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

mnist = input_data.read_data_sets('MNIST_data', one_hot = True)


def add_layers(inputs, in_size,out_size,activation_function = None):
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

def compute_accuracy(v_xs,v_ys):
    global predictions
    y_pre = sess.run(predictions,feed_dict= {xs: v_xs})
    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # change the data type
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})
    return result



# define placeholder for inputs to network

xs = tf.placeholder(tf.float32,[None,784]) # 28 * 28
ys = tf.placeholder(tf.float32, [None,10])

#add output layer
predictions = add_layers(xs,784,10,activation_function = tf.nn.softmax) #in_size = 784, out_size is result

# loss function # use cross_entropy

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(predictions),reduction_indices = [1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()
sess.run(tf.global_variables_initializer())

for i in range(2000):
    batch_xs, batch_ys = mnist.train.next_batch(100) # use mini batch to learn, save time
    sess.run(train_step, feed_dict={xs:batch_xs, ys: batch_ys})
    if i % 50 == 0:
        print(compute_accuracy(mnist.test.images, mnist.test.labels))