Optimizer = (model.parameters(), lr=learning_rate, weight_decay=1e-3)ДЇor batch_idx, data in enumerate(dataloader):Д®ncoded_batch, recon_batch, mu, logvar = model(data)

Model = VAE(img.shape, 50, num_output_vars) Return BCE + KLD def img_VAE(img, num_output_vars):Дataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False) Mu, logvar = self.encode(x.view(-1, self.output_size))Дef loss_function(recon_x, x, mu, logvar):Д«CE = F.binary_cross_entropy(recon_x.view(-1, x.shape), x.view(-1, x.shape), reduction='sum') Self.fc4 = nn.Linear(hidden_size, self.output_size) Self.fc3 = nn.Linear(latent_size, hidden_size) Self.fc22 = nn.Linear(hidden_size, latent_size) Self.fc21 = nn.Linear(hidden_size, latent_size) Self.fc1 = nn.Linear(self.input_size, hidden_size) Gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) (cA, (cH, cV, cD)) = pywt.dwt2(cA, wavelet='haar') (cA, (cH, cV, cD)) = pywt.dwt2(image, wavelet='haar')ДЇeatures = Waveletsе°Џжіўж®µ def extract_wavelets(image, num_levels=4): Img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) From sklearn.preprocessing import MinMaxScaler

Skimage color2gray