import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_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 avg_class==None:
        layer1 = tf.nn.relu(tf.matmul(input_tensor,weights1)+biases1)
        return tf.matmul(layer1,weights2)+biases2
    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(tf.float32,[None,input_node],name='x-input')
    y_=tf.placeholder(tf.float32,[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]))

    y=inference(x,None,weights1,biases1,weights2,biases2)

    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,5500/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(name='train')
    correct_prediction=tf.equal(tf.argmax(average_y,1),tf.argmax(y_,1))
    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

    with tf.Session() as sess:
        tf.global_variables_initializer().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=sess.run(accuracy,feed_dict=validate_feed)
                print("After {} steps,validate accuracy is {}".format(i,validate_acc))

            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 {} steps,test accuracy is {}".format(training_steps, test_acc))



def main(argv=None):
    mnist = input_data.read_data_sets(r"C:\Users\Natsu\Desktop\MacroMap\TENSORV\data", one_hot=True)
    train(mnist)

if __name__ == '__main__':
    tf.app.run()


