tf_mnist.py

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

# mnist = input_data.read_data_sets("mnist_data")

INPUT_NODE = 784
OUTPUT_NODE = 10

LAYER1_NODE = 500
BATCH_SIZE = 100

LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99

REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000

MOVING_AVERAGE_DECAY = 0.99

def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):
    # if not using Exponential Moving Average
    if avg_class == None:
        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)
        return tf.matmul(layer1, weights2) + biases2
    # if using Exponential Moving Average
    else:
        layer1 = tf.nn.relu(
            tf.matmul(input_tensor, avg_class.average(weights1)) +
                      avg_class.average(biases1))
        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)

def train(mnist):
    x = tf.placeholder(dtype=tf.float32, shape=[None, INPUT_NODE], name='x-input')
    y_ = tf.placeholder(dtype=tf.float32, shape=[None, OUTPUT_NODE], name='y-input')

    weights1 = tf.Variable(
        tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1)
    )
    biases1 = tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))

    weights2 = tf.Variable(
        tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1)
    )
    biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))

    # not using average
    y = inference(
        x, None, weights1, biases1, weights2, biases2
    )

    # ================== using average
    global_step = tf.Variable(0, trainable=False)

    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)

    variable_averages_op = variable_averages.apply(tf.trainable_variables())

    average_y = inference(
        x, variable_averages, weights1, biases1, weights2, biases2
    )
    # =================================================================================

    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
    cross_entropy_mean = tf.reduce_mean(cross_entropy)

    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
    regularization = regularizer(weights1) + regularizer(weights2)

    loss = cross_entropy_mean + regularization

    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,
                                               global_step,
                                               mnist.train.num_examples / BATCH_SIZE,
                                               LEARNING_RATE_DECAY)

    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

    # with tf.control_dependencies([train_step, variable_averages_op]):
    #     train_op = tf.no_op('train')

    train_op = tf.group(train_step, variable_averages_op)

    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))
    # correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    with tf.Session() as sess:
        tf.initialize_all_variables().run()
        validate_feed = {x: mnist.validation.images,
                         y_: mnist.validation.labels}

        test_feed = {x: mnist.test.images, y_: mnist.test.labels}

        for i in range(TRAINING_STEPS):
            if i % 1000 == 0:
                validate_acc, lss = sess.run([accuracy, loss], feed_dict=validate_feed)
                print("After %d training step(s), validation accuracy (using average model) is %g, loss is %g"
                      % (i, validate_acc, lss))

            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            sess.run(train_op, feed_dict={x: xs, y_: ys})

        test_acc = sess.run(accuracy, feed_dict=test_feed)
        print("After %d training step(s), test accuracy (using average model) is %g"
              % (TRAINING_STEPS, test_acc))


mnist = input_data.read_data_sets("mnist_data", one_hot=True)
train(mnist)

# xa, ya = mnist.train.next_batch(10)
# with tf.Session() as sess:
#     a = sess.run(tf.argmax(ya, 1))
#     print(a)