# Import data

mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

# Create the model

x = tf.placeholder(tf.float32, [None, 784])

W_2 = tf.Variable(tf.random_normal([784, 30]))

b_2 = tf.Variable(tf.random_normal([30]))

z_2 = tf.matmul(x, W_2) + b_2

a_2 = tf.sigmoid(z_2)

W_3 = tf.Variable(tf.random_normal([30, 10]))

b_3 = tf.Variable(tf.random_normal([10]))

z_3 = tf.matmul(a_2, W_3) + b_3

a_3 = tf.sigmoid(z_3)

# Define loss and optimizer

y_ = tf.placeholder(tf.float32, [None, 10])

# loss = tf.reduce_mean(tf.norm(y_ - a_3, axis=1)**2) / 2

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=z_3))

train_step = tf.train.GradientDescentOptimizer(3.0).minimize(loss)

sess = tf.InteractiveSession()

tf.global_variables_initializer().run()

# Train

best = 0

for epoch in range(30):

for _ in range(5000):

batch_xs, batch_ys = mnist.train.next_batch(10)

sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

# Test trained model

correct_prediction = tf.equal(tf.argmax(a_3, 1), tf.argmax(y_, 1))

accuracy = tf.reduce_sum(tf.cast(correct_prediction, tf.int32))

accuracy_currut = sess.run(accuracy, feed_dict={x: mnist.test.images,

y_: mnist.test.labels})

print("Epoch %s: %s / 10000" % (epoch, accuracy_currut))

best = (best, accuracy_currut)[best <= accuracy_currut]

# Test trained model