x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean( ????????tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) train_step = ????????tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.InteractiveSession() tf.global_variables_initializer().run() # Train for _ in range(1000):
????????batch_xs, batch_ys = mnist.train.next_batch(100)
????????_,cost = sess.run([train_step,cross_entropy], feed_dict={x: ????????????????batch_xs, y_: batch_ys})
? ? ? ? ?print(cost)
輸出為：
2.30259
1.96883
1.76687
1.39246
1.22997
1.19243
1.08657
1.12234
1.30965
0.833457
0.963737
0.767362
0.763479
0.75128
0.709319
使用adam算法：
tf.train.AdamOptimizer(0.5).minimize(cross_entropy)
輸出為：
2.30259
16.8401
17.2679
16.3104
14.2131
16.3672
18.8276
11.4264
15.4074
8.97122
3.11424
5.39649
5.58269
7.20344
7.48695
11.5576
8.18053
4.38636
8.00477
3.82183
10.1561
4.68156
5.63022
3.41254
6.28483
需要在之后思考解答？
