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

# number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


def compute_accuracy(v_xs, v_ys):  # validation xs and validation ys
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
    correct_prediction = tf.equal(tf.arg_max(y_pre, 1), tf.arg_max(v_ys, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
    return result


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def conv2d(x, W):
    # stride [1, x_movement, y_movement, 1]
    # must have strides[0] = stride[4] = 1
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    # stride [1, x_movement, y_movement, 1]
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# define placeholder for inputs to network

xs = tf.placeholder(tf.float32, [None, 784])/255  # 28x28
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs, [-1, 28, 28, 1])

# conv1 layer #
W_conv1 = weight_variable([5, 5, 1, 32])  # patch 5x5, insize 1, outsize 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)  # output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1)  # output size 14x14x32

# conv2 layer #
W_conv2 = weight_variable([5, 5, 32, 64])  # patch 5x5, insize 32, outsize 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  # output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2)  # output size 7x7x64

# func1 layer #
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
# [n_samples, 7 ,7, 64] ->> [n_samples, 7*7*64]
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)


# func2 layer #
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)


# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))  # loss
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess = tf.Session()
# important step
sess.run(tf.global_variables_initializer())

for i in range(10000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})
    if i % 500 == 0:
        print(compute_accuracy(mnist.test.images[:1000], mnist.test.labels[:1000]))

Accuracy:

Accuracy
Accuracy