import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

print(tf.__version__)

# 1. create data
mnist = input_data.read_data_sets(‘../MNIST_data‘, one_hot=True)
with tf.variable_scope(‘Input‘):
    tf_x = tf.placeholder(tf.float32, [None, 28 * 28], name=‘x‘)
    image = tf.reshape(tf_x, [-1, 28, 28, 1], name=‘image‘)
    tf_y = tf.placeholder(tf.float32, [None, 10], name=‘y‘)
    is_training = tf.placeholder(tf.bool, None)

# 2. define Network
with tf.variable_scope(‘Net‘):
    """
    "SAME" 类型的padding:
    out_height = ceil(in_height / strides[1]); ceil向上取整
    out_width = ceil(in_width / strides[2])

    "VALID"类型的padding:
    out_height = ceil((in_height - filter_height + 1) / striders[1])
    out_width = ceil((in_width - filter_width + 1) / striders[2]
    """
    conv1 = tf.layers.conv2d(inputs=image, filters=32, kernel_size=5,
                             strides=1, padding=‘same‘, activation=tf.nn.relu)  # 32x28x28
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=2, strides=2)  # 32x14x14
    conv2 = tf.layers.conv2d(pool1, 64, 3, 1, ‘same‘, activation=tf.nn.relu)  # 64x14x14
    pool2 = tf.layers.max_pooling2d(conv2, 2, 2)  # 64x7x7
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
    fc1 = tf.layers.dense(pool2_flat, 1024, tf.nn.relu)
    fc1 = tf.layers.dropout(fc1, rate=0.5, training=is_training)
    predict = tf.layers.dense(fc1, 10, tf.nn.softmax)

# 3. define loss
with tf.name_scope(‘loss‘):
    loss = tf.losses.softmax_cross_entropy(onehot_labels=tf_y, logits=predict)
    tf.summary.scalar(‘loss‘, loss)
with tf.name_scope(‘accuracy‘):
    # tf.metrics.accuracy() 返回 [上次的accuracy， 这次的accuracy]
    accuracy = tf.metrics.accuracy(labels=tf.argmax(tf_y, axis=1), predictions=tf.argmax(predict, axis=1))[1]
    tf.summary.scalar(‘accuracy‘, accuracy)

# 4. define optimizer
with tf.name_scope(‘train‘):
    optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)

# 5. initialize
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())

# 6. train
saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(init_op)
    # =================
    merge_op = tf.summary.merge_all()
    train_writer = tf.summary.FileWriter(‘logs/train‘, sess.graph)
    test_writer = tf.summary.FileWriter(‘logs/test‘, sess.graph)
    # tensorboard --logdir=logs
    # =================
    for step in range(10001):
        batch_x, batch_y = mnist.train.next_batch(100)
        _, ls, train_output = sess.run([optimizer, loss, merge_op],
                                       feed_dict={tf_x: batch_x, tf_y: batch_y, is_training: True})
        if step % 100 == 0:
            acc_test, test_output = sess.run([accuracy, merge_op],
                                             feed_dict={tf_x: mnist.test.images, tf_y: mnist.test.labels,
                                                        is_training: False})
            print(‘Step:‘, step, ‘| train loss: %.4f‘ % ls, ‘| test accuracy: %.2f‘ % acc_test)
            train_writer.add_summary(train_output, step)
            test_writer.add_summary(test_output, step)
    save_path = saver.save(sess, ‘./cnn_mnist.ckpt‘)

with tf.Session() as sess:
    sess.run(init_op)
    saver.restore(sess, ‘./cnn_mnist.ckpt‘)
    acc_test = sess.run(accuracy, feed_dict={tf_x: mnist.test.images,
                                             tf_y: mnist.test.labels,
                                             is_training: False})
    print(acc_test)