匯入所需要的包
import tensorflow as tf# x:[60k,28,28],
# y:[60k]
(x, y)
,_ = tf.keras.datasets.mnist.load_data(
)# x:[0-255]->[0,1] y:[0-9]
# 將x,y 轉換為tensor,並且將x歸一化
x = tf.convert_to_tensor(x, dtype=tf.float32)
/255
.y = tf.convert_to_tensor(y, dtype=tf.int32)
print
(x.shape,y.shape,x.dtype,y.dtype)
# 設定batch為128
train_db = tf.data.dataset.from_tensor_slices(
(x,y)
).batch(
128)
train_iter =
iter
(train_db)
sample =
next
(train_iter)
# 乙個batch的形狀
print
('batch:'
,sample[0]
.shape,sample[1]
.shape)
# 建立權值
# 降維過程 [b,784]->[b,256]->[b,128]->[b,10]
# [dim_in, dim_out],[dim_out]
# 隨機生成乙個權重矩陣，並且初始化每一層的偏置
# 由於下文中的梯度下降法，tape預設只會跟蹤tf.variable型別的資訊，所以進行轉換。
w1 = tf.variable(tf.random.truncated_normal(
[784
,256
],stddev=
0.1)
)b1 = tf.variable(tf.zeros(
[256])
)w2 = tf.variable(tf.random.truncated_normal(
[256
,128
],stddev=
0.1)
)b2 = tf.variable(tf.zeros(
[128])
)w3 = tf.variable(tf.random.truncated_normal(
[128,10
],stddev=
0.1)
)b3 = tf.variable(tf.zeros([10
]))lr =1e-
3#0.001 10的-3次方
for epoch in
range(10
):# enumerate處理後可以返回當前步驟的step，便於列印當前資訊
print
('epoch'
,epoch)
for step,
(x,y)
inenumerate
(train_db)
:#x :[128,28,28]
#y :[128]
x = tf.reshape(x,[-
1,28*
28])with tf.gradienttape(
)as tape:
# x :[128,28*28]
# h1 = x@w1+b1
# [b,784]@[784*256]+[256]->[b,256]+[256]->[b,256]+[b,256]
h1 = x@w1 +tf.broadcast_to(b1,
[x.shape[0]
,256])
h1 = tf.nn.relu(h1)
h2 = h1@w2 + b2
h2 = tf.nn.relu(h2)
out = h2@w3 + b3
# compute loss 計算誤差
# out:[b,10]
y_onehot = tf.one_hot(y,depth=10)
# mse = mean(sum(y-out)^2)
loss = tf.square(y_onehot-out)
# mean: scalar
loss = tf.reduce_mean(loss)
# compute gradients
grads = tape.gradient(loss,
[w1,b1,w2,b2,w3,b3]
)# w1 = w1 - lr * w1_grad
w1.assign_sub(lr * grads[0]
)# 保持w1原地更新，保持引用不變，型別不變
b1.assign_sub(lr * grads[1]
) w2.assign_sub(lr * grads[2]
) b2.assign_sub(lr * grads[3]
) w3.assign_sub(lr * grads[4]
) b3.assign_sub(lr * grads[5]
)if step %
100==0:
print
(step,
' loss:'
,float
(loss)
)
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