jerheff

def binary_crossentropy_with_ranking(y_true, y_pred):
    """ Trying to combine ranking loss with numeric precision"""
    # first get the log loss like normal
    logloss = K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)
    
    # next, build a rank loss
    
    # clip the probabilities to keep stability
    y_pred_clipped = K.clip(y_pred, K.epsilon(), 1-K.epsilon())

JerryZhong

def sigmoid(X):
    '''Compute the sigmoid function '''
    #d = zeros(shape=(X.shape))

    den = 1.0 + e ** (-1.0 * X)

    d = 1.0 / den

    return d

JerryZhong

# Back-Propagation Neural Networks
# another way: solve it as a Regression Problem
# Written in Python.  See http://www.python.org/
# Modified by JSun to solve the problem here: http://www.weibo.com/1497035431/ynPEvC78V
# Neil Schemenauer <[email protected]>

import math
import random
import string

lastland

# -*- coding=utf-8 -*-
import collections

# Usage:
#   我的做法是把WordsDetector.py里的结果输出到文件，
#   然后把文件名放到下面的names列表中，运行本程序。

names = ['name0',
         'name1',
         'name2',

fxsjy

# Back-Propagation Neural Networks
# another way: solve it as a Regression Problem
# Written in Python.  See http://www.python.org/
# Modified by JSun to solve the problem here: http://www.weibo.com/1497035431/ynPEvC78V
# Neil Schemenauer <[email protected]>

import math
import random
import string