prompt: beautiful, masterpiece, best quality, extremely detailed face, perfect lighting, (1girl, solo, 1boy, 1girl, NemoNelly, Slight penetration, lying, on back, spread legs:1.5), street, crowd, ((skinny)), ((puffy eyes)), brown hair, medium hair, cowboy shot, medium breasts, swept bangs, walking, outdoors, sunshine, light_rays, fantasy, rococo, hair_flower,low tied hair, smile, half-closed eyes, dating, (nude), nsfw, (heavy breathing:1.5), tears, crying, blush, wet, sweat, <lora:koreanDollLikeness_v15:0.4>, <lora:povImminentPenetration_ipv1:0>, <lora:breastinclassBetter_v14:0.1>

$ git clone https ://github.com/AUTOMATIC1111/stable-diffusion-webui.git



$ cd stable-diffusion-webui



$ git checkout 22bcc7be428c94e9408f589966c2040187245d81



# 我们需要 CPU 版本的 torch

$ export TORCH_COMMAND="pip install torch==1.13.1 torchvision==0.14.1 --index-url https: //download.pytorch.org/whl/cpu"

$ export USE_NNPACK=0



# 前 4 个参数是为了让其运行在 CPU 上, 最后一个参数是让 WebUI 可以远程访问

$ bash webui.sh --skip-torch-cuda-test --no-half --precision full --use-cpu all --listen

Stable Diffusion 是一种通过文字描述创造出图像的 AI 模型. 它是一个开源软件, 有许多人愿意在网络上免费分享他们的计算资源, 使得新手可以在线尝试.

$ dd if=/dev/zero of=/mnt/swap bs=64M count=256

$ chmod 0600 /mnt/swap

$ mkswap /mnt/swap

$ swapon /mnt/swap

bash webui.sh --skip-torch-cuda-test --no-half --precision full --use-cpu all --listen

$ cd models/Stable-diffusion​

# Stable diffusion v1.4

# 下面的URL超链接过长有省略号，需要右键复制url

$ wget https://huggingface.co/CompVis ... .ckpt

# Stable diffusion v1.5

$ wget https://huggingface.co/runwaym ... .ckpt

# F222

$ wget https://huggingface.co/acheong ... .ckpt

# Anything V3

$ wget 

https://huggingface.co/Linaqru ... nsors

# Open Journey

$ wget https://huggingface.co/prompth ... .ckpt

# DreamShaper

$ wget https://civitai.com/api/download/models/5636 -O dreamshaper_331BakedVae.safetensors

# ChilloutMix

$ wget https://civitai.com/api/download/models/11745 -O chilloutmix_NiPrunedFp32Fix.safetensors

# Robo Diffusion

$ wget 

https://huggingface.co/nousr/r ... .ckpt

# Mo-di-diffusion

$ wget 

https://huggingface.co/nitroso ... .ckpt

# Inkpunk Diffusion

$ wget 

https://huggingface.co/Envvi/I ... .ckpt

{

    "txt2img/Batch size/value": 4,

    "txt2img/Width/value": 480,

    "txt2img/Height/value": 270

}

model: chilloutmix_NiPrunedFp32Fix.safetensors



prompt: beautiful, masterpiece, best quality, extremely detailed face, perfect lighting, (1girl, solo, 1boy, 1girl, NemoNelly, Slight penetration, lying, on back, spread legs:1.5), street, crowd, ((skinny)), ((puffy eyes)), brown hair, medium hair, cowboy shot, medium breasts, swept bangs, walking, outdoors, sunshine, light_rays, fantasy, rococo, hair_flower,low tied hair, smile, half-closed eyes, dating, (nude), nsfw, (heavy breathing:1.5), tears, crying, blush, wet, sweat, <lora:koreanDollLikeness_v15:0.4>, <lora:povImminentPenetration_ipv1:0>, <lora:breastinclassBetter_v14:0.1>

prompt: paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, (ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit, bad body, pubic

$ cd models/Lora



$ wget 

https://huggingface.co/amornln ... nsors

$ wget 

https://huggingface.co/samle/s ... nsors

$ wget https://huggingface.co/jomcs/N ... nsors

# -*- coding: utf-8 -*-

# @Time : 2021/4/24 20:50

# @File : baidu_voice_service.py

# @Author : Rocky C@www.30daydo.com



import os

import time



import requests

import sys

import pickle

sys.path.append('..')

from config import API_KEY,SECRET_KEY

from base64 import b64encode

from pathlib import PurePath

import subprocess



BASE = PurePath(__file__).parent



# 需要识别的文件

# 文件格式

# 文件后缀只支持 pcm/wav/amr 格式，极速版额外支持m4a 格式



CUID = '24057753' # 随意

# 采样率

RATE = 16000  # 固定值





ASR_URL = 'http://vop.baidu.com/server_api'



#测试自训练平台需要打开以下信息， 自训练平台模型上线后，您会看见 第二步：“”获取专属模型参数pid:8001，modelid:1234”，按照这个信息获取 dev_pid=8001，lm_id=1234

'''

http://vop.baidu.com/server_api

1537	普通话(纯中文识别)	输入法模型	有标点	支持自定义词库

1737	英语	英语模型	无标点	不支持自定义词库

1637	粤语	粤语模型	有标点	不支持自定义词库

1837	四川话	四川话模型	有标点	不支持自定义词库

1936	普通话远场

'''

DEV_PID = 1737



SCOPE = 'brain_enhanced_asr'  # 有此scope表示有asr能力，没有请在网页里开通极速版





class DemoError(Exception):

    pass





TOKEN_URL = 'http://openapi.baidu.com/oauth/2.0/token'



def fetch_token():



    params = {'grant_type': 'client_credentials',

              'client_id': API_KEY,

              'client_secret': SECRET_KEY}

    r = requests.post(

        url=TOKEN_URL,

        data=params

    )



    result = r.json()

    if ('access_token' in result.keys() and 'scope' in result.keys()):

        if SCOPE and (not SCOPE in result['scope'].split(' ')):  # SCOPE = False 忽略检查

            raise DemoError('scope is not correct')



        return result['access_token']



    else:

        raise DemoError('MAYBE API_KEY or SECRET_KEY not correct: access_token or scope not found in token response')





"""  TOKEN end """



def dump_token(token):

    with open(os.path.join(BASE,'token.pkl'),'wb') as fp:

        pickle.dump({'token':token},fp)



def load_token(filename):



    if not os.path.exists(filename):

        token=fetch_token()

        dump_token(token)

        return token

    else:

        with open(filename,'rb') as fp:

            token = pickle.load(fp)

            return token['token']



def recognize_service(token,filename):

    FORMAT = filename[-3:]

    with open(filename, 'rb') as speech_file:

        speech_data = speech_file.read()



    length = len(speech_data)

    if length == 0:

        raise DemoError('file %s length read 0 bytes' % filename)



    b64_data = b64encode(speech_data)

    params = {'cuid': CUID, 'token': token, 'dev_pid': DEV_PID,'speech':b64_data,'len':length,'format':FORMAT,'rate':RATE,'channel':1}



    headers = {

        'Content-Type':'application/json',

    }

    r = requests.post(url=ASR_URL,json=params,headers=headers)

    return r.json()



def rate_convertor(filename):

    filename = filename.split('.')[0]

    CMD=f'ffmpeg.exe -y -i {filename}.mp3 -ac 1 -ar 16000 {filename}.wav'

    try:

        p=subprocess.Popen(CMD, stdin=subprocess.PIPE)

        p.communicate()

        time.sleep(1)

    except Exception as e:

        print(e)

        return False,None

    else:

        return True,f'{filename}.wav'



def clear(file):

    try:

        os.remove(file)

    except Exception as e:

        print(e)



def get_voice_text(audio_file):



    filename = 'token.pkl'

    token = load_token(filename)

    convert_status,file = rate_convertor(audio_file)

    clear(file)

    if not convert_status:

        return None



    result = recognize_service(token,file)



    return result['result'][0]



if __name__ == '__main__':

    get_voice_text('1.mp3')

# -*- coding: utf-8 -*-

# @Time : 2021/4/25 15:16

# @File : download_mp3.py

# @Author : Rocky C@www.30daydo.com







#!/usr/bin/env python3

import os

import subprocess

import time

import re

import requests

import urllib.request

import zipfile

import io

from google.cloud import speech_v1

from random import randint, uniform

from bs4 import BeautifulSoup

from selenium import webdriver

from selenium.common.exceptions import NoSuchElementException

from selenium.webdriver.common.by import By

from selenium.webdriver.common.action_chains import ActionChains

from selenium.webdriver.support import expected_conditions as EC

from selenium.webdriver.support.ui import WebDriverWait

from google_recaptcha.baidu_voice_service import get_voice_text,clear





class Gcaptcha:



    def __init__(self, url):

        self.response = None



        self.attempts = 0

        self.successful = 0

        self.failed = 0

        self.solved = 0



        self.mp3 = 'audio.mp3'

        # self.wav='audio.wav'



        # Set Chrome to run in headless mode and mute the audio

        opts = webdriver.ChromeOptions()

        # opts.headless = True

        opts.add_argument("--mute-audio")

        # opts.add_argument("--headless",)



      

        CHROME_PATH = r'C:\git\EZProject\bin\chromedriver.exe'

        self.driver = webdriver.Chrome(executable_path=CHROME_PATH,options=opts)

        self.driver.maximize_window()

        self.driver.get(url)

        self.__bypass_webdriver_check()



        # Initialize gcaptcha solver

        self.__initialize()



        while True:

            # Download MP3 file

            mp3_file = self.__download_mp3()



            # Transcribe MP3 file

            result = get_voice_text(self.mp3)



            # audio_transcription = transcribe(mp3_file)

            # self.transcription.attempts += 1



            # If the MP3 file is properly transcribed

            if result is not None:

                # self.transcription.successful += 1



                # Verify transcription

                verify = self.__submit_transcription(result)



                # Transcription successful with confidence >60%

                if verify:

                    gcaptcha_response = self.__get_response()

                    self.response = gcaptcha_response

                    # self.recaptcha.solved += 1



                    # Delete MP3 file



                    self.driver.close()

                    self.driver.quit()

                    break

                # Multiple correct solutions required. Solving again.

                else:

                    self.solved += 1

                    clear(self.mp3)



            # If the MP3 file could not be transcribed

            else:

                self.failed += 1

                clear(self.mp3)



                # Click on the "Get a new challenge" button to use a new MP3 file

                self.__refresh_mp3()

                # time.sleep(uniform(2, 4))







    def __initialize(self):

        # Access initial gcaptcha iframe

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=a]'))

        self.__bypass_webdriver_check()



        # Click the gcaptcha checkbox

        checkbox = self.driver.find_element(By.CSS_SELECTOR, '#recaptcha-anchor')

        self.__mouse_click(checkbox)



        # Go back to original content to access second gcaptcha iframe

        self.driver.switch_to.default_content()



        # Wait roughly 3 seconds for second gcaptcha iframe to load

        time.sleep(uniform(2.5, 3))



        # Find second gcaptcha iframe

        gcaptcha = WebDriverWait(self.driver, 10).until(

            EC.presence_of_element_located((By.CSS_SELECTOR, 'iframe[name^=c]'))

        )



        # Access second gcaptcha iframe

        self.driver.switch_to.frame(gcaptcha)

        self.__bypass_webdriver_check()



        # Click the audio button

        audio_button = WebDriverWait(self.driver, 10).until(

            EC.presence_of_element_located((By.CSS_SELECTOR, '.rc-button-audio'))

        )

        self.__mouse_click(audio_button)

        time.sleep(0.5)



    def __mouse_click(self, element):

        cursor = ActionChains(self.driver)

        cursor.move_to_element(element)

        cursor.pause(uniform(0.3, 0.5))

        cursor.click()

        cursor.perform()



    def __bypass_webdriver_check(self):

        self.driver.execute_script(

            'const newProto = navigator.__proto__; delete newProto.webdriver; navigator.__proto__ = newProto;')



    def __download_mp3(self):

        self.driver.switch_to.default_content()

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=c]'))

        self.__bypass_webdriver_check()



        # Check if the Google servers are blocking us

        if len(self.driver.find_elements(By.CSS_SELECTOR, '.rc-doscaptcha-body-text')) == 0:

            audio_file = WebDriverWait(self.driver, 10).until(

                EC.presence_of_element_located((By.CSS_SELECTOR, '.rc-audiochallenge-tdownload-link'))

            )



            # Click the play button

            play_button = WebDriverWait(self.driver, 10).until(

                EC.presence_of_element_located((By.CSS_SELECTOR, '.rc-audiochallenge-play-button > button'))

            )

            self.__mouse_click(play_button)



            # Get URL of MP3 file

            audio_url = audio_file.get_attribute('href')



            # Predefine the MP3 file name



            # Download the MP3 file

            try:

                urllib.request.urlretrieve(audio_url, self.mp3)

            except Exception as e:

                print(e)

                return None

            else:

                return self.mp3

        else:

            Error('Too many requests have been sent to Google. You are currently being blocked by their servers.')

            exit(-1)



    def __refresh_mp3(self):

        self.driver.switch_to.default_content()

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=c]'))

        self.__bypass_webdriver_check()



        # Click on the refresh button to retrieve a new mp3 file

        refresh_button = self.driver.find_element(By.CSS_SELECTOR, '#recaptcha-reload-button')

        self.__mouse_click(refresh_button)



    def __submit_transcription(self, text):

        self.driver.switch_to.default_content()

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=c]'))

        self.__bypass_webdriver_check()



        # Input field for response

        input_field = self.driver.find_element(By.CSS_SELECTOR, '#audio-response')



        # Instantly type the full text without delays because Google isn't checking delays between keystrokes

        input_field.send_keys(text)



        # Click "Verify" button

        verify_button = self.driver.find_element(By.CSS_SELECTOR, '#recaptcha-verify-button')

        self.__mouse_click(verify_button)



        # Wait roughly 3 seconds for verification to complete

        time.sleep(uniform(2, 3))



        self.driver.switch_to.default_content()

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=a]'))

        self.__bypass_webdriver_check()



        # Check to see if verified by recaptcha

        try:

            self.driver.find_element(By.CSS_SELECTOR, '.recaptcha-checkbox-checked')

        except NoSuchElementException:

            return False

        else:

            return True



    def __get_response(self):

        # Switch back to main parent window and get gcaptcha response

        self.driver.switch_to.default_content()

        response = self.driver.find_element(By.CSS_SELECTOR, '#g-recaptcha-response').get_attribute('value')

        return response





class Error(Exception):

    def __init__(self, message):

        get_files = os.listdir()

        match_regex = re.compile(r'^audio\d+.mp3|chromedriver_\w+\d+.zip$')

        filtered_files = [f for f in get_files if match_regex.match(f)]



        for file in filtered_files:

            clear(file)



        raise Exception(f'ERROR: {message}')





if __name__=='__main__':

    gcaptcha = Gcaptcha('https://www.google.com/recaptcha/api2/demo')

ffmpeg.exe -i 3.mp3 -ac 1 -ar 16000 16k.wav

#Read two files and merge

df1 = pd.read_csv('../data/ipo_list', sep='\t', index_col='code')

df2 = pd.read_csv('../data/ipo_details', sep= '\t', index_col = 0)

#Use combine_first to avoid duplicate columns

df = df1.combine_first(df2)

df.columns.values



array(['area', 'banks', 'buy_ratio', 'category', 'date', 'draw_prob',

       'eipo', 'firstday_performance', 'hk_portion', 'ipo_price',

       'ipo_price_range', 'market_type', 'name', 'now_price', 'one_hand',

       'predict_profile_market_ratio', 'predict_profit_ratio',

       'profit_ratio', 'recommender', 'sales', 'shares_per_hand',

       'stock_type', 'total_performance', 'total_value', 'website'], dtype=object)

# Drop unrelated columns

to_del = ['date', 'banks', 'eipo', 'name', 'now_price', 'website', 'total_performance','predict_profile_market_ratio', 'predict_profit_ratio', 'profit_ratio']

for item in to_del:

    del df[item]

  

#Drop non_public ipo stocks

df = df[df.draw_prob.notnull()]

def per2float(x):

    if not pd.isnull(x):

        x = x.strip('%')

        return float(x)/100.

    else:

        return x

    

#Format percentage

df['draw_prob'] = df['draw_prob'].apply(per2float)

df['firstday_performance'] = df['firstday_performance'].apply(per2float)

df['hk_portion'] = df['hk_portion'].apply(per2float)

def buy_ratio_process(x):

    if x == '认购不足':

        return 0.0

    else:

        return float(x)



#Format buy_ratio

df['buy_ratio'] = df['buy_ratio'].apply(buy_ratio_process)

def get_low_bound(x):

    if ',' in str(x):

        x = x.replace(',', '')

    try:

        if pd.isnull(x) or '-' not in x:

            return float(x)

        else:

            x = x.split('-')

            return float(x[0])

    except Exception as e:

        print(e)

        print(x)



def get_up_bound(x):

    if ',' in str(x):

        x = x.replace(',', '')

    try:

        if pd.isnull(x) or '-' not in x:

            return float(x)

        else:

            x = x.split('-')

            return float(x[1])

    except Exception as e:

        print(e)

        print(x)

        

def get_ipo_range_prop(x):

    if pd.isnull(x):

        return x

    low_bound = get_low_bound(x)

    up_bound = get_up_bound(x)

    return (up_bound-low_bound)*2/(up_bound+low_bound)

  

#Merge ipo_price_range to proportion of middle

df['ipo_price_range_ratio'] = df['ipo_price_range'].apply(get_ipo_range_prop)

del df['ipo_price_range']

def get_total_value_mid(x):

    if pd.isnull(x):

        return x

    low_bound = get_low_bound(x)

    up_bound = get_up_bound(x)

    return (up_bound+low_bound)/2

  

df['total_value_mid'] = df['total_value'].apply(get_total_value_mid)/1000000000.

del df['total_value']

#Now do one-hot encoding for all categorical columns

#One problem is that we have to split('、') first for contents with multiple companies



dftest = df.copy()



def one_hot_encoding(df, column_name):

#Reads a df and target column, does tailored one-hot encoding, and return new df for merge



    cat_list = df[column_name].unique().tolist()

    cat_set = set()

    for items in cat_list:

        if pd.isnull(items):

            continue

        items = items.split('、')

        for item in items:

            item = item.strip()

            cat_set.add(item)

    for item in cat_set:

        item = column_name + '_' + item

        df[item] = 0

    

    def check_onehot(x, cat):

        if pd.isnull(x):

            return 0

        x = x.split('、')

        for item in x:

            if cat == item.strip():

                return 1

        return 0

    

    for item in cat_set:

        df[column_name + '_' + item] = df[column_name].apply(check_onehot, args=(item, ))

    

    del df[column_name]

    return df

    

dftest = one_hot_encoding(dftest, 'area')

dftest = one_hot_encoding(dftest, 'category')

dftest = one_hot_encoding(dftest, 'market_type')

dftest = one_hot_encoding(dftest, 'recommender')

dftest = one_hot_encoding(dftest, 'sales')

dftest = one_hot_encoding(dftest, 'stock_type')

# load data and split feature and label

df = pd.read_csv('../data/hk_ipo_feature_engineered', sep='\t', index_col='code', encoding='utf-8')

Y = df['firstday_performance']

X = df.drop('firstday_performance', axis = 1)

# split data into train and test sets

seed = 7

test_size = 0.33

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)

# fit model no training data

eval_set = [(X_test, y_test)]

# fit model no training data

xgb_model = xgb.XGBRegressor().fit(X_train,y_train)

predictions = xgb_model.predict(X_test)

actuals = y_test

print mean_squared_error(actuals, predictions)



0.0643324471123

importance = xgb_model.booster().get_score(importance_type='weight')

tuples = [(k, importance[k]) for k in importance]

Here, we will see how we can upgrade our code to work with tensorflow 2.0.



This error is usually faced when we are loading pre-trained model with tensorflow session/graph or we are building flask api over a pre-trained model and loading model in tensorflow graph to avoid collision of sessions while application is getting multiple requests at once or say in case of multi-threadinng



After tensorflow 2.0 upgrade, i also started facing above error in one of my project when i had built api of pre-trained model with flask. So i looked around in tensorflow 2.0 documents to find a workaround, to avoid this runtime error and upgrade my code to work with tensorflow 2.0 as well rather than downgrading it to tensorflow 1.x .



I had a project on which i had written tutorial as well on how to build Flask api on trained keras model of text classification and then use it in production



But this project was not working after tensorflow upgrade and was facing runtime error.



Stacktrace of error was something like below:



 File "/Users/Upasana/Documents/playground/deploy-keras-model-in-production/src/main.py", line 37, in model_predict

    with backend.get_session().graph.as_default() as g:

  File "/Users/Upasana/Documents/playground/deploy-keras-model-in-production/venv-tf2/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 379, in get_session

    '`get_session` is not available '

RuntimeError: `get_session` is not available when using TensorFlow 2.0.

Related code to get model

with backend.get_session().graph.as_default() as g:

        model = SentimentService.get_model1()

Related code to load model

def load_deep_model(self, model):

        json_file = open('./src/mood-saved-models/' + model + '.json', 'r')

        loaded_model_json = json_file.read()

        loaded_model = model_from_json(loaded_model_json)



        loaded_model.load_weights("./src/mood-saved-models/" + model + ".h5")



        loaded_model._make_predict_function()

        return loaded_model

get_session is removed in tensorflow 2.0 and hence not available.



so, in order to load saved model we switched methods. Rather than using keras’s load_model, we used tensorflow to load model so that we can load model using distribution strategy.



Note

The tf.distribute.Strategy API provides an abstraction for distributing your training across multiple processing units.



New code to get model

another_strategy = tf.distribute.MirroredStrategy()

with another_strategy.scope():

    model = SentimentService.get_model1()

New code to load model

def load_deep_model(self, model):

        loaded_model = tf.keras.models.load_model("./src/mood-saved-models/"model + ".h5")

        return loaded_model

This worked and solved the problem with runtime error of get_session not available in tensorflow 2.0 . You can refer to Tensorflow 2.0 upgraded article too



Hope, this will solve your problem too. Thanks for following this article.

import xml.etree.ElementTree as ET

import pickle

import os

from os import listdir, getcwd

from os.path import join









sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]



# 20类

classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",

           "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]



# size w,h

# box x-min,x-max,y-min,y-max

def convert(size, box):

    dw = 1. / size[0]

    dh = 1. / size[1]

    x = (box[0] + box[1]) / 2.0 # 中心点位置

    y = (box[2] + box[3]) / 2.0

    w = box[1] - box[0]

    h = box[3] - box[2]

    x = x * dw

    w = w * dw

    y = y * dh

    h = h * dh # 全部转化为相对坐标

    return (x, y, w, h)





def convert_annotation(year, image_id):

    # 找到2个同样的文件

    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))

    out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, image_id), 'w')



    tree = ET.parse(in_file)

    root = tree.getroot()

    size = root.find('size')

    w = int(size.find('width').text)

    h = int(size.find('height').text)



    for obj in root.iter('object'):

        difficult = obj.find('difficult').text

        cls = obj.find('name').text

        if cls not in classes or int(difficult) == 1: # difficult ==1 的不要了

            continue

        cls_id = classes.index(cls) # 排在第几位

        xmlbox = obj.find('bndbox')

        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),

             float(xmlbox.find('ymax').text))

        # 传入的是w,h 与框框的周边

        bb = convert((w, h), b)

        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')





wd = getcwd()



for year, image_set in sets:

    # ('2012', 'train') 循环5次

    # 创建目录 一次性

    if not os.path.exists('VOCdevkit/VOC%s/labels/' % (year)):

        os.makedirs('VOCdevkit/VOC%s/labels/' % (year))



    # 图片的id数据

    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()



    # 结果写入这个文件

    list_file = open('%s_%s.txt' % (year, image_set), 'w')



    for image_id in image_ids:

        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (wd, year, image_id)) # 补全路径

        convert_annotation(year, image_id)

    list_file.close()

import matplotlib.pyplot as plt

import numpy as np

import time

from collections import OrderedDict



def softmax(a):

    a = a - np.max(a)

    exp_a = np.exp(a)

    exp_a_sum = np.sum(exp_a)

    return exp_a / exp_a_sum





def cross_entropy_error(t, y):

    delta = 1e-7

    s = -1 * np.sum(t * np.log(y + delta))

    #     print('cross entropy ',s)

    return s





class simpleNet:

    def __init__(self):

        self.W = np.random.randn(2, 3)



    def predict(self, x):

        print('current w',self.W)

        return np.dot(x, self.W)



    def loss(self, x, t):

        z = self.predict(x)

        #         print(z)

        #         print(z.ndim)

        y = softmax(z)

        #         print('y',y)

        loss = cross_entropy_error(y, t)  # y为预测的值

        return loss







def numerical_gradient_(f, x):  # 针对2维的情况 甚至是多维

    h = 1e-4  # 0.0001

    grad = np.zeros_like(x)



    it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])

    while not it.finished:

        idx = it.multi_index

        print('idx', idx)

        tmp_val = x[idx]

        x[idx] = float(tmp_val) + h

        fxh1 = f(x)  # f(x+h)

        print('fxh1 ', fxh1)

        # print('current W', net.W)

        x[idx] = tmp_val - h

        fxh2 = f(x)  # f(x-h)

        print('fxh2 ', fxh2)

        # print('next currnet W ', net.W)

        grad[idx] = (fxh1 - fxh2) / (2 * h)



        x[idx] = tmp_val  # 还原值

        it.iternext()



    return grad







net = simpleNet()

x=np.array([0.6,0.9])

t = np.array([0.0,0.0,1.0])



def f(W):

    return net.loss(x,t)



grads =numerical_gradient_(f,net.W)

print(grads)

ModuleNotFoundError                       Traceback (most recent call last)

ModuleNotFoundError: No module named 'numpy.core._multiarray_umath'

---------------------------------------------------------------------------

ImportError                               Traceback (most recent call last)

ImportError: numpy.core.multiarray failed to import



The above exception was the direct cause of the following exception:



SystemError                               Traceback (most recent call last)

C:\ProgramData\Anaconda3\lib\importlib\_bootstrap.py in _find_and_load(name, import_)



SystemError: <class '_frozen_importlib._ModuleLockManager'> returned a result with an error set

---------------------------------------------------------------------------

ImportError                               Traceback (most recent call last)

ImportError: numpy.core._multiarray_umath failed to import

---------------------------------------------------------------------------

ImportError                               Traceback (most recent call last)

ImportError: numpy.core.umath failed to import

2019-03-26 18:01:48.643796: F tensorflow/python/lib/core/bfloat16.cc:675] Check failed: PyBfloat16_Type.tp_base != nullptr

sgdc = SGDClassifier()

sgdc.fit(X_train,y_train)

sgdc_predict_y = sgdc.predict(X_test)

print 'Accuary of SGD classifier ', sgdc.score(X_test,y_test)

print classification_report(y_test,sgdc_predict_y,target_names=['Benign','Malignant'])