from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

import tensorflow as tf
sess = tf.InteractiveSession()

# placeholders
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])

# variables
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

sess.run(tf.global_variables_initializer())

## prediction
y = tf.matmul(x,W) + b
cross_entropy = tf.reduce_mean(
	tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits = y))

# train the model
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

for _ in range(1000):
	batch = mnist.train.next_batch(100)
	train_step.run(feed_dict={x:batch[0],y_:batch[1]})

correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print(accuracy.eval(feed_dict={x:mnist.test.images, y_:mnist.test.labels}))

## improvement
# weights
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)
# convolution and pooling
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')

# first convolutional layer
W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x,[-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# second convolutional 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)

# densely 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 layer
W_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop,W_fc2) + b_fc2

# train and evaluate the model
cross_entropy = tf.reduce_mean(
	tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())
	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:0.5})
			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}))