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How to use outputs_list method in localstack
Best Python code snippet using localstack_python
class_neural.py
Source:class_neural.py 
1# импоÑÑиÑование библиоÑеки ÑипизаÑии даннÑÑ
2from typing import List, NoReturn, Union3# импоÑÑиÑование библиоÑеки Ð´Ð»Ñ ÑабоÑÑ Ñ Ð¼Ð°ÑÑиÑами, векÑоÑами4import numpy as np5# импоÑÑиÑование библиоÑеки Ð´Ð»Ñ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð¸Ñ ÑÑнкÑии ÑигмоидÑ6import scipy.special as sc7# опÑеделение клаÑÑа нейÑонной ÑеÑи8class neuralNetwork:9 # иниÑиализиÑование нейÑонной ÑеÑи10 def __init__(self, input_nodes: int, hidden_nodes: List[int],11 output_nodes: int, learning_rate: float) -> NoReturn:12 # задаем колиÑеÑÑво Ñзлов во вÑ
одном, ÑкÑÑÑом и вÑÑ
одном ÑлоÑÑ
13 self.inodes = input_nodes14 self.hnodes = hidden_nodes15 self.onodes = output_nodes16 # коÑÑÑиÑÐ¸ÐµÐ½Ñ Ð¾Ð±ÑÑениÑ17 self.lr = learning_rate18 # ÑпиÑок маÑÑÐ¸Ñ Ð²ÐµÑовÑÑ
коÑÑиÑиенÑов19 self.w = self.create_w()20 # иÑполÑзование ÑÐ¸Ð³Ð¼Ð¾Ð¸Ð´Ñ Ð² каÑеÑÑве ÑÑнкÑии акÑиваÑии21 self.activation_function = lambda x: sc.expit(x)22 # Ñоздание ÑпиÑка маÑÑÐ¸Ñ Ð²ÐµÑовÑÑ
коÑÑиÑиенÑов23 def create_w(self) -> List[Union[np.ndarray, float]]:24 list_w = []25 """еÑли длина ÑкÑÑÑÑÑ
Ñлов 0, Ñо Ð¼Ñ Ñоздаем Ð¾Ð´Ð½Ñ Ð¼Ð°ÑÑиÑÑ Ð²ÐµÑов Ñ26 ÑазмеÑам колиÑеÑÑво вÑÑ
однÑÑ
нейÑонов и вÑ
однÑÑ
"""27 if len(self.hnodes) == 0:28 """запиÑÑваем в ÑпиÑок веÑов ÑгенеÑиÑованнÑе ÑлÑÑайнÑм обÑазом29 веÑовÑе коÑÑÑиÑиенÑÑ Ñ Ð¿Ñименением"""30 # ноÑмалÑного ÑаÑпÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¸ Ñдвигом -0.531 list_w.append(np.random.normal(0.0, pow(self.onodes, -0.5),32 (self.onodes, self.inodes)))33 else:34 """инаÑе пÑоÑ
одим по ÑпиÑÐºÑ ÐºÐ¾Ð»Ð¸ÑеÑÑва нейÑонов ÑкÑÑÑом Ñлое и35 генеÑиÑÑем веÑовÑе коÑÑÑиÑиенÑÑ"""36 for i in range(len(self.hnodes)):37 if i == 0:38 list_w.append(np.random.normal(0.0,39 pow(self.hnodes[i], -0.5),40 (self.hnodes[i],41 self.inodes)))42 if len(self.hnodes) > 1:43 list_w.append(np.random.normal(0.0,44 pow(self.hnodes[i + 1],45 -0.5),46 (self.hnodes[i + 1],47 self.hnodes[i])))48 else:49 list_w.append(np.random.normal(0.0, pow(self.onodes,50 -0.5),51 (self.onodes,52 self.hnodes[i])))53 elif i == len(self.hnodes) - 1:54 list_w.append(np.random.normal(0.0, pow(self.onodes, -0.5),55 (self.onodes,56 self.hnodes[i])))57 else:58 list_w.append(np.random.normal(0.0, pow(self.hnodes[i + 1],59 -0.5),60 (self.hnodes[i + 1],61 self.hnodes[i])))62 return list_w63 # меÑод Ð´Ð»Ñ ÑÑениÑовки нейÑонной ÑеÑи64 def train(self, inputs_list: List[float], targets_list: List[float]) -> \65 NoReturn:66 # пÑеобÑазоваÑÑ ÑпиÑок вÑ
однÑÑ
в векÑоÑ67 inputs = np.array(inputs_list, ndmin=2).T68 targets = np.array(targets_list, ndmin=2).T69 outputs_list = []70 outputs = 071 i = 072 # полÑÑение вÑÑ
однÑÑ
Ñигналов на каждом Ñлое73 for hn in self.w:74 if i == 0:75 outputs = self.activation_function(np.dot(hn, inputs))76 else:77 outputs = self.activation_function(np.dot(hn, outputs))78 outputs_list.append(outputs)79 i += 180 # полÑÑение оÑибки в завиÑимоÑÑи Ð¾Ñ Ñелевого знаÑÐµÐ½Ð¸Ñ Ð½Ð° вÑÑ
оде81 output_errors = targets - outputs82 errors = []83 # пÑоÑеÑÑ Ð³ÑадиенÑного ÑпÑÑка в завиÑимоÑÑи Ð¾Ñ ÐºÐ¾Ð»Ð»Ð¸ÑеÑÑва ÑкÑÑÑÑ
Ñлоев84 if len(self.hnodes) == 0:85 self.w[0] += self.lr * np.dot((output_errors * outputs_list[0] *86 (1.0 - outputs_list[0])),87 np.transpose(inputs))88 else:89 i = 090 outputs_list = outputs_list[::-1]91 for out in outputs_list:92 if i == 0:93 self.w[-(i + 1)] += self.lr * np.dot((output_errors * out94 * (1.0 - out)),95 np.transpose(96 outputs_list[i +97 1]))98 elif i == len(outputs_list) - 1:99 if len(self.w) > 2:100 errors = np.dot(self.w[1].T, errors)101 self.w[0] += self.lr * np.dot((errors * out *102 (1.0 - out)),103 np.transpose(inputs))104 elif len(self.w) == 2:105 errors = np.dot(self.w[-1].T, output_errors)106 self.w[-(i + 1)] += self.lr * np.dot((errors *107 out * (1.0 -108 out)),109 np.transpose(110 inputs))111 elif i == 1:112 errors = np.dot(self.w[-1].T, output_errors)113 self.w[-(i + 1)] += self.lr * np.dot((errors * out *114 (1.0 - out)),115 np.transpose(116 outputs_list[i +117 1]))118 else:119 errors = np.dot(self.w[-i].T, errors)120 self.w[-(i + 1)] += self.lr * np.dot((errors * out *121 (1.0 - out)),122 np.transpose(123 outputs_list[i +124 1]))125 i += 1126 # опÑÐ¾Ñ Ð½ÐµÐ¹Ñонной ÑеÑи127 def query(self, inputs_list: List[float]) -> List[float]:128 # пÑеобÑазоваÑÑ ÑпиÑок вÑ
однÑÑ
знаÑений в двÑÑ
меÑнÑй маÑÑив129 inputs = np.array(inputs_list, ndmin=2).T130 # полÑÑение вÑÑ
одного Ñигнала131 for hn in self.w:132 inputs = self.activation_function(np.dot(hn, inputs))...
hill.py
Source:hill.py
1import numpy as np2def encrypt(messages, key, n):3 messages_list = [(ord(i) - ord('a') + 1) for i in messages]4 messages_list = np.reshape(messages_list, (n, n))5 key = np.reshape(key, (n, n))6 outputs_list = []7 for i in messages_list:8 outputs_list.append(np.dot(key, i) % 26)9 outputs_list = np.reshape(outputs_list, (1, n ** 2))[0]10 o = [chr(i + ord('a')) for i in outputs_list]11 print(''.join(o))12def isPerfectSquare(x):13 sr = np.sqrt(x)14 return ((sr - np.floor(sr)) == 0), np.ceil(sr - np.floor(sr))15def decrypt(messages_decdecrypted, key, n):16 messages_decdecrypted_list = [(ord(i) - ord('a')) for i in messages_decdecrypted]17 messages_list = np.reshape(messages_decdecrypted_list, (n, n))18 print(messages_decdecrypted_list)19 key = np.reshape(key, (n, n))20 # print(key)21 key = np.linalg.inv(key) % 2622 print(key)23 outputs_list = []24 for i in messages_list:25 outputs_list.append(np.dot(key, i) % 26)26 outputs_list = np.reshape(outputs_list, (1, n ** 2))[0]27 print(outputs_list)28 o = [chr(round(i) + ord('a')-1) for i in outputs_list]29 print(''.join(o))30choice=int(input("select\n 1 for encrypt\n 2 for decrypt"))31if choice==1:32 messages = input("enter the message: ")33 key = list(map(int, input("in 1xn spaces array: ").split(" ")))34 n=int(np.sqrt(len(key)))35 encrypt(messages,key,n)36else:37 messages = input("enter the encrypted message: ")38 key = list(map(int, input("in 1xn spaces array: ").split(" ")))39 n=int(np.sqrt(len(key)))40 decrypt(messages, key, n)41# messages = 'hwkb'#input('message')42# conditions = isPerfectSquare(len(key))43# print(conditions)...
tools.py
Source:tools.py
1## @package tools2# Module caffe2.python.helpers.tools3from __future__ import absolute_import4from __future__ import division5from __future__ import print_function6from __future__ import unicode_literals7def image_input(8 model, blob_in, blob_out, order="NCHW", use_gpu_transform=False, **kwargs9):10 assert 'is_test' in kwargs, "Argument 'is_test' is required"11 if order == "NCHW":12 if (use_gpu_transform):13 kwargs['use_gpu_transform'] = 1 if use_gpu_transform else 014 # GPU transform will handle NHWC -> NCHW15 outputs = model.net.ImageInput(blob_in, blob_out, **kwargs)16 pass17 else:18 outputs = model.net.ImageInput(19 blob_in, [blob_out[0] + '_nhwc'] + blob_out[1:], **kwargs20 )21 outputs_list = list(outputs)22 outputs_list[0] = model.net.NHWC2NCHW(outputs_list[0], blob_out[0])23 outputs = tuple(outputs_list)24 else:25 outputs = model.net.ImageInput(blob_in, blob_out, **kwargs)26 return outputs27def video_input(model, blob_in, blob_out, **kwargs):28 # size of outputs can vary depending on kwargs29 outputs = model.net.VideoInput(blob_in, blob_out, **kwargs)...
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