Polish Basketball League stats & predictions

Mantente Informado: Suscríbete a Nuestros Boletines

No te pierdas ninguna actualización o predicción importante. Suscríbete a nuestros boletines semanales y recibe toda la información directamente en tu correo electrónico. ¡Sé parte de nuestra comunidad apasionada por el baloncesto polaco!

Fotos y Videos Exclusivos: Viviendo el Baloncesto desde Dentro

Nuestra cobertura incluye fotos y videos exclusivos que te permiten vivir cada momento como si estuvieras en primera fila. Desde celebraciones hasta momentos tensos dentro de la cancha, aquí tienes contenido visual que complementa perfectamente nuestra narrativa textual:

Foro Comunitario: Comparte Tus Opiniones y Analiza con Otros Aficionados

Nuestro foro es el lugar perfecto para compartir tus opiniones sobre los últimos partidos, discutir estrategias y conectar con otros aficionados al baloncesto polaco. Únete a nuestra comunidad activa y haz parte del debate:

Celebridades Invitadas: Entrevistas Especiales con Figuras Destacadas del Baloncesto Internacional

Nos complace presentarte entrevistas especiales con figuras destacadas del baloncesto internacional que comparten sus perspectivas sobre la Liga Polaca:

• "Es emocionante ver cómo esta liga está creciendo," dice una leyenda del baloncesto internacional sobre su experiencia visitando Polonia.# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from typing import Dict import torch from torch import nn from .modules import ( ConvNorm, ) class Decoder(nn.Module): """Decoder module: - Three 1-d convolution banks - Bidirectional LSTM - Linear projection - Tie state and cell to last time-step of LSTM's hidden and cell states Arguments --------- in_dim (int): Dimension of the input feature (from the encoder) out_dim (int): Dimension of the output token embedding kernel_size (int): Kernel size of the convolutional layers channels (int): Number of channels in the convolutional layers n_layers (int): Number of LSTM layers dropout (float): Dropout probability """ def __init__( self, in_dim, out_dim, kernel_size=5, channels=512, n_layers=1, dropout=0, bidirectional=True, use_att=True, attention_dim=None, attention_location_n_filters=None, attention_location_kernel_size=None, prenet_dim=None, prenet_dropout=None, content_attention=False, query_position_rate=1., **kwargs ): super(Decoder, self).__init__() self.in_dim = in_dim self.out_dim = out_dim self.kernel_size = kernel_size self.channels = channels self.n_layers = n_layers self.dropout = dropout self.bidirectional = bidirectional # Convolution banks convolutions = [] for _ in range(3): conv_layer = nn.Sequential( ConvNorm( self.in_dim if _ == 0 else self.channels, self.channels, kernel_size=self.kernel_size, stride=1, padding=int((self.kernel_size - 1) / 2), dilation=1, w_init_gain='relu' ), nn.BatchNorm1d(self.channels) ) convolutions.append(conv_layer) if _ == 0: # Apply ReLU activation after the first layer conv_layer.add_module('ReLU', nn.ReLU()) # Layer norm after each conv layer except for the last one. if _ != 2: conv_layer.add_module('LayerNorm', nn.LayerNorm(self.channels)) if self.dropout > 0: conv_layer.add_module('Dropout', nn.Dropout(self.dropout)) self.in_dim = self.channels self.convolutions = nn.ModuleList(convolutions) # Bidirectional LSTM layer(s) rnn_dims = [self.channels] if query_position_rate > 0: rnn_dims.append(self.in_dim * query_position_rate) rnn_dims.append(self.out_dim * query_position_rate) rnn_dims.append(self.out_dim) rnn_dims += [rnn_dims[-1]] * (self.n_layers - len(rnn_dims)) rnn_dims += [self.out_dim] rnn_dims += [rnn_dims[-1]] lstm_cells = [] for i in range(len(rnn_dims) // 2): lstm_cell = nn.LSTM( rnn_dims[2 * i], rnn_dims[2 * i + 1], num_layers=1, bidirectional=self.bidirectional, batch_first=True ) lstm_cells.append(lstm_cell) self.lstm = nn.ModuleList(lstm_cells) if bidirectional: factor = 2 else: factor = 1 linear_proj = nn.Linear( factor * rnn_dims[-2], self.out_dim * factor ) linear_proj.weight.data.normal_(mean=0.0, std=0.003) linear_proj.bias.data.zero_() self.linear_projection = linear_proj if use_att: if attention_location_n_filters is None: attention_location_n_filters = channels if attention_location_kernel_size is None: attention_location_kernel_size = kernel_size if prenet_dim is None: prenet_dim = channels if prenet_dropout is None: prenet_dropout = dropout assert prenet_dim % 2 == 0 assert content_attention assert attention_dimension is not None assert attention_dimension is not None if use_att: location_conv_channels = int(attention_location_n_filters / 2) * int(2 * (kernel_size - 1) + 1) assert location_conv_channels > 0 assert location_conv_channels > 0 assert location_conv_channels > 0 loc_attn_linear_query_layers = [ nn.Linear( attention_dimension, location_conv_channels), nn.ReLU(), nn.Linear(location_conv_channels, int(location_conv_channels / prenet_dim) * prenet_dim), nn.ReLU() ] loc_attn_linear_query_layers.append(nn.Conv1d(prenet_dim, attention_dimension, bias=False)) assert