import tensorflow as tf
import numpy as np
import time
import sys
path = '.data/models/tutorials/image/cifar10'
sys.path.append(path)
import cifar10,cifar10_input

max_steps = 3000
batch_size = 128
data_dir = '/tmp/cifar10_data/cifar-10-batches-bin'

def variable_with_weight_loss(shape,stddev,wl):
    var = tf.Variable(tf.truncated_normal(shape,stddev=stddev))
    if wl is not None:
        weight_loss = tf.multiply(tf.nn.l2_loss(var),wl,name='weight_loss')
        tf.add_to_collection('losses',weight_loss)
    return var

cifar10.maybe_download_and_extract()
images_train,labels_train = cifar10_input.distorted_inputs(data_dir,
                                                           batch_size = batch_size)
images_test,labels_test = cifar10_input.inputs(eval_data=True,
                                              data_dir = data_dir,
                                              batch_size=batch_size)

image_holder = tf.placeholder(tf.float32,[batch_size,24,24,3])
label_holder = tf.placeholder(tf.int32,[batch_size])

weight1 = variable_with_weight_loss(shape=[5,5,3,64],stddev=5e-2,wl=0.0)
kernel1 = tf.nn.conv2d(image_holder,weight1,[1,1,1,1],padding='SAME')
bias1 = tf.Variable(tf.constant(0.0,shape=[64]))
conv1 = tf.nn.relu(tf.nn.bias_add(kernel1,bias1))
pool1 = tf.nn.max_pool(conv1,ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME')
norm1 = tf.nn.lrn(pool1,4,bias = 1.0,alpha=0.001/9.0,beta=0.75)

weight2 = variable_with_weight_loss(shape=[5,5,64,64],stddev=5e-2,wl=0.0)
kernel2 = tf.nn.conv2d(norm1,weight2,[1,1,1,1],padding='SAME')
bias2 = tf.Variable(tf.constant(0.1,shape=[64]))
conv2 = tf.nn.relu(tf.nn.bias_add(kernel2,bias2))
norm2 = tf.nn.lrn(conv2,4,bias = 1.0,alpha=0.001/9.0,beta=0.75)
pool2 = tf.nn.max_pool(norm2,ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME')

reshape = tf.reshape(pool2,[batch_size,-1])
dim = reshape.get_shape()[1].value
weight3 = variable_with_weight_loss(shape=[dim,384],stddev=0.04,wl=0.004)
bias3 = tf.Variable(tf.constant(0.1,shape = [384]))
local3 = tf.nn.relu(tf.matmul(reshape,weight3)+bias3)

weight4 = variable_with_weight_loss([384,192],stddev=0.04,wl = 0.004)
bias4 = tf.Variable(tf.constant(0.1,shape=[192]))
local4 = tf.nn.relu(tf.matmul(local3,weight4)+bias4)

weight5 = variable_with_weight_loss([192,10],stddev=1/192.0,wl = 0.0)
bias5 = tf.Variable(tf.constant(0.0,shape=[10]))
logits = tf.nn.relu(tf.matmul(local4,weight5)+bias5)

def loss(logits,labels):
    labels = tf.cast(labels,tf.int64)
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits = logits,labels = labels,name = 'cross_entropy_per_example'
    )
    cross_entropy_mean = tf.reduce_mean(cross_entropy,
                                        name = 'cross_entropy')
    tf.add_to_collection('losses',cross_entropy_mean)

    return tf.add_n(tf.get_collection('losses'),name = 'total_loss')

loss = loss(logits,label_holder)
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)
top_k_op = tf.nn.in_top_k(logits,label_holder,1)

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
tf.train.start_queue_runners()

for step in range(max_steps):
    start_time = time.time()
    image_batch,label_batch = sess.run([images_train,labels_train])

    _,loss_value,acc = sess.run([train_op,loss,top_k_op],
                            feed_dict={image_holder:image_batch,label_holder:label_batch})
    duration = time.time() - start_time

    if step % 10 == 0:
        examples_per_sec = batch_size/duration
        sec_per_batch = float(duration)

        format_str = 'step %d,loss = %.2f, accuracy is %.2f (%.1f examples/sec;%.3f sec/batch)'
        print(format_str % (step,loss_value,np.sum(acc)/batch_size,examples_per_sec,sec_per_batch))

num_examples = 10000
import math
num_iter = int(math.ceil(num_examples/batch_size))
true_count = 0
total_sample_count = num_iter * batch_size
step = 0
while step < num_iter:
    image_batch,label_batch = sess.run([images_test,labels_test])
    predictions = sess.run([top_k_op],feed_dict={image_holder:image_batch,
                                                 label_holder:label_batch})
    true_count += np.sum(predictions)
    step +=1

precision = true_count/total_sample_count
print('precision @ 1 = %.3f'% precision)