import tensorflow as tf
import numpy as np
import numpy as np
from utils.cifar10_input import CIFAR_10_DATA

cifar_1 = CIFAR_10_DATA(one_hot = True)
cifar_1 = cifar_1.get_train_data()

cifar_2 = CIFAR_10_DATA(data_batch_name = "data_batch_2")
cifar_2 = cifar_2.get_train_data()

x = tf.placeholder("float", shape=[None, 3072])
y_ = tf.placeholder("float", shape=[None, 10])

# data_1 用做训练好了，data_2 中选出500测试一下

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

# b
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')

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

W_conv1 = weight_variable([5, 5, 3, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x,[-1,32,32,3])

# use relu as the function  and use max pooling at the end
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# the second 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)


# full-connect layer:Now we got a image witch with a size into 7x7
W_fc1 = weight_variable([8 * 8 * 64, 2048])
b_fc1 = bias_variable([2048])

h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)


keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([2048, 10])
b_fc2 = bias_variable([10])

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

sess = tf.InteractiveSession()
# 交叉熵 损失函数的计算
cross_entropy = -tf.reduce_sum(y_*tf.log(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, "float"))
sess.run(tf.initialize_all_variables())


for i in range(10000):
  batch = cifar_1.next_batch(50)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:batch[0], y_: batch[1], keep_prob: 1.0})
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

