ceshiji.py

#coding:utf-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os
import struct
import numpy as np
from matplotlib import pyplot as plt
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import cv2,csv
import res_dnn

			
def encode_labels( y, k):
	onehot = np.zeros((y.shape[0],k ))
	for idx, val in enumerate(y):
		onehot[idx,val] = 1.0 
	return onehot

def load_mnist(path, kind='train'):
	if kind=='train':
		labels_path=os.path.abspath('../mnist/train-labels-idx1-ubyte')		
		images_path=os.path.abspath('../mnist/train-images-idx3-ubyte')
	else:
		labels_path=os.path.abspath('../mnist/t10k-labels-idx1-ubyte')		
		images_path=os.path.abspath('../mnist/t10k-images-idx3-ubyte')
	
	with open(labels_path, 'rb') as lbpath:
		magic, n = struct.unpack('>II',
								 lbpath.read(8))
		labels = np.fromfile(lbpath,
							 dtype=np.uint8)

	with open(images_path, 'rb') as imgpath:
		magic, num, rows, cols = struct.unpack(">IIII",
											   imgpath.read(16))
		images = np.fromfile(imgpath,
							 dtype=np.uint8).reshape(len(labels), 784)

	return images, labels

BATCH_SIZE = 100 
LEARNING_RATE_BASE = 0.01
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 5000
MOVING_AVERAGE_DECAY = 0.99 
MODEL_SAVE_PATH = "./lenet5/"
MODEL_NAME = "lenet5_model"
INPUT_NODE = 784
OUTPUT_NODE = 10
IMAGE_SIZE = 28
NUM_CHANNELS = 1
NUM_LABELS = 10
display_step = 100
learning_rate_flag=True


def train(X_test,y_test_lable):
	x_ = tf.placeholder(tf.float32, [None, INPUT_NODE],name='x-input')	
	x = tf.reshape(x_, shape=[-1, 28, 28, 1])
	
	y_ = tf.placeholder(tf.float32, [None,OUTPUT_NODE], name='y-input')
	regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
	cosine,loss= res_dnn.inference(x,True,regularizer,tf.argmax(y_,1))

	pred_max=tf.argmax(cosine,1)
	y_max=tf.argmax(y_,1)
	correct_pred = tf.equal(pred_max,y_max)
	accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
	
	saver = tf.train.Saver()
	with tf.Session() as sess:
	        saver.restore(sess,"res_DNN/res_model") 
		X_test = np.reshape(X_test, (
                                        X_test.shape[0],
                                        IMAGE_SIZE,
                                        IMAGE_SIZE,
                                        NUM_CHANNELS))

		acc = sess.run(accuracy, feed_dict={x: X_test, y_:y_test_lable})
                print('Test accuracy: %.2f%%' % (acc * 100))
		
def main(argv=None):
	X_test, y_test = load_mnist('mnist', kind='t10k')
	mms=MinMaxScaler()
        X_test=mms.fit_transform(X_test) 
        y_test_lable = encode_labels(y_test,10) 
	train(X_test,y_test_lable)

if __name__ == '__main__':
        start = time.time()
	main()
        end = time.time()
        print  end-start
        print  'I have trained %d mins and %d seconds'%((end-start)/60,(end-start)%60)