MortisHuang

# 匯入各種第三方函式庫(Library)
library(clusterProfiler)
library(dplyr)
library(enrichplot)
library(org.Hs.eg.db)
library(msigdbr)
library(DOSE)
library(rlang)
library(export)
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MortisHuang

class Solution:
    def twoSum(self, nums: List[int], target: int) -> List[int]:
        dic = {}
        for i, num in enumerate(nums):
            if target - num in dic:
                return([dic[target - num], i])
            dic[num] = i
        return([])

MortisHuang

from __future__ import absolute_import, division, print_function, unicode_literals

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# import matplotlib.pyplot as plt

print(tf.__version__)

MortisHuang

#%% GPU Device Detection 
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

MortisHuang

from sklearn import manifold, datasets
digits = datasets.load_digits(n_class=6)
X, y = digits.data, digits.target
X_tsne = manifold.TSNE(n_components=2, init='random', random_state=5, verbose=1).fit_transform(X)

MortisHuang

import numpy as np
import matplotlib.pyplot as plt
from sklearn import manifold, datasets
#Prepare the data
digits = datasets.load_digits(n_class=6)
X, y = digits.data, digits.target
n_samples, n_features = X.shape
n = 20  
img = np.zeros((10 * n, 10 * n))
for i in range(n):

MortisHuang

from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.users import GetUserInfo
from wordpress_xmlrpc.methods.posts import GetPosts, NewPost

#網站登入資訊
id="你的後台登入帳號"
password="你的後台登入密碼"

#網站網址，請把example.com替換成你的網址，並且先試著連上該網址，應該會出現「XML-RPC server accepts POST requests only.」才對。
url="https://example.com/xmlrpc.php"

MortisHuang

width=28
height=28
channels=1
noise_plot = np.random.normal(0, 1, (16, 10))

shape = (width, height, channels)

optimizer = Adam(lr=0.0002, beta_1=0.5, decay=8e-8)

G = generator()

MortisHuang

def stacked_generator_discriminator(G,D):

    D.trainable = False

    model = Sequential()
    model.add(G)
    model.add(D)

    return model
    

MortisHuang

def discriminator():
    """ Declare discriminator """

    model = Sequential()
    model.add(Flatten(input_shape=shape))
    model.add(Dense((width * height * channels), input_shape=shape))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(int((width * height * channels)/2)))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(1, activation='sigmoid'))