200)       #batch为200表示一次加载200张的图片

 

#降维    Dense是全连接
model = keras.Sequential([ 
    layers.Dense(512, activation='relu'),   #relu是非线性参数
    layers.Dense(256, activation='relu'),
    layers.Dense(10)])

optimizer = optimizers.SGD(learning_rate=0.001)


def train_epoch(epoch):

    # Step4.loop
    for step, (x, y) in enumerate(train_dataset):     #循环300次     60kb/200等于大概300次


        with tf.GradientTape() as tape:
            # [b, 28, 28] => [b, 784]
            x = tf.reshape(x, (-1, 28*28))
            # Step1. compute output
            # [b, 784] => [b, 10]
            out = model(x)
            # Step2. compute loss
            loss = tf.reduce_sum(tf.square(out - y)) / x.shape[0]

        # Step3. optimize and update w1, w2, w3, b1, b2, b3
        grads = tape.gradient(loss, model.trainable_variables)     #grads里包含了对w1，w2，w3和b1,b2,b3的loss对其的求导
        # w' = w - lr * grad
        optimizer.apply_gradients(zip(grads, model.trainable_variables))

        if step % 100 == 0:
            print(epoch, step, 'loss:', loss.numpy())



def train():
#对整个数据集迭代次30次
    for epoch in range(30):

        train_epoch(epoch)






if __name__ == '__main__':
    train()

结果：

(60000, 28, 28) (60000, 10)
0 0 loss: 2.1289964
0 100 loss: 0.96601397
0 200 loss: 0.8044617
1 0 loss: 0.65632385
1 100 loss: 0.71072084
1 200 loss: 0.6174767
2 0 loss: 0.53884405
2 100 loss: 0.61792874
2 200 loss: 0.53729916
3 0 loss: 0.48332796
3 100 loss: 0.5644321
3 200 loss: 0.48922828
4 0 loss: 0.44779533
4 100 loss: 0.5270611
4 200 loss: 0.45555627
5 0 loss: 0.42214122
5 100 loss: 0.49914017
5 200 loss: 0.42974195
6 0 loss: 0.4022831
6 100 loss: 0.4767412
6 200 loss: 0.4090542
7 0 loss: 0.38604406
7 100 loss: 0.45791557
7 200 loss: 0.39167565
8 0 loss: 0.3723324
8 100 loss: 0.44173408
8 200 loss: 0.37691337
9 0 loss: 0.360519
9 100 loss: 0.42779246
9 200 loss: 0.36422646
10 0 loss: 0.35006583
10 100 loss: 0.41538823
10 200 loss: 0.3530626
11 0 loss: 0.3407312
11 100 loss: 0.40423894
11 200 loss: 0.34306836
12 0 loss: 0.3323893
12 100 loss: 0.3939416
12 200 loss: 0.3339965
13 0 loss: 0.3248109
13 100 loss: 0.38446128
13 200 loss: 0.32582656
14 0 loss: 0.31788555
14 100 loss: 0.37571213
14 200 loss: 0.3183561
15 0 loss: 0.3113761
15 100 loss: 0.3676333
15 200 loss: 0.31151268
16 0 loss: 0.30531833
16 100 loss: 0.36009517
16 200 loss: 0.30516908
17 0 loss: 0.2996593
17 100 loss: 0.35302532
17 200 loss: 0.29931957
18 0 loss: 0.29437816
18 100 loss: 0.34642395
18 200 loss: 0.2938386
19 0 loss: 0.2894483
19 100 loss: 0.34028184
19 200 loss: 0.2887537
20 0 loss: 0.28483075
20 100 loss: 0.3345565
20 200 loss: 0.28399432
21 0 loss: 0.2804789
21 100 loss: 0.3291541
21 200 loss: 0.27953643
22 0 loss: 0.27633134
22 100 loss: 0.32407936
22 200 loss: 0.27533495
23 0 loss: 0.27240857
23 100 loss: 0.3192857
23 200 loss: 0.27136424
24 0 loss: 0.26872116
24 100 loss: 0.31474534
24 200 loss: 0.26758516
25 0 loss: 0.2652039
25 100 loss: 0.31041327
25 200 loss: 0.26399314
26 0 loss: 0.26185223
26 100 loss: 0.30627567
26 200 loss: 0.2605623
27 0 loss: 0.25865546
27 100 loss: 0.3023752
27 200 loss: 0.25727862
28 0 loss: 0.2556298
28 100 loss: 0.29863724
28 200 loss: 0.25413704
29 0 loss: 0.25273502
29 100 loss: 0.29504693
29 200 loss: 0.2511155