re4388

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re4388

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re4388

FROM nvidia/cuda:9.2-cudnn7-devel-ubuntu16.04
ARG PYTHON_VERSION=3.6

ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
ENV PATH /opt/conda/bin:$PATH

# Install basic packages for Ubuntu
RUN apt-get update --fix-missing && apt-get install -y wget bzip2 ca-certificates \
    libglib2.0-0 libxext6 libsm6 libxrender1 \
    git mercurial subversion && \

re4388

import sys
from PyQt5.QtWidgets import QApplication, QWidget,QLabel, QMessageBox, QPushButton
from PyQt5.QtGui import QIcon, QPixmap
from PyQt5.QtCore import pyqtSlot, QTimer, QDateTime, QDate, QTime
from Ui_Pomodoro import Ui_Dialog
from PyQt5 import QtCore
import time

re4388

def get_expire_date(fid):
    tree = ET.fromstring(info_s)
    for root in tree.iter():
        for child_of_root in root:
            if child_of_root.tag == 'feature':
                print(child_of_root.attrib['id'])

re4388

''' 给定一个字符串，

要求把字符串前面的若干个字符移动到字符串的尾部，如把字符串“abcdef”前面的2个字符'a'和'b'移动到字符串的尾部，
使得原字符串变成字符串“cdefab”。

请写一个函数完成此功能，

要求对长度为n的字符串操作的时间复杂度为 O(n)，空间复杂度为 O(1)。 '''

re4388

@when('I start to train')
def start_train(tmpdir, mocker, context, qtbot, monkeypatch):
    
    model_folder = os.path.join(tmpdir, 'model_folder')
    mocker.patch.object(window.main_view.dialog_train, 'model_folder', return_value=(model_folder, None))

    # click the train
    window.main_view._ui.actionTrain.trigger()

    # begin to train

re4388

!pip3 install --quiet "tensorflow>=1.7"
!pip3 install --quiet tensorflow-hub
!pip3 install seaborn

import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd

re4388

!wget https://raw.githubusercontent.com/Tony607/Keras-Text-Transfer-Learning/master/train_5500.txt
!wget https://raw.githubusercontent.com/Tony607/Keras-Text-Transfer-Learning/master/test_data.txt



def get_dataframe(filename):
    lines = open(filename, 'r').read().splitlines()
    data = []
    for i in range(0, len(lines)):
        label = lines[i].split(' ')[0]

re4388

module_url = "https://tfhub.dev/google/universal-sentence-encoder-large/3"
embed = hub.Module(module_url)
embed_size = embed.get_output_info_dict()['default'].get_shape()[1].value