jsrimr

import 'package:flutter/material.dart';
import 'package:speech_to_text/speech_recognition_error.dart';
import 'package:speech_to_text/speech_recognition_result.dart';
import 'package:speech_to_text/speech_to_text.dart';

void main() {
  runApp(const MyApp());
}

class MyApp extends StatelessWidget {

jsrimr

from itertools import permutations

def solution(board, r, c):
    answer = float('inf')
    n_set = set()
    for row in board:
        for el in row:
            n_set.add(el)

    def compute_needed_move(dst, r,c):

jsrimr

from collections import defaultdict, deque


def solution(edges):
    graph = defaultdict(list)
    for (n1, n2) in edges:
        graph[n1].append(n2)
        graph[n2].append(n1)

jsrimr

"""
ref : https://ccurity.tistory.com/231
"""

import datetime
import time
import pyautogui as m
import schedule
import argparse

jsrimr

def _wider_conv(self, teacher_w1, teacher_b1, teacher_w2, width_coeff, verification):
        new_width = int(width_coeff * teacher_w1.shape[3])
        rand = np.random.randint(teacher_w1.shape[3], size=(new_width - teacher_w1.shape[3]))
        replication_factor = np.bincount(rand)
        student_w1 = teacher_w1.copy()
        student_w2 = teacher_w2.copy()
        student_b1 = teacher_b1.copy()
        # target layer update (i)
        for i in range(len(rand)):
            teacher_index = rand[i]

jsrimr

ctpu up --noconf --preemptible --zone=us-central1-f --tf-version=1.15.3 --name=incremental-scale-width-232-1-0 -tpu-only &
sleep 15
ctpu up --noconf --preemptible --zone=us-central1-f --tf-version=1.15.3 --name=incremental-scale-width-232-2-0 -tpu-only &
sleep 15
ctpu up --noconf --preemptible --zone=us-central1-f --tf-version=1.15.3 --name=incremental-scale-width-232-3-0 -tpu-only &
sleep 15
ctpu up --noconf --preemptible --zone=us-central1-f --tf-version=1.15.3 --name=incremental-scale-width-232-4-0 -tpu-only &
sleep 15
ctpu up --noconf --preemptible --zone=us-central1-f --tf-version=1.15.3 --name=incremental-scale-width-232-5-0 -tpu-only &
sleep 15

jsrimr

#compound scaling
scale_coeff = [0.5, 1.0, 1.5]
for coeff in scale_coeff:
    print(a ** coeff, b**(coeff) , c**(coeff))
    #1.0954451150103321 1.0488088481701516 1.0723805294763609
    #1.2 1.1 1.15
    #1.3145341380123985 1.153689732987167 1.2332376088978148

# W scaling
alpha = .8

jsrimr

from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR

jsrimr

"""
most code from
https://colab.research.google.com/github/pytorch/xla/blob/master/contrib/colab/mnist-training.ipynb#scrollTo=pTmxZL5ymp8P
"""

import math
from matplotlib import pyplot as plt

M, N = 4, 6
RESULT_IMG_PATH = '/tmp/test_result.png'

jsrimr

from multiprocessing import Process, Pipe
import time

remotes, work_remotes = zip(*[Pipe() for _ in range(10)])

def worker(remote, parent_remote):
    parent_remote.close()
    print(f"closed {id(parent_remote)}, {time.time()}")
    # print(f"In worker : {id(remotes[0])} is closed. {remotes[0].closed}")