详细代码如下:

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

in_units = 784
h1_units = 256
h2_units = 256
h3_units = 256
out_units = 10

W1 = tf.Variable(tf.truncated_normal([in_units, h1_units], stddev=0.1))
b1 = tf.Variable(tf.zeros([h1_units]))

W2 = tf.Variable(tf.truncated_normal([h1_units, h2_units], stddev=0.1))
b2 = tf.Variable(tf.zeros([h2_units]))

W3 = tf.Variable(tf.truncated_normal([h2_units, h3_units], stddev=0.1))
b3 = tf.Variable(tf.zeros([h3_units]))

W4 = tf.Variable(tf.zeros([h3_units, out_units]))
b4 = tf.Variable(tf.zeros([out_units]))

x = tf.placeholder(tf.float32, [None, in_units])
keep_prob = tf.placeholder(tf.float32)

hidden1 = tf.nn.relu(tf.matmul(x, W1) + b1)
hidden1_drop = tf.nn.dropout(hidden1, keep_prob)

hidden2 = tf.nn.relu(tf.matmul(hidden1_drop, W2) + b2)
hidden2_drop = tf.nn.dropout(hidden2, keep_prob)

hidden3 = tf.nn.relu(tf.matmul(hidden2_drop, W3) + b3)
hidden3_drop = tf.nn.dropout(hidden3, keep_prob)

y = tf.nn.softmax(tf.matmul(hidden3_drop, W4) + b4)

y_ = tf.placeholder(tf.float32, [None, out_units])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdagradOptimizer(0.1).minimize(cross_entropy)

tf.global_variables_initializer().run()
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

for i in range(3000):
batch_xs, batch_ys = mnist.train.next_batch(100)
train_step.run({x: batch_xs, y_: batch_ys, keep_prob: 0.6})
if i % 100 == 0:
accuracy_train = accuracy.eval({x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, accuracy_train))

print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

哦我找到原因了， 调用环境时得加一个env=env.unwrapped语句，不然好像游戏到达一定分数就结束了

@12321 遇到了同样的问题，多谢

最后两章代码没有开源~

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