#%from tensorflow.examples.tutorials.mnist import input_data
#mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
#x = tf.placeholder(tf.float32, [None, 784])
#W = tf.Variable(tf.zeros([784, 10]))
#b = tf.Variable(tf.zeros([10]))
#y = tf.nn.softmax(tf.matmul(x, W) + b)
#y_ = tf.placeholder(tf.float32, [None, 10])
#cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
#train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
#sess = tf.InteractiveSession()
#tf.global_variables_initializer().run()
#for _ in range(1000):
 # batch_xs, batch_ys = mnist.train.next_batch(100)
  #sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
   # correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
#accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))%


import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
try:
    from scipy import misc
except ImportError:
    !pip install scipy
    from scipy import misc

    ############################################ train
training_size = 300
img_size = 20*20*3

class Struct:
    "A structure that can have any fields defined."
    def __init__(self, **entries): self.__dict__.update(entries)
training_images=[];
training_labels=[];
training_data= Struct(training_set=training_images, labels=training_labels)
training_images = np.empty(shape=(training_size,20,20,3))
import glob
i = 0
for filename in glob.glob('D:/Minutia/PrincipleWrinkleMinutia/*.jpg'):
    image = misc.imread(filename)
    training_images[i] = image
    i+=1
print(training_images[0].shape)

a= [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
    2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2]
training_labels = tf.one_hot(a,3)
sess = tf.Session()
sess.run(training_labels)

#################################################### test
test_size = 300
img_size = 20*20*3
class Struct:
    "A structure that can have any fields defined."
    def __init__(self, **entries): self.__dict__.update(entries)
test_images=[];
test_labels=[];
test_data= Struct(training_set=test_images, labels=test_labels)

test_images = np.empty(shape=(test_size,20,20,3))
import glob
i = 0
for filename in glob.glob('D:/Minutia/PrincipleWrinkleMinutia/*.jpg'):
    image = misc.imread(filename)
    test_images[i] = image
    i+=1
print(test_images[0].shape)
a= [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
    2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2]
test_labels = tf.one_hot(a,3)
sess = tf.Session()
sess.run(test_labels)

x = tf.placeholder(tf.float64, [None, img_size])
W = tf.Variable(tf.zeros([img_size, 3], dtype=tf.float64))
b = tf.Variable(tf.zeros([3], dtype=tf.float64))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float64, [None, 3])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

for _ in range(300):
 batch_xs, batch_ys = training_data.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
correct_prediction = tf.equal(training_images, training_labels)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))
print(sess.run(accuracy, feed_dict={x: test_images, y_: test_labels}))