#-*-coding:utf-8-*-
import tensorflow as tf

#输入数据
from tensorflow.examples.tutorials.mnist import input_data
#mnist =input_data.read_data_sets("/tmp/data/mnist",one_hot=True)
#本地路径
MNIST_data_folder="/home/syx/PycharmProjects/NN_Rep/data/mnist"
mnist=input_data.read_data_sets(MNIST_data_folder,one_hot=True)

#定义网络超参数
learning_rate=0.001
training_iters= 200000
batch_size =128
display_step = 10

#定义网络的参数
n_input =784 #输入的维度(img shape: 28x28)
n_classes=10 #标记的维度(0-9)
dropout =0.75 #Dropout的概率，输出的可能性

#输出占位符
x=tf.placeholder(tf.float32,[None,n_input])
y=tf.placeholder(tf.float32,[None,n_classes])
keep_prob=tf.placeholder(tf.float32) #dropout

#定义卷积操作
def conv2d(name, x, W, b, strides=1):
    x = tf.nn.conv2d(x, W, strides=[1,strides,strides,1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x, name=name)


#定义池化层操作
def maxpool2d(name, x, k=2):
    return tf.nn.max_pool(x,ksize=[1,k,k,1],strides=[1,k,k,1],padding='SAME',name=name)

#正则化操作
def norm(name,l_input,lsize=4):
    return tf.nn.lrn(l_input,lsize,bias=1.0,alpha=0.001 /9.0,beta=0.75,name=name)

#定义所有网络参数
weights={
    'wc1': tf.Variable(tf.random_normal([11,11,1,96])),
    'wc2': tf.Variable(tf.random_normal([5,5,96,256])),
    'wc3': tf.Variable(tf.random_normal([3,3,256,384])),
    'wc4': tf.Variable(tf.random_normal([3,3,384,384])),
    'wc5': tf.Variable(tf.random_normal([3,3,384,256])),
    'wd1': tf.Variable(tf.random_normal([4*4*256, 4096])),
    'wd2': tf.Variable(tf.random_normal([4096, 4096])),
    'out': tf.Variable(tf.random_normal([4096, 10]))
}

biases={
    'bc1': tf.Variable(tf.random_normal([96])),
    'bc2': tf.Variable(tf.random_normal([256])),
    'bc3': tf.Variable(tf.random_normal([384])),
    'bc4': tf.Variable(tf.random_normal([384])),
    'bc5': tf.Variable(tf.random_normal([256])),
    'bd1': tf.Variable(tf.random_normal([4096])),
    'bd2': tf.Variable(tf.random_normal([4096])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}


#定义整个网络
def alex_net(x, weights, biases, dropout):
    # Reshape input picture
    x= tf.reshape(x, shape=[-1, 28, 28, 1])
    #第一层卷积
    #卷积
    conv1=conv2d('conv1', x, weights['wc1'], biases['bc1'])
    # 下采样
    pool1=maxpool2d('pool1',conv1,k=2)
    # 规范化
    norm1=norm('norm1',pool1,lsize=4)

    #第二层卷积
    #卷积
    conv2=conv2d('conv2', norm1, weights['wc2'], biases['bc2'])
    #最大池化
    pool2 = maxpool2d('pool2',conv2,k=2)
    #规范化
    norm2=norm('norm2',pool2,lsize=4)

    #第三层卷积
     #卷积
    conv3=conv2d('conv3', norm2, weights['wc3'], biases['bc3'])
    #最大池化
    pool3 = maxpool2d('pool3',conv3,k=2)
    #规范化
    norm3 =norm('norm3', pool3, lsize=4)
    #第四层卷积
    conv4=conv2d('conv4', norm3, weights['wc4'], biases['bc4'])
    #第五层卷积
    conv5 = conv2d('conv5', conv4, weights['wc5'], biases['bc5'])
    #下采样
    pool5= maxpool2d('pool5', conv5, k=2)
    #规范化
    norm5 =norm('norm5', pool5, lsize=4)

    #全连接1
    fc1 = tf.reshape(norm5, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matul(fc1, weights['wd1']),biases['bd1'])
    fc1 = tf.nn.relu(fc1)

    #dropout
    fc1 = tf.nn.dropout(fc1, dropout)

    #全连接层2
    fc2 = tf.reshape(fc1, [-1, weights['wd2'].get_shape().as_list()[0]])
    fc2 = tf.add(tf.matmul(fc2, weights['wd2']),biases['bd2'])
    fc2 = tf.nn.relu(fc2)
    #dropout
    fc2 = tf.nn.dropout(fc2, dropout)

    #输出层
    out= tf.add(tf.matmul(fc2, weights['out']),biases['out'])
    return out
#构建模型
pred = alex_net(x, weights, biases, keep_prob)

#定义损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

#评估函数
correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

#初始化变量
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    step = 1
    # 开始训练，直到达到training_iters,即200000
    while step * batch_size < training_iters:
        batch_x,batch_y = mnist.train.next_batch(batch_size)
        sess.run(optimizer,feed_dict={x: batch_x,
                                      y: batch_y,
                                      keep_prob: dropout})
        if step % display_step == 0:
        #计算损失值和准确度，输出
            loss,acc = sess.run([cost,accuracy], feed_dict={x: batch_x, y: batch_y,keep_prob: 1.})
            print("Iter "+ str(step*batch_size)+", Minibatch Loss= "+ \
                      "{:.6f}".format(loss) + ", Training Accuracy= "+ \
                      "{:.5f}".format(acc))
        step += 1
    print("Optimization Finished!")

     #计算测试集的准确度
    print("Testing Accuracy:", \
           sess.run(accuracy, feed_dict={x: mnist.test.images[:256],
                                         y: mnist.test.labels[:256],
                                         keep_prob: 1.}))

