import
tensorflow.compat.v1 as tf
tf.compat.v1.disable_eager_execution()
from
data
import
train_images, train_labels
DLEN
=
len
(train_images.data[
0
])
DNUM
=
len
(train_images.data)
x
=
tf.placeholder(tf.float32, [
None
, DLEN])
W
=
tf.Variable(tf.zeros([DLEN,
10
]))
b
=
tf.Variable(tf.zeros([
10
]))
y
=
tf.nn.softmax(tf.matmul(x, W)
+
b)
y_
=
tf.placeholder(
"float"
, [
None
,
10
])
cross_entropy
=
-
tf.reduce_sum(y_
*
tf.log(y))
train_step
=
tf.train.GradientDescentOptimizer(
0.001
).minimize(cross_entropy)
saver
=
tf.train.Saver()
sess
=
tf.Session()
sess.run(tf.global_variables_initializer())
for
i
in
range
(DNUM):
batch_xs
=
[train_images.data[i]]
batch_ys
=
[train_labels.data[i]]
sess.run(train_step, feed_dict
=
{x: batch_xs, y_: batch_ys})
correct_prediction
=
tf.equal(tf.argmax(y,
1
), tf.argmax(y_,
1
))
accuracy
=
tf.reduce_mean(tf.cast(correct_prediction,
"float"
))
saver.save(sess,
'model/model'
)
sess.close()