EURO U19 Qualification Group 7 stats & predictions

Ejemplos prácticos aplicando estas estrategias

• Ejemplo Martingale:
  Supongamos que realizaste tres apuestas seguidas perdiendo $10 cada una; ahora debes hacer una cuarta apuesta por $30 (suma total perdida) para recuperar tus pérdidas previas.
  
  
• Ejemplo Paroli:
  Si realizaste dos apuestas consecutivas ganando $20 cada una (total $40), debes hacer ahora una tercera apuesta doble ($40) buscando alcanzar $80 antes de volver al valor inicial.
• Ejemplo D'Alembert:
  Si perdiste $10 hace unas horas pero ganaste $10 hace unos minutos antes (neto cero), deberías hacer ahora otra apuesta por $10.

Leyendas emergentes: ¿Quiénes serán las estrellas futuras?

Mientras seguimos la fase clasificatoria del EURO U19 Group 7 International Football Tournament,<|...|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|>|[0]: import os [1]: import sys [2]: import torch [3]: import torch.nn as nn [4]: import torch.nn.functional as F [5]: from torchvision.models.resnet import BasicBlock as ResnetBasicBlock [6]: from torchvision.models.resnet import Bottleneck as ResnetBottleneck [7]: from torch.autograd import Variable [8]: from utils.utils import weights_init [9]: class ResNetBase(nn.Module): [10]: def __init__(self): [11]: super(ResNetBase,self).__init__() [12]: def forward(self,x): [13]: out = self.conv1(x) [14]: out = self.bn1(out) [15]: out = self.relu(out) [16]: out = self.maxpool(out) [17]: out = self.layer1(out) [18]: out = self.layer2(out) [19]: out = self.layer3(out) [20]: return out [21]: class ResNetBottleneck(nn.Module): [22]: expansion = 4 [23]: def __init__(self,inplanes=64,outplanes=256,kernel_size=1,stride=1,bias=False): [24]: super(ResNetBottleneck,self).__init__() [25]: self.conv1 = nn.Conv2d(inplanes,outplanes//self.expansion,kernel_size=1,stride=stride,bias=bias) [26]: self.bn1 = nn.BatchNorm2d(outplanes//self.expansion) [27]: self.conv2 = nn.Conv2d(outplanes//self.expansion,outplanes//self.expansion,kernel_size=kernel_size,stride=1,padding=(kernel_size-1)//2,bias=bias) [28]: self.bn2 = nn.BatchNorm2d(outplanes//self.expansion) [29]: self.conv3 = nn.Conv2d(outplanes//self.expansion,outplanes,kernel_size=1,stride=1,bias=bias) [30]: self.bn3 = nn.BatchNorm2d(outplanes) [31]: def forward(self,x): [32]: residual = x [33]: out = self.conv1(x) [34]: out = self.bn1(out) [35]: out = F.relu(out,inplace=True) out = self.conv3(self.bn2(self.conv2(F.relu(self.bn2(out),inplace=True)))) residual = self.conv3(self.bn3(self.conv2(F.relu(self.bn2(out),inplace=True)))) if x.size() != residual.size(): residual = F.pad(residual,(0,residual.size(3)-x.size(3),0,residual.size(2)-x.size(2),0,0)) out += residual out = F.relu(out,inplace=True) return out ***** Tag Data ***** ID: 1 description: The initialization and forward methods of the ResNetBottleneck class, which includes advanced neural network layer configurations and operations such as convolutional layers with different kernel sizes and batch normalization. start line: 23 end line: 35 dependencies: - type: Class name: ResNetBottleneck start line: 21 end line: 22 context description: This snippet defines the core structure and forward pass of the ResNetBottleneck block used in deep learning models for tasks like image classification. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: 5 self contained: Y ************ ## Challenging aspects ### Challenging aspects in above code: The provided snippet defines the core structure and forward pass of the `ResNetBottleneck` block used in deep learning models for tasks like image classification. Here are some challenging aspects and specific nuances that students need to consider: 1. **Parameter Management**: - The `expansion` factor is crucial for understanding how the dimensions of the layers are transformed throughout the bottleneck block. - Proper management of input/output channels and ensuring correct dimensions across layers. 2. **Residual Connections**: - Implementing the residual connection correctly to ensure that the input (`res