import tensorflow as tf
mnist=input_data.read_data_sets('MNIST',one_hot=True)

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre=sess.run(prediction,feed_dict=[xs:v_xs,keep_prob:1])
    correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:1})
    return result

def weight_variable(shape):
    initial=tf.truncated_normal(shape,stddev=0.1)
    return tf.Varible(initial)


def bias_variable(shape):
    initial=tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

def conv2d(x,W):
    #stride [1,x_movement,y_movement,1]
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') #must have strides

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

xs=tf.placeholder(tf.float32,[None,784])
ys=tf.placeholder(tf.float32,[None,10])
keep_prob=tf.placeholder(tf.float32)
x_image=tf.reshape(xs,[-1,28,28,1])
print(x_image.shape)


###conv1 layer
W_conv1=weight_variable([5,5,1,32]) #patch 5*5 in size 1 out size 32
b_conv1=bias_variable([32])
h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)#output size 28*28*32
h_pool1=max_pool_2x2(h_conv1) #output size 14*14*32


#conv2 layer
W_conv2=weight_variable([5,5,32,64]) #patch 5*5 in size 32 out size 64
b_conv2=bias_variable([32])
h_conv2=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)#output size 14*14*64
h_pool2=max_pool_2x2(h_conv1) #output size 7*7*64

#func1 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]) #{n_samples,7,7,64} 变成 [n_samples,7*7*64]
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)

#func2 layer
W_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])
prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_conv2)

#the error between prediction and real data
cross_entropy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess=tf.Session()

#important step
sess.run(tf.initialize_all_variables())





