tensorflowでmnistを識別したい

前提・実現したいこと

機械学習を勉強中の学生です。
CNNを使ってMNISTの０〜９の画像を認識しようとしています。

該当のソースコード

python
1import tensorflow as tf
2from tensorflow.examples.tutorials.mnist import input_data
3
4mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
5tf_x = tf.placeholder(tf.float32, [None, 784], name="tf_x")
6tf_y = tf.placeholder(tf.float32, [None, 10], name="tf_y")
7
8def cnn():
9    x = tf.reshape(tf_x, [-1, 28, 28, 1], name="x")
10    conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)
11    conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
12    conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)
13    conv2 = tf.layers.max_pooling2d(conv2, 2, 2)
14    fc1 = tf.contrib.layers.flatten(conv2)
15    fc1 = tf.layers.dense(fc1, 1024)
16    fc1 = tf.layers.dropout(fc1, 0.3)
17    out = tf.layers.dense(fc1, 10)
18    return out
19
20
21y = cnn()
22
23loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=tf_y))
24
25
26lr = 0.25
27train_step = tf.train.AdamOptimizer(lr).minimize(loss)
28
29
30correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(tf_y, 1))
31accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
32
33
34sess = tf.InteractiveSession()
35sess.run(tf.global_variables_initializer())
36
37for i in range(1000):
38    batch_xs, batch_ys = mnist.train.next_batch(100)
39    _, acc = sess.run([train_step, accuracy], feed_dict={ tf_x: batch_xs, tf_y: batch_ys})
40    print(acc)
41
42
43acc = sess.run(accuracy, feed_dict={tf_x: mnist.test.images, tf_y: mnist.test.labels})
44print(acc)
45