MrRjxrby

import torch
import torch.nn as nn
import numpy as np

# Определяем инвариантные функции
class UniVariateFunction(nn.Module):
 def __init__(self, output_size):
   super(UniVariateFunction, self).__init__()
   self.linear = nn.Linear(1, output_size)
 def forward(self, x):

MrRjxrby

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import numpy as np

class CapsuleLayer(nn.Module):
    def __init__(self, num_capsules, num_route_nodes, in_channels, out_channels, kernel_size=None, stride=None):
        super(CapsuleLayer, self).__init__()

MrRjxrby

import numpy as np
import torch
import torch.nn as nn
from torchvision import datasets
from torchvision import transforms
from torch.utils.data.sampler import SubsetRandomSampler
import gc

# Конфигурация устройства (GPU/CPU)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

MrRjxrby

import tensorflow as tf
from tensorflow.keras import layers

# Генератор
def build_generator():
    model = tf.keras.Sequential()
    model.add(layers.Dense(128, input_dim=100, activation='relu'))
    model.add(layers.Dense(784, activation='sigmoid'))
    return model

MrRjxrby

import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerEncoderLayer(nn.Module):
    """
    Один слой энкодера трансформера (Post-LN).

    Аргументы:
        d_model (int): Размерность эмбеддингов (например, 512)