from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot = True)

import tensorflow as tf
sess = tf.compat.v1.InteractiveSession()
x = tf.compat.v1.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.compat.v1.placeholder("float", [None,10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.math.log(y), reduction_indices = [1]))
train_step = tf.compat.v1.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)
tf.compat.v1.global_variables_initializer().run()
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
train_step.run({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"))
print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}))
修改過後可以運行的程式碼
結果：正確率0.9099