zhpmatrix

import tensorflow as tf
from numpy import *
from random import randint

max_length = 2
batch_size = 3

label = array([[[0,0] for _ in range(max_length)] for _ in range(batch_size)])
for i in range(batch_size):
	for j in range(max_length):

zhpmatrix

import tensorflow as tf
from numpy import *
from random import randint

max_length = 3
batch_size = 5

targets = array([[1 for _ in range(max_length)] for _ in range(batch_size)])
logits = array([[[randint(0,10)/10,randint(0,10)/10] for _ in range(max_length)] for _ in range(batch_size)])
sequence_length = array([randint(1,max_length) for _ in range(batch_size)])

zhpmatrix

import math
import numpy as np

def get_cosh(x):
    N = 100000
    t = 0.0
    for i in range(0, N):
        t += math.pow(x,2*i)*1.0/math.factorial(2*i)*1.0
    return 2 * t * 1.0 / 2

zhpmatrix

function top = nms(boxes, overlap)
% top = nms_fast(boxes, overlap)
% Non-maximum suppression. (FAST VERSION)
% Greedily select high-scoring detections and skip detections
% that are significantly covered by a previously selected
% detection.
% NOTE: This is adapted from Pedro Felzenszwalb's version (nms.m),
% but an inner loop has been eliminated to significantly speed it
% up in the case of a large number of boxes
% Tomasz Malisiewicz (tomasz@cmu.edu)

zhpmatrix

import argparse
parser = argparse.ArgumentParser(description='Main Application')
parser.add_argument('--n',type=str,default='magic',help='name of dataset')
parser.add_argument('--m',type=str,default='m11',help='method name(11,21,22)')
parser.add_argument('--dr',type=int,default=1,help='random state of train_test_split')

args = parser.parse_args()
train_X, test_X, label_X, label_Y = get_data(args.n, args.dr)

zhpmatrix

#include <stdio.h>
#include <xmmintrin.h>
int main(){
	short in1[] = {1,2,3,4};
	short in2[] = {2,3,4,5};
	int out1;
	int out2;
	__m64 m1;
	__m64 m2;
	__m128 m128;

zhpmatrix

from retrying import retry
from random import random

@retry(stop_max_attempt_number=7)
def do():
    if random() > 0.5:
        print('Success!')
    else:
        print('Fail!')
        raise

zhpmatrix

python中try/except/else/finally语句的完整格式如下所示：
try:
     Normal execution block
except A:
     Exception A handle
except B:
     Exception B handle
except:
     Other exception handle
else:

zhpmatrix

import numpy as np

def drop_first_last(grades):
    first, *middle, last = grades
    return middle

def args(*args):
    for value in args:
        print(value)

zhpmatrix

def dropout(x, level, noise_shape=None, seed=None):
    """Sets entries in `x` to zero at random, while scaling the entire tensor.

    # Arguments
        x: tensor
        level: fraction of the entries in the tensor
            that will be set to 0.
        noise_shape: shape for randomly generated keep/drop flags,
            must be broadcastable to the shape of `x`