import tensorflow as tf

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):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

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

def createModel():
    x = tf.placeholder(tf.float32, [None, 784])
    x_image = tf.reshape(x, [-1,28,28,1])

    # first layer
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    # second layer
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    # fully connected layer
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])
    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)

    # dropout
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # readout via softmax
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    #convolutional network result
    y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

    #give back the non hidden stuff
    return x, keep_prob, y_conv

#setup model
x, keep_prob, y_conv = createModel()


#restore
sess = tf.Session()

def restore():
    saver = tf.train.Saver()
    saver.restore(sess, "./mnist-model.ckpt")
    print("Model restored.")

def predict(images):
    #prediction, index of max confidence in one-hot is the number
    prediction = tf.argmax(y_conv,1)
    # prediction = [tf.reduce_max(y_conv),tf.argmax(y_conv,1)]
    #run
    result = sess.run(prediction, feed_dict={x: images, keep_prob: 1.0})
    # result = sess.run(y_conv, feed_dict={x: images, keep_prob: 1.0})
    return result

def close():
    sess.close()

def train(images):
    sess.run(tf.initialize_all_variables())

    for i in range(20000):
        batch = mnist.train.next_batch(50)
        if i%100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})
            print("step %d, training accuracy %g"%(i, train_accuracy))
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    print("test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

    save_path = saver.save(sess, "./mnist-model.ckpt")
    print("Model saved in file: %s" % save_path)

    writer = tf.train.SummaryWriter('./mnist_logs',sess.graph)
