手寫數字問題

import

osos.environ[
'tf_cpp_min_log_level
']='2'
#使tensorflow少列印一些不必要的資訊
import
tensorflow.compat.v1 as tf
from tensorflow import
keras
from tensorflow.keras import
layers, optimizers, datasets
tf.enable_eager_execution() 
#保證sess.run()能夠正常執行
#資料集載入
(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為200表示一次載入200張的
#降維 dense是全連線
model =keras.sequential([ 
layers.dense(512, activation='
relu
'), #
relu是非線性引數
layers.dense(256, activation='
relu'),
layers.dense(10)])
optimizer = optimizers.sgd(learning_rate=0.001)
deftrain_epoch(epoch):
#step4.loop
for step, (x, y) in enumerate(train_dataset): #
迴圈300次 60kb/200等於大概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裡包含了對w1，w2，w3和b1,b2,b3的loss對其的求導
#w' = w - lr * grad
if step % 100 ==0:
print(epoch, step, '
loss:
', loss.numpy())
deftrain():
#對整個資料集迭代次30次
for epoch in range(30):
train_epoch(epoch)
if__name__ == '
__main__':
train()

結果：

(60000, 28, 28) (60000, 10)

0 0 loss: 2.12899640 100 loss: 0.966013970 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

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