# -*- coding: utf-8 -*-
"""
Created on Tue May  9 16:54:32 2017
@author: 代码医生 qq群：40016981，公众号：xiangyuejiqiren
@blog：http://blog.csdn.net/lijin6249
"""


import  cifar10_input
import tensorflow as tf
import numpy as np


batch_size = 128
data_dir = '/tmp/cifar10_data/cifar-10-batches-bin'
print("begin")
images_train, labels_train = cifar10_input.inputs(eval_data = False,data_dir = data_dir, batch_size = batch_size)
images_test, labels_test = cifar10_input.inputs(eval_data = True, data_dir = data_dir, batch_size = batch_size)
print("begin data")



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)
  
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')  
                        
def avg_pool_6x6(x):
  return tf.nn.avg_pool(x, ksize=[1, 6, 6, 1],
                        strides=[1, 6, 6, 1], padding='SAME')

# tf Graph Input
x = tf.placeholder(tf.float32, [None, 24,24,3]) # mnist data维度 28*28=784
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 数字=> 10 classes


W_conv1 = weight_variable([5, 5, 3, 64])
b_conv1 = bias_variable([64])

x_image = tf.reshape(x, [-1,24,24,3])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 64, 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)


keep_prob = tf.placeholder("float")
W_fc1 = weight_variable([6 * 6 * 64, 256])
b_fc1 = bias_variable([256])

h_pool2_flat = tf.reshape(h_pool2, [-1, 6*6*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)

W_fc2 = weight_variable([256, 128])
b_fc2 = bias_variable([128])
h_fc2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

W_fc3 = weight_variable([128, 10])
b_fc3 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc2, W_fc3) + b_fc3)


cross_entropy = -tf.reduce_sum(y*tf.log(y_conv))

#不同的优化方法测测效果
#train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy)
#train_step = tf.train.AdagradOptimizer(1e-5).minimize(cross_entropy)
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, "float"))


sess = tf.Session()
sess.run(tf.global_variables_initializer())
tf.train.start_queue_runners(sess=sess)
for i in range(15000):#20000
  image_batch, label_batch = sess.run([images_train, labels_train])
  label_b = np.eye(10,dtype=float)[label_batch] #one hot
  
  train_step.run(feed_dict={x:image_batch, y: label_b, keep_prob: 0.5},session=sess)
  
  if i%200 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:image_batch, y: label_b, keep_prob: 1.0},session=sess)
    print( "step %d, training accuracy %g"%(i, train_accuracy))


image_batch, label_batch = sess.run([images_test, labels_test])
label_b = np.eye(10,dtype=float)[label_batch]#one hot
print ("finished！ test accuracy %g"%accuracy.eval(feed_dict={
     x:image_batch, y: label_b, keep_prob: 1.0},session=sess))
